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are excited to share today that the Apache Software Foundation (ASF) voted to make DevLake an officially supported project of the Apache Incubator.","s":"Apache Incubator Welcomes DevLake, A Dev-Data Platform Serving Developers","u":"/zh/blog/apache-welcomes-devlake","h":"","p":15123},{"i":15126,"t":"Launched in December of 2021, Apache DevLake is an open-source dev data platform that ingests, analyzes, and visualizes the fragmented data in developer tools. Software development is complex, requiring many tools and processes, and as a result creates a storm of data scattered across tools in many formats. This makes it difficult to organize, query, and make sense of. We built Apache DevLake, to make it easy to make sense of this rich data and to translate it into actionable insights.","s":"What is DevLake?","u":"/zh/blog/apache-welcomes-devlake","h":"#what-is-devlake","p":15123},{"i":15128,"t":"DevOps data collection across software development lifecycle (SDLC) to connect data islands Standardized data models with out-of-the-box metrics and customizable dashboards Flexible plugin system for user-defined data integration and transformation Below is the architecture of Apache DevLake:","s":"Apache DevLake's key features:","u":"/zh/blog/apache-welcomes-devlake","h":"#apache-devlakes-key-features","p":15123},{"i":15130,"t":"First and foremost, we firmly believe in Apache's principle of \"Community over Code\" and \"The Apache Way\". We look forward to building a vibrant, inclusive, and diverse community under the guidance of our mentors through the incubator journey. We also feel that the Apache community has one of the strongest ecosystems when it comes to data-oriented open-source projects. Last and not least, the support and enthusiasm of the Apache community and mentors made it clear to us that this would be a place where we can truly evolve and nurture Apache DevLake. In the months leading up to DevLake's acceptance, our mentors provided guidance and suggestions instrumental to making this important moment a reality. We want to take this opportunity to thank and acknowledge them: Willem Ning Jiang: Apache DevLake Champion; ASF Member & Board Director Liang Zhang: Founder & CEO of SphereEx; ASF Member, Founder & PMC Chair of Apache ShardingSphere Lidong Dai: ASF Member; Apache DolphinScheduler PMC Chair Sijie Guo: ASF Member, PMC member of Apache Pulsar; Founder & CEO of StreamNative Felix Cheung: ASF Member, PMC on Spark, Superset, Yunikorn, Zeppelin, Pinot, and Incubator. SVP of Engineering at SafeGraph. Jean-Baptiste Onofré: ASF Member, Karaf PMC Chair, PMC on ActiveMQ, Archiva, Aries, Beam, Brooklyn, Camel, Carbondata, Felix, Incubator, and many more.","s":"Why join Apache Incubator?","u":"/zh/blog/apache-welcomes-devlake","h":"#why-join-apache-incubator","p":15123},{"i":15132,"t":"Enhance system scalability and performance in large-scale data scenarios. Integrate more data sources and tools (JIRA, GitHub, GitLab, and Jenkins are already supported.) Enable support for OLAP databases, providing users with more choices. Provide more scenario-specific, out-of-the-box dashboards and templates reflecting best practices and well-known methodologies to improve usability.","s":"Apache DevLake Future Roadmap","u":"/zh/blog/apache-welcomes-devlake","h":"#apache-devlake-future-roadmap","p":15123},{"i":15134,"t":"We invite developers and those passionate about data-driven engineering to 'dive into the lake' with us, and welcome contributions of all kinds. Join us on Slack and at our weekly open source community meetups🥳 Apache DevLake (Incubating) Links: GitHub: https://github.com/apache/incubator-devlake Official Website: https://devlake.apache.org/ Slack: https://join.slack.com/t/devlake-io/shared_invite/zt-20envwfbk-JUTZ4z9jSeRnrvNhBFLg9w Podling Website：https://incubator.apache.org/projects/devlake.html","s":"Join us!","u":"/zh/blog/apache-welcomes-devlake","h":"#join-us","p":15123},{"i":15137,"t":"Apache DevLake is a dev data platform that can collect and integrate data from different dev tools including Jira, Github, GitLab and Jenkins. 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If 0 or 1 is still used in SQL to query, there will be a report of error. Here is an example(only relevant fields are shown in the example). The lookup statement works in MySQL, but will lead to an error in PostgreSQL. type GitlabMergeRequestNote struct { MergeRequestId int `gorm:\"index\"` System bool } db.Where(\"merge_request_id = ? AND `system` = 0\", gitlabMr.GitlabId). After changing the sentence as it follows, an error will still be reported. The reason will be shown in the part about backticks. db.Where(\"merge_request_id = ? 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When inserting data, MySQL will accept it, but ProstgresSQL will report an error. type GithubPullRequest struct { GithubId int `gorm:\"primaryKey\"` RepoId int `gorm:\"index\"` Number int `gorm:\"index\"` State string `gorm:\"type:varchar(255)\"` Title string `gorm:\"type:varchar(255)\"` GithubCreatedAt time.Time GithubUpdatedAt time.Time `gorm:\"index\"` ClosedAt *time.Time // In order to get the following fields, we need to collect PRs individually from GitHub Additions int Deletions int Comments int Commits int ReviewComments int Merged bool MergedAt *time.Time Body string Type string `gorm:\"type:varchar(255)\"` Component string `gorm:\"type:varchar(255)\"` MergeCommitSha string `gorm:\"type:varchar(40)\"` HeadRef string `gorm:\"type:varchar(255)\"` BaseRef string `gorm:\"type:varchar(255)\"` BaseCommitSha string `gorm:\"type:varchar(255)\"` HeadCommitSha string `gorm:\"type:varchar(255)\"` Url string `gorm:\"type:varchar(255)\"` AuthorName string `gorm:\"type:varchar(100)\"` AuthorId int common.NoPKModel }","s":"Inconsistent definition of model with schema","u":"/zh/blog/compatibility-of-apache-devLake-with-postgreSQL","h":"#inconsistent-definition-of-model-with-schema","p":15136},{"i":15149,"t":"We used the GROUP_CONCATfunction in a complex query. Although there are similar functions in PostgreSQL, the function names are different and the usage is slightly different. cursor2, err := db.Table(\"pull_requests pr1\"). Joins(\"left join pull_requests pr2 on pr1.parent_pr_id = pr2.id\").Group(\"pr1.parent_pr_id, pr2.created_date\").Where(\"pr1.parent_pr_id != ''\"). Joins(\"left join repos on pr2.base_repo_id = repos.id\"). Order(\"pr2.created_date ASC\"). Select(`pr2.key as parent_pr_key, pr1.parent_pr_id as parent_pr_id, GROUP_CONCAT(pr1.base_ref order by pr1.base_ref ASC) as cherrypick_base_branches, GROUP_CONCAT(pr1.key order by pr1.base_ref ASC) as cherrypick_pr_keys, repos.name as repo_name, concat(repos.url, '/pull/', pr2.key) as parent_pr_url`).Rows() Solution: We finally decided to use two steps to achieve the GROUP_CONCAT function. First we used the simplest SQL query to get multiple pieces of the sorted data, and then used the code to group them. After modification: cursor2, err := db.Raw( ` SELECT pr2.pull_request_key AS parent_pr_key, pr1.parent_pr_id AS parent_pr_id, pr1.base_ref AS cherrypick_base_branch, pr1.pull_request_key AS cherrypick_pr_key, repos.NAME AS repo_name, Concat(repos.url, '/pull/', pr2.pull_request_key) AS parent_pr_url, pr2.created_date FROM pull_requests pr1 LEFT JOIN pull_requests pr2 ON pr1.parent_pr_id = pr2.id LEFT JOIN repos ON pr2.base_repo_id = repos.id WHERE pr1.parent_pr_id != '' ORDER BY pr1.parent_pr_id, pr2.created_date, pr1.base_ref ASC `).Rows()","s":"3.MySQL-Specific Functions","u":"/zh/blog/compatibility-of-apache-devLake-with-postgreSQL","h":"#3mysql-specific-functions","p":15136},{"i":15152,"t":"We used backticks in some SQL statements to protect field names from conflicting with MySQL reserved words, which can lead to errors in PostgreSQL. To solve this problem we revisited our code, modified all field names that conflict with reserved words, and removed the backticks in the SQL statement. In the example just mentioned: db.Where(\"merge_request_id = ? AND `system` = ?\", gitlabMr.GitlabId, false) Solution: We changed system to is_system to avoid the usage of backticks. db.Where(\"merge_request_id = ? AND is_system = ?\", gitlabMr.GitlabId, false)","s":"Backticks","u":"/zh/blog/compatibility-of-apache-devLake-with-postgreSQL","h":"#backticks","p":15136},{"i":15154,"t":"There were delete statements as followed in our code, which are legal in MySQL but will report an error in PostgreSQL. err := db.Exec(` DELETE ic FROM jira_issue_commits ic LEFT JOIN jira_board_issues bi ON (bi.source_id = ic.source_id AND bi.issue_id = ic.issue_id) WHERE ic.source_id = ? AND bi.board_id = ? `, sourceId, boardId).Error","s":"Non-standard delete statement","u":"/zh/blog/compatibility-of-apache-devLake-with-postgreSQL","h":"#non-standard-delete-statement","p":15136},{"i":15156,"t":"DevLake is a compelling offering. It collects and normalizes data from many of our favorite development tools and visualizes it using Grafana dashboards. Like the sleuths we are, we feel the urge to look beyond the dashboard overviews and find the golden nuggets buried deep within the data. So, we'd like to introduce the DevLake Playground, a place where you can unleash the power of Python on your data. In the DevLake Playground, we can explore the data using Jupyter Notebooks. There are some predefined notebooks and you can write your own. A Jupyter notebook combines Python code and documentation, which you can easily customize to your needs with some tweaks. The benefits of these Jupyter notebooks as opposed to Grafana are: Grafana is limited to SQL queries for gathering and transforming data, and visualizations for data tables. Python (code) offers more flexibility in transforming the data and can easily provide feedback on intermediate steps. The playground also supports more visualization types; for example (see the first use case below,) when the data is structured as a graph, we can visualize it with Graphviz.","s":"DevLake Playground: How to explore your data","u":"/zh/blog/DevLake-Playground-How-to-explore-your-data","h":"","p":15155},{"i":15159,"t":"The DevLake Domain model exposes the changes of issues of our issue tracker, including status changes. If we use that to visualize how issues really flow, we get a rudimentary (automated) value stream map. We can use this to identify bottlenecks in our process or flaws in our process design. This is inspired by this blog post: For example, in the chart above, we see that it takes on average 15 days for Stories to go from \"Ready\" to \"In Progress\". And, it happened 476x within the selected time frame. And now that we have this data in our playground, we can easily change how we represent it. If we focus on the most common status transitions, we can visualize the distribution of durations in a box plot out of the same data: This functionality is made available through a predefined notebook, so you can easily run it with your own data.","s":"Analyzing the development process through JIRA statuses","u":"/zh/blog/DevLake-Playground-How-to-explore-your-data","h":"#analyzing-the-development-process-through-jira-statuses","p":15155},{"i":15161,"t":"Let's say we have a hypothesis: \"Defect fixes are more quickly merged than new functionality.\" Before building a dashboard, we want to determine whether the data quality is good enough and whether we can test this hypothesis. With pandas, we can quickly join different tables from the data model. With the following code, we were able to get a preliminary view: import pandas as pd from playground.db_engine import create_db_engine # the default notebook DB_URL = \"mysql://merico:merico@127.0.0.1:3306/lake\" engine = create_db_engine(DB_URL) # read tables from database df_pr_issues = pd.read_sql(\"select * from pull_request_issues\", engine) df_prs = pd.read_sql(\"select * from pull_requests\", engine) df_issues = pd.read_sql(\"select * from issues\", engine) # join pull requests and issues based on rows in pull_request_issues df = pd.merge(df_pr_issues, df_prs, left_on=\"pull_request_id\", right_on=\"id\", suffixes=('_pr_issues', '_prs')) df = pd.merge(df, df_issues, left_on=\"issue_id\", right_on=\"id\", suffixes=('_prs', '_issues')) # set data types correctly df['created_date_issues'] = pd.to_datetime(df['created_date_issues']) df['resolution_date'] = pd.to_datetime(df['resolution_date']) df['created_date_prs'] = pd.to_datetime(df['created_date_prs']) df['merged_date'] = pd.to_datetime(df['merged_date']) # calculate lead times df['issue_lead_time'] = df['resolution_date'] - df['created_date_issues'] df['pr_lead_time'] = df['merged_date'] - df['created_date_prs'] # drop unnecessary columns df = df[['type_issues', 'title_issues', 'issue_lead_time', 'title_prs', 'pr_lead_time']] # group lead times by issue_type, add count df_grouped = df.groupby('type_issues').agg({ 'title_issues': 'count', 'issue_lead_time': ['mean', 'median', 'std'], 'pr_lead_time': ['mean', 'median', 'std'] }) df.rename(columns={'title_issues': 'issue_count'}, inplace=True) display(df_grouped) If we run this example on the Devlake GitHub issues and pull requests (up to March 2024), we get the following output: title_issues issue_lead_time pr_lead_time count mean median std mean median std type_issues 34 66 days 14:53:28.709677419 42 days 05:59:04 67 days 14:35:49.143870568 3 days 11:42:34.857142857 0 days 12:54:43.500000 12 days 17:24:54.878541108 BUG 141 10 days 07:27:20.572463768 1 days 22:42:39 20 days 14:51:35.075965706 1 days 16:51:51.529411764 0 days 01:04:54 10 days 10:20:54.566875184 INCIDENT 2 0 days 00:50:49 0 days 00:50:49 0 days 00:59:50.688234865 0 days 00:06:39 0 days 00:06:39 0 days 00:00:42.426406871 REQUIREMENT 40 37 days 02:50:22.500000 16 days 03:56:45.500000 60 days 02:39:39.606995949 9 days 11:13:27.270270270 2 days 00:14:04 22 days 12:32:27.522638402","s":"Explore data across different domains","u":"/zh/blog/DevLake-Playground-How-to-explore-your-data","h":"#explore-data-across-different-domains","p":15155},{"i":15163,"t":"We hope you are as excited as we are. We look forward to you joining our community to get your feedback and contributions. Want to get started? Have a look at the playground repository.","s":"Getting started","u":"/zh/blog/DevLake-Playground-How-to-explore-your-data","h":"#getting-started","p":15155},{"i":15165,"t":"Apache DevLake is an integration tool with the DevOps data collection functionality, which presents a different stage of data to development teams via Grafana. which also can leverage teams to improve the development process with a data-driven model.","s":"How DevLake is Up and Running","u":"/zh/blog/how-DevLake-is-up-and-running","h":"","p":15164},{"i":15167,"t":"The left side of the following screenshot is an integrative DevOps data plugin, the existing plugins include Github, GitLab, JIRA, Jenkins, Tapd, Feishu, and the most featured analysis engine in the Simayi platform. The main framework in the middle of the following screenshot, completes data collection, expansion, and conversion to the domain layer by running subtasks in the plugins. The user can trigger the tasks by config-UI or all API. RMDBS currently supports Mysql and PostgresSQL, more databases will be supported in the future. Grafana can generate different types of needed data by using SQL. Then let’s move on to how to start running DevLake.","s":"Apache DevLack Architecture Overview","u":"/zh/blog/how-DevLake-is-up-and-running","h":"#apache-devlack-architecture-overview","p":15164},{"i":15169,"t":"Before the Golang program runs, it will automatically call the init() method in the package. We need to focus on the loading of the services package. The following code has detailed comments: func init() { var err error // get initial config information cfg = config.GetConfig() // get Database db, err = runner.NewGormDb(cfg, logger.Global.Nested(\"db\")) // configure time zone location := cron.WithLocation(time.UTC) // create scheduled task manager cronManager = cron.New(location) if err != nil { panic(err) } // initialize the data migration migration.Init(db) // register the framework's data migration scripts migrationscripts.RegisterAll() // load plugin, loads all .so files in the folder cfg.GetString(\"PLUGIN_DIR\")，in th LoadPlugins method()，specifically, LoadPlugins stores the pluginName:PluginMeta key-value pair into core.plugins by calling runner. err = runner.LoadPlugins( cfg.GetString(\"PLUGIN_DIR\"), cfg, logger.Global.Nested(\"plugin\"), db, ) if err != nil { panic(err) } // run data migration scripts to complete the initializztion work of tables in the databse framework layer. err = migration.Execute(context.Background()) if err != nil { panic(err) } // call service init pipelineServiceInit() }","s":"Start the system","u":"/zh/blog/how-DevLake-is-up-and-running","h":"#start-the-system","p":15164},{"i":15171,"t":"The running process of the pipeline Before we go through the pipeline process, we need to know the Blueprint first. Blueprint is a timed task that contains all the subtasks and plans that need to be executed. Each execution record of Blueprint is a historical run, AKA Pipeline. Which presents a trigger for DevLack to complete one or more data collection transformation tasks through one or more plugins. The following is the pipeline running flow chart. A pipeline contains a two-dimensional array of tasks, mainly to ensure that a series of tasks are executed in a preset order. Like the following screenshot if the plugin of Stage 3 needs to rely on the other plugin to prepare the data(eg: the operation of refdiff needs to rely on gitextractor and Github, for more information on data sources and plugins, please refer to the documentation, then when Stage 3 starts to execute, it needs to ensure that its dependencies have been executed in Stage 1 and Stage 2. Task running process The plugin tasks in stage1, stage2, and stage3 are executed in parallel: The next step is to execute the subtasks in the plugin sequentially. The work before the RunTask is to prepare the params for the RunTask method to call, like logger, db, context and etc. The main method of RunTask is mainly to update the tasks in the Database, at the same time prepare to run the options of the plugins task. RunpluginTask will obtain the corresponding PluginMeta through core.Getplugin(pluginName), then obtains the PluginTask via PluginMeta, and then executes RunPluginSubTasks. The running process of each plugin subtask(the relevant interface and func will be explained in the next section) Get all available subtasks subtaskMeta of all th4e plugins by calling SubTaskMetas(). Use options[‘task’] and subtaskMeta to form a set of subtasks to be executed subtaskMetas. Calculate how many subtasks in total Build taskCtx via helper.NewDefaultTaskContext. Build taskData via call pluginTask.PrepareTaskData. Iterate over all subtasks in subtaskMetas. Get subtaskCtx of subtask via call taskCtx.SubTaskContext(subtaskMeta.Name). Run subtaskMeta.EntryPoint(subtaskCtx)","s":"The execution principle of DevLake","u":"/zh/blog/how-DevLake-is-up-and-running","h":"#the-execution-principle-of-devlake","p":15164},{"i":15173,"t":"PluginMeta: Contains the two basic methods of plugins, which all plugins need to implement. And stored in core.plugins when the system starts. And obtained through core.GetPlugin when executing plugin tasks. type PluginMeta interface { Description() string //PkgPath information will be lost when compiled as plugin(.so), this func will return that info RootPkgPath() string } PluginTask: It can be obtained by PluginMeta, after the plugin implemented this method, Framework can run the subtask directly, instead of letting the plugin itself run it, the biggest benefit of this is that the subtasks of the plugin are easier to implement, and we can more easily leverage(such as adding logs, etc.) during the operation of the plugin. type PluginTask interface { // return all available subtasks, framework will run them for you in order SubTaskMetas() []SubTaskMeta // based on task context and user input options, return data that shared among all subtasks PrepareTaskData(taskCtx TaskContext, options map[string]interface{}) (interface{}, error) } Every plugin has a taskData, which contains configuration options, apiClient, and other properties of plugins.(like the github has repo information) SubTaskMeta:: the meta data of a subtask, every subtask will define a SubTaskMeta. var CollectMeetingTopUserItemMeta = core.SubTaskMeta{ Name: \"collectMeetingTopUserItem\", EntryPoint: CollectMeetingTopUserItem, EnabledByDefault: true, Description: \"Collect top user meeting data from Feishu api\", } ExecContext: defines all resources needed to execute (sub)tasks. SubTaskContext: defines all resources need to execute subtask(including ExecContext). TaskContext: defines all resources need to execute tasks(including ExecContext). The difference with SubTaskContext is the TaskContext() method in SubTaskContext can retire TaskContext, but SubTaskContext(subtask string) method in TaskContext can return SubTaskContext, which means the subtask belongs to the plugin task, so we use the different contexts to distinguish this. SubTaskEntryPoint: all the subtasks in the plugin have to implement this function so that they can be coordinated and arranged by the framework layer.","s":"Important interfaces in DevLake","u":"/zh/blog/how-DevLake-is-up-and-running","h":"#important-interfaces-in-devlake","p":15164},{"i":15175,"t":"This blog introduced the basics of the DevLack framework and how it starts and runs, there are 3 more contexts api_collector, api_extractor, and data_convertor will be explained in the next blog.","s":"Further Plan","u":"/zh/blog/how-DevLake-is-up-and-running","h":"#further-plan","p":15164},{"i":15177,"t":"In the marathon of software development, teams can be overwhelmed by the complexity of tasks and the pressure to perform. The real challenge lies in understanding which efforts lead to success and which need refinement. That's the power of metrics. They transform endless streams of data into actionable insights. Apache DevLake provides the means to harness these insights, offering a metrics dashboard that aligns daily tasks with strategic goals. This guide is your first step towards a more informed and intentional approach to development. Let’s embark on this journey to make every commit, issue, and pull request an opportunity for improvement.","s":"Quick Start Guide: Setup Your First Engineering Metrics Dashboard in 5 Minutes","u":"/zh/blog/Quick-Start-Guide-Setup-Your-First-Engineering-Metrics-Dashboard-in-5-Minutes","h":"","p":15176},{"i":15179,"t":"Before setting up your first Engineering Metrics Dashboard with Apache DevLake, ensure you have the following pre-requisites ready: DevLake Version: This feature is available from 1.0.0-beta3 or higher [Installation Guide] Repository: Your code should be on GitHub, GitLab, BitBucket, or Azure DevOps. This requirement is just for the initial setup; you can connect other tools later.","s":"Getting Started","u":"/zh/blog/Quick-Start-Guide-Setup-Your-First-Engineering-Metrics-Dashboard-in-5-Minutes","h":"#getting-started","p":15176},{"i":15181,"t":"After installation, connect to your first repository by opening the Config UI or localhost:4000/onboard and you will start the onboarding session as shown in the screenshot below","s":"Set Up The Workflow","u":"/zh/blog/Quick-Start-Guide-Setup-Your-First-Engineering-Metrics-Dashboard-in-5-Minutes","h":"#set-up-the-workflow","p":15176},{"i":15183,"t":"In this step, you'll define a project that aims to enhance your software development process within your organization or team. Remember, the true value lies in the goals behind the metrics rather than the metrics themselves. Focus on setting clear, strategic objectives to ensure that the metrics you track are meaningful and directly contribute to your broader organizational goals.","s":"Step-1: Create Project","u":"/zh/blog/Quick-Start-Guide-Setup-Your-First-Engineering-Metrics-Dashboard-in-5-Minutes","h":"#step-1-create-project","p":15176},{"i":15185,"t":"Initiate by creating a Personal Access Token (PAT) on your chosen platform—be it GitHub, GitLab, BitBucket, or Azure DevOps This token empowers DevLake to securely interact with the specific repository you're setting up for metric analysis.","s":"Step-2: Configure Connection","u":"/zh/blog/Quick-Start-Guide-Setup-Your-First-Engineering-Metrics-Dashboard-in-5-Minutes","h":"#step-2-configure-connection","p":15176},{"i":15187,"t":"Adding your data scope is a crucial step where you select the specific repository you want to monitor. Once selected, the dashboard aggregates data pertaining to issues, deployments, pull requests (PRs), workflow runs, GitHub actions, and more, giving you a comprehensive view of your operations. To streamline the onboarding experience and swiftly deliver your initial metrics, the system is configured to collect only the last 14 days of data by default. This ensures a rapid setup so you can start analyzing your metrics almost immediately. However, this setting is flexible— you can adjust the data collection window at any time to suit your project’s needs and objectives.","s":"Step-3: Add Data Scope","u":"/zh/blog/Quick-Start-Guide-Setup-Your-First-Engineering-Metrics-Dashboard-in-5-Minutes","h":"#step-3-add-data-scope","p":15176},{"i":15189,"t":"This metrics dashboard provides a clear visualization of the development processes by tracking various key performance indicators from GitHub. The dashboard covers: User Requirements (Issues): Monitors new issues and how they are resolved over time, providing insights into the demands and responsiveness of the team. Issue Resolution: Shows the number of closed issues and the average time taken for issue resolution, offering a measure of efficiency and agility in problem-solving. Pull Requests: Details the number of new pull requests, an indicator of collaboration, alongside the merge time, indicating the pace of code integration. Workflow Runs: Highlights the total number of workflow runs and their success rates, illustrating the robustness of the CI/CD pipeline. Queue and Processing Times: Analyzes the time taken from issue opening to closure and pull request opening to merge, shedding light on the throughput and process optimization. By consolidating these metrics, the dashboard serves as a crucial tool for teams to evaluate their development lifecycle and make data-driven improvements.","s":"Metrics Dashboard Overview","u":"/zh/blog/Quick-Start-Guide-Setup-Your-First-Engineering-Metrics-Dashboard-in-5-Minutes","h":"#metrics-dashboard-overview","p":15176},{"i":15191,"t":"Congratulations on setting up your first engineering metrics dashboard using GitHub and Grafana in just a few minutes! As an open-source tool, DevLake supports various data sources and plugins while ensuring that your data remains securely under your control. Learn more about how DevLake can help you by checking the DORA guide, how to customize dashboards, and how to customize data model. If you haven't already, star DevLake GitHub repository to follow the updates and feel free to post your queries on slack - our vibrant community includes engineering leaders, users, experts, and contributors from diverse backgrounds around the world. Watch this tutorial on YouTube","s":"Next Steps","u":"/zh/blog/Quick-Start-Guide-Setup-Your-First-Engineering-Metrics-Dashboard-in-5-Minutes","h":"#next-steps","p":15176},{"i":15194,"t":"An API key, also known as an API token, is a string used for authentication when making requests to DevLake's open API or webhook. It serves as a form of identification to ensure authorized access. An API key contains three components: Name: This is the descriptive name assigned to the key for easy identification. Expiration: You have the option to set an expiration period for the API key, such as 7 days, 30 days, 90 days, or choose for it to never expire. Allowed Path: The API URL or endpoint that the API key is permitted to access. It defines the specific resources that the key can interact with.","s":"What is an 'API Key'","u":"/zh/docs/v1.0/Configuration/APIKeys","h":"#what-is-an-api-key","p":15192},{"i":15197,"t":"Check out the API docs.","s":"Accessing DevLake Open APIs","u":"/zh/docs/v1.0/Configuration/APIKeys","h":"#accessing-devlake-open-apis","p":15192},{"i":15199,"t":"It is typically not necessary to manually create an API key from the 'API keys' page. Instead, you can follow these steps: Navigate to the 'Data Connection' page. Create a new webhook, and an API key will be automatically generated for you. You can copy the provided curl commands, which include the API key, and save them in your local environment for future use. If you happen to forget to save the API key, do not worry. You can view the webhook details and regenerate a new API key if needed. It is important to note that the API keys automatically generated for webhooks will not be displayed on the 'API keys' page.","s":"Utilizing API Key in Incoming Webhooks","u":"/zh/docs/v1.0/Configuration/APIKeys","h":"#utilizing-api-key-in-incoming-webhooks","p":15192},{"i":15201,"t":"If you run into any problems, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/APIKeys","h":"#troubleshooting","p":15192},{"i":15203,"t":"Visit Config UI at http://localhost:4000 and go to the Connections page.","s":"Bitbucket Cloud","u":"/zh/docs/v1.0/Configuration/BitBucket","h":"","p":15202},{"i":15206,"t":"Connection Name​ Give your connection a unique name to help you identify it in the future. Endpoint URL​ For Bitbucket Cloud, you do not need to enter the REST API endpoint URL, which is always https://api.bitbucket.org/2.0/. Username and App Password​ Learn about how to create a Bitbucket app password. The following permissions are required to collect data from Bitbucket repositories: Account:Read Workspace membership:Read Projects:Read Repositories:Read Pull requests:Read Issues:Read Pipelines:Read Runners:Read Proxy URL (Optional)​ If you are behind a corporate firewall or VPN you may need to utilize a proxy server. Enter a valid proxy server address on your network, e.g. http://your-proxy-server.com:1080 Fixed Rate Limit (Optional)​ DevLake uses a dynamic rate limit to collect Bitbucket data. You can adjust the rate limit if you want to increase or lower the speed. The maximum rate limit for different entities in Bitbucket(Cloud) varies from 1,000 - 60,000 requests/hour. The rate limit to access repository data is 1,000 requests/hour, but we find it can still run when we input a value that exceeds 1,000. You can try using a larger rate limit if you have large repositories. Test and Save Connection​ Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Step 1.1 - Authentication","u":"/zh/docs/v1.0/Configuration/BitBucket","h":"#step-11---authentication","p":15202},{"i":15208,"t":"Choose the Bitbucket repositories you wish to collect either by finding them in the miller column, or searching. You can only add public repositories through the search box.","s":"Step 1.2 - Add Data Scopes","u":"/zh/docs/v1.0/Configuration/BitBucket","h":"#step-12---add-data-scopes","p":15202},{"i":15210,"t":"Entities​ The entities of which domain you wish to collect: Usually, you don't have to modify this part. However, if you don't want to collect certain Bitbucket entities, you can unselect some entities to accelerate the collection speed. - Issue Tracking: Bitbucket issues, issue comments, etc. - Source Code Management: Bitbucket repos, refs, commits, etc. - Code Review: Bitbucket PRs, PR comments, etc. - CI/CD: Bitbucket Pipelines, Bitbucket Deployments, etc. - Cross Domain: Bitbucket users, etc. Transformations​ The transformations on the Bitbucket data you are going to collect. The details of the transformations will be explained below. Without adding transformation rules, you can still view the 'Bitbucket' dashboard. However, if you want to view more pre-built dashboards, finish the transformations required. Each Bitbucket repo has at most ONE set of transformations. Issue Tracking > Issue Status Mapping​ The given settings transformed the Bitbucket issue statuses to the issue statuses used by DevLake, enabling you to measure metrics like the Issue Delivery Rate on the pre-built dashboards, as DevLake understands your definition of when an issue is considered as completed (status = 'DONE'). TODO: The issues that are planned but have not been worked on yet IN-PROGRESS: The issues that are work-in-progress DONE: The issues that are completed OTHER: Other issues statuses that do not belong to the three statuses above The original status will be saved in the column original_status of the table 'issues', and the new status will be saved in the column status of the same table. Issue Tracking > Issue Type Mapping​ DevLake will convert the issue types of 'enhancement', 'proposal', and 'task' from Bitbucket into the new issue type 'REQUIREMENT' for DevLake. In contrast, any issues classified as 'bug' in Bitbucket will be converted into the new issue type 'BUG' for DevLake. The original type will be saved in the column original_type of the table 'issues', and the new type will be saved in the column type of the same table. CI/CD​ The CI/CD configuration for Bitbucket is used for calculating DORA metrics. By default, DevLake will identify the deployment and environment settings that are defined in the Bitbucket CI .yml file. However, to ensure this works properly, you must specify the deployment settings in the .yml file. The pipeline steps with the deployment key will be recognized as DevLake deployments. The value of the deployment key will be recognized as the environment of DevLake deployments. All Bitbucket pipeline steps will be saved in table 'cicd_tasks', but DevLake deployments will be set as type = 'deployment' and environment = '{Bitbucket-pipeline-step.deployment.value}'. If you have not defined these settings in the .yml file, please select 'Detect Deployments from Pipeline steps in Bitbucket', and input the RegEx in the following fields: Deployment: A pipeline step with a name that matches the given RegEx will be recognized as a DevLake deployment. Production: A pipeline step with a name that matches the given RegEx will be recognized as a DevLake cicd_task in the production environment. Introduction to Bitbucket CI entities​ Bitbucket has several key CI entities: pipelines, pipeline steps, and deployments. A Bitbucket pipeline contains several pipeline steps. Each pipeline step defined with a deployment key can be mapped to a Bitbucket deployment. Each Bitbucket pipeline is converted to a cicd_pipeline in DevLake's domain layer schema and each Bitbucket pipeline step is converted to a cicd_task in DevLake's domain layer. If a pipeline step defines deployment with a value (usually indicating the environment), this pipeline step is also a Bitbucket deployment. Additional Settings (Optional)​ Tags Limit: DevLake compares the last N pairs of tags to get the \"commit diff', \"issue diff\" between tags. N defaults to 10. commit diff: new commits for a tag relative to the previous one issue diff: issues solved by the new commits for a tag relative to the previous one Tags Pattern: Only tags that meet the given Regular Expression will be counted. Tags Order: Only \"reverse semver\" order is supported for now. Please click Save to save the transformation rules for the repo. In the data scope list, click Next Step to continue configuring.","s":"Step 1.3 - Add Scope Config (Optional)","u":"/zh/docs/v1.0/Configuration/BitBucket","h":"#step-13---add-scope-config-optional","p":15202},{"i":15213,"t":"Collecting Bitbucket data requires creating a project first. You can visit the Project page from the side menu and create a new project by following the instructions on the user interface.","s":"Step 2.1 - Create a Project","u":"/zh/docs/v1.0/Configuration/BitBucket","h":"#step-21---create-a-project","p":15202},{"i":15215,"t":"You can add a previously configured Bitbucket connection to the project and select the boards for which you wish to collect the data for. Please note: if you don't see the repositories you are looking for, please check if you have added them to the connection first.","s":"Step 2.2 - Add a Bitbucket Connection","u":"/zh/docs/v1.0/Configuration/BitBucket","h":"#step-22---add-a-bitbucket-connection","p":15202},{"i":15217,"t":"There are three settings for Sync Policy: Data Time Range: You can select the time range of the data you wish to collect. The default is set to the past six months. Sync Frequency: You can choose how often you would like to sync your data in this step by selecting a sync frequency option or enter a cron code to specify your preferred schedule. Skip Failed Tasks: sometime a few tasks may fail in a long pipeline; you can choose to skip them to avoid spending more time in running the pipeline all over again.","s":"Step 2.3 - Set the Sync Policy","u":"/zh/docs/v1.0/Configuration/BitBucket","h":"#step-23---set-the-sync-policy","p":15202},{"i":15219,"t":"Click on \"Collect Data\" to start collecting data for the whole project. You can check the status in the Status tab on the same page.","s":"Step 2.4 - Start Data Collection","u":"/zh/docs/v1.0/Configuration/BitBucket","h":"#step-24---start-data-collection","p":15202},{"i":15221,"t":"If you run into any problems, please check the Troubleshooting or create an issue.","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/BitBucket","h":"#troubleshooting","p":15202},{"i":15224,"t":"Advanced mode allows users to create any pipeline by writing JSON. This is useful for users who want to: Collect multiple GitHub/GitLab repos or Jira projects within a single pipeline Have fine-grained control over what entities to collect or what subtasks to run for each plugin Orchestrate a complex pipeline that consists of multiple stages of plugins. Advanced mode gives utmost flexibility to users by exposing the JSON API.","s":"Why advanced mode?","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#why-advanced-mode","p":15222},{"i":15226,"t":"Click on \"+ New Blueprint\" on the Blueprint page. In step 1, click on the \"Advanced Mode\" link. The pipeline editor expects a 2D array of plugins. The first dimension represents different stages of the pipeline and the second dimension describes the plugins in each stage. Stages run in sequential order and plugins within the same stage runs in parallel. We provide some templates for users to get started. Please also see the next section for some examples. You can choose how often you would like to sync your data in this step by selecting a sync frequency option or enter a cron code to specify your preferred schedule. After setting up the Blueprint, you will be prompted to the Blueprint's activity detail page, where you can track the progress of the current run and wait for it to finish before the dashboards become available. You can also view all historical runs of previously created Blueprints from the list on the Blueprint page.","s":"How to use advanced mode to create pipelines?","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#how-to-use-advanced-mode-to-create-pipelines","p":15222},{"i":15229,"t":"Collect multiple GitHub repos sequentially. Below is an example for collecting 2 GitHub repos sequentially. It has 2 stages, each contains a GitHub task. [ [ { \"Plugin\": \"github\", \"Options\": { \"connectionId\": 1, \"repo\": \"incubator-devlake\", \"owner\": \"apache\" } }, { \"Plugin\": \"github\", \"Options\": { \"connectionId\": 1, \"repo\": \"lake\", \"owner\": \"merico-dev\" } } ] ] GitHub: connectionId: The ID of your GitHub connection at page http://localhost:4000/connections/github. owner: Just take a look at the URL: https://github.com/apache/incubator-devlake, owner is apache. repo: Just take a look at the URL: https://github.com/apache/incubator-devlake, repo is incubator-devlake.","s":"1. GitHub","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#1-github","p":15222},{"i":15231,"t":"Collect multiple GitLab repos sequentially. When there're multiple collection tasks against a single data source, we recommend running these tasks sequentially since the collection speed is mostly limited by the API rate limit of the data source. Running multiple tasks against the same data source is unlikely to speed up the process and may overwhelm the data source. Below is an example for collecting 2 GitLab repos sequentially. It has 2 stages, each contains a GitLab task. [ [ { \"Plugin\": \"gitlab\", \"Options\": { \"connectionId\": 1, \"projectId\": 152***74 } } ], [ { \"Plugin\": \"gitlab\", \"Options\": { \"connectionId\": 2, \"projectId\": 116***98 } } ] ] connectionId: The ID of your GitLab connection at page http://localhost:4000/connections/gitlab. projectId: GitLab repo's Project ID.","s":"2. GitLab","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#2-gitlab","p":15222},{"i":15233,"t":"Collect multiple Jenkins jobs sequentially. Below is an example for collecting 2 Jenkins jobs sequentially. It has 2 stages, each contains a Jenkins task. [ [ { \"plugin\": \"jenkins\", \"options\": { \"connectionId\": 1, \"scopeId\": \"auto_deploy\" } } ], [ { \"plugin\": \"jenkins\", \"options\": { \"connectionId\": 2, \"scopeId\": \"Deploy test\" } } ] ] connectionId: The ID of your Jenkins connection at page http://localhost:4000/connections/jenkins. scopeId: Jenkins job name.","s":"3. Jenkins","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#3-jenkins","p":15222},{"i":15235,"t":"Collect multiple Jira boards sequentially. Below is an example for collecting 2 Jira boards sequentially. It has 2 stages, each contains a Jira task. [ [ { \"plugin\": \"jira\", \"options\": { \"boardId\": 8, \"connectionId\": 1 } } ], [ { \"plugin\": \"jira\", \"options\": { \"boardId\": 26, \"connectionId\": 1 } } ] ] connectionId: The ID of your Jira connection at page http://localhost:4000/connections/jira. boardId: Just take a look at the URL - it will be the last number in the address. Should look something like this at the end: RapidBoard.jspa?rapidView=8 or /projects/xxx/boards/8. So 8 would be the board ID in that case.","s":"4. Jira","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#4-jira","p":15222},{"i":15237,"t":"Below is an example for collecting a GitLab repo and a Jira board in parallel. It has a single stage with a GitLab task and a Jira task. As GitLab and Jira are using their own tokens, they can be executed in parallel. [ [ { \"plugin\":\"jira\", \"options\":{ \"boardId\":8, \"connectionId\":1 } } ], [ { \"Plugin\":\"gitlab\", \"Options\":{ \"connectionId\":1, \"projectId\":116***98 } } ] ]","s":"5. Jira + GitLab","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#5-jira--gitlab","p":15222},{"i":15239,"t":"Below is an example for collecting a TAPD workspace. Since users can configure multiple TAPD connection, it's required to pass in a connectionId for TAPD task to specify which connection to use. [ [ { \"plugin\": \"tapd\", \"options\": { \"createdDateAfter\": \"2006-01-02T15:04:05Z\", \"workspaceId\": 34***66, \"connectionId\": 1 } } ] ] createdDateAfter: The data range you wish to collect after the given date. connectionId: The ID of your TAPD connection at page http://localhost:4000/connections/tapd. workspaceId: TAPD workspace id, you can get it from two ways: url: db: you can check workspace info from db._tool_tapd_workspaces and get all workspaceId you want to collect after execution of the following json in advanced mode [ [ { \"plugin\": \"tapd\", \"options\": { \"companyId\": 558***09, \"workspaceId\": 1, \"connectionId\": 1 }, \"subtasks\": [\"collectCompanies\", \"extractCompanies\"] } ] ]","s":"6. TAPD","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#6-tapd","p":15222},{"i":15241,"t":"Below is an example for collecting a TAPD workspace and a GitLab repo in parallel. It has a single stage with a TAPD task and a GitLab task. [ [ { \"plugin\": \"tapd\", \"options\": { \"createdDateAfter\": \"2006-01-02T15:04:05Z\", \"workspaceId\": 6***14, \"connectionId\": 1 } } ], [ { \"Plugin\":\"gitlab\", \"Options\":{ \"connectionId\":1, \"projectId\":116***98 } } ] ]","s":"7. TAPD + GitLab","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#7-tapd--gitlab","p":15222},{"i":15243,"t":"Below is an example for collecting a Zentao workspace. Since users can configure multiple Zentao connection, it's required to pass in a connectionId for Zentao task to specify which connection to use. [ [ { plugin: 'zentao', options: { connectionId: 1, productId: 1, projectId: 1, executionId: 1 } } ] ] connectionId: The ID of your Zentao connection at page http://localhost:4000/connections/zentao. productId: optional, ZENTAO product id, see \"Find Product Id\" for details. projectId: optional, ZENTAO product id, see \"Find Project Id\" for details. executionId: optional, ZENTAO product id, see \"Find Execution Id\" for details. You must choose at least one of productId, projectId and executionId. Find Product Id​ Navigate to the Zentao Product in the browser Click the red square annotated in the pic above Then the number in the red circle above is ProductId Find Project Id​ Navigate to the Zentao Project in the browser Then the number in the red square above is ProjectId Find Execution Id​ Navigate to the Zentao Execution in the browser Then the number in the red square above is ExecutionId","s":"8. Zentao","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#8-zentao","p":15222},{"i":15245,"t":"Below is an example for collecting a bitbucket repo. [ [ { \"plugin\": \"bitbucket\", \"options\": { \"connectionId\": 1, \"owner\": \"apache\", \"repo\": \"devlake\" } } ] ] connectionId: The ID of your bitbucket connection at page http://localhost:4000/connections/bitbucket. owner: the owner of the repository. repo: the bitbucket repository name.","s":"9. BitBucket","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#9-bitbucket","p":15222},{"i":15247,"t":"Below is an example for collecting a SonarQube project. [ [ { \"plugin\": \"sonarqube\", \"options\": { \"connectionId\": 1, \"projectKey\": \"testDevLake\" } } ] ] connectionId: The ID of your SonarQube connection at page http://localhost:4000/connections/sonarqube. projectKey: The project key of the SonarQube. To find the project key in SonarQube, please follow the steps: Log in to the SonarQube management page. Find the project for which you want to find the project key. Click on the project name to enter the project homepage. In the top menu bar of the project homepage, select \"Project Information\". On the \"Project Information\" page, you will see the project key.","s":"10. SonarQube","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#10-sonarqube","p":15222},{"i":15249,"t":"Below is an example for collecting a Teambition project. Since users can configure multiple Teambition connection, it's required to pass in a connectionId for Teambition task to specify which connection to use. [ [ { \"plugin\": \"teambition\", \"options\": { \"createdDateAfter\": \"2006-01-02T15:04:05Z\", \"projectId\": \"5e5****376\", \"connectionId\": 1 } } ] ] connectionId: The ID of your Teambition connection at page http://localhost:4000/connections/teambition. projectId: Teambition project id, you can get it from url:","s":"11. Teambition","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#11-teambition","p":15222},{"i":15251,"t":"This section is for editing a Blueprint in the Advanced Mode. To edit in the Normal mode, please refer to this guide. To edit a Blueprint created in the Advanced mode, you can simply go the Configuration page of that Blueprint and edit its configuration.","s":"Editing a Blueprint (Advanced Mode)","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#editing-a-blueprint-advanced-mode","p":15222},{"i":15253,"t":"Create a Blueprint in the Advanced Mode. You can skip collectors in a Blueprint by setting skipCollectors to true in the request body of the trigger API. For example: curl --request POST \\ --url http://localhost:8080/blueprints/:blueprintId/trigger \\ --header 'content-type: application/json' \\ --data '{ \"skipCollectors\": true }'","s":"How to skip collectors in a Blueprint (Advanced Mode)?","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#how-to-skip-collectors-in-a-blueprint-advanced-mode","p":15222},{"i":15255,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/AdvancedMode","h":"#troubleshooting","p":15222},{"i":15257,"t":"Visit Config UI at http://localhost:4000 and go to the Connections page.","s":"Bitbucket Server/Data Center","u":"/zh/docs/v1.0/Configuration/BitBucketServer","h":"","p":15256},{"i":15260,"t":"Connection Name​ Give your connection a unique name to help you identify it in the future. Endpoint URL​ For Bitbucket Server/Data Center, you do need to enter the REST API endpoint URL, which generally is https://<bitbucket-server>/. Username and App Password​ The following permissions are required to collect data from Bitbucket repositories: Repository read Proxy URL (Optional)​ If you are behind a corporate firewall or VPN you may need to utilize a proxy server. Enter a valid proxy server address on your network, e.g. http://your-proxy-server.com:1080 Fixed Rate Limit (Optional)​ DevLake uses a dynamic rate limit to collect Bitbucket Server/Data Center data. You can adjust the rate limit if you want to increase or lower the speed. Bitbucket admins could set up the rate limit in the Bitbucket instance referring to this doc. Test and Save Connection​ Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Step 1.1 - Authentication","u":"/zh/docs/v1.0/Configuration/BitBucketServer","h":"#step-11---authentication","p":15256},{"i":15262,"t":"Choose the Bitbucket repositories you wish to collect either by finding them in the miller column, or searching. You can only add public repositories through the search box.","s":"Step 1.2 - Add Data Scopes","u":"/zh/docs/v1.0/Configuration/BitBucketServer","h":"#step-12---add-data-scopes","p":15256},{"i":15264,"t":"Entities​ The entities of which domain you wish to collect: Usually, you don't have to modify this part. However, if you don't want to collect certain Bitbucket Server/Data Center entities, you can unselect some entities to accelerate the collection speed. - Source Code Management: Bitbucket repos, refs, commits, etc. - Code Review: Bitbucket PRs, PR comments, etc. - Cross Domain: Bitbucket users, etc. Please noted that compared to Bitbucket Cloud, Bitbucket Server/Data Center DOES NOT collect issues or CI/CD data. Transformations​ The transformations on the Bitbucket Server data you are going to collect. The details of the transformations will be explained below. Without adding transformation rules, you can still view the 'Bitbucket' dashboard. However, if you want to view more pre-built dashboards, finish the transformations required. Each Bitbucket repo has at most ONE set of transformations. Code Review​ Type: The type of pull requests will be parsed from PR labels by given regular expression. For example: when your labels for PR types are like 'type/feature-development', 'type/bug-fixing' and 'type/docs', please input 'type/(.*)$' when your labels for PR types are like 'feature-development', 'bug-fixing' and 'docs', please input '(feature-development|bug-fixing|docs)$' Component: The component of pull requests will be parsed from PR labels by given regular expression. Additional Settings (Optional)​ Tags Limit: DevLake compares the last N pairs of tags to get the \"commit diff', \"issue diff\" between tags. N defaults to 10. commit diff: new commits for a tag relative to the previous one issue diff: issues solved by the new commits for a tag relative to the previous one Tags Pattern: Only tags that meet the given Regular Expression will be counted. Tags Order: Only \"reverse semver\" order is supported for now. Please click Save to save the transformation rules for the repo. In the data scope list, click Next Step to continue configuring.","s":"Step 1.3 - Add Scope Config (Optional)","u":"/zh/docs/v1.0/Configuration/BitBucketServer","h":"#step-13---add-scope-config-optional","p":15256},{"i":15267,"t":"Collecting Bitbucket data requires creating a project first. You can visit the Project page from the side menu and create a new project by following the instructions on the user interface.","s":"Step 2.1 - Create a Project","u":"/zh/docs/v1.0/Configuration/BitBucketServer","h":"#step-21---create-a-project","p":15256},{"i":15269,"t":"You can add a previously configured Bitbucket Server connection to the project and select the boards for which you wish to collect the data for. Please note: if you don't see the repositories you are looking for, please check if you have added them to the connection first.","s":"Step 2.2 - Add a Bitbucket Server Connection","u":"/zh/docs/v1.0/Configuration/BitBucketServer","h":"#step-22---add-a-bitbucket-server-connection","p":15256},{"i":15271,"t":"There are three settings for Sync Policy: Data Time Range: You can select the time range of the data you wish to collect. The default is set to the past six months. Sync Frequency: You can choose how often you would like to sync your data in this step by selecting a sync frequency option or enter a cron code to specify your preferred schedule. Skip Failed Tasks: sometime a few tasks may fail in a long pipeline; you can choose to skip them to avoid spending more time in running the pipeline all over again.","s":"Step 2.3 - Set the Sync Policy","u":"/zh/docs/v1.0/Configuration/BitBucketServer","h":"#step-23---set-the-sync-policy","p":15256},{"i":15273,"t":"Click on \"Collect Data\" to start collecting data for the whole project. You can check the status in the Status tab on the same page.","s":"Step 2.4 - Start Data Collection","u":"/zh/docs/v1.0/Configuration/BitBucketServer","h":"#step-24---start-data-collection","p":15256},{"i":15275,"t":"If you run into any problems, please check the Troubleshooting or create an issue.","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/BitBucketServer","h":"#troubleshooting","p":15256},{"i":15277,"t":"Visit Config UI at : http://localhost:4000 and go to Connections page.","s":"CircleCI","u":"/zh/docs/v1.0/Configuration/CircleCI","h":"","p":15276},{"i":15280,"t":"Connection Name​ Give your connection a unique name to help you identify it in the future. Endpoint URL​ For CircleCI, you do not need to enter the REST API endpoint URL, which is always https://circleci.com/api/. Token​ Learn about Managing API Tokens. Tokens you have generated that can be used to access the CircleCI API. Apps using these tokens can act as you and have full read- and write-permissions! There are two types of API token(Personal and Project) you can create within CircleCI. Proxy URL (Optional)​ If you are behind a corporate firewall or VPN you may need to utilize a proxy server. Enter a valid proxy server address on your network, e.g. http://your-proxy-server.com:1080 Fixed Rate Limit (Optional)​ DevLake uses a dynamic rate limit to collect CircleCI data. You can adjust the rate limit if you want to increase or lower the speed. Learn more about CircleCI API rate limit. Test and Save Connection​ Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Step 1.1 - Authentication","u":"/zh/docs/v1.0/Configuration/CircleCI","h":"#step-11---authentication","p":15276},{"i":15282,"t":"Projects​ Select the CircleCI projects to collect.","s":"Step 1.2 - Configure Data Scope","u":"/zh/docs/v1.0/Configuration/CircleCI","h":"#step-12---configure-data-scope","p":15276},{"i":15284,"t":"You can add a transformation to standardize the data. A transformation acts on the CircleCI data in the tool layer, transforming it to the domain layer.","s":"Step 1.3 - Adding Scope Config (Optional)","u":"/zh/docs/v1.0/Configuration/CircleCI","h":"#step-13---adding-scope-config-optional","p":15276},{"i":15287,"t":"Collecting CircleCI data requires creating a project first. You can visit the Project page from the side menu and create a new project by following the instructions on the user interface.","s":"Step 2.1 - Create a Project","u":"/zh/docs/v1.0/Configuration/CircleCI","h":"#step-21---create-a-project","p":15276},{"i":15289,"t":"You can add a previously configured CircleCI connection to the project and select the boards for which you wish to collect the data for. Please note: if you don't see the repositories you are looking for, please check if you have added them to the connection first.","s":"Step 2.2 - Add a CircleCI Connection","u":"/zh/docs/v1.0/Configuration/CircleCI","h":"#step-22---add-a-circleci-connection","p":15276},{"i":15291,"t":"There are three settings for Sync Policy: Data Time Range: You can select the time range of the data you wish to collect. The default is set to the past six months. Sync Frequency: You can choose how often you would like to sync your data in this step by selecting a sync frequency option or enter a cron code to specify your preferred schedule. Skip Failed Tasks: sometime a few tasks may fail in a long pipeline; you can choose to skip them to avoid spending more time in running the pipeline all over again.","s":"Step 2.3 - Set the Sync Policy","u":"/zh/docs/v1.0/Configuration/CircleCI","h":"#step-23---set-the-sync-policy","p":15276},{"i":15293,"t":"Click on \"Collect Data\" to start collecting data for the whole project. You can check the status in the Status tab on the same page.","s":"Step 2.4 - Start Data Collection","u":"/zh/docs/v1.0/Configuration/CircleCI","h":"#step-24---start-data-collection","p":15276},{"i":15295,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/CircleCI","h":"#troubleshooting","p":15276},{"i":15297,"t":"Visit Config UI at: http://localhost:4000.","s":"Azure DevOps","u":"/zh/docs/v1.0/Configuration/AzureDevOps","h":"","p":15296},{"i":15299,"t":"On the Connections page, you can select GitHub and create a new connection or it.","s":"Step 1 - Add Data Connections","u":"/zh/docs/v1.0/Configuration/AzureDevOps","h":"#step-1---add-data-connections","p":15296},{"i":15301,"t":"Connection Name​ Give your connection a unique name to help you identify it in the future. Token​ Paste your Azure DevOps personal access token (PAT) here. Check Azure's official doc on how to create a PAT. Make sure that the Organization field is set to \"All accessible organizations\" when creating the PAT. Test and Save Connection​ Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Step 1.1 - Authentication","u":"/zh/docs/v1.0/Configuration/AzureDevOps","h":"#step-11---authentication","p":15296},{"i":15303,"t":"Select repositories​ Select the repositories you want to collect data from. Data Entities​ Azure DevOps supports the following data entities. CI/CD: builds and jobs. Source Code Management: repositories and their commits. Code Review: pull requests and their commits.","s":"Step 1.2 - Add Data Scopes","u":"/zh/docs/v1.0/Configuration/AzureDevOps","h":"#step-12---add-data-scopes","p":15296},{"i":15305,"t":"Scope config contains two parts: The entities of which domain you wish to collect: Usually, you don't have to modify this part. However, if you don't want to collect certain Azure DevOps entities, you can unselect some entities to accelerate the collection speed. Source Code Management: Azure repos, refs, commits, etc. Code Review: Azure PRs, PR comments and reviews, etc. CI/CD: Azure pipelines, jobs, etc. Cross Domain: Azure accounts, etc. The transformations on the Azure DevOps data you are going to collect. CI/CD​ To effectively measure DORA metrics through Azure DevOps, it is necessary to define the concept of a 'deployment'. DevLake considers an Azure Pipeline Run (see the blue rectangle) as a DevLake deployment using specific conditions expressed through regular expressions (regex): Deployment: The provided regex should match either the name of the Azure pipeline (see the red rectangle) that the pipeline run belongs to or any of the job display names (see the yellow rectangle) associated with the pipeline run. This will designate it as a deployment. For example, if the deployment pipeline is named 'build-and-push-image', you can input (push-image) as the regex. To ensure case insensitivity, include (?i) before the regex. Production: The given regex should match either the pipeline run's name or any of its job display names to classify it as a deployment within the production environment. For instance, if the deployment pipeline is named 'build-to-prod', you can input (prod) as the regex. To ensure case insensitivity, include (?i) before the regex. Additional Settings​ The additional settings for transformations are RefDiff options: Tags Limit: the number of tags to compare. Tags Pattern: Only tags that match the given regex are taken into account.","s":"Step 1.3 - Add Scope Config (Optional)","u":"/zh/docs/v1.0/Configuration/AzureDevOps","h":"#step-13---add-scope-config-optional","p":15296},{"i":15308,"t":"Collecting Azure DevOps data requires creating a project first. You can visit the Project page from the side menu and create a new project by following the instructions on the user interface.","s":"Step 2.1 - Create a Project","u":"/zh/docs/v1.0/Configuration/AzureDevOps","h":"#step-21---create-a-project","p":15296},{"i":15310,"t":"You can add a previously configured GitLab connection to the project and select the boards for which you wish to collect the data for. Please note: if you don't see the repositories you are looking for, please check if you have added them to the connection first.","s":"Step 2.2 - Add a Azure DevOps Connection","u":"/zh/docs/v1.0/Configuration/AzureDevOps","h":"#step-22---add-a-azure-devops-connection","p":15296},{"i":15312,"t":"There are three settings for Sync Policy: Data Time Range: You can select the time range of the data you wish to collect. The default is set to the past six months. Sync Frequency: You can choose how often you would like to sync your data in this step by selecting a sync frequency option or entering a cron code to specify your prefered schedule. Skip Failed Tasks: sometime a few tasks may fail in a long pipeline; you can choose to skip them to avoid spending more time in running the pipeline all over again.","s":"Step 2.3 - Set the Sync Policy","u":"/zh/docs/v1.0/Configuration/AzureDevOps","h":"#step-23---set-the-sync-policy","p":15296},{"i":15314,"t":"Click on \"Collect Data\" to start collecting data for the whole project. You can check the status in the Status tab on the same page.","s":"Step 2.4 - Start Data Collection","u":"/zh/docs/v1.0/Configuration/AzureDevOps","h":"#step-24---start-data-collection","p":15296},{"i":15316,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/AzureDevOps","h":"#troubleshooting","p":15296},{"i":15318,"t":"This tutorial shows how to leverage Grafana's role-based access control (RBAC) to manage what dashboards a user has access to. If you're setting up a single DevLake instance to be shared by multiple teams in your organization, this tutorial can help you achieve data segregation between teams.","s":"Dashboard Access Control","u":"/zh/docs/v1.0/Configuration/Dashboards/AccessControl","h":"","p":15317},{"i":15320,"t":"One of the simplest solutions is to create one Grafana folder for each team and assign permissions to teams at the folder level. Below is a step-by-step walk through. Sign in as Grafana admin and create a new folder Click \"Permissions\" tab and remove the default access of \"Editor (Role)\" and \"Viewer (Role)\" After removing default permissions: Add \"View\" permission to the target team (you'll need to create this team in Grafana first) Copy/move dashboards into this folder (you may need to edit the dashboard so that it only shows data belongs to this team)","s":"Example solution: one folder for each team","u":"/zh/docs/v1.0/Configuration/Dashboards/AccessControl","h":"#example-solution-one-folder-for-each-team","p":15317},{"i":15322,"t":"Manage dashboard permissions by Grafana","s":"Reference","u":"/zh/docs/v1.0/Configuration/Dashboards/AccessControl","h":"#reference","p":15317},{"i":15324,"t":"When first visiting Grafana, you will be provided with a sample dashboard with some basic charts setup from the database.","s":"Grafana","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"","p":15323},{"i":15326,"t":"Section Link Logging In View Section Viewing All Dashboards View Section Customizing a Dashboard View Section Dashboard Settings View Section Provisioning a Dashboard View Section Troubleshooting DB Connection View Section","s":"Contents","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#contents","p":15323},{"i":15328,"t":"Once the app is up and running, visit http://localhost:3002 to view the Grafana dashboard. Default login credentials are: Username: admin Password: admin","s":"Logging In","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#logging-in","p":15323},{"i":15330,"t":"To see all dashboards created in Grafana visit /dashboards Or, use the sidebar and click on Manage:","s":"Viewing All Dashboards","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#viewing-all-dashboards","p":15323},{"i":15332,"t":"If you want to import a dashboard to Grafana which you have backed up earlier, please follow the steps below: Click the Import icon Click the Upload JSON file button If the dashboard conflicts with an existing one, please change the Name and uid Click Import","s":"Importing a Dashboard","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#importing-a-dashboard","p":15323},{"i":15334,"t":"When viewing a dashboard, click the top bar of a panel, and go to edit Edit Dashboard Panel Page:","s":"Customizing a Dashboard","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#customizing-a-dashboard","p":15323},{"i":15336,"t":"Top Left is the variable select area (custom dashboard variables, used for switching projects, or grouping data) Top Right we have a toolbar with some buttons related to the display of the data: View data results in a table Time range selector Refresh data button The Main Area will display the chart and should update in real time Note: Data should refresh automatically, but may require a refresh using the button in some cases","s":"1. Preview Area","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#1-preview-area","p":15323},{"i":15338,"t":"Here we form the SQL query to pull data into our chart, from our database Ensure the Data Source is the correct database Select Format as Table, and Edit SQL buttons to write/edit queries as SQL The Main Area is where the queries are written, and in the top right is the Query Inspector button (to inspect returned data)","s":"2. Query Builder","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#2-query-builder","p":15323},{"i":15340,"t":"In the top right of the window are buttons for: Dashboard settings (regarding entire dashboard) Save/apply changes (to specific panel)","s":"3. Main Panel Toolbar","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#3-main-panel-toolbar","p":15323},{"i":15342,"t":"Change chart style (bar/line/pie chart etc) Edit legends, chart parameters Modify chart styling Other Grafana specific settings","s":"4. Grafana Parameter Sidebar","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#4-grafana-parameter-sidebar","p":15323},{"i":15344,"t":"When viewing a dashboard click on the settings icon to view dashboard settings. Here are 2 important sections to use: Variables Create variables to use throughout the dashboard panels, that are also built on SQL queries JSON Model Copy json code here and save it to a new file in /grafana/dashboards/ with a unique name in the lake repo. This will allow us to persist dashboards when we load the app","s":"Dashboard Settings","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#dashboard-settings","p":15323},{"i":15346,"t":"To save a dashboard in the lake repo and load it: Create a dashboard in browser (visit /dashboard/new, or use sidebar) Save dashboard (in top right of screen) Go to dashboard settings (in top right of screen) Click on JSON Model in sidebar Copy code into a new .json file in /grafana/dashboards","s":"Provisioning a Dashboard","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#provisioning-a-dashboard","p":15323},{"i":15348,"t":"To ensure we have properly connected our database to the data source in Grafana, check database settings in ./grafana/datasources/datasource.yml, specifically: database user secureJsonData/password","s":"Troubleshooting DB Connection","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#troubleshooting-db-connection","p":15323},{"i":15350,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/Dashboards/GrafanaUserGuide","h":"#troubleshooting","p":15323},{"i":15352,"t":"Visit Config UI at: http://localhost:4000.","s":"GitHub","u":"/zh/docs/v1.0/Configuration/GitHub","h":"","p":15351},{"i":15354,"t":"On the Connections page, you can select GitHub and create a new connection or it.","s":"Step 1 - Add Data Connections","u":"/zh/docs/v1.0/Configuration/GitHub","h":"#step-1---add-data-connections","p":15351},{"i":15356,"t":"Connection Name​ Give your connection a unique name to help you identify it in the future. Endpoint URL​ This should be a valid REST API endpoint, e.g. https://api.github.com/. The URL should end with /. Personal Access Token(s)​ You can use one of the following GitHub tokens: personal access tokens(PATs) or fine-grained personal access tokens. GitHub Personal Access Tokens(Recommended)​ Prerequisites: please make sure your organization has enabled Personal Access Token before configuration. See the detailed doc. Learn about how to create a GitHub personal access token. The following permissions are required to collect data from repositories: repo:status repo_deployment read:user read:org However, if you want to collect data from private repositories, the following permissions are required: repo read:user read:org The difference is that you have to give full permission for repos, not just repo:status and repo_deployment. Starting from v0.18.0, DevLake provides the auto-check for the permissions of your token(s). The data collection speed is restricted by the rate limit of 5,000 requests per hour per token (15,000 requests/hour if you pay for GitHub enterprise). You can accelerate data collection by configuring multiple personal access tokens. Please note that multiple tokens should be created by different GitHub accounts. Tokens belonging to the same GitHub account share the rate limit. Fine-grained Personal Access Tokens​ Note: this token doesn't support GraphQL APIs. You have to disable Use GraphQL APIs on the connection page if you want to use it. However, this will significantly increase the data collection time. If you're concerned with giving classic PATs full unrestricted access to your repositories, you can use fine-grained PATs announced by GitHub recently. With fine-grained PATs, GitHub users can create read-only PATs that only have access to repositories under certain GitHub orgs. But in order to do that, org admin needs to enroll that org with fine-grained PATs beta feature first. Please check this doc for more details. The token should be granted read-only permission for the following entities. Actions Contents Discussions Issues Metadata Pull requests GitHub Apps​ Learn about how to create a GitHub Apps. The following permissions are required to collect data from repositories: Repository Actions Administration Checks Commit statuses Contents Deployments Issues Metadata Pull requests Organization Members Use Graphql APIs​ If you are using github.com or your on-premise GitHub version supports GraphQL APIs, toggle on this setting to collect data quicker. GraphQL APIs are 10+ times faster than REST APIs, but they may not be supported in GitHub on-premise versions. Instead of using multiple tokens to collect data, you can use ONLY ONE token because GraphQL APIs are quick enough. Proxy URL (Optional)​ If you are behind a corporate firewall or VPN you may need to utilize a proxy server. Enter a valid proxy server address on your network, e.g. http://your-proxy-server.com:1080 Fixed Rate Limit (Optional)​ DevLake uses a dynamic rate limit to collect GitHub data. You can adjust the rate limit if you want to increase or lower the speed. The maximum rate limit for GitHub is 5,000 requests/hour (15,000 requests/hour if you pay for GitHub enterprise). Please do not use a rate that exceeds this number. Test and Save Connection​ Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Step 1.1 - Authentication","u":"/zh/docs/v1.0/Configuration/GitHub","h":"#step-11---authentication","p":15351},{"i":15358,"t":"Repositories​ Choose the GitHub repositories you wish to collect either by finding them in the miller column, or searching. You can only add public repositories through the search box.","s":"Step 1.2 - Add Data Scopes","u":"/zh/docs/v1.0/Configuration/GitHub","h":"#step-12---add-data-scopes","p":15351},{"i":15360,"t":"Scope config contains two parts: The entities of which domain you wish to collect: Usually, you don't have to modify this part. However, if you don't want to collect certain GitHub entities, you can unselect some entities to accelerate the collection speed. Issue Tracking: GitHub issues, issue comments, issue labels, etc. Source Code Management: GitHub repos, refs, commits, etc. Code Review: GitHub PRs, PR comments and reviews, etc. CI/CD: GitHub Workflow runs, GitHub Workflow jobs, etc. Cross Domain: GitHub accounts, etc. The transformations on the GitHub data you are going to collect. The details of the transformations will be explained below. Without adding transformation rules, you can still view the \"GitHub Metrics\" dashboard. However, if you want to view \"Weekly Bug Retro\", \"Weekly Community Retro\" or other pre-built dashboards, the following transformation rules, especially \"Type/Bug\", should be added. Each GitHub repo has at most ONE set of transformations. Issue Tracking​ Severity: Parse the value of severity from issue labels. when your issue labels for severity level are like 'severity/p0', 'severity/p1', 'severity/p2', then input 'severity/(.*)$' when your issue labels for severity level are like 'p0', 'p1', 'p2', then input '(p0|p1|p2)$' Component: Same as \"Severity\". Priority: Same as \"Severity\". Type/Requirement: The type of issues with labels that match given regular expression will be set to \"REQUIREMENT\". Unlike \"PR.type\", submatch does nothing, because for issue management analysis, users tend to focus on 3 kinds of types (Requirement/Bug/Incident), however, the concrete naming varies from repo to repo, time to time, so we decided to standardize them to help analysts metrics. Type/Bug: Same as \"Type/Requirement\", with type setting to \"BUG\". Type/Incident: Same as \"Type/Requirement\", with type setting to \"INCIDENT\". CI/CD​ This set of configurations is used to define 'deployments'. Deployments are related to measure DORA metrics. For GitHub deployments, DevLake recognizes them as deployments by specifying a regular expression (regex) to identify the production environments among all 'GitHub environments'. If your deployments are not performed through GitHub deployments but rather specific workflow runs in GitHub, you have the option to convert a workflow run into a DevLake deployment. In this case, you need to configure two regular expressions (regex): Deployment: The given regex should match the name of the GitHub workflow run or one of its jobs to be considered as a deployment. For example, if the workflow run used for deployment is named 'build-and-push-image', you can input (push-image). To make the regex case insensitive, you can include (?i) before the regex. Production: The given regex should match either the workflow run's name or its branch's name to be considered a deployment within the production environment. For instance: If the workflow run used for deployment is named 'build-to-prod', you can input (prod). To make the regex case insensitive, you can include (?i) before the regex. Also, many users in GitHub utilize the same workflow for both staging and prod deployments, executing it on the release branch would indicate a production deployment. Code Review​ Type: The type of pull requests will be parsed from PR labels by given regular expression. For example: when your labels for PR types are like 'type/feature-development', 'type/bug-fixing' and 'type/docs', please input 'type/(.*)$' when your labels for PR types are like 'feature-development', 'bug-fixing' and 'docs', please input '(feature-development|bug-fixing|docs)$' Component: The component of pull requests will be parsed from PR labels by given regular expression. Additional Settings (Optional)​ Tags Limit: It'll compare the last N pairs of tags to get the \"commit diff\", \"issue diff\" between tags. N defaults to 10. commit diff: new commits for a tag relative to the previous one issue diff: issues solved by the new commits for a tag relative to the previous one Tags Pattern: Only tags that meet given regular expression will be counted. Tags Order: Only \"reverse semver\" order is supported for now. Please click Save to save the transformation rules for the repo. In the data scope list, click Next Step to continue configuring.","s":"Step 1.3 - Add Scope Config (Optional)","u":"/zh/docs/v1.0/Configuration/GitHub","h":"#step-13---add-scope-config-optional","p":15351},{"i":15363,"t":"Collecting GitHub data requires creating a project first. You can visit the Project page from the side menu and create a new project by following the instructions on the user interface.","s":"Step 2.1 - Create a Project","u":"/zh/docs/v1.0/Configuration/GitHub","h":"#step-21---create-a-project","p":15351},{"i":15365,"t":"You can add a previously configured GitHub connection to the project and select the boards for which you wish to collect the data for. Please note: if you don't see the repositories you are looking for, please check if you have added them to the connection first.","s":"Step 2.2 - Add a GitHub Connection","u":"/zh/docs/v1.0/Configuration/GitHub","h":"#step-22---add-a-github-connection","p":15351},{"i":15367,"t":"There are three settings for Sync Policy: Data Time Range: You can select the time range of the data you wish to collect. The default is set to the past six months. Sync Frequency: You can choose how often you would like to sync your data in this step by selecting a sync frequency option or enter a cron code to specify your preferred schedule. Skip Failed Tasks: sometime a few tasks may fail in a long pipeline; you can choose to skip them to avoid spending more time in running the pipeline all over again.","s":"Step 2.3 - Set the Sync Policy","u":"/zh/docs/v1.0/Configuration/GitHub","h":"#step-23---set-the-sync-policy","p":15351},{"i":15369,"t":"Click on \"Collect Data\" to start collecting data for the whole project. You can check the status in the Status tab on the same page.","s":"Step 2.4 - Start Data Collection","u":"/zh/docs/v1.0/Configuration/GitHub","h":"#step-24---start-data-collection","p":15351},{"i":15371,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/GitHub","h":"#troubleshooting","p":15351},{"i":15373,"t":"Visit Config UI: http://localhost:4000.","s":"GitLab","u":"/zh/docs/v1.0/Configuration/GitLab","h":"","p":15372},{"i":15375,"t":"On the Connections page, you can select GitHub and create a new connection or it.","s":"Step 1 - Add Data Connections","u":"/zh/docs/v1.0/Configuration/GitLab","h":"#step-1---add-data-connections","p":15372},{"i":15377,"t":"Connection Name​ Give your connection a unique name to help you identify it in the future. GitLab Version​ Select if you use GitLab Cloud or GitLab Server (v11+). Endpoint URL​ This should be a valid REST API endpoint. If you use GitLab cloud, you do not need to enter the endpoint, which is always https://gitlab.com/api/v4/. If you GitLab Server (v11+), the endpoint will look like https://gitlab.example.com/api/v4/. Please note: the endpoint URL should end with /. Personal Access Token​ Your GitLab personal access token (PAT) is required to add a connection. Learn about how to create a GitLab personal access token. Personal Access Token Permissions​ At least one of the following permissions is required to collect data from repositories: api read_api You also have to double-check your GitLab user permission settings. Go to the Project information -> Members page of the GitLab projects you wish to collect. Check your role in this project from the Max role column. Make sure you are not the Guest role, otherwise, you will not be able to collect data from this project. Proxy URL (Optional)​ If you are behind a corporate firewall or VPN you may need to utilize a proxy server. Enter a valid proxy server address on your network, e.g. http://your-proxy-server.com:1080 Fixed Rate Limit (Optional)​ DevLake uses a dynamic rate limit at around 12,000 requests/hour to collect GitLab data. You can adjust the rate limit if you want to increase or lower the speed. The maximum rate limit for GitLab Cloud is 120,000 requests/hour. Tokens under the same IP address share the rate limit, so the actual rate limit for your token will be lower than this number. For self-managed GitLab rate limiting, please contact your GitLab admin to get or set the maximum rate limit of your GitLab instance. Please do not use a rate that exceeds this number. Test and Save Connection​ Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Step 1.1 - Authentication","u":"/zh/docs/v1.0/Configuration/GitLab","h":"#step-11---authentication","p":15372},{"i":15379,"t":"Repositories​ Select the GitLab repositories you want to collect from the miller column. Please note that repositories with guest permissions or those that are archived have already been excluded. You can also use the search function to find them. Limited by the GitLab API, You need to type more than 2 characters to search. The repositories only with guest permissions are not shown in the list.","s":"Step 1.2 - Add Data Scopes","u":"/zh/docs/v1.0/Configuration/GitLab","h":"#step-12---add-data-scopes","p":15372},{"i":15381,"t":"Scope config contains two parts: The entities of which domain you wish to collect: Usually, you don't have to modify this part. However, if you don't want to collect certain GitLab entities, you can unselect some entities to accelerate the collection speed. Issue Tracking: GitLab issues, issue comments, issue labels, etc. Source Code Management: GitLab repos, refs, commits, etc. Code Review: GitLab MRs, MR comments and reviews, etc. CI/CD: GitLab pipelines, jobs, etc. Cross Domain: GitLab accounts, etc. The transformations on the GitLab data you are going to collect. The details of the transformations will be explained below. Without adding transformation rules, you can still view some of the dashboards. Each GitLab repo has at most ONE set of transformations. CI/CD​ This set of configurations is used to define 'deployments'. Deployments are related to measure DORA metrics. For GitLab deployments, DevLake recognizes them as deployments by specifying a regular expression (regex) to identify the production environments among all 'GitLab environments'. If your deployments are not performed through GitLab deployments but rather specific pipelines in GitLab, you have the option to convert a GitLab pipeline run into a DevLake deployment. In this case, you need to configure two regular expressions (regex): Deployment: The given regex should match the name of the GitLab pipeline's branch name or one of its job names to be considered as a deployment. For example, if the pipeline is executet on the 'build-and-push-image', you can input (push-image). To make the regex case insensitive, you can include (?i) before the regex. Production: The given regex should match either the pipeline's branch name or one of its job names to be considered a deployment within the production environment. For instance: If the pipeline used for deployment is named 'build-to-prod', you can input (prod). To make the regex case insensitive, you can include (?i) before the regex. Also, many users in GitLab utilize the same pipeline for both staging and prod deployments, executing it on the release branch would indicate a production deployment.","s":"Step 1.3 - Add Scope Config (Optional)","u":"/zh/docs/v1.0/Configuration/GitLab","h":"#step-13---add-scope-config-optional","p":15372},{"i":15384,"t":"Collecting GitLab data requires creating a project first. You can visit the Project page from the side menu and create a new project by following the instructions on the user interface.","s":"Step 2.1 - Create a Project","u":"/zh/docs/v1.0/Configuration/GitLab","h":"#step-21---create-a-project","p":15372},{"i":15386,"t":"You can add a previously configured GitLab connection to the project and select the boards for which you wish to collect the data for. Please note: if you don't see the repositories you are looking for, please check if you have added them to the connection first.","s":"Step 2.2 - Add a GitLab Connection","u":"/zh/docs/v1.0/Configuration/GitLab","h":"#step-22---add-a-gitlab-connection","p":15372},{"i":15388,"t":"There are three settings for Sync Policy: Data Time Range: You can select the time range of the data you wish to collect. The default is set to the past six months. Sync Frequency: You can choose how often you would like to sync your data in this step by selecting a sync frequency option or entering a cron code to specify your prefered schedule. Skip Failed Tasks: sometime a few tasks may fail in a long pipeline; you can choose to skip them to avoid spending more time in running the pipeline all over again.","s":"Step 2.3 - Set the Sync Policy","u":"/zh/docs/v1.0/Configuration/GitLab","h":"#step-23---set-the-sync-policy","p":15372},{"i":15390,"t":"Click on \"Collect Data\" to start collecting data for the whole project. You can check the status in the Status tab on the same page.","s":"Step 2.4 - Start Data Collection","u":"/zh/docs/v1.0/Configuration/GitLab","h":"#step-24---start-data-collection","p":15372},{"i":15392,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/GitLab","h":"#troubleshooting","p":15372},{"i":15394,"t":"This guide provides a step-by-step approach to organizing projects in DevLake, enabling you to measure DORA metrics according to your specific use cases.","s":"How to Organize DevLake Projects","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"","p":15393},{"i":15396,"t":"On a high level, a DevLake project can be viewed as a real-world project or product line. It represents a specific initiative or endeavor within the software development domain. On a lower level, a DevLake project is a way of organizing and grouping data from different domains. DevLake uses various data scopes, such as repos, boards, cicd_scopes, and cq_projects as the 'container' to associate different types of data to a specific project. See more on this doc.","s":"1. What is a DevLake project?","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"#1-what-is-a-devlake-project","p":15393},{"i":15398,"t":"This is crucial due to the fact that DevLake measures DORA metrics at the project level. Each project is associated with specific key entities, such as 'pull requests', 'deployments', and 'incidents', which are used to calculate the corresponding DORA metrics. Therefore, proper project organization ensures accurate and meaningful DORA metric calculations for effective analysis and evaluation. How are four DORA metrics calculated from 'pull requests', 'deployments', and 'incidents'? Deployment Frequency: How often does a project deploys? Lead Time for Changes: How fast are the pull requests delivered? Change Failure Rate: How many deployments lead to incidents? Median Time to Restore: How fast are incidents solved?","s":"2. Why is it important to organize projects?","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"#2-why-is-it-important-to-organize-projects","p":15393},{"i":15400,"t":"There are several challenges associated with organizing projects in DevLake due to different development practices within teams. Some of these challenges include: Managing multiple Git repos, issue boards, and CI/CD pipelines within a project. Having a Git repo, issue board, or CI/CD pipeline associated with multiple projects, such as in the case of mono-repos or boards used to track incidents from user feedback. Managing multiple projects within a team and the need to measure DORA metrics at the team level. Projects contributed by multiple teams, with each team requiring the ability to measure their own DORA metrics. This document serves as a guide to address these challenges and provide assistance in effectively dealing with these diverse development practices.","s":"3. Challenges in project organization","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"#3-challenges-in-project-organization","p":15393},{"i":15403,"t":"It is advisable to create DevLake projects that align with the number of real-life projects you have. For example, if you have a team (Team A) responsible for managing multiple projects, it is recommended to create separate DevLake projects for each individual project instead of creating a single project named 'Team A'. This approach allows for better organization and tracking of metrics specific to each project.","s":"4.1. Determining the number of DevLake projects","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"#41-determining-the-number-of-devlake-projects","p":15393},{"i":15405,"t":"When organizing projects in DevLake, it is important to associate all relevant data scopes, such as repos, issue boards, and CI/CD scopes, with the corresponding DevLake project based on real-life practices. In situations where a repo or board is shared by multiple projects in real life, it is recommended to include them in all of these projects within DevLake. This is because DevLake cannot differentiate which commits or issues belong to specific projects. Rather than excluding shared resources from DORA measurements, it is advisable to consider them in all relevant projects.","s":"4.2 Principles of organizing projects","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"#42-principles-of-organizing-projects","p":15393},{"i":15407,"t":"To clarify the concepts, let's define three terms: Project: Refers to a real-world project or product line, such as Apache DevLake or Apache Spark. It focuses on the work to be done. Team: Represents a department, such as the 'product team' or 'engineering team'. It focuses on the people and their roles. Note that people within the same team may not always work on the same projects. Project Team: Comprises individuals working on a specific project. DevLake does support measuring DORA metrics at the project-team level, which is essentially the same as measuring at the project level. However, it is important to note that DevLake does not recommend measuring DORA metrics at the team level. Despite the existence of the 'DORA by team' dashboard contributed by the community. Doing so may introduce inaccuracies and dilute the significance of measuring DORA metrics from the outset.","s":"4.3 Measuring DORA at the team level","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"#43-measuring-dora-at-the-team-level","p":15393},{"i":15409,"t":"This section demonstrates real-life practices and how they get reflected in DevLake. Disclaimer: To keep this guide shorter, some technical details are only mentioned in Use Case 1, so if you read this page for the first time, make sure to go through them in order. Note: If you use webhooks, check the quick note about them below.","s":"5. Use Cases","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"#5-use-cases","p":15393},{"i":15411,"t":"Apache Software Foundation (ASF) has and is developing many projects. To take an example we will analyze 2 projects: DevLake and Spark. Both are independent of each other. Assume that ASF wants to check the health of the development and maintenance of these projects with DORA. DevLake manages 3 repos: incubator-devlake, incubator-devlake-website, and incubator-devlake-helm-chart. There are many repos related to Spark in one way or another. To keep it simple, we will also pick 3 repos: spark, spark-website, and incubator-livy. Both projects use GitHub for storing code (including pull requests), deployments on GitHub Actions, and incidents. Note: To avoid confusion between DevLake as a project in this use case and DevLake as a platform, we will use complete names i.e. project DevLake and platform DevLake respectively. 5.1.1. Organizing Projects​ First, create two projects on the DevLake platform, one for DevLake and one for Spark. These will represent real-world projects. Once these are created, the connections created in the following steps will be bound to them. 5.1.2. Creating Connections​ Since all is on GitHub in this case, we can use just 1 connection with the following properties: it includes all the project's repos its scope includes everything we work with (i.e. pull requests, deployments, and incidents) If you store incidents on Jira, for example, you will need to create a separate connection just for them. The same applies to deployments, a separate connection is needed in case they are stored in Jenkins (or any other host for deployments). 5.1.3. Configuring Connections​ This part is described in GitHub connection configuration. Please check the configuration guide for configuring other data sources. 5.1.4. Using Connections​ At this point, we have projects and connections created on the platform DevLake. It is time to bind those connections to the projects. To do so, follow the steps described in the Tutorial. 5.1.5. Resulting Metrics​ To know if the data of a project is successfully collected to your DORA Dashboard: If everything goes well, you should see all the 4 charts. If something is wrong, and you are puzzled as to why, check out the Debugging Dora Issue Metrics page. 5.1.6. How can I observe metrics by project?​ In the same DORA dashboard check out this menu point: The metrics should change when you select or deselect projects, representing the projects you selected.","s":"5.1. Use Case 1: Apache Projects","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"#51-use-case-1-apache-projects","p":15393},{"i":15413,"t":"Consider a scenario where a company operates with several teams, each managing one or more projects. For illustration, we will explore two such teams: the Payments team and the Internal Tools team. Here's a simplified representation of this scenario: Quick Overview:​ The Payments team works on a single project: “payments”. The Internal Tools team manages two projects: “it-legacy” and “it-new”. Both teams use different sets of tools and boards. Step-by-Step Simplification:​ Define the Teams and Projects: Payments Team: One project: \"payments\". Internal Tools Team: Two projects: \"it-legacy\" and \"it-new\". Understand the Tools: Assume both teams utilize GitHub for repos and Jenkins for CI/CD. The Payments team uses Jira boards. The Internal Tools team uses webhooks for reporting incidents. 5.2.1. Organizing Projects​ DORA is effective for observing the impacts of methodology changes within a team. From DORA’s standpoint, the concept of distinct teams is not recognized; only projects exist. Adding a team concept introduces unnecessary complexity without providing any substantial benefit. In DevLake, we create three projects: payments, it-legacy, and it-new. It is crucial to maintain atomic projects, representing the smallest, independent units, to prevent complexity and ensure precise data representation. Atomic projects allow for a more flexible and accurate data comparison and combination between projects. 5.2.2. Adding Connections​ Create just one connection and reuse it across projects by adding data scopes. This method optimizes data collection, minimizing redundancy and ensuring more efficient use of resources. It is NOT recommended to create multiple connections, for instance, GitHub repos, as it will increase the time to collect the data due to the storage of multiple copies of shared repos in the database. The only exception is the webhooks: we must have 1 connection per project, as this is the only way DevLake can accurately assign incidents to the corresponding project. So, in total we will have only these connections: 1 connection for all GitHub repos to collect pull requests 1 connection to Jenkins to collect all deployments 1 connection to Jira to collect incidents 2 webhook connections to collect incidents: 1 per each project that uses webhooks (it-legacy and it-new) The step-by-step Configuration Guide shows how to both add connections and set scopes as described in the next chapter. 5.2.3. Setting Scopes​ Now, add the connections to our projects and set the scope to them: For payments project: add 1 scope to GitHub connection for p1...p10 repos to collect their pull requests add 1 scope to Jenkins for deployments of p1...p10 repos add 1 scope to Jira to collect incidents For it-legacy project: add 1 scope to GitHub for repos it-legacy-1, it-legacy-2, it-core-1 and it-core-2 to collect their pull requests add 1 scope to Jenkins for deployments of it-legacy-1, it-legacy-2, it-core-1 and it-core-2 repos include the it-legacy webhook for collecting incidents For it-new project: add 1 scope to GitHub for repos it-new-1, it-new-2, it-core-1 and it-core-2 to collect their pull requests add 1 scope to Jenkins for deployments of it-new-1, it-new-2, it-core-1 and it-core-2 repos include the it-new webhook for collecting incidents 5.2.4. Resulting Metrics​ See 5.1.5 Resulting Metrics","s":"5.2. Use Case 2: Multiple Teams with Distinct Projects","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"#52-use-case-2-multiple-teams-with-distinct-projects","p":15393},{"i":15415,"t":"Assigning a UNIQUE webhook to each project is critical. This ensures that the DevLake platform correctly associates the incoming data with the corresponding project through the webhook. If you use the same webhook across multiple projects, the data sent by it will be replicated per each project that uses that webhook. More information available on the Webhook page","s":"6. About Webhooks","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"#6-about-webhooks","p":15393},{"i":15417,"t":"Please check out the Debugging DORA Issue Metrics to debug DORA dashboard. If you still run into any problems, please check the Troubleshooting or create an issue","s":"7. Troubleshooting","u":"/zh/docs/v1.0/Configuration/HowToOrganizeDevlakeProjects","h":"#7-troubleshooting","p":15393},{"i":15419,"t":"Visit Config UI at: http://localhost:4000.","s":"Jenkins","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"","p":15418},{"i":15422,"t":"Give your connection a unique name to help you identify it in the future.","s":"Connection Name","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"#connection-name","p":15418},{"i":15424,"t":"This should be a valid REST API endpoint. Eg. https://ci.jenkins.io/. The endpoint url should end with /.","s":"Endpoint URL","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"#endpoint-url","p":15418},{"i":15426,"t":"Your User ID for the Jenkins Instance.","s":"Username (E-mail)","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"#username-e-mail","p":15418},{"i":15428,"t":"For help on Username and Password, please see Jenkins docs on using credentials. You can also use \"API Access Token\" for this field, which can be generated at User -> Configure -> API Token section on Jenkins.","s":"Password","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"#password","p":15418},{"i":15430,"t":"DevLake uses a dynamic rate limit to collect Jenkins data. You can adjust the rate limit if you want to increase or lower the speed. There is no doc about Jenkins rate limiting. Please create an issue if you find related information.","s":"Fixed Rate Limit (Optional)","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"#fixed-rate-limit-optional","p":15418},{"i":15432,"t":"Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Test and Save Connection","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"#test-and-save-connection","p":15418},{"i":15435,"t":"Choose the Jenkins jobs. All Jenkins builds under these jobs will be collected.","s":"Jobs","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"#jobs","p":15418},{"i":15437,"t":"Jenkins only supports CI/CD domain entities, transformed from Jenkins builds and stages. CI/CD: Jenkins builds, stages, etc.","s":"Data Entities","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"#data-entities","p":15418},{"i":15439,"t":"This set of configurations is used for calculating DORA metrics. If you'd like to define deployments with Jenkins, please select \"Detect Deployment from Jenkins Builds\", and provide the following regexes Deployment: Jenkins stages whose names match the regex will be registered as deployments (if a Jenkins build has no stage, its job name will be used to match the regex) Production: Jenkins stages whose names match the regex will be assigned environment 'PRODUCTION' (if a Jenkins build has no stage, its job name will be used to match the regex) This is how it works behind the scene: If a Jenkins build has stages, it's converted to a cicd_pipeline and its stages are converted to cicd_tasks in DevLake's domain layer schema. If a Jenkins build has no stage, it's converted to both a cicd_pipeline and a cicd_task whose names are the build's jobName. After the conversion, the two regexes are applied to the records in the cicd_tasks table. You can also select \"Not using Jenkins builds as Deployments\" if you're not using Jenkins to conduct deployments.","s":"Step 3 - Adding Transformation Rules (Optional)","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"#step-3---adding-transformation-rules-optional","p":15418},{"i":15441,"t":"You can choose how often you would like to sync your data in this step by selecting a sync frequency option or enter a cron code to specify your prefered schedule.","s":"Step 4 - Setting Sync Policy","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"#step-4---setting-sync-policy","p":15418},{"i":15443,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/Jenkins","h":"#troubleshooting","p":15418},{"i":15445,"t":"Visit Config UI at: http://localhost:4000.","s":"Jira","u":"/zh/docs/v1.0/Configuration/Jira","h":"","p":15444},{"i":15447,"t":"On the Connections page, you can select Jira and create a new connection or it.","s":"Step 1 - Add Data Connections","u":"/zh/docs/v1.0/Configuration/Jira","h":"#step-1---add-data-connections","p":15444},{"i":15449,"t":"Connection Name​ Give your connection a unique name to help you identify it in the future. Please note: the following configuration for the endpoint and authentication for your Jira connection depends on your Jira version, Jira Cloud or Server/Data Center. Jira Cloud​ Endpoint URL​ This should be a valid REST API endpoint: https://<mydomain>.atlassian.net/rest/ Please note: the endpoint url should end with /. E-mail​ Please enter the e-mail of your Jira account. API Token​ Learn about how to create an API token. Please note: Jira API token and Personal Access Token are two different tokens. Jira Server/Data Center​ Endpoint URL​ This should be a valid REST API endpoint: https://jira.<mydomain>.com/rest/ Please note: the endpoint url should end with /. Authentication Method​ Jira Server supports two ways of authentication: using basic authentication or Personal Access Token. Username​ Please enter the username of your Jira account. Password​ Please enter the password of your Jira account. Personal Access Token​ Learn about how to create a Personal Access Token. Token Permissions​ For both Jira Cloud and Jira Server, when accessing Jira API, users may encounter access restrictions if their token does not have sufficient permissions. This is typically caused by insufficient scope or role settings for the Jira token. To solve this issue, users can take the following steps: Checking User Permissions Users can confirm whether they have sufficient permissions by checking their permissions in Jira. For Cloud users, they can view their global and project permissions through the \"Permissions\" tab on the \"Profile\" page. For Server users, they can log in to Jira as an administrator and view user permissions on the \"User Management\" page. Ensuring Sufficient Permissions Before using the Jira API, users need to ensure that their account has at least the necessary project or global permissions. Global permissions include various Jira system settings and management operations, while project permissions control specific operations and configurations for each Jira project. Users can assign roles such as Project Administrator, Project Lead, Developer, etc. for the corresponding projects, or assign global permissions such as Jira Administrators, Jira Software Administrators, etc. It is recommended to minimize the permissions granted to the API to ensure system security. Solving Access Restrictions To solve access restrictions caused by insufficient Jira token permissions, users should check the token's permission settings to ensure the correct scope and role are set. If the permission settings are correct but the required API is still inaccessible, consider using other authentication methods, such as authenticating with a username and password. If the issue persists, contact the Jira administrator for further assistance. Proxy URL (Optional)​ If you are behind a corporate firewall or VPN you may need to utilize a proxy server. Enter a valid proxy server address on your network, e.g. http://your-proxy-server.com:1080 Fixed Rate Limit (Optional)​ DevLake uses a dynamic rate limit to collect Jira data. You can adjust the rate limit if you want to increase or lower the speed. If you encounter a 403 error during data collection, please lower the rate limit. Jira(Cloud) uses a dynamic rate limit and has no clear rate limit. For Jira Server's rate limiting, please contact your Jira Server admin to get or set the maximum rate limit of your Jira instance. Please do not use a rate that exceeds this number. Test and Save Connection​ Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Stept 1.1 - Authentication","u":"/zh/docs/v1.0/Configuration/Jira","h":"#stept-11---authentication","p":15444},{"i":15451,"t":"Boards​ Choose the Jira boards to collect.","s":"Step 1.2 - Add Data Scopes","u":"/zh/docs/v1.0/Configuration/Jira","h":"#step-12---add-data-scopes","p":15444},{"i":15453,"t":"Scope config contains two parts: The entities of which domain you wish to collect: Usually, you don't have to modify this part. However, if you don't want to collect certain Jira entities, you can unselect some entities to accelerate the collection speed. Issue Tracking: Jira issues, issue comments, issue labels, etc. Cross Domain: Jira accounts, etc. The transformations on the Jira data you are going to collect Issue Type: Standardize the issue types to DevLake's pre-defined three issue types: REQUIREMENT, BUG, and INCIDENT. Story Points: Map the custom field that represents story_point in your Jira to DevLake's issue story point. Cross-domain: Get the commit(s) associated with Jira issues from Jira issues' remote links or development panels. Although this configuration is optional, some of the above transformations are required to measure metrics such as Requirement Lead Time, Bug Age or DORA - Median Time to Restore Service in the built-in Grafana dashboards. Without adding transformation rules, you can not view all charts in \"Jira\" or \"Engineering Throughput and Cycle Time\" dashboards. Each Jira board has at most ONE set of transformations. Issue Tracking​ Requirement: choose the issue types to be transformed to \"REQUIREMENT\". Bug: choose the issue types to be transformed to \"BUG\". Incident: choose the issue types to be transformed to \"INCIDENT\". Epic Key: choose the custom field that represents Epic key. In most cases, it is \"Epic Link\". Story Point: choose the custom field that represents story points. In most cases, it is \"Story Points\". Additional Settings​ Remotelink Commit SHA: parse the commits from an issue's remote links by the given regular expression so that the relationship between issues and commits can be created. You can directly use the regular expression /commit/([0-9a-f]{40})$.","s":"Step 1.3 - Add Scope Config (Optional)","u":"/zh/docs/v1.0/Configuration/Jira","h":"#step-13---add-scope-config-optional","p":15444},{"i":15456,"t":"Collecing Jira data reuiqres creating a project first. You can visit the Project page from the side menu and create a new project by following the instructions on the user interface.","s":"Step 2.1 - Create a Project","u":"/zh/docs/v1.0/Configuration/Jira","h":"#step-21---create-a-project","p":15444},{"i":15458,"t":"You can add a previously configured Jira connection to the project and select the boards for which you wish to collect the data for. Please note: if you don't see the boards you are looking for, please check if you have added them to the connection first.","s":"Step 2.2 - Add a Jira Connection","u":"/zh/docs/v1.0/Configuration/Jira","h":"#step-22---add-a-jira-connection","p":15444},{"i":15460,"t":"There are three settings for Sync Policy: Data Time Range: You can select the time range of the data you wish to collect. The default is set to the past six months. Sync Frequency: You can choose how often you would like to sync your data in this step by selecting a sync frequency option or enter a cron code to specify your prefered schedule. Skip Failed Tasks: sometime a few tasks may fail in a long pipeline; you can choose to skip them to avoid spending more time in running the pipeline all over again.","s":"Step 2.3 - Set the Sync Policy","u":"/zh/docs/v1.0/Configuration/Jira","h":"#step-23---set-the-sync-policy","p":15444},{"i":15462,"t":"Click on \"Collect Data\" to start collecting data for the whole project. You can check the status in the Status tab on the same page.","s":"Step 2.4 - Start Data Collection","u":"/zh/docs/v1.0/Configuration/Jira","h":"#step-24---start-data-collection","p":15444},{"i":15464,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/Jira","h":"#troubleshooting","p":15444},{"i":15466,"t":"Visit Config UI at: http://localhost:4000.","s":"Opsgenie","u":"/zh/docs/v1.0/Configuration/Opsgenie","h":"","p":15465},{"i":15468,"t":"On the Connections page, you can select Opsgenie and create a new connection on it.","s":"Step 1 - Add Data Connections","u":"/zh/docs/v1.0/Configuration/Opsgenie","h":"#step-1---add-data-connections","p":15465},{"i":15470,"t":"Connection Name​ Give your connection a unique name to help you identify it in the future. Endpoint URL​ Opsgenie makes available two types of REST API endpoints, US and EU, eg. https://api.opsgenie.com/ or https://api.eu.opsgenie.com/. You can choose wich instance by selecting the due radio input. API Access Key​ In the API key management section of your Atlassian Opsgenie account settings, you can create a API key to manage your requests. Learn about how to create a Opsgenie API key. The following permissions are required to collect data from repositories: Read Create and Update Configuration Access Proxy URL (Optional)​ If you are behind a corporate firewall or VPN you may need to utilize a proxy server. Enter a valid proxy server address on your network, e.g. http://your-proxy-server.com:1080 Fixed Rate Limit (Optional)​ DevLake uses a dynamic rate limit to collect Opsgenie data. You can adjust the rate limit if you want to increase or lower the speed. Opsgenie doesn't establishes a maximum rate limit for its API request, thus you should check this detailed doc on how to calculate your rate limit, based on number of user and account plan that you hired. For now, the default rate limit is in 6,000 request/hour (100 request/minute). Test and Save Connection​ Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Step 1.1 - Authentication","u":"/zh/docs/v1.0/Configuration/Opsgenie","h":"#step-11---authentication","p":15465},{"i":15472,"t":"Services​ Choose the Opsgenie services you wish to collect either by finding them in the miller column, or searching.","s":"Step 1.2 - Add Data Scopes","u":"/zh/docs/v1.0/Configuration/Opsgenie","h":"#step-12---add-data-scopes","p":15465},{"i":15475,"t":"Collecing Opsgenie data requires creating a project first. You can visit the Project page from the side menu and create a new project by following the instructions on the user interface.","s":"Step 2.1 - Create a Project","u":"/zh/docs/v1.0/Configuration/Opsgenie","h":"#step-21---create-a-project","p":15465},{"i":15477,"t":"You can add a previously configured Opsgenie connection to the project and select the boards for which you wish to collect the data for. Please note: if you don't see the services you are looking for, please check if you have added them to the connection first.","s":"Step 2.2 - Add a Opsgenie Connection","u":"/zh/docs/v1.0/Configuration/Opsgenie","h":"#step-22---add-a-opsgenie-connection","p":15465},{"i":15479,"t":"There are three settings for Sync Policy: Data Time Range: You can select the time range of the data you wish to collect. The default is set to the past six months. Sync Frequency: You can choose how often you would like to sync your data in this step by selecting a sync frequency option or enter a cron code to specify your prefered schedule. Skip Failed Tasks: sometime a few tasks may fail in a long pipeline; you can choose to skip them to avoid spending more time in running the pipeline all over again.","s":"Step 2.3 - Set the Sync Policy","u":"/zh/docs/v1.0/Configuration/Opsgenie","h":"#step-23---set-the-sync-policy","p":15465},{"i":15481,"t":"Click on \"Collect Data\" to start collecting data for the whole project. You can check the status in the Status tab on the same page.","s":"Step 2.4 - Start Data Collection","u":"/zh/docs/v1.0/Configuration/Opsgenie","h":"#step-24---start-data-collection","p":15465},{"i":15483,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/Opsgenie","h":"#troubleshooting","p":15465},{"i":15485,"t":"Visit Config UI at: http://localhost:4000.","s":"PagerDuty","u":"/zh/docs/v1.0/Configuration/PagerDuty","h":"","p":15484},{"i":15488,"t":"Give your connection a unique name to help you identify it in the future.","s":"Connection Name","u":"/zh/docs/v1.0/Configuration/PagerDuty","h":"#connection-name","p":15484},{"i":15490,"t":"Paste your PagerDuty personal access token (PAT) here. You may make it a Read-Only token for the plugin's purposes.","s":"Token","u":"/zh/docs/v1.0/Configuration/PagerDuty","h":"#token","p":15484},{"i":15492,"t":"Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Test and Save Connection","u":"/zh/docs/v1.0/Configuration/PagerDuty","h":"#test-and-save-connection","p":15484},{"i":15494,"t":"Create a project for PagerDuty by adding the connection created above to it.","s":"Step 1 - Add Data Connection","u":"/zh/docs/v1.0/Configuration/PagerDuty","h":"#step-1---add-data-connection","p":15484},{"i":15497,"t":"Select the services you want to collect data from.","s":"Select services","u":"/zh/docs/v1.0/Configuration/PagerDuty","h":"#select-services","p":15484},{"i":15499,"t":"PagerDuty supports the following data entities. Issue Tracking: These map to PagerDuty incidents.","s":"Data Entities","u":"/zh/docs/v1.0/Configuration/PagerDuty","h":"#data-entities","p":15484},{"i":15501,"t":"Currently, this plugin does not support transformation rules, so skip this page by clicking Next Step.","s":"Step 3 - Adding Transformation Rules (Optional)","u":"/zh/docs/v1.0/Configuration/PagerDuty","h":"#step-3---adding-transformation-rules-optional","p":15484},{"i":15503,"t":"Set the sync policy as you see fit. Note that PagerDuty can only collect data from up to 6 months prior to the present time.","s":"Step 4 - Set Sync Policy","u":"/zh/docs/v1.0/Configuration/PagerDuty","h":"#step-4---set-sync-policy","p":15484},{"i":15505,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/PagerDuty","h":"#troubleshooting","p":15484},{"i":15507,"t":"Visit Config UI at: http://localhost:4000 and go to Connections page.","s":"TAPD","u":"/zh/docs/v1.0/Configuration/Tapd","h":"","p":15506},{"i":15510,"t":"Name your connection.","s":"Connection Name","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#connection-name","p":15506},{"i":15512,"t":"This should be a valid REST API endpoint https://api.tapd.cn/ The endpoint url should end with /.","s":"Endpoint URL","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#endpoint-url","p":15506},{"i":15514,"t":"Input the username and password of your Tapd account, you can follow the steps as below.","s":"Username/Password","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#usernamepassword","p":15506},{"i":15516,"t":"If you are behind a corporate firewall or VPN you may need to utilize a proxy server. Enter a valid proxy server address on your network, e.g. http://your-proxy-server.com:1080","s":"Proxy URL (Optional)","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#proxy-url-optional","p":15506},{"i":15518,"t":"For TAPD, we suggest you setting the rate limit to 3500","s":"Ralte Limit (Optional)","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#ralte-limit-optional","p":15506},{"i":15520,"t":"Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Test and Save Connection","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#test-and-save-connection","p":15506},{"i":15522,"t":"You can find the campany id in your Tapd company dashboard.","s":"Step 2 - Setting Data Scope","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#step-2---setting-data-scope","p":15506},{"i":15524,"t":"Choose the Tapd workspaces to collect.","s":"Workspaces","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#workspaces","p":15506},{"i":15526,"t":"Usually, you don't have to modify this part. However, if you don't want to collect certain Tapd entities, you can unselect some entities to accerlerate the collection speed. Issue Tracking: Tapd issues, issue comments, issue labels, etc. Cross Domain: Tapd accounts, etc.","s":"Data Entities","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#data-entities","p":15506},{"i":15528,"t":"Without adding transformation rules, you can not view all charts in \"Engineering Throughput and Cycle Time\" dashboards. Each Tapd workspace can be configured with different transformation rules.","s":"Step 3 - Adding Transformation Rules (Optional)","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#step-3---adding-transformation-rules-optional","p":15506},{"i":15530,"t":"Requirement: choose the issue types to be transformed to \"REQUIREMENT\". Bug: choose the issue types to be transformed to \"BUG\". Incident: choose the issue types to be transformed to \"INCIDENT\". TODO: The issues that are planned but have not been worked on yet IN-PROGRESS: The issues that are work-in-progress DONE: The issues that are completed","s":"Issue Tracking","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#issue-tracking","p":15506},{"i":15532,"t":"You can choose how often you would like to sync your data in this step by selecting a sync frequency option or enter a cron code to specify your prefered schedule.","s":"Step 4 - Setting Sync Policy","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#step-4---setting-sync-policy","p":15506},{"i":15534,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/Tapd","h":"#troubleshooting","p":15506},{"i":15536,"t":"Visit Config UI: http://localhost:4000 and go to Connections page.","s":"Teambition(WIP)","u":"/zh/docs/v1.0/Configuration/Teambition","h":"","p":15535},{"i":15539,"t":"Give your connection a unique name to help you identify it in the future. Endpoint URL​ This should be a valid REST API endpoint https://open.teambition.com/api/ The endpoint url should end with /.","s":"Connection Name","u":"/zh/docs/v1.0/Configuration/Teambition","h":"#connection-name","p":15535},{"i":15541,"t":"Input the app id and secret key of your Teambition account, you can follow the steps as below. You should ensure that you have added all the necessary \"get\" and \"list\" authentication methods. Tenant Type/Tenant Id​ It is important to add your app before finding the Tenant Id.","s":"App Id/Secret Key","u":"/zh/docs/v1.0/Configuration/Teambition","h":"#app-idsecret-key","p":15535},{"i":15543,"t":"If you are behind a corporate firewall or VPN you may need to utilize a proxy server. Enter a valid proxy server address on your network, e.g. http://your-proxy-server.com:1080","s":"Proxy URL (Optional)","u":"/zh/docs/v1.0/Configuration/Teambition","h":"#proxy-url-optional","p":15535},{"i":15545,"t":"For Teambition, we suggest you setting the rate limit to 5000","s":"Ralte Limit (Optional)","u":"/zh/docs/v1.0/Configuration/Teambition","h":"#ralte-limit-optional","p":15535},{"i":15547,"t":"Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Test and Save Connection","u":"/zh/docs/v1.0/Configuration/Teambition","h":"#test-and-save-connection","p":15535},{"i":15549,"t":"Similar to other beta plugins, Teambition does not support project, which means, you can only collect Teambition data via blueprint's advanced mode. Please go to the Blueprints page and switch to advanced mode. See how to use advanced mode and JSON examples.","s":"Step 2 - Configure Blueprint","u":"/zh/docs/v1.0/Configuration/Teambition","h":"#step-2---configure-blueprint","p":15535},{"i":15551,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/Teambition","h":"#troubleshooting","p":15535},{"i":15553,"t":"The Apache DevLake Config UI provides a user-friendly interface for configuring the data collection process. To access the Config UI, please visit http://localhost:4000.","s":"Tutorial","u":"/zh/docs/v1.0/Configuration/Tutorial","h":"","p":15552},{"i":15555,"t":"To ensure the proper functioning of DevLake, follow these two key steps:","s":"Basic Configuration","u":"/zh/docs/v1.0/Configuration/Tutorial","h":"#basic-configuration","p":15552},{"i":15557,"t":"Step 1.1 - Add a connection. Configure the endpoint and authentication details to connect to the source data. Step 1.2 - Add data scope, such as Git repositories, issue boards, or CI/CD pipelines, to determine what data should be collected. Step 1.3 - Add scope config (optional). Define the specific data entities within the data scope for collection or apply transformation rules to the raw API responses.","s":"Step 1 - Add Data Connections","u":"/zh/docs/v1.0/Configuration/Tutorial","h":"#step-1---add-data-connections","p":15552},{"i":15559,"t":"Step 2.1 - Create a project. DevLake assesses DORA metrics at the project level. For more information on organizing DevLake projects, please refer to how to organize DevLake projects for more details. Step 2.2 - Associate connection(s) with the project. When associating a connection with a project, you can select specific data scopes. All connections linked to the same project will be considered part of the same project for calculating DORA metrics. Step 2.3 - Set the synchronization policy. Specify the sync frequency, time range and the skip-on-fail option for your data. Step 2.4 - Start data collection. Choose the desired mode for collecting data.","s":"Step 2 - Collect Data in a Project","u":"/zh/docs/v1.0/Configuration/Tutorial","h":"#step-2---collect-data-in-a-project","p":15552},{"i":15561,"t":"To view the collected data, click on the \"Dashboards\" button located in the top-right corner of Config UI. For detailed instructions, please refer to the Grafana manuals.","s":"Step 3 - Check the Data in Grafana Dashboards","u":"/zh/docs/v1.0/Configuration/Tutorial","h":"#step-3---check-the-data-in-grafana-dashboards","p":15552},{"i":15563,"t":"For detailed examples, please refer to the respective documentation files available in this folder, such as GitHub configuration, GitLab configuration, Jira configuration and more. They provide step-by-step instructions and guidance for configuring DevLake with different platforms.","s":"Examples","u":"/zh/docs/v1.0/Configuration/Tutorial","h":"#examples","p":15552},{"i":15565,"t":"Q1. What are the specific sync policies to configure?​ Time Filter: This allows you to select the desired time range for syncing data, optimizing the collection process. Frequency: You can determine the frequency of data synchronization by choosing a sync frequency option or specifying a cron code for a custom schedule. Running Policy: By default, the \"Skip failed tasks\" option is enabled. This helps prevent data loss in scenarios where you are collecting a large volume of data (e.g., 10+ GitHub repositories, Jira boards, etc.). When a task fails, this policy allows the pipeline to continue running, preserving the data collected by successful tasks. You can rerun the failed tasks later from the blueprint's detail page. Q2. What data collection modes do DevLake support?​ Three modes. Collect Data (Default): This mode retrieves data within the specified time range. Tools and entities that support incremental refresh will utilize this method, while those that only support full refresh will perform a full refresh. This mode is the default choice for recurring pipelines. Collect Data in Full Refresh Mode: In this mode, all existing data within the designated time range will be deleted and re-collected. It is useful for removing outdated or irrelevant data from DevLake that no longer exists in the original tools. Re-transform Data: This mode does not collect new data. Instead, it applies the latest transformation rules from the Scope Config to the existing data.","s":"Q&A","u":"/zh/docs/v1.0/Configuration/Tutorial","h":"#qa","p":15552},{"i":15567,"t":"1. What can be done when a data collection failed or partially succeeded?​ First, re-run the failed task once all other tasks have completed. If the task still fails, proceed to the next steps. Capture a screenshot of the error message associated with the failed task. Download the logs from the pipeline for further analysis. Visit the GitHub repository and create a bug report. Include the captured screenshot and the downloaded logs in the bug report. For other problems, please check the troubleshooting doc, create an issue, or contact us on Slack.","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/Tutorial","h":"#troubleshooting","p":15552},{"i":15570,"t":"To organize and display metrics by team, Apache DevLake needs to know about the team configuration in an organization, specifically: What are the teams? Who are the users(unified identities)? Which users belong to a team? Which accounts(identities in specific tools) belong to the same user? Each of the questions above corresponds to a table in DevLake's schema, illustrated below: teams table stores all the teams in the organization. users table stores the organization's roster. An entry in the users table corresponds to a person in the org. team_users table stores which users belong to a team. user_accounts table stores which accounts belong to a user. An account refers to an identiy in a DevOps tool and is automatically created when importing data from that tool. For example, a user may have a GitHub account as well as a Jira account. Apache DevLake uses a simple heuristic algorithm based on emails and names to automatically map accounts to users and populate the user_accounts table. When Apache DevLake cannot confidently map an account to a user due to insufficient information, it allows DevLake users to manually configure the mapping to ensure accuracy and integrity.","s":"What is 'Team Configuration' and how it works?","u":"/zh/docs/v1.0/Configuration/TeamConfiguration","h":"#what-is-team-configuration-and-how-it-works","p":15568},{"i":15572,"t":"In the following sections, we'll walk through how to configure teams and create the five aforementioned tables (teams, users, team_users, accounts, and user_accounts). The overall workflow is: Create the teams table Create the users and team_users table Populate the accounts table via data collection Run a heuristic algorithm to populate user_accounts table Manually update user_accounts when the algorithm can't catch everything Note: Please replace /path/to/*.csv with the absolute path of the CSV file you'd like to upload. Please replace http://127.0.0.1:4000 with your actual Config UI service IP and port number. If you have enabled https, please replace it accordingly.","s":"A step-by-step guide","u":"/zh/docs/v1.0/Configuration/TeamConfiguration","h":"#a-step-by-step-guide","p":15568},{"i":15574,"t":"You can create the teams table by sending a PUT request to /plugins/org/teams.csv with a teams.csv file. To jumpstart the process, you can download a template teams.csv from /plugins/org/teams.csv?fake_data=true. Below are the detailed instructions: Download the template teams.csv file. You can do it by a. Pasting the URL into your browser to download the template. http://127.0.0.1:4000/api/plugins/org/teams.csv?fake_data=true b. Or using CURL: curl 'http://127.0.0.1:4000/api/rest/plugins/org/teams.csv?fake_data=true' -X 'GET' -H 'Authorization: Bearer {API_key}' Fill out teams.csv file and upload it to DevLake (If you are using Excel to modify the CSV file, please save it with UTF-8 encoding. See how) a. Fill out teams.csv with your org data. Please don't modify the column headers or the file suffix. b. Upload teams.csv to DevLake with the following curl command curl 'http://127.0.0.1:4000/api/rest/plugins/org/teams.csv' -X 'PUT' -H 'Authorization: Bearer {API_key}' --form 'file=@\"/path/to/teams.csv\"' c. The PUT request would populate the teams table with data from teams.csv file. You can connect to the database and verify the data in the teams table. See 'Appendix A' for how to connect to the database.","s":"Step 1 - Create the teams table","u":"/zh/docs/v1.0/Configuration/TeamConfiguration","h":"#step-1---create-the-teams-table","p":15568},{"i":15576,"t":"You can create the users and team_users table by sending a single PUT request to /plugins/org/users.csv with a users.csv file. To jumpstart the process, you can download a template users.csv from /plugins/org/users.csv?fake_data=true. Below are the detailed instructions: Download the template users.csv file. You can do it by a. Pasting the URL into your browser to download the template. http://127.0.0.1:4000/api/plugins/org/users.csv?fake_data=true b. Or using CURL: curl 'http://127.0.0.1:4000/api/rest/plugins/org/users.csv?fake_data=true' -X 'GET' -H 'Authorization: Bearer {API_key}' Fill out users.csv and upload it to DevLake (If you are using Excel to modify the CSV file, please save it with UTF-8 encoding. See how) a. Fill out users.csv with your org data. Please do not modify the column headers or the file suffix. b. Upload users.csv to DevLake with the following curl command: curl 'http://127.0.0.1:4000/api/rest/plugins/org/users.csv' -X 'PUT' -H 'Authorization: Bearer {API_key}' --form 'file=@\"/path/to/users.csv\"' c. The PUT request would populate the users table along with the team_users table with data from users.csv file. You can connect to the database and verify these two tables. If you want to update team_users or users table, simply upload the updated users.csv to DevLake again following the previous step.","s":"Step 2 - Create the users and team_users table","u":"/zh/docs/v1.0/Configuration/TeamConfiguration","h":"#step-2---create-the-users-and-team_users-table","p":15568},{"i":15578,"t":"The accounts table is automatically populated when you collect data from data sources like GitHub and Jira through DevLake. For example, the GitHub plugin would create one entry in the accounts table for each GitHub user involved in your repository. For demo purposes, we will insert some mock data into the accounts table using SQL: INSERT INTO `accounts` (`id`, `created_at`, `updated_at`, `_raw_data_params`, `_raw_data_table`, `_raw_data_id`, `_raw_data_remark`, `email`, `full_name`, `user_name`, `avatar_url`, `organization`, `created_date`, `status`) VALUES ('github:GithubAccount:1:1234', '2022-07-12 10:54:09.632', '2022-07-12 10:54:09.632', '{\\\"ConnectionId\\\":1,\\\"Owner\\\":\\\"apache\\\",\\\"Repo\\\":\\\"incubator-devlake\\\"}', '_raw_github_api_pull_request_reviews', 28, '', 'TyroneKCummings@teleworm.us', '', 'Tyrone K. Cummings', 'https://avatars.githubusercontent.com/u/101256042?u=a6e460fbaffce7514cbd65ac739a985f5158dabc&v=4', '', NULL, 0), ('jira:JiraAccount:1:629cdf', '2022-07-12 10:54:09.632', '2022-07-12 10:54:09.632', '{\\\"ConnectionId\\\":1,\\\"BoardId\\\":\\\"76\\\"}', '_raw_jira_api_users', 5, '', 'DorothyRUpdegraff@dayrep.com', '', 'Dorothy R. Updegraff', 'https://avatars.jiraxxxx158dabc&v=4', '', NULL, 0);","s":"Step 3 - Populate the accounts table via data collection","u":"/zh/docs/v1.0/Configuration/TeamConfiguration","h":"#step-3---populate-the-accounts-table-via-data-collection","p":15568},{"i":15580,"t":"Now that we have data in both the users and accounts table, we can tell DevLake to infer the mappings between users and accounts with a simple heuristic algorithm based on names and emails. Send an API request to DevLake to run the mapping algorithm curl --location --request POST '127.0.0.1:4000/api/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw '{ \"name\": \"test\", \"plan\":[ [ { \"plugin\": \"org\", \"subtasks\":[\"connectUserAccountsExact\"] } ] ] }' After successful execution, you can verify the data in user_accounts in the database.","s":"Step 4 - Run a heuristic algorithm to populate user_accounts table","u":"/zh/docs/v1.0/Configuration/TeamConfiguration","h":"#step-4---run-a-heuristic-algorithm-to-populate-user_accounts-table","p":15568},{"i":15582,"t":"It is recommended to examine the generated user_accounts table after running the algorithm. We will demonstrate how to manually update user_accounts when the mapping is inaccurate or incomplete in this section. To make manual verification easier, DevLake provides an API for users to download user_accounts as a CSV file. Alternatively, you can verify and modify user_accounts all by SQL, see Appendix for more info. Download the template by pasting the following URL to your browser: http://127.0.0.1:4000/api/plugins/org/user_account_mapping.csv b. Or using CURL: curl 'http://127.0.0.1:4000/api/rest/plugins/org/user_account_mapping.csv' -X 'GET' -H 'Authorization: Bearer {API_key}' If you find the mapping inaccurate or incomplete, you can modify the user_account_mapping.csv file and then upload it to DevLake. For example, here we change the UserId of row 'Id=github:GithubAccount:1:1234' in the user_account_mapping.csv file to 2. Save and upload the updated user_account_mapping.csv file with the following curl command (If you are using Excel to modify the CSV file, please save it with UTF-8 encoding. See how): curl 'http://127.0.0.1:4000/api/rest/plugins/org/user_account_mapping.csv' -X 'PUT' -H 'Authorization: Bearer {API_key}' --form 'file=@\"/path/to/user_account_mapping.csv\"' You can verify the data in the user_accounts table has been updated.","s":"Step 5 - Manually update user_accounts when the algorithm can't catch everything","u":"/zh/docs/v1.0/Configuration/TeamConfiguration","h":"#step-5---manually-update-user_accounts-when-the-algorithm-cant-catch-everything","p":15568},{"i":15584,"t":"There are many ways to connect to the database: Through Grafana. Go to Grafana's Explore page. Switch the SQL editor to the code mode. Then, you can write SQL to describe database and query data. Through database management tools such as Navicat, phyMyAdmin, DataGrip, MySQLWorkbench, etc. Type in your host, port, username and password to connect to the DB. The username and password can be fount in the image or .env file. Through the command line. Take MySQL as an example: mysql -h <ip> -u <username> -p -P <port>","s":"Appendix A: how to connect to the database","u":"/zh/docs/v1.0/Configuration/TeamConfiguration","h":"#appendix-a-how-to-connect-to-the-database","p":15568},{"i":15586,"t":"SELECT a.id as account_id, a.email, a.user_name as account_user_name, u.id as user_id, u.name as real_name FROM accounts a join user_accounts ua on a.id = ua.account_id join users u on ua.user_id = u.id","s":"Appendix B: how to examine user_accounts via SQL","u":"/zh/docs/v1.0/Configuration/TeamConfiguration","h":"#appendix-b-how-to-examine-user_accounts-via-sql","p":15568},{"i":15588,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/TeamConfiguration","h":"#troubleshooting","p":15568},{"i":15590,"t":"Visit Config UI at: http://localhost:4000.","s":"SonarQube Server","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"","p":15589},{"i":15593,"t":"Name your connection.","s":"Connection Name","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"#connection-name","p":15589},{"i":15595,"t":"This should be a valid REST API endpoint http://<host>:<port>/api/ The endpoint url should end with /.","s":"Endpoint URL","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"#endpoint-url","p":15589},{"i":15597,"t":"Please use a system admin account to create the SonarQube token, because some SonarQube APIs require this permission to list all projects. Learn about how to create a SonarQube token.","s":"Token","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"#token","p":15589},{"i":15599,"t":"If you are behind a corporate firewall or VPN you may need to utilize a proxy server. Enter a valid proxy server address on your network, e.g. http://your-proxy-server.com:1080","s":"Proxy URL (Optional)","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"#proxy-url-optional","p":15589},{"i":15601,"t":"DevLake uses a dynamic rate limit at around 18,000 requests/hour to collect SonarQube data. You can adjust the rate limit if you want to increase or lower the speed.","s":"Fixed Rate Limit (Optional)","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"#fixed-rate-limit-optional","p":15589},{"i":15603,"t":"Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Test and Save Connection","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"#test-and-save-connection","p":15589},{"i":15606,"t":"Choose the SonarQube projects to collect.","s":"Projects","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"#projects","p":15589},{"i":15608,"t":"Usually, you don't have to modify this part. However, if you don't want to collect certain SonarQube entities, you can unselect some entities to accerlerate the collection speed. Code Quality Domain: SonarQube issues, issue code blocks, file metrics, hotspots, etc. Cross Domain: SonarQube accounts, etc.","s":"Data Entities","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"#data-entities","p":15589},{"i":15610,"t":"SonarQube does not have transformation and this step will be skipped.","s":"Step 3 - Adding Transformation Rules (Optional)","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"#step-3---adding-transformation-rules-optional","p":15589},{"i":15612,"t":"You can choose how often you would like to sync your data in this step by selecting a sync frequency option or enter a cron code to specify your prefered schedule.","s":"Step 4 - Setting Sync Policy","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"#step-4---setting-sync-policy","p":15589},{"i":15614,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/SonarQube","h":"#troubleshooting","p":15589},{"i":15616,"t":"Visit config-ui: http://{localhost}:4000.","s":"Webhooks","u":"/zh/docs/v1.0/Configuration/webhook","h":"","p":15615},{"i":15618,"t":"Go to the 'Connections' page. Create a webhook with a unique name.","s":"Step 1 - Add a new incoming webhook","u":"/zh/docs/v1.0/Configuration/webhook","h":"#step-1---add-a-new-incoming-webhook","p":15615},{"i":15620,"t":"Click on Generate POST URL, and you will find three webhook URLs. Copy the ones that suit your usage into your CI/CD or issue-tracking systems. You can always come back to the webhook page to copy the URLs later on. A non-expired API key is auto-generated for the authentication of the webhook. This API key only shows in the payload when you create the webhook. However, you can always revoke and generate a new token when you view the webhook details. See the full payload schema of webhooks.","s":"Step 2 - Create webhooks connection","u":"/zh/docs/v1.0/Configuration/webhook","h":"#step-2---create-webhooks-connection","p":15615},{"i":15622,"t":"If you want to use webhook data to measure DORA metrics, you have to associate it with a DevLake project. Go to the 'Incoming Webhooks' tab on a project's page. Add webhook by selecting the existing webhook. Go to the project's blueprint page and click 'Collect Data'. This will trigger the DORA plugin to measure DORA metrics with the data collected by the data connections and webhooks associated with this project. Put webhook on the internet​ For the new webhook to work, it needs to be accessible from the DevOps tools from which you would like to push data to DevLake. If DevLake is deployed in your private network and your DevOps tool (e.g. CircleCI) is a cloud service that lives outside of your private network, then you need to make DevLake's webhook accessible to the outside cloud service. There are many tools for this: For testing and quick setup, ngrok is a useful utility that provides a publicly accessible web URL to any locally hosted application. You can put DevLake's webhook on the internet within 5 mins by following ngrok's Getting Started guide. Note that, when posting to webhook, you may need to replace the localhost part in the webhook URL with the forwarding URL that ngrok provides. If you prefer DIY, please check out open-source reverse proxies like fatedier/frp or go for the classic nginx.","s":"Step 3 - Use webhook in a project","u":"/zh/docs/v1.0/Configuration/webhook","h":"#step-3---use-webhook-in-a-project","p":15615},{"i":15624,"t":"If you run into any problems, please check the Troubleshooting or create an issue.","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/webhook","h":"#troubleshooting","p":15615},{"i":15626,"t":"Visit Config UI at: http://localhost:4000 and go to Connections page.","s":"Zentao","u":"/zh/docs/v1.0/Configuration/Zentao","h":"","p":15625},{"i":15629,"t":"Give your connection a unique name to help you identify it in the future.","s":"Connection Name","u":"/zh/docs/v1.0/Configuration/Zentao","h":"#connection-name","p":15625},{"i":15631,"t":"Please ensure that the REST API endpoint URL is valid. It should be in the format of either http://<host>:<port>/api.php/v1/ or http://<host>:<port>/zentao/api.php/v1/. If the initial test fails, please try another endpoint URL as the URL depends on where Zentao is deployed. Additionally, please ensure that the endpoint URL ends with a forward slash /.","s":"Endpoint URL","u":"/zh/docs/v1.0/Configuration/Zentao","h":"#endpoint-url","p":15625},{"i":15633,"t":"Input the username and password of your Zentao account.","s":"Username/Password","u":"/zh/docs/v1.0/Configuration/Zentao","h":"#usernamepassword","p":15625},{"i":15635,"t":"If you are behind a corporate firewall or VPN you may need to utilize a proxy server. Enter a valid proxy server address on your network, e.g. http://your-proxy-server.com:1080","s":"Proxy URL (Optional)","u":"/zh/docs/v1.0/Configuration/Zentao","h":"#proxy-url-optional","p":15625},{"i":15637,"t":"Click Test Connection, if the connection is successful, click Save Connection to add the connection.","s":"Test and Save Connection","u":"/zh/docs/v1.0/Configuration/Zentao","h":"#test-and-save-connection","p":15625},{"i":15640,"t":"Please select the Zentao products for collecting stories/bugs and the Zentao projects for collecting executions. Both will also collect information on accounts/departments.","s":"Projects and Products","u":"/zh/docs/v1.0/Configuration/Zentao","h":"#projects-and-products","p":15625},{"i":15642,"t":"Usually, you don't have to modify this part. However, if you don't want to collect certain Lento entities, you can unselect some entities to accerlerate the collection speed. Issue Tracking: Lento issues, issue comments, issue labels, etc.","s":"Data Entities","u":"/zh/docs/v1.0/Configuration/Zentao","h":"#data-entities","p":15625},{"i":15644,"t":"Zentao does not have transformation and this step will be skipped.","s":"Step 3 - Adding Transformation Rules (Optional)","u":"/zh/docs/v1.0/Configuration/Zentao","h":"#step-3---adding-transformation-rules-optional","p":15625},{"i":15646,"t":"You can choose how often you would like to sync your data in this step by selecting a sync frequency option or enter a cron code to specify your prefered schedule.","s":"Step 4 - Setting Sync Policy","u":"/zh/docs/v1.0/Configuration/Zentao","h":"#step-4---setting-sync-policy","p":15625},{"i":15648,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/Configuration/Zentao","h":"#troubleshooting","p":15625},{"i":15651,"t":"This document describes Apache DevLake's domain layer schema. Referring to DevLake's architecture, the data in the domain layer is transformed from the data in the tool layer. The tool layer schema is based on the data from specific tools such as Jira, GitHub, GitLab, Jenkins, etc. The domain layer schema can be regarded as an abstraction of tool-layer schemas. DevLake Dataflow","s":"Summary","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#summary","p":15649},{"i":15653,"t":"All metrics from pre-built dashboards are based on this data schema. As a user, you can create your own customized dashboards based on this data schema. As a contributor, you can refer to this data schema while working on the ETL logic when adding/updating data source plugins.","s":"Use Cases","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#use-cases","p":15649},{"i":15655,"t":"This is the up-to-date domain layer schema for DevLake. Tables (entities) are categorized into 5 domains. Issue tracking: Jira issues, GitHub issues, GitLab issues, etc. Source code management: Git/GitHub/GitLab commits and refs(tags and branches), etc. Code review: GitHub PRs, GitLab MRs, etc. CI/CD: Jenkins jobs & builds, etc. Code Quality: SonarQube issues, hotspots, file metrics, etc. Cross-domain: entities that map entities from different domains to break data isolation.","s":"Data Models","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#data-models","p":15649},{"i":15657,"t":"When reading the schema, you'll notice that many tables' primary key is called id. Unlike auto-increment id or UUID, id is a string composed of several parts to uniquely identify similar entities (e.g. repo) from different platforms (e.g. GitHub/GitLab) and allow them to co-exist in a single table. Tables that end with WIP are still under development.","s":"Schema Diagram","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#schema-diagram","p":15649},{"i":15659,"t":"The name of a table is in plural form. E.g. boards, issues, etc. The name of a table which describe the relation between 2 entities is in the form of [BigEntity in singular form]_[SmallEntity in plural form]. E.g. board_issues, sprint_issues, pull_request_comments, etc. Value of the field in enum type are in capital letters. E.g. table.issues.type has 3 values, REQUIREMENT, BUG, INCIDENT. Values that are phrases, such as 'IN_PROGRESS' of table.issues.status, are separated with underscore '_'.","s":"Naming Conventions","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#naming-conventions","p":15649},{"i":15661,"t":"Apache DevLake provides 2 plugins: customize: to create/delete columns in the domain layer schema with the data extracted from raw layer tables dbt: to transform data based on the domain layer schema and generate new tables","s":"How to Customize Data Models","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#how-to-customize-data-models","p":15649},{"i":15664,"t":"issues​ An issue is the abstraction of GitHub/GitLab/BitBucket/Jira/TAPD/Zentao... issues. field type length description key id varchar 255 An issue's id is composed of < plugin >:< Entity >:< PK0 >[:PK1]...\" For Github issues, a Github issue's id is like \"github:GithubIssues:< GithubIssueId >\". E.g. 'github:GithubIssues:1049355647' For Jira issues, a Github repo's id is like \"jira:JiraIssues:< JiraSourceId >:< JiraIssueId >\". E.g. 'jira:JiraIssues:1:10063'. < JiraSourceId > is used to identify which jira source the issue came from, since DevLake users can import data from several different Jira instances at the same time. PK issue_key varchar 255 The key of this issue. For example, the key of this Github issue is 1145. url varchar 255 The url of the issue. It's a web address in most cases. title varchar 255 The title of an issue description longtext The detailed description/summary of an issue type varchar 100 The standard type of this issue. There are 3 standard types: REQUIREMENT: this issue is a feature BUG: this issue is a bug found during test INCIDENT: this issue is a bug found after release The 3 standard types are transformed from the original types of an issue. The transformation rule is set in the '.env' file or 'config-ui' before data collection. For issues with an original type that has not mapped to a standard type, the value of type will be the issue's original type. original_type varchar 100 The original type of an issue. status varchar 100 The standard statuses of this issue. There are 3 standard statuses: TODO: this issue is in backlog or to-do list IN_PROGRESS: this issue is in progress DONE: this issue is resolved or closed The 3 standard statuses are transformed from the original statuses of an issue. The transformation rule: For Jira issue status: transformed from the Jira issue's statusCategory. Jira issue has 3 default status categories: 'To Do', 'In Progress', 'Done'. For Github issue status: open -> TODO closed -> DONE original_status varchar 100 The original status of an issue. story_point double The story point of this issue. Only certain types(e.g. story) of Jira or TAPD issues has story points priority varchar 255 The priority of the issue urgency varchar 255 The urgency of the issue component varchar 255 The component a bug-issue affects. This field only supports Github plugin for now. The value is transformed from Github issue labels by the rules set according to the user's configuration of .env by end users during DevLake installation. severity varchar 255 The severity level of a bug-issue. This field only supports Github plugin for now. The value is transformed from Github issue labels by the rules set according to the user's configuration of .env by end users during DevLake installation. parent_issue_id varchar 255 The id of its parent issue epic_key varchar 255 The key of the epic this issue belongs to. For tools with no epic-type issues such as Github and GitLab, this field is default to an empty string original_estimate_minutes int The original estimation of the time allocated for this issue time_spent_minutes int The original estimation of the time allocated for this issue time_remaining_minutes int The remaining time to resolve the issue creator_id varchar 255 The id of issue creator creator_name varchar 255 The name of the creator assignee_id varchar 255 The id of issue assignee. For Github issues: this is the last assignee of an issue if the issue has multiple assignees For Jira issues: this is the assignee of the issue at the time of collection assignee_name varchar 255 The name of the assignee created_date datetime 3 The time issue created updated_date datetime 3 The last time issue gets updated resolution_date datetime 3 The time the issue changes to 'DONE'. lead_time_minutes int Describes the cycle time from issue creation to issue resolution. For issues whose type = 'REQUIREMENT' and status = 'DONE', lead_time_minutes = resolution_date - created_date. The unit is minute. For issues whose type != 'REQUIREMENT' or status != 'DONE', lead_time_minutes is null original_project varchar 255 The name of the original project this issue belongs to. Transformed from a Jira project's name, a TAPD workspace's name, etc. icon_url varchar 255 The url of the issue icon. x_custom_field_1 It depends on the type of the converted field - The value of the custom field. This field is available when utilizing the customize plugin to convert Jira's raw layer fields to the domain layer fields. issue_assignees​ This table shows the assignee(s) of issues. Multiple entries can exist per issue, as a GitHub/TAPD issue may have multiple assignees at the same time. This table can be used to get the detailed information of all issue assignees. field type length description key issue_id varchar 255 Issue ID FK_issues.id assignee_id varchar 255 Assignee ID FK_accounts.id assignee_name varchar 255 Assignee Name issue_labels​ This table shows the labels of issues. Multiple entries can exist per issue. This table can be used to filter issues by label name. field type length description key label_name varchar 255 Label name. Collect from GitHub issue labels or Jira issue labels issue_id varchar 255 Issue ID FK_issues.id issue_comments​ This table shows the comments of issues. Only GitHub and TAPD issue comments are collected. Issues with multiple comments are shown as multiple records. This table can be used to calculate metric - issue response time. field type length description key id varchar 255 The unique id of a comment PK issue_id varchar 255 Issue ID FK_issues.id account_id varchar 255 The id of the account who made the comment FK_accounts.id body longtext The body/detail of the comment created_date datetime 3 The creation date of the comment updated_date datetime 3 The update date of the comment issue_changelogs​ This table shows the changelogs of issues. Only Jira issue changelogs are collected for now. Issues with multiple changelogs are shown as multiple records. This is transformed from Jira or TAPD changelogs. field type length description key id varchar 255 The unique id of an issue changelog PK issue_id varchar 255 Issue ID FK_issues.id author_id varchar 255 The id of the user who made the change FK_accounts.id author_name varchar 255 The id of the user who made the change field_id varchar 255 The id of changed field field_name varchar 255 The id of changed field original_from_value longtext The original value of the changed field original_to_value longtext The new value of the changed field from_value longtext The transformed/standardized original value of the changed field to_value longtext The transformed/standardized new value of the changed field created_date datetime 3 The creation date of the changelog issue_worklogs​ This table shows the work logged under issues. Only Jira issue worklogs are collected for now. Usually, an issue has multiple worklogs logged by different developers. field type length description key id varchar 255 The id of the worklog. PK author_id varchar 255 The id of the author who logged the work FK_accounts.id comment longtext 255 The comment made while logging the work. time_spent_minutes int The time logged. The unit of value is normalized to minute. E.g. 1d =) 480, 4h30m =) 270 logged_date datetime 3 The time of this logging action started_date datetime 3 Start time of the worklog issue_id varchar 255 Issue ID FK_issues.id issue_relationships​ This table shows the metadata of information about relationships between issues. field type length description key id varchar 255 Issue ID PK source_issue_id int ID of the source issue in the relationship target_issue_id int ID of the target issue in the relationship original_type varchar 255 Type of relationship between the source and target issues issue_repo_commits​ This table shows the metadata of commits made to a code repository associated with specific issues. field type length description key issue_id varchar 255 Issue ID PK repo_url varchar 255 The URL of the code repository PK commit_sha varchar 255 The SHA of the commit. PK host varchar 255 The hostname namespace varchar 255 The namespace of the code repository repo_name varchar 255 The name of the code repository. issue_custom_array_fields​ The table below presents the custom fields of issues in an 'array' type. This table is available when utilizing the customize plugin to convert Jira's raw layer fields to the domain layer fields. It is important to note that custom fields of other types will be displayed as 'x_custom_field_1' in the issues table. field type length description key issue_id varchar 255 Issue ID PK field_id varchar 255 The ID of the array field of the issue. It starts with 'x_', e.g. x_product_lines. field_value varchar 255 The value of the array field. E.g. DevLake, DevSea, DevPond. You can refer to the following SQL to use this table. -- query issue count by product lines select count(*) as issue_count, field_value from issues join issue_custom_array_fields on issues.id = issue_custom_array_fields.issue_id where field_id = 'x_product_lines' group by field_value; boards​ A board is an issue list or a collection of issues. It's the abstraction of a Jira board, a Jira or TAPD project, a GitHub repo's issue list or a GitLab repo's issue list. This table can be used to filter issues by the boards they belong to. field type length description key id varchar 255 A board's id is composed of \"< plugin >:< Entity >:< PK0 >[:PK1]...\" For a Github repo's issue list, the board id is like \"< github >:< GithubRepos >:< ConnectionId >:< GithubRepoId >\". E.g. \"github:GithubRepo:384111310\" For a Jira Board, the id is like \"< jira >:< JiraSourceId >< JiraBoards >:< ConnectionId >:< JiraBoardsId >\". E.g. \"jira:1:JiraBoards:1:12\" PK name varchar 255 The name of the board. Note: the board name of a Github repo 'apache/incubator-devlake' is 'apache/incubator-devlake', representing the default issue list. description varchar 255 The description of the board. url varchar 255 The url of the board. E.g. https://github.com/apache/incubator-devlake/issues created_date datetime 3 Board creation time type varchar 255 Identify scrum and non-scrum board board_issues​ This table shows the relation between boards and issues. This table can be used to filter issues by board. field type length description key board_id varchar 255 Board id FK_boards.id issue_id varchar 255 Issue id FK_issues.id sprints​ A sprint is the abstraction of Jira sprints, TAPD iterations and GitHub milestones. A sprint contains a list of issues. field type length description key id varchar 255 A sprint's id is composed of \"< plugin >:< Entity >:< PK0 >[:PK1]...\" A sprint in a Github repo is a milestone, the sprint id is like \"< github >:< GithubRepos >:< GithubRepoId >:< milestoneNumber >\". Eg. The id for this sprint is \"github:GithubRepo:384111310:5\" For a Jira Board, the id is like \"< jira >:< JiraSourceId >< JiraBoards >:< JiraBoardsId >\". Eg. \"jira:1:JiraBoards:12\" PK name varchar 255 The name of sprint. For Github projects, the sprint name is the milestone name. For instance, 'v0.10.0 - Introduce Temporal to DevLake' is the name of this sprint. url varchar 255 The url of sprint. status varchar 255 There are 3 statuses of a sprint: CLOSED: a completed sprint ACTIVE: a sprint started but not completed FUTURE: a sprint that has not started SUSPENDED: a sprint that has been suspended started_date datetime 3 The start time of a sprint ended_date datetime 3 The planned/estimated end time of a sprint. It's usually set when planning a sprint. completed_date datetime 3 The actual time to complete a sprint. original_board_id datetime 3 The id of board where the sprint first created. This field is not null only when this entity is transformed from Jira sprints. In Jira, sprint and board entities have 2 types of relation: A sprint is created based on a specific board. In this case, board(1):(n)sprint. This field original_board_id is used to show the relation. A sprint can be mapped to multiple boards, a board can also show multiple sprints. In this case, board(n):(n)sprint. This relation is shown in table.board_sprints FK_boards.id sprint_issues​ This table shows the relation between sprints and issues that have been added to sprints. This table can be used to show metrics such as 'ratio of unplanned issues', 'completion rate of sprint issues', etc field type length description key sprint_id varchar 255 Sprint id FK_sprints.id issue_id varchar 255 Issue id FK_issues.id board_sprints​ field type length description key board_id varchar 255 Board id FK_boards.id sprint_id varchar 255 Sprint id FK_sprints.id","s":"Domain 1 - Issue Tracking","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#domain-1---issue-tracking","p":15649},{"i":15666,"t":"repos​ GitHub, GitLab or BitBucket repositories. field type length description key id varchar 255 A repo's id is composed of \"< plugin >:< Entity >:< PK0 >[:PK1]...\" For example, a Github repo's id is like \"< github >:< GithubRepos >:< ConnectionId >:< GithubRepoId >\". E.g. 'github:GithubRepos:1:384111310' PK name longtext The name of repo. For DevLake, it's 'apache/incubator-devlake' description longtext The description of repo. url longtext The url of repo. E.g. https://github.com/apache/incubator-devlake owner_id varchar 255 The id of the owner of repo FK_accounts.id language varchar 255 The major language of repo. E.g. The language for apache/incubator-devlake is 'Go' forked_from longtext Empty unless the repo is a fork in which case it contains the id of the repo the repo is forked from. deleted tinyint 1 0: repo is active 1: repo has been deleted created_date datetime 3 Repo creation date updated_date datetime 3 Last full update was done for this repo repo_commits​ The commits belong to the history of a repository. More than one repo can share the same commits if one is a fork of the other. field type length description key repo_id varchar 255 Repo id FK_repos.id commit_sha char 40 Commit sha FK_commits.sha refs​ A ref is the abstraction of a branch or tag. field type length description key id varchar 255 A ref's id is composed of \"< plugin >:< Entity >:< PK0 >[:PK1]...\" For example, a Github ref is composed of \"github:GithubRepos:< GithubRepoId >:< RefUrl >\". E.g. The id of release v5.3.0 of PingCAP/TiDB project is 'github:GithubRepos:384111310:refs/tags/v5.3.0' A repo's id is composed of \"< plugin >:< Entity >:< PK0 >[:PK1]...\" PK name varchar 255 The name of the ref. E.g. 'refs/tags/v0.9.3' or 'origin/main' repo_id varchar 255 The id of repo this ref belongs to FK_repos.id commit_sha char 40 The commit this ref points to at the time of collection is_default tinyint 1 0: not the default branch 1: the ref is the default branch. By the definition of Github, the default branch is the base branch for pull requests and code commits. ref_type varchar 64 There are 2 typical types: BRANCH TAG commits_diffs​ This table shows the commits added in a new commit compared to an old commit. This table can be used to support tag-based and deploy-based metrics. The records of this table are computed by RefDiff plugin. The computation should be manually triggered after using GitRepoExtractor to collect commits and refs. The algorithm behind is similar to this. field type length description key new_commit_sha char 40 The commit new ref/deployment points to at the time of collection PK old_commit_sha char 40 The commit old ref/deployment points to at the time of collection PK commit_sha char 40 One of the added commits in the new ref compared to the old ref/deployment PK sorting_index bigint An index for debugging, please skip it ref_commits​ field type length description key new_ref_id varchar 255 The new ref's id for comparison PK old_ref_id varchar 255 The old ref's id for comparison PK new_commit_sha char 40 The commit new ref points to at the time of collection old_commit_sha char 40 The commit old ref points to at the time of collection commits​ field type length description key sha char 40 One of the added commits in the new ref compared to the old ref FK_commits.sha message varchar 255 Commit message author_name varchar 255 The value is set with command git config user.name xxxxx commit author_email varchar 255 The value is set with command git config user.email xxxxx author authored_date datetime 3 The date when this commit was originally made author_id varchar 255 The id of commit author FK_accounts.id committer_name varchar 255 The name of committer committer_email varchar 255 The email of committer committed_date datetime 3 The last time the commit gets modified. For example, when rebasing the branch where the commit is in on another branch, the committed_date changes. committer_id varchar 255 The id of committer FK_accounts.id additions bigint Added lines of code deletions bigint Deleted lines of code dev_eq int A metric that quantifies the amount of code contribution. The data can be retrieved from AE plugin. commit_files​ The files that have been changed by commits. field type length description key id varchar 255 The id is composed of \"< Commit_sha >:< file_path >\" FK_commits.sha commit_sha char 40 Commit sha FK_commits.sha file_path varchar 255 Path of a changed file in a commit additions bigint The added lines of code in this file by the commit deletions bigint The deleted lines of code in this file by the commit components​ The components of files extracted from the file paths. This can be used to analyze Git metrics by component. field type length description key repo_id varchar 255 The repo id FK_repos.id name varchar 255 The name of component path_regex varchar 255 The regex to extract components from this repo's paths commit_file_components​ The relationship between commit_file and component_name. field type length description key commit_file_id varchar 255 The id of commit file FK_commit_files.id component_name varchar 255 The component name of a file commit_parents​ The parent commit(s) for each commit, as specified by Git. field type length description key commit_sha char 40 commit sha FK_commits.sha parent_commit_sha char 40 Parent commit sha FK_commits.sha","s":"Domain 2 - Source Code Management","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#domain-2---source-code-management","p":15649},{"i":15668,"t":"pull_requests​ Pull requests are the abstraction of GitHub pull requests, GitLab merge requests, BitBucket pull requests, etc. field type length description key id varchar 255 A pull request's id is composed of \"< plugin >:< Entity >:< PK0 >[:PK1]...\" E.g. For 'github:GithubPullRequests:1347' title longtext The title of pull request description longtext The body/description of pull request status varchar 100 The PR/MR statuses are standardized to 'OPEN', 'MERGED' and 'CLOSED'. Learn how each plugin's PR statuses are standardized. original_status varchar 100 The original status of pull requests. parent_pr_id varchar 255 The id of the parent PR pull_request_key varchar 255 The key of PR. E.g. 1536 is the key of this PR base_repo_id varchar 255 The repo that will be updated. head_repo_id varchar 255 The repo containing the changes that will be added to the base. If the head repository is NULL, this means that the corresponding project had been deleted when DevLake processed the pull request. author_name varchar 100 The author's name of the pull request author_id varchar 100 The author's id of the pull request url varchar 255 the web link of the pull request type varchar 255 The work-type of a pull request.For example: feature-development, bug-fix, docs,etc. component varchar 255 The component this PR affects. The value is transformed from Github/GitLab pull request labels by configuring GITHUB_PR_COMPONENT in .env file during installation. created_date datetime 3 The time PR created. merged_date datetime 3 The time PR gets merged. Null when the PR is not merged. closed_date datetime 3 The time PR closed. Null when the PR is not closed. merge_commit_sha char 40 the merge commit of this PR. By the definition of Github, when you click the default Merge pull request option on a pull request on Github, all commits from the feature branch are added to the base branch in a merge commit. FK_commits.sha base_ref varchar 255 The branch name in the base repo that will be updated head_ref varchar 255 The branch name in the head repo that contains the changes that will be added to the base base_commit_sha char 40 The base commit of this PR. head_commit_sha char 40 The head commit of this PR. pull_request_labels​ This table shows the labels of pull request. Multiple entries can exist per pull request. This table can be used to filter pull requests by label name. field type length description key label_name varchar 255 Label name pull_request_id varchar 255 Pull request ID FK_pull_requests.id pull_request_commits​ A commit associated with a pull request. The list is additive. This means if a rebase with commit squashing takes place after the commits of a pull request have been processed, the old commits will not be deleted. field type length description key pull_request_id varchar 255 Pull request id FK_pull_requests.id commit_sha char 40 Commit sha FK_commits.sha commit_author_name varchar 255 The name of the person who authored the commit commit_author_email varchar 255 The email address of the person who authored the commit. commit_authored_date varchar 255 The date and time when the commit was authored. pull_request_comments​ Normal comments, review bodies, reviews' inline comments of GitHub's pull requests or GitLab's merge requests. field type length description key id varchar 255 Comment id PK pull_request_id varchar 255 Pull request id FK_pull_requests.id body longtext The body of the comments account_id varchar 255 The account who made the comment FK_accounts.id created_date datetime 3 Comment creation time position int Deprecated type varchar 255 - For normal comments: NORMAL - For review comments, ie. diff/inline comments: DIFF - For reviews' body (exist in GitHub but not GitLab): REVIEW review_id varchar 255 Review_id of the comment if the type is REVIEW or DIFF status varchar 255 Status of the comment","s":"Domain 3 - Code Review","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#domain-3---code-review","p":15649},{"i":15670,"t":"cicd_scopes​ The entity to filter or group 'cicd_pipelines'. For GitHub: a GitHub repo is converted to a cicd_scope For GitLab: a GitLab project is converted to a cicd_scope For Jenkins: a Jenkins job is converted to a cicd_scope For Bamboo CI: a Bamboo plan is converted to a cicd_scope field type length description key id varchar 255 A cicd_scope's id is composed of \"< plugin >:< Entity >:< PluginConnectionId >[:PK1]...\" For example, a GitHub cicd_scope's id is \"github:GithubRepos:< GithubConnectionId >:< GithubRepoId >\", a Bamboo cicd_scope's id is 'bamboo:BambooPlan:< BambooConnectionId >:< BambooPlanKey >' PK name varchar 255 The name of cicd_scope. description longtext The description of cicd_scope. url varchar 255 The url of cicd_scope. E.g. https://github.com/apache/incubator-devlake or https://jenkins.xxx.cn/view/PROD/job/OPS_releasev2/ created_date datetime 3 Creation date of the cicd_scope, nullable updated_date datetime 3 Updation date of the cicd_scope, nullable cicd_pipelines​ A cicd_pipeline is the abstraction of a top-level CI/CD execution, e.g. a GitHub workflow run, a GitLab pipeline, a BitBucket pipeline, a Jenkins build, a Bamboo plan build, etc. A cicd_pipeline contains one or more of cicd_tasks. field type length description key id varchar 255 This key is generated based on details from the original plugin PK name varchar 255 For gitlab, as there is no name for pipeline, so we use projectId, others have their own name result varchar 100 The result of the pipeline. It will be standardized to 'SUCCESS', 'FAILURE', '' in DevLake based on each plugin's possible results. original_result varchar 100 The original_result of the pipeline. Its value depends on the state of the corresponding entity in the different plugins. status varchar 100 The status of the pipeline. It will be standardized to 'DONE', 'IN_PROGRESS', 'OTHER' in DevLake based on each plugin's possible statues. original_status varchar 100 The original_status of the pipeline. Its value depends on the state of the corresponding entity in the different plugins. type varchar 100 The value will be set to 'DEPLOYMENT' if it matched the regex configured in the Scope Config, otherwise it is an empty string. environment varchar 255 The value will be set to 'PRODUCTION' if it matched the regex configured in the Scope Config, otherwise it is an empty string. duration_sec double how long does this pipeline take queued_duration_sec double how long does this pipeline take queuing created_date datetime 3 When this pipeline created. queued_date datetime 3 The queued time of the pipeline. started_date datetime 3 The started time of the pipeline. finished_date datetime 3 When this pipeline finished. cicd_scope_id longtext The id of cicd_scope this pipeline belongs to FK_cicd_scopes.id cicd_pipeline_commits​ field type length description key pipeline_id varchar 255 This key is generated based on details from the original plugin PK commit_sha varchar 255 The commit that triggers this pipeline PK branch varchar 255 The branch that triggers this pipeline repo_url varchar 255 repo_id varchar 255 The repo that this pipeline belongs to cicd_tasks​ A cicd_task is the abstraction of the bottom-level CI/CD execution. For GitHub: a cicd_task is a GitHub job run in a GitHub workflow run. For GitLab: a cicd_task is a GitLab job run of a GitLab pipeline run. For Jenkins: a cicd_task is a subtask of a Jenkins build. If a build does not have subtask(s), then the build will also be saved as a cicd_task in this table. For Bamboo CI: a cicd_task is a Bamboo job build in a Bamboo plan build. field type length description key id varchar 255 This key is generated based on details from the original plugin PK name varchar 255 pipeline_id varchar 255 The id of the cicd_pipeline it belongs to result varchar 100 The result of the task. It will be standardized to 'SUCCESS', 'FAILURE', '' in DevLake based on each plugin's possible. original_result varchar 100 The original_result of the task. Its value depends on the state of the corresponding entity in the different plugins. status varchar 100 The status of the task. It will be standardized to 'DONE', 'IN_PROGRESS', 'OTHER' in DevLake based on each plugin's possible states. original_status varchar 100 The original_status of the task. Its value depends on the state of the corresponding entity in the different plugins. type varchar 100 The value will be set to 'DEPLOYMENT' if it matched the regex configured in the Scope Config, otherwise it is an empty string. environment varchar 255 The value will be set to 'PRODUCTION' if it matched the regex configured in the Scope Config, otherwise it is an empty string. duration_sec double How long does this task take ququed_duration_sec double How long does this task take queuing created_date datetime 3 The created time of the task. queued_date datetime 3 The queued time of the task. started_date datetime 3 When this task started finished_date datetime 3 When this task finished cicd_scope_id longtext The id of cicd_scope this task belongs to FK_cicd_scopes.id cicd_deployments​ A cicd_deployment refers to a deployment at the project level. In the case where a pipeline run or build deploys across three distinct repositories, it will be categorized as ONE cicd_deployment while being recorded as THREE separate cicd_deployment_commits. It may come from several sources: Domain layer cicd_pipelines, such as GitHub workflow runs, GitLab pipelines, Jenkins builds and BitBucket pipelines, etc. Deployments from cicd_pipelines will be transformed according to the regex configuration set in the Blueprint transformation before adding to this table. Tool layer deployments: in v0.20, only the BitBucket\\Bamboo\\GitLab and GitHub(Use GraphQL APIs) plugins collect the independent deployment entity which you can find in table._tool_bitbucket_deployments and _tool_bamboo_deploy_builds, there will be more in the future. Deployments pushed directly from webhooks Additional Notes CICD deployments are recorded at the project level, not the repository level. CICD deployment commits are individual commits that represent a single deployment across multiple repositories. field type length description key id varchar 255 The deployment_id of this deployment. The value will be set with id when it comes from webhooks. PK cicd_scope_id varchar 255 The id of cicd_scope this deployment belongs to FK_cicd_scopes.id name varchar 255 The name of the deployment result varchar 100 The result of the deployment, enum. 'SUCCESS', 'FAILUR'E, '' original_result varchar 100 The original_result of the deployment. Its value depends on the state of the corresponding entity in the different plugins. status varchar 100 The status of this deployment, enum: 'IN_PROGRESS', 'DONE', 'OTHER' original_status varchar 100 The original_status of the deployment. Its value depends on the state of the corresponding entity in the different plugins. environment varchar 255 The environment to deploy, only 'PRODUCTION' deployment will appear in v0.17 original_environment varchar 255 The original environment field of the deployment. It appeared since v1.0. created_date datetime 3 The created time of the deployment. queued_date datetime 3 The queued time of the deployment. started_date datetime 3 The started time of the deployment. finished_date datetime 3 The finished time of the deployment duration_sec double The time this deployment takes ququed_duration_sec double The time this deployment takes queuing cicd_deployment_commits​ A cicd_deployment_commit is a deployment in a specific repo. A deployment may come from several sources: Domain layer cicd_pipelines, such as GitHub workflow runs, GitLab pipelines, Jenkins builds and BitBucket pipelines, etc. Deployments from cicd_pipelines will be transformed according to the regex configuration set in the Blueprint transformation before adding to this table. Tool layer deployments: in v0.18, only the BitBucket and Bamboo plugins collect the independent deployment entity which you can find in table._tool_bitbucket_deployments and _tool_bamboo_deploy_builds, but there will be more in the future. Deployments pushed directly from webhooks You can query deployments from this table by SELECT DISTINCT cicd_deployment_id FROM cicd_deployments_commits. Normally, one deployment only deploy to one repo. But in some cases, one deployment may deploy in multiple repos with different commits. In these cases, there will be multiple pairs of deployment-commit-repo, appeared in multiple entries in this table. field type length description key id varchar 255 This key is the combination of the deployment's id and repo_url, e.g. - from a GitHub workflow run: github:GithubRun:1:384111310:3521097091:https://github.com/apache/incubator-devlake - from a Jenkins build, jenkins:JenkinsBuild:1:deploy#7:https://github.com/apache/incubator-devlake - from a webhook, webhook:1:90489d3951711d72:e6bde456807818c5c78d7b265964d6d48b653af6 PK cicd_scope_id varchar 255 The id of cicd_scope this deployment_commit belongs to FK_cicd_scopes.id cicd_deployment_id varchar 255 The deployment_id of this deployment_commit. The value will be set with id when it comes from webhooks. name varchar 255 The name of the deployment result varchar 100 The result of the deployment, enum: 'SUCCESS', 'FAILURE', '' original_result varchar 100 The original_result of the deployment. Its value depends on the state of the corresponding entity in the different plugins. status varchar 100 The status of this deployment, enum: 'IN_PROGRESS', 'DONE', 'OTHER' original_status varchar 100 The original_status of the deployment. Its value depends on the state of the corresponding entity in the different plugins. environment varchar 255 The environment to deploy original_environment varchar 255 The original environment of the deployment. It appeared since v1.0. created_date datetime 3 The created time of the deployment. queued_date datetime 3 The queued time of the deployment. started_date datetime 3 The started time of the deployment. finished_date datetime 3 The finished time of the deployment duration_sec double The time this deployment takes queued_duration_sec double The time this deployment takes queueing commit_sha char 40 The commit sha that triggers the deployment ref_name varchar 255 The ref (branch/tag) name of the commit repo_id varchar 255 - repo_url varchar 191 The url of the repo prev_success_deployment_commit_id varchar 255 The last successful deployment_commit_id before this one in the same cicd_scope, repo and environment, which is used to calculate the new commits deployed by this deployment, thereby measuring DORA - change lead time.","s":"Domain 4 - CI/CD","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#domain-4---cicd","p":15649},{"i":15672,"t":"The names of tables in the 'Code Quality' domain will start with a prefix cq_ cq_projects​ field type length description key id varchar 255 This key is generated based on details from the original plugin PK name varchar 255 The name of the project in SonarQube qualifier varchar 255 The type of project. Examples include \"TRK\" for regular projects and \"VW\" for views visibility varchar 64 The visibility of the project. Examples include \"public\" and \"private\" last_analysis_date datatime 3 The date and time of the most recent analysis of the project commit_sha varchar 128 It represents the version number or code version identifier of a project cq_issues​ field type length description key id varchar 255 This key is generated based on details from the original plugin PK rule varchar 255 The key of the rule that the issue is violating severity varchar 255 The severity level of the issue component varchar 255 The name of the component where the issue was found project_key varchar 255 The key of the project that the issue belongs to line bigint The line number where the issue was found status varchar 255 The status of the issue message longtext The message associated with the issue debt bigint The estimated time required to fix the issue effort bigint The effort required to fix the issue commit_author_email varchar 255 The email address of the author of the commit that introduced the issue assignee varchar 255 The person assigned to fix the issue hash varchar 255 A hash code for the issue tags varchar 255 Any tags associated with the issue type varchar 255 The type of the issue scope varchar 128 The scope of the issue start_line bigint 255 The starting line of the issue end_line bigint 255 The ending line of the issue start_offset bigint 255 The starting offset of the issue end_offset bigint 255 The ending offset of the issue vulnerability_probability varchar 100 The probability of the issue being a vulnerability security_category varchar 100 The security category of the issue created_date datetime 3 The time when the issue was created updated_date datetime 3 The time when the issue was last updated cq_issue_code_blocks​ field type length description key id varchar 255 This key is generated based on details from the original plugin PK issue_key varchar 255 A string that stores the key of the issue that the code block is associated with component varchar 255 A string that stores the name of the component that the code block is associated with start_line bigint 255 An integer that stores the line number where the code block starts end_line bigint 255 An integer that stores the line number where the code block ends start_offset bigint 255 An integer that stores the offset where the code block starts end_offset bigint 255 An integer that stores the offset where the code block ends msg longtext A long text field that stores the message associated with the code block cq_file_metrics​ field type length description key id varchar 255 This key is generated based on details from the original plugin PK project_key varchar 255 The key of the project that the issue belongs to PK file_name longtext longtext fields that store the name of the file file_path longtext longtext fields that store the path of the file file_language longtext longtext fields that store the language of the file code_smells bigint Code smells of this file sqale_index bigint Sqale index of the file sqale_rating double Sqale rating of the file bugs bigint Bugs rating of the file reliability_rating longtext Reliability rating of the file vulnerabilities bigint Vulnerabilities of the file security_rating longtext Security rating of the file security_hotspots bigint Security hotspots of the file security_hotspots_reviewed double Security hotspots reviewed of the file security_review_rating longtext Security review rating of the file ncloc bigint Ncloc of the file coverage double Ncoverage of the file lines_to_cover bigint Lines to cover of the file duplicated_lines_density double Duplicated lines density of the file duplicated_blocks bigint Duplicated blocks of the file duplicated_files bigint Duplicated files of the file duplicated_lines bigint Duplicated lines of the file effort_to_reach_maintainability_rating_a bigint Effort to reach maintainability rating a of the file complexity bigint Complexity of the file cognitive_complexity bigint Cognitive complexity of the file num_of_lines bigint Num of lines of the file","s":"Domain 5 - Code Quality","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#domain-5---code-quality","p":15649},{"i":15674,"t":"These entities are used to map entities between different domains. They are the key players to break data isolation. There are low-level entities such as issue_commits, users, and higher-level cross domain entities such as board_repos issue_commits​ A low-level mapping between \"issue tracking\" and \"source code management\" domain by mapping issues and commits. Issue(n): Commit(n). The original connection between these two entities lies in either issue tracking tools like Jira or source code management tools like GitLab. You have to use tools to accomplish this. For example, a common method to connect Jira issue and GitLab commit is a GitLab plugin Jira Integration. With this plugin, the Jira issue key in the commit message written by the committers will be parsed. Then, the plugin will add the commit urls under this jira issue. Hence, DevLake's Jira plugin can get the related commits (including repo, commit_id, url) of an issue. field type length description key issue_id varchar 255 Issue id FK_issues.id commit_sha char 40 Commit sha FK_commits.sha pull_request_issues​ This table shows the issues closed by pull requests. It's a medium-level mapping between \"issue tracking\" and \"source code management\" domain by mapping issues and commits. Issue(n): Commit(n). The data is extracted from the body of pull requests conforming to certain regular expression. The regular expression can be defined in GITHUB_PR_BODY_CLOSE_PATTERN in the .env file field type length description key pull_request_id varchar 255 Pull request id FK_pull_requests.id issue_id varchar 255 Issue id FK_issues.id pull_request_number varchar 255 Pull request key issue_number varchar 255 Issue key board_repos (Deprecated)​ A way to link \"issue tracking\" and \"source code management\" domain by mapping boards and repos. Board(n): Repo(n). field type length description key board_id varchar 255 Board id FK_boards.id repo_id varchar 255 Repo id FK_repos.id accounts​ This table stores of user accounts across different tools such as GitHub, Jira, GitLab, etc. This table can be joined to get the metadata of all accounts. metrics, such as 'No. of Issue closed by contributor', 'No. of commits by contributor', field type length description key id varchar 255 An account's id is the identifier of the account of a specific tool. It is composed of \"< Plugin >:< Entity >:< PK0 >[:PK1]...\" For example, a Github account's id is composed of \"< github >:< GithubAccounts >:< GithubUserId >)\". E.g. 'github:GithubUsers:14050754' PK email varchar 255 Email of the account full_name varchar 255 Full name user_name varchar 255 Username, nickname or Github login of an account avatar_url varchar 255 organization varchar 255 User's organization(s) created_date datetime 3 User creation time status int 0: default, the user is active. 1: the user is not active users​ field type length description key id varchar 255 id of a person PK email varchar 255 the primary email of a person name varchar 255 name of a person user_accounts​ field type length description key user_id varchar 255 users.id Composite PK, FK account_id varchar 255 accounts.id Composite PK, FK teams​ field type length description key id varchar 255 id from the data sources, decided by DevLake users PK name varchar 255 name of the team. E.g. team A, team B, etc. alias varchar 255 alias or abbreviation of a team parent_id varchar 255 teams.id, default to null FK sorting_index int 255 the field to sort team team_users​ field type length description key team_id varchar 255 Full name of the team. E.g. team A, team B, etc. Composite PK, FK user_id varchar 255 users.id Composite PK, FK project​ field type length description key name varchar 255 name for project PK description longtext description of the project created_at datetime 3 created time of project updated_at datetime 3 last updated time of project project_metric_settings​ field type length description key project_name varchar 255 name for project PK plugin_name varchar 255 name for plugin PK plugin_option longtext check if metric plugins have been enabled by the project enable tinyint 1 if the metric plugins is enabled project_mapping​ field type length description key project_name varchar 255 name for project PK table varchar 255 the table name of Scope PK row_id varchar 255 the row_id in the Scope table PK project_pr_metrics​ field type length description key id varchar 255 Id of PR PK project_name varchar 100 The project that this PR belongs to PK first_review_id longtext The id of the first review on this pr first_commit_sha longtext The sha of the first commit pr_coding_time bigint The time it takes from the first commit until a PR is issued pr_pickup_time bigint The time it takes from when a PR is issued until the first comment is added to that PR pr_review_time bigint The time it takes to complete a code review of a PR before it gets merged deployment_id longtext The id of cicd_task which deploy the commits of this PR pr_deploy_time bigint The time it takes from when a PR is merged to when it is deployed pr_cycle_time bigint The total time from the first commit to when the PR is deployed project_issue_metrics​ field type length description key id varchar 255 Id of Issue PK project_name varchar 100 The project that this Issue belongs to PK deployment_id longtext The id of cicd_task which cause an incident refs_issues_diffs​ This table shows the issues fixed by commits added in a new ref compared to an old one. The data is computed from table.commits_diffs, table.pull_requests, table.pull_request_commits, and table.pull_request_issues. This table can support tag-based analysis, for instance, 'No. of bugs closed in a tag'. field type length description key new_ref_id varchar 255 The new ref's id for comparison FK_refs.id old_ref_id varchar 255 The old ref's id for comparison FK_refs.id new_ref_commit_sha char 40 The commit new ref points to at the time of collection old_ref_commit_sha char 40 The commit old ref points to at the time of collection issue_number varchar 255 Issue number issue_id varchar 255 Issue id FK_issues.id","s":"Domain 6 - Cross-Domain Entities","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#domain-6---cross-domain-entities","p":15649},{"i":15676,"t":"When developing a new plugin, you need to refer to domain layer models, as all raw data should be transformed to domain layer data to provide standardized metrics across tools. Please use the following method to access the domain data models. import \"github.com/apache/incubator-devlake/models/domainlayer/domaininfo\" domaininfo := domaininfo.GetDomainTablesInfo() for _, table := range domaininfo { // do something } If you want to learn more about plugin models, please visit PluginImplementation","s":"Get Domain Layer Models in Developer Mode","u":"/zh/docs/v1.0/DataModels/DevLakeDomainLayerSchema","h":"#get-domain-layer-models-in-developer-mode","p":15649},{"i":15679,"t":"This document describes Apache DevLake's raw layer schema. Referring to DevLake's architecture, the raw layer stores the API responses from data sources (DevOps tools) in JSON. This saves developers' time if the raw data is to be transformed differently later on. Please note that communicating with data sources' APIs is usually the most time-consuming step.","s":"Summary","u":"/zh/docs/v1.0/DataModels/RawLayerSchema","h":"#summary","p":15677},{"i":15681,"t":"As a user, you can check raw data tables to verify data quality if you have concerns about the domain layer data. As a developer, you can customize domain layer schema based on raw data tables via customize.","s":"Use Cases","u":"/zh/docs/v1.0/DataModels/RawLayerSchema","h":"#use-cases","p":15677},{"i":15683,"t":"Raw layer tables start with a prefix _raw_. Each plugin contains multiple raw data tables, the naming convension of these tables is _raw_{plugin}_{entity}. For instance, _raw_jira_issues _raw_jira_boards _raw_jira_board_issues ... Normally, you do not need to use these tables, unless you have one of the above use cases.","s":"Data Models","u":"/zh/docs/v1.0/DataModels/RawLayerSchema","h":"#data-models","p":15677},{"i":15686,"t":"This document describes Apache DevLake's tool layer schema. Referring to DevLake's architecture, the Tool layer extracts raw data from JSONs into a relational schema that's easier to consume by analytical tasks. Each DevOps tool would have a schema that's tailored to its data structure, hence the name, the Tool layer.","s":"Summary","u":"/zh/docs/v1.0/DataModels/ToolLayerSchema","h":"#summary","p":15684},{"i":15688,"t":"As a user, you can check tool data tables to verify data quality if you have concerns about the domain layer data.","s":"Use Cases","u":"/zh/docs/v1.0/DataModels/ToolLayerSchema","h":"#use-cases","p":15684},{"i":15690,"t":"Tool layer tables start with a prefix _tool_. Each plugin contains multiple tool data tables, the naming convension of these tables is _tool_{plugin}_{entity}. For instance, _tool_jira_issues _tool_jira_boards _tool_jira_board_issues` ... Normally, you do not need to use tool layer tables, unless you have one of the above use cases.","s":"Data Models","u":"/zh/docs/v1.0/DataModels/ToolLayerSchema","h":"#data-models","p":15684},{"i":15693,"t":"This document describes Apache DevLake's data models of its own entities. These tables are used and managed by the Devlake framework.","s":"Summary","u":"/zh/docs/v1.0/DataModels/SystemTables","h":"#summary","p":15691},{"i":15695,"t":"As a user, you can check _devlake_blueprints and _devlake_pipelines when failing to collect data via DevLake's blueprint. As a contributor, you can check these tables to debug task concurrency or data migration features.","s":"Use Cases","u":"/zh/docs/v1.0/DataModels/SystemTables","h":"#use-cases","p":15691},{"i":15697,"t":"These tables start with a prefix _devlake. Unlike raw or tool data tables, DevLake only contains one set of system tables. The naming convension of these tables is _raw_{plugin}_{entity}, such as _devlake_blueprints _devlake_pipelines _devlake_tasks _devlake_subtasks ... Normally, you do not need to use these tables, unless you have one of the above use cases.","s":"Data Models","u":"/zh/docs/v1.0/DataModels/SystemTables","h":"#data-models","p":15691},{"i":15700,"t":"The Dal (Data Access Layer) is designed to decouple the hard dependency on gorm in v0.12. The advantages of introducing this isolation are: Unit Test: Mocking an Interface is easier and more reliable than Patching a Pointer. Clean Code: DBS operations are more consistence than using gorm directly. Replaceable: It would be easier to replace gorm in the future if needed.","s":"Summary","u":"/zh/docs/v1.0/DeveloperManuals/Dal","h":"#summary","p":15698},{"i":15702,"t":"type Dal interface { AutoMigrate(entity interface{}, clauses ...Clause) error Exec(query string, params ...interface{}) error RawCursor(query string, params ...interface{}) (*sql.Rows, error) Cursor(clauses ...Clause) (*sql.Rows, error) Fetch(cursor *sql.Rows, dst interface{}) error All(dst interface{}, clauses ...Clause) error First(dst interface{}, clauses ...Clause) error Count(clauses ...Clause) (int64, error) Pluck(column string, dest interface{}, clauses ...Clause) error Create(entity interface{}, clauses ...Clause) error Update(entity interface{}, clauses ...Clause) error CreateOrUpdate(entity interface{}, clauses ...Clause) error CreateIfNotExist(entity interface{}, clauses ...Clause) error Delete(entity interface{}, clauses ...Clause) error AllTables() ([]string, error) }","s":"The Dal Interface","u":"/zh/docs/v1.0/DeveloperManuals/Dal","h":"#the-dal-interface","p":15698},{"i":15705,"t":"// Get a database cursor user := &models.User{} cursor, err := db.Cursor( dal.From(user), dal.Where(\"department = ?\", \"R&D\"), dal.Orderby(\"id DESC\"), ) if err != nil { return err } for cursor.Next() { err = dal.Fetch(cursor, user) // fetch one record at a time ... } // Get a database cursor by raw sql query cursor, err := db.Raw(\"SELECT * FROM users\") // USE WITH CAUTIOUS: loading a big table at once is slow and dangerous // Load all records from database at once. users := make([]models.Users, 0) err := db.All(&users, dal.Where(\"department = ?\", \"R&D\")) // Load a column as Scalar or Slice var email string err := db.Pluck(\"email\", &username, dal.Where(\"id = ?\", 1)) var emails []string err := db.Pluck(\"email\", &emails) // Execute query err := db.Exec(\"UPDATE users SET department = ? WHERE department = ?\", \"Research & Development\", \"R&D\")","s":"Query","u":"/zh/docs/v1.0/DeveloperManuals/Dal","h":"#query","p":15698},{"i":15707,"t":"err := db.Create(&models.User{ Email: \"hello@example.com\", // assuming this the Primarykey Name: \"hello\", Department: \"R&D\", })","s":"Insert","u":"/zh/docs/v1.0/DeveloperManuals/Dal","h":"#insert","p":15698},{"i":15709,"t":"err := db.Create(&models.User{ Email: \"hello@example.com\", // assuming this the Primarykey Name: \"hello\", Department: \"R&D\", })","s":"Update","u":"/zh/docs/v1.0/DeveloperManuals/Dal","h":"#update","p":15698},{"i":15711,"t":"err := db.CreateOrUpdate(&models.User{ Email: \"hello@example.com\", // assuming this is the Primarykey Name: \"hello\", Department: \"R&D\", })","s":"Insert or Update","u":"/zh/docs/v1.0/DeveloperManuals/Dal","h":"#insert-or-update","p":15698},{"i":15713,"t":"err := db.CreateIfNotExist(&models.User{ Email: \"hello@example.com\", // assuming this is the Primarykey Name: \"hello\", Department: \"R&D\", })","s":"Insert if record(by PrimaryKey) didn't exist","u":"/zh/docs/v1.0/DeveloperManuals/Dal","h":"#insert-if-recordby-primarykey-didnt-exist","p":15698},{"i":15715,"t":"err := db.CreateIfNotExist(&models.User{ Email: \"hello@example.com\", // assuming this is the Primary key })","s":"Delete","u":"/zh/docs/v1.0/DeveloperManuals/Dal","h":"#delete","p":15698},{"i":15717,"t":"// Returns all table names allTables, err := db.AllTables() // Automigrate: create/add missing table/columns // Note: it won't delete any existing columns, nor does it update the column definition err := db.AutoMigrate(&models.User{})","s":"DDL and others","u":"/zh/docs/v1.0/DeveloperManuals/Dal","h":"#ddl-and-others","p":15698},{"i":15719,"t":"First, run the command make mock to generate the Mocking Stubs, the generated source files should appear in mocks folder. mocks ├── ApiResourceHandler.go ├── AsyncResponseHandler.go ├── BasicRes.go ├── CloseablePluginTask.go ├── ConfigGetter.go ├── Dal.go ├── DataConvertHandler.go ├── ExecContext.go ├── InjectConfigGetter.go ├── InjectLogger.go ├── Iterator.go ├── Logger.go ├── Migratable.go ├── PluginApi.go ├── PluginBlueprintV100.go ├── PluginInit.go ├── PluginMeta.go ├── PluginTask.go ├── RateLimitedApiClient.go ├── SubTaskContext.go ├── SubTaskEntryPoint.go ├── SubTask.go └── TaskContext.go With these Mocking stubs, you may start writing your TestCases using the mocks.Dal. import \"github.com/apache/incubator-devlake/mocks\" func TestCreateUser(t *testing.T) { mockDal := new(mocks.Dal) mockDal.On(\"Create\", mock.Anything, mock.Anything).Return(nil).Once() userService := &services.UserService{ Dal: mockDal, } userService.Post(map[string]interface{}{ \"email\": \"helle@example.com\", \"name\": \"hello\", \"department\": \"R&D\", }) mockDal.AssertExpectations(t)","s":"How to do Unit Test","u":"/zh/docs/v1.0/DeveloperManuals/Dal","h":"#how-to-do-unit-test","p":15698},{"i":15722,"t":"Docker v19.03.10+ Golang v1.19+ GNU Make- Mac (Preinstalled) - Windows: [Download](http://gnuwin32.sourceforge.net/packages/make.htm) - Ubuntu: `sudo apt-get install build-essential libssl-dev`","s":"Requirements","u":"/zh/docs/v1.0/DeveloperManuals/DeveloperSetup","h":"#requirements","p":15720},{"i":15724,"t":"The following guide will walk through how to run DevLake's frontend (config-ui) and backend in dev mode. Navigate to where you would like to install this project and clone the repository: git clone https://github.com/apache/incubator-devlake.git cd incubator-devlake Install dependencies for plugins: RefDiff Install Go packages cd backend go get cd .. Copy the sample config file to new local file: cp env.example .env Update the following variables in the file .env: DB_URL: Replace mysql:3306 with 127.0.0.1:3306 Start the MySQL and Grafana containers: Make sure the Docker daemon is running before this step. docker-compose -f docker-compose-dev.yml up -d mysql grafana Run devlake and config-ui in dev mode in two separate terminals: # specify the plugins that you need for both backend and frontend export DEVLAKE_PLUGINS=bamboo,bitbucket,circleci,customize,dora,gitextractor,github,github_graphql,gitlab,jenkins,jira,org,pagerduty,refdiff,slack,sonarqube,trello,webhook # run devlake make dev # run config-ui make configure-dev For common errors, please see Troubleshooting. Config UI is running at localhost:4000 For how to use Config UI, please refer to our tutorial","s":"How to setup dev environment","u":"/zh/docs/v1.0/DeveloperManuals/DeveloperSetup","h":"#how-to-setup-dev-environment","p":15720},{"i":15726,"t":"# install mockery go install github.com/vektra/mockery/v2@latest # generate mocking stubs make mock # run tests make test","s":"Running Tests","u":"/zh/docs/v1.0/DeveloperManuals/DeveloperSetup","h":"#running-tests","p":15720},{"i":15728,"t":"Please refer to the Migration Doc.","s":"DB migrations","u":"/zh/docs/v1.0/DeveloperManuals/DeveloperSetup","h":"#db-migrations","p":15720},{"i":15730,"t":"All DevLake APIs (core service + plugin API) are documented with swagger. To see API doc live with swagger: - Install [swag](https://github.com/swaggo/swag). - Run `make swag` to generate the swagger documentation. - Visit `http://localhost:8080/swagger/index.html` while `devlake` is running.","s":"Using DevLake API","u":"/zh/docs/v1.0/DeveloperManuals/DeveloperSetup","h":"#using-devlake-api","p":15720},{"i":15732,"t":"To access Grafana, click View Dashboards button in the top left corner of Config UI, or visit localhost:3002 (username: admin, password: admin). For provisioning, customizing, and creating dashboards, please refer to our Grafana Doc.","s":"Developing dashboards","u":"/zh/docs/v1.0/DeveloperManuals/DeveloperSetup","h":"#developing-dashboards","p":15720},{"i":15734,"t":"Q: Running `make dev` yields error: `libgit2.so.1.3: cannot open share object file: No such file or directory` A: `libgit2.so.1.3` is required by the gitextractor plugin and should be . Make sure your program can find `libgit2.so.1.3`. `LD_LIBRARY_PATH` can be assigned like this if your `libgit2.so.1.3` is located at `/usr/local/lib`: ```sh export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib ``` Note that the version has to be pinned to 1.3.0. If you don't have it, you may need to build it manually with CMake from [source](https://github.com/libgit2/libgit2/releases/tag/v1.3.0).","s":"Troubleshooting","u":"/zh/docs/v1.0/DeveloperManuals/DeveloperSetup","h":"#troubleshooting","p":15720},{"i":15736,"t":"- Compile all plugins: `make build-plugin` - Compile specific plugins: `PLUGIN=<PLUGIN_NAME> make build-plugin` - Compile server: `make build` - Compile worker: `make build-worker`","s":"Compiling","u":"/zh/docs/v1.0/DeveloperManuals/DeveloperSetup","h":"#compiling","p":15720},{"i":15738,"t":"To dig deeper into developing and utilizing our built-in functions and have a better developer experience, feel free to dive into our godoc reference.","s":"References","u":"/zh/docs/v1.0/DeveloperManuals/DeveloperSetup","h":"#references","p":15720},{"i":15741,"t":"Starting in v0.10.0, DevLake provides a lightweight migration tool for executing migration scripts. Both the framework and the plugins can define their migration scripts in their own migration folder. The migration scripts are written with gorm in Golang to support different SQL dialects.","s":"Summary","u":"/zh/docs/v1.0/DeveloperManuals/DBMigration","h":"#summary","p":15739},{"i":15743,"t":"The migration scripts describe how to do database migration and implement the MigrationScript interface. When DevLake starts, the scripts register themselves to the framework by invoking the Register function. The method Up contains the steps of migration. type MigrationScript interface { // this function will contain the business logic of the migration (e.g. DDL logic) Up(basicRes BasicRes) errors.Error // the version number of the migration. typically in date format (YYYYMMDDHHMMSS), e.g. 20220728000001. Migrations are executed sequentially based on this number. Version() uint64 // The name of this migration Name() string }","s":"Migration Scripts","u":"/zh/docs/v1.0/DeveloperManuals/DBMigration","h":"#migration-scripts","p":15739},{"i":15745,"t":"For each migration, we define a \"snapshot\" datamodel of the model that we wish to perform the migration on. The fields on this model shall be identical to the actual model; but unlike the actual one, this one will never change in the future. The naming convention of these models is <ModelName>YYYYMMDD and they must implement the func TableName() string method, and consumed by the Script::Up method.","s":"The Migration Model","u":"/zh/docs/v1.0/DeveloperManuals/DBMigration","h":"#the-migration-model","p":15739},{"i":15747,"t":"The table tracks migration scripts execution and schemas changes, and from which, DevLake can figure out the current state of database schemas.","s":"Table migration_history","u":"/zh/docs/v1.0/DeveloperManuals/DBMigration","h":"#table-migration_history","p":15739},{"i":15749,"t":"Each plugin has a migrationscripts subpackage that lists all the migrations to be executed for that plugin. You will need to add your migration to that list for the framework to pick it up. Similarly, there is a package for the framework-only migrations defined under the models package.","s":"Execution","u":"/zh/docs/v1.0/DeveloperManuals/DBMigration","h":"#execution","p":15739},{"i":15751,"t":"Check migration_history table, calculate all the migration scripts need to be executed. Sort scripts by Version and Name in ascending order. Please do NOT change these two values for the script after release for any reasons; otherwise, users may fail to upgrade due to the duplicated execution. Execute the scripts. Save the results in the migration_history table.","s":"How It Works","u":"/zh/docs/v1.0/DeveloperManuals/DBMigration","h":"#how-it-works","p":15739},{"i":15753,"t":"When you write a new migration script, please pay attention to the fault tolerance and the side effect. It would be better if the failed script could be safely retried, in case if something goes wrong during the migration. For this purpose, the migration scripts should be well-designed. For example, if you have created a temporary table in the Up method, it should be dropped before exiting, regardless of success or failure. Suppose we want to change the type of the Primary Key name of table users from int to varchar(255) Rename users to users_20221018 (stop if error, otherwise define a defer to rename back on error) Create new users (stop if error, otherwise define a defer to drop the table on error) Convert data from users_20221018 to users (stop if error) Drop table users_20221018 With these steps, the defer functions would be executed in reverse order if any error occurred during the migration process so the database would roll back to the original state in most cases. However, you don't neccessary deal with all the mess. We had summarized some of the most useful code examples for you to follow: [Create new tables]https://github.com/apache/incubator-devlake/blob/main/backend/core/models/migrationscripts/20220406_add_frame_tables.go) Rename column Add columns with default value Change the values(or type) of Primary Key Change the values(or type) of Column The above examples should cover most of the scenarios you may encounter. If you come across other scenarios, feel free to create issues in our GitHub Issue Tracker for discussions. In order to help others understand the script you have written, there are a couple of rules we suggest to follow: Name your script in a meaningful way. For instance, renamePipelineStepToStage is more descriptive than modifyPipelines. The script should keep only the targeted fields you are attempting to operate except when using migrationhelper.Transform, which is a full table tranformation that requires full table definition. If this is the case, add comment to the end of the fields to indicate which ones are the targets. Add comments to the script when the operation is too complicated to be expressed in plain code. Other rules to follow when writing a migration script: The migration script should only use the interfaces and packages offered by the framework like core, errors and migrationhelper. Do NOT import gorm or package from plugin directly. The name of model struct defined in your script should be suffixed with the Version of the script to distinguish from other scripts in the same package to keep it self-contained, i.e. tasks20221018. Do NOT refer struct defined in other scripts. All scripts and models names should be camelCase to avoid accidental reference from other packages.","s":"Best Practices","u":"/zh/docs/v1.0/DeveloperManuals/DBMigration","h":"#best-practices","p":15739},{"i":15756,"t":"E2E testing, as a part of automated testing, generally refers to black-box testing at the file and module level or unit testing that allows the use of some external services such as databases. The purpose of writing E2E tests is to shield some internal implementation logic and see whether the same external input can output the same result in terms of data aspects. In addition, compared to the black-box integration tests, it can avoid some chance problems caused by network and other factors. More information about the plugin can be found here: Why write E2E tests (incomplete). In DevLake, E2E testing consists of interface testing and input/output result validation for the plugin Extract/Convert subtask. This article only describes the process of writing the latter. As the Collectors invoke external services we typically do not write E2E tests for them.","s":"Why write E2E tests","u":"/zh/docs/v1.0/DeveloperManuals/E2E-Test-Guide","h":"#why-write-e2e-tests","p":15754},{"i":15758,"t":"Let's take a simple plugin - Feishu Meeting Hours Collection as an example here. Its directory structure looks like this. Next, we will write the E2E tests of the sub-tasks. The first step in writing the E2E test is to run the Collect task of the corresponding plugin to complete the data collection; that is, to have the corresponding data saved in the table starting with _raw_feishu_ in the database. This data will be presumed to be the \"source of truth\" for our tests. Here are the logs and database tables using the DirectRun (cmd) run method. $ go run plugins/feishu/main.go --numOfDaysToCollect 2 --connectionId 1 (Note: command may change with version upgrade) [2022-06-22 23:03:29] INFO failed to create dir logs: mkdir logs: file exists press `c` to send cancel signal [2022-06-22 23:03:29] INFO [feishu] start plugin [2022-06-22 23:03:33] INFO [feishu] scheduler for api https://open.feishu.cn/open-apis/vc/v1 worker: 13, request: 10000, duration: 1h0m0s [2022-06-22 23:03:33] INFO [feishu] total step: 2 [2022-06-22 23:03:33] INFO [feishu] executing subtask collectMeetingTopUserItem [2022-06-22 23:03:33] INFO [feishu] [collectMeetingTopUserItem] start api collection [2022-06-22 23:03:34] INFO [feishu] [collectMeetingTopUserItem] finished records: 1 [2022-06-22 23:03:34] INFO [feishu] [collectMeetingTopUserItem] end api collection error: %!w(<nil>) [2022-06-22 23:03:34] INFO [feishu] finished step: 1 / 2 [2022-06-22 23:03:34] INFO [feishu] executing subtask extractMeetingTopUserItem [2022-06-22 23:03:34] INFO [feishu] [extractMeetingTopUserItem] get data from _raw_feishu_meeting_top_user_item where params={\"connectionId\":1} and got 148 [2022-06-22 23:03:34] INFO [feishu] [extractMeetingTopUserItem] finished records: 1 [2022-06-22 23:03:34] INFO [feishu] finished step: 2 / 2 It is also worth mentioning that the plugin runs two tasks, collectMeetingTopUserItem and extractMeetingTopUserItem. The former is the task of collecting, which is needed to run this time, and the latter is the task of extracting data. It doesn't matter whether the extractor runs in the prepared data session. Next, we need to export the data to .csv format. This step can be done in a variety of different ways - you can show your skills. I will only introduce a few common methods here.","s":"Preparing data","u":"/zh/docs/v1.0/DeveloperManuals/E2E-Test-Guide","h":"#preparing-data","p":15754},{"i":15760,"t":"Run go run generator/main.go create-e2e-raw directly and follow the guidelines to complete the export. This solution is the simplest, but has some limitations, such as the exported fields being fixed. You can refer to the next solutions if you need more customisation options.","s":"DevLake Code Generator Export","u":"/zh/docs/v1.0/DeveloperManuals/E2E-Test-Guide","h":"#devlake-code-generator-export","p":15754},{"i":15762,"t":"This solution is very easy to use and will not cause problems using Postgres or MySQL. The success criteria for csv export is that the go program can read it without errors, so several points are worth noticing. the values in the csv file should be wrapped in double quotes to avoid special symbols such as commas in the values that break the csv format double quotes in csv files are escaped. generally \"\" represents a double quote pay attention to whether the column data is the actual value, not the value after base64 or hex After exporting, move the .csv file to plugins/feishu/e2e/raw_tables/_raw_feishu_meeting_top_user_item.csv.","s":"GoLand Database export","u":"/zh/docs/v1.0/DeveloperManuals/E2E-Test-Guide","h":"#goland-database-export","p":15754},{"i":15764,"t":"This is MySQL's solution for exporting query results to a file. The MySQL currently started in docker-compose-dev.yml comes with the --security parameter, so it does not allow select ... into outfile. The first step is to turn off the security parameter, which is done roughly as follows. After closing it, use select ... into outfile to export the csv file. The export result is rough as follows. Notice that the data field has extra hexsha fields, which need to be manually converted to literal quantities.","s":"MySQL Select Into Outfile","u":"/zh/docs/v1.0/DeveloperManuals/E2E-Test-Guide","h":"#mysql-select-into-outfile","p":15754},{"i":15766,"t":"This is Vscode's solution for exporting query results to a file, but it is not easy to use. Here is the export result without any configuration changes However, it is obvious that the escape symbol does not conform to the csv specification, and the data is not successfully exported. After adjusting the configuration and manually replacing \\\" with \"\", we get the following result. The data field of this file is encoded in base64, so it needs to be decoded manually to a literal amount before using it.","s":"Vscode Database","u":"/zh/docs/v1.0/DeveloperManuals/E2E-Test-Guide","h":"#vscode-database","p":15754},{"i":15768,"t":"This tool must write the SQL yourself to complete the data export, which can be rewritten by imitating the following SQL. SELECT id, params, CAST(`data` as char) as data, url, input,created_at FROM _raw_feishu_meeting_top_user_item; Select csv as the save format and export it for use.","s":"MySQL workbench","u":"/zh/docs/v1.0/DeveloperManuals/E2E-Test-Guide","h":"#mysql-workbench","p":15754},{"i":15770,"t":"Copy(SQL statement) to '/var/lib/postgresql/data/raw.csv' with csv header; is a common export method for PG to export csv, which can also be used here. COPY ( SELECT id, params, convert_from(data, 'utf-8') as data, url, input,created_at FROM _raw_feishu_meeting_top_user_item ) to '/var/lib/postgresql/data/raw.csv' with csv header; Use the above statement to complete the export of the file. If pg runs in docker, just use the command docker cp to export the file to the host.","s":"Postgres Copy with csv header","u":"/zh/docs/v1.0/DeveloperManuals/E2E-Test-Guide","h":"#postgres-copy-with-csv-header","p":15754},{"i":15772,"t":"First, create a test environment. For example, let's create meeting_test.go. Then enter the test preparation code in it as follows. The code is to create an instance of the feishu plugin and then call ImportCsvIntoRawTable to import the data from the csv file into the _raw_feishu_meeting_top_user_item table. func TestMeetingDataFlow(t *testing.T) { var plugin impl.Feishu dataflowTester := e2ehelper.NewDataFlowTester(t, \"feishu\", plugin) // import raw data table dataflowTester.ImportCsvIntoRawTable(\"./raw_tables/_raw_feishu_meeting_top_user_item.csv\", \"_raw_feishu_meeting_top_user_item\") } The signature of the import function is as follows. func (t *DataFlowTester) ImportCsvIntoRawTable(csvRelPath string, rawTableName string) It has a twin, with only slight differences in parameters. func (t *DataFlowTester) ImportCsvIntoTabler(csvRelPath string, dst schema.Tabler) The former is used to import tables in the raw layer. The latter is used to import arbitrary tables. Note: These two functions will delete the db table and use gorm.AutoMigrate to re-create a new table to clear data in it. After importing the data is complete, run this tester and it must be PASS without any test logic at this moment. Then write the logic for calling the call to the extractor task in TestMeetingDataFlow. func TestMeetingDataFlow(t *testing.T) { var plugin impl.Feishu dataflowTester := e2ehelper.NewDataFlowTester(t, \"feishu\", plugin) taskData := &tasks.FeishuTaskData{ Options: &tasks.FeishuOptions{ ConnectionId: 1, }, } // import raw data table dataflowTester.ImportCsvIntoRawTable(\"./raw_tables/_raw_feishu_meeting_top_user_item.csv\", \"_raw_feishu_meeting_top_user_item\") // verify extraction dataflowTester.FlushTabler(&models.FeishuMeetingTopUserItem{}) dataflowTester.Subtask(tasks.ExtractMeetingTopUserItemMeta, taskData) } The added code includes a call to dataflowTester.FlushTabler to clear the table _tool_feishu_meeting_top_user_items and a call to dataflowTester.Subtask to simulate the running of the subtask ExtractMeetingTopUserItemMeta. Now run it and see if the subtask ExtractMeetingTopUserItemMeta completes without errors. The data results of the extract run generally come from the raw table, so the plugin subtask will run correctly if written without errors. We can observe if the data is successfully parsed in the db table in the tool layer. In this case the _tool_feishu_meeting_top_user_items table has the correct data. If the run is incorrect, maybe you can troubleshoot the problem with the plugin itself before moving on to the next step.","s":"Writing E2E tests","u":"/zh/docs/v1.0/DeveloperManuals/E2E-Test-Guide","h":"#writing-e2e-tests","p":15754},{"i":15774,"t":"Let's continue writing the test and add the following code at the end of the test function func TestMeetingDataFlow(t *testing.T) { ...... dataflowTester.VerifyTable( models.FeishuMeetingTopUserItem{}, \"./snapshot_tables/_tool_feishu_meeting_top_user_items.csv\", []string{ \"meeting_count\", \"meeting_duration\", \"user_type\", \"_raw_data_params\", \"_raw_data_table\", \"_raw_data_id\", \"_raw_data_remark\", }, ) } Its purpose is to call dataflowTester.VerifyTable to complete the validation of the data results. The third parameter is all the fields of the table that need to be verified. The data used for validation exists in . /snapshot_tables/_tool_feishu_meeting_top_user_items.csv, but of course, this file does not exist yet. There is a twin, more generalized function, that could be used instead: dataflowTester.VerifyTableWithOptions(models.FeishuMeetingTopUserItem{}, dataflowTester.TableOptions{ CSVRelPath: \"./snapshot_tables/_tool_feishu_meeting_top_user_items.csv\" }, ) The above usage will be default to validating against all fields of the models.FeishuMeetingTopUserItem model. There are additional fields on TableOptions that can be specified to limit which fields on that model to perform validation on. To facilitate the generation of the file mentioned above, DevLake has adopted a testing technique called Snapshot, which will automatically generate the file based on the run results when the VerifyTable or VerifyTableWithOptions functions are called without the csv existing. But note! Please do two things after the snapshot is created: 1. check if the file is generated correctly 2. re-run it to make sure there are no errors between the generated results and the re-run results. These two operations are critical and directly related to the quality of test writing. We should treat the snapshot file in .csv format like a code file. If there is a problem with this step, there are usually 2 ways to solve it. The validated fields contain fields like create_at runtime or self-incrementing ids, which cannot be repeatedly validated and should be excluded. there is \\n or \\r\\n or other escape mismatch fields in the run results. Generally, when parsing the httpResponse error, you can follow these solutions: modify the field type of the content in the api model to `json. convert it to string when parsing so that the \\n symbol can be kept intact, avoiding the parsing of line breaks by the database or the operating system For example, in the github plugin, this is how it is handled. Well, at this point, the E2E writing is done. We have added a total of 3 new files to complete the testing of the meeting length collection task. It's pretty easy.","s":"Verify that the results of the task are correct","u":"/zh/docs/v1.0/DeveloperManuals/E2E-Test-Guide","h":"#verify-that-the-results-of-the-task-are-correct","p":15754},{"i":15776,"t":"It's straightforward. Just run make e2e-plugins because DevLake has already solidified it into a script~","s":"Run E2E tests for all plugins like CI","u":"/zh/docs/v1.0/DeveloperManuals/E2E-Test-Guide","h":"#run-e2e-tests-for-all-plugins-like-ci","p":15754},{"i":15779,"t":"Example request POST /lake/notify?nouce=3-FDXxIootApWxEVtz&sign=424c2f6159bd9e9828924a53f9911059433dc14328a031e91f9802f062b495d5 {\"TaskID\":39,\"PluginName\":\"jenkins\",\"CreatedAt\":\"2021-09-30T15:28:00.389+08:00\",\"UpdatedAt\":\"2021-09-30T15:28:00.785+08:00\"}","s":"Request","u":"/zh/docs/v1.0/DeveloperManuals/Notifications","h":"#request","p":15777},{"i":15781,"t":"If you want to use the notification feature, you should add two configuration key to .env file. # .env # notification request url, e.g.: http://example.com/lake/notify NOTIFICATION_ENDPOINT= # secret is used to calculate signature NOTIFICATION_SECRET=","s":"Configuration","u":"/zh/docs/v1.0/DeveloperManuals/Notifications","h":"#configuration","p":15777},{"i":15783,"t":"You should check the signature before accepting the notification request. We use sha256 algorithm to calculate the checksum. // calculate checksum sum := sha256.Sum256([]byte(requestBody + NOTIFICATION_SECRET + nouce)) return hex.EncodeToString(sum[:])","s":"Signature","u":"/zh/docs/v1.0/DeveloperManuals/Notifications","h":"#signature","p":15777},{"i":15785,"t":"Plugins are code extensions that enable you to pull data from data-sources and present them in DevLake. They can be implemented in both Go and Python. The framework itself is written in Go, and Python (called PyDevLake) is a supplemental extension to support developers who prefer it using it. PyDevLake is relatively brand new, and we would like to see it gain more traction; we encourage you to give it a try if you are familiar with Python. The Go development manual can be found here. This manual also covers the framework in detail. The Python development manual can be found here.","s":"Plugin Implementation","u":"/zh/docs/v1.0/DeveloperManuals/PluginImplementation","h":"","p":15784},{"i":15787,"t":"For some metric calculations such as the DORA metric, we often encounter situations requiring comprehensive calculations based on data from multiple data sources. For example, we may use GitLab for code hosting, Jenkins for CI/CD, to calculate PR deployment cycle time, we need to know which GitLab Projects and Jenkins Jobs are related for correctness and performance reasons. However, in most cases, we have multiple GitLab Projects / Jenkins Jobs that belong to different teams in our Apache DevLake database. To distinguish them into different groups. The Project is introduced in v0.15. Essentially, a project consists of a set of Scopes, i.e., a couple of GitLab Projects, Jira Boards or Jenkins Jobs, etc. Project is a set of Scope from different domains, a way to group different resources, and it is crucial for some metric calculation like Dora. Next, let us introduce Project in the following order: Project related models Related APIs that can be used to manipulate Project models The interface that needs to be implemented when developing various plugins to support the Project. The interface that needs to be implemented to develop the Data Source Plugin The interface that needs to be implemented to develop the Metric Plugins","s":"Summary","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"","p":15786},{"i":15789,"t":"To support project we contains the following three models: projects describes a project object, including its name, creation and update time and other basic information project_metric_settings describes what metric plugins a project had enabled. project_mapping describes the mapping relationship of project and scope, including the name of the project、the table name of Scope and the row_id in the Scope table.","s":"Models","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"","p":15786},{"i":15791,"t":"field type length description key name varchar 255 name for project PK description longtext description of the project created_at datetime 3 created time of project updated_at datetime 3 last updated time of project","s":"projects","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"#projects","p":15786},{"i":15793,"t":"name describe created_at updated_at project_1 this is one of the test projects 2022-11-01 01:22:13.000 2022-11-01 02:24:15.000 project_2 this is another project test project 2022-11-01 01:23:29.000 2022-11-01 02:27:24.000","s":"example","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"#example","p":15786},{"i":15795,"t":"field type length description key project_name varchar 255 name for project PK plugin_name varchar 255 name for plugin PK plugin_option longtext check if metric plugins have been enabled by the project enable tinyint 1 if the metric plugins is enabled","s":"project_metric_settings","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"#project_metric_settings","p":15786},{"i":15797,"t":"project_name plugin_name plugin_option enable project_1 dora {} true project_2 dora {} false","s":"example","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"#example-1","p":15786},{"i":15799,"t":"field type length description key project_name varchar 255 name for project PK table varchar 255 the table name of Scope PK row_id varchar 255 the row_id in the Scope table PK","s":"project_mapping","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"#project_mapping","p":15786},{"i":15801,"t":"project_name table row_id project_1 Repo gitlab:GithubRepo:1:lake project_1 Board jira:JiraBoard:1:lake project_2 Repo github:GithubRepo:1:lake","s":"example","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"#example-2","p":15786},{"i":15803,"t":"For API specification, please check the swagger doc(by visiting [Your Config-UI Host]/api/swagger/index.html). Related endpoints: /projects /projects/:projectName/metrics /plugins","s":"How to manage project via API","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"","p":15786},{"i":15805,"t":"We divide plugins into two categories The first category is Data Source Plugin, such as GitLab GitHub Jira Jenkins, etc. These plugins collect data from various data sources The second category is Metric Plugin, such as Dora, etc. These plugins do not directly contact the data source but do secondary calculations based on the collected data after the Data Source Plugin works","s":"The interface that needs to be implemented","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"","p":15786},{"i":15807,"t":"For example GitLab GitHub Jira Jenkins etc. These plugins, from various data sources, extract data into the database and store them, they deal directly with the data source, so we classify them as Data Source Plugin.","s":"Data Source Plugin","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"#data-source-plugin","p":15786},{"i":15809,"t":"Data Source Plugin needs to implement DataSourcePluginBlueprintV200 interface to support project The interface definition for this interface is as follows // DataSourcePluginBlueprintV200 extends the V100 to provide support for // Project, so that complex metrics like DORA can be implemented based on a set // of Data Scopes type DataSourcePluginBlueprintV200 interface { MakeDataSourcePipelinePlanV200( connectionId uint64, scopes []*BlueprintScopeV200, syncPolicy BlueprintSyncPolicy, ) (PipelinePlan, []Scope, errors.Error) } scopes in input parameters is a set of arrays containing IDs, Names, and Entities. The input data format is as follows: []*core.BlueprintScopeV200{ { Entities: []string{\"CODE\", \"TICKET\", \"CICD\"}, Id: \"37\", Name: \"test\", }, } syncPolicy in input parameters contains some option settings, whose structure is defined as follows: type BlueprintSyncPolicy struct { Version string `json:\"version\" validate:\"required,semver,oneof=1.0.0\"` SkipOnFail bool `json:\"skipOnFail\"` CreatedDateAfter *time.Time `json:\"createdDateAfter\"` } PipelinePlan in output is a part of blueprint JSON: The input data format is as follows:(Take GitLab plugin as an example) core.PipelinePlan{ { { Plugin: \"gitlab\", Subtasks: []string{ tasks.ConvertProjectMeta.Name, tasks.CollectApiIssuesMeta.Name, tasks.ExtractApiIssuesMeta.Name, tasks.ConvertIssuesMeta.Name, tasks.ConvertIssueLabelsMeta.Name, tasks.CollectApiJobsMeta.Name, tasks.ExtractApiJobsMeta.Name, tasks.CollectApiPipelinesMeta.Name, tasks.ExtractApiPipelinesMeta.Name, }, Options: map[string]interface{}{ \"connectionId\": uint64(1), \"projectId\": testID, }, }, { Plugin: \"gitextractor\", Options: map[string]interface{}{ \"proxy\": \"\", \"repoId\": repoId, \"url\": \"https://git:nddtf@this_is_cloneUrl\", }, }, }, { { Plugin: \"refdiff\", Options: map[string]interface{}{ \"tagsLimit\": 10, \"tagsOrder\": \"reverse semver\", \"tagsPattern\": \"pattern\", }, }, }, } project needs to provide a specific set of Scopes for a specific connection to the plugin through this interface, and then obtain the plugin involved in the PipelineTask All plugins and corresponding parameter information. At the same time, the plugin needs to convert entities like repo and board in the data source into a scope interface that project can understand The corresponding scope interface has been implemented at following files of in the framework layer: models/domainlayer/devops/cicd_scope.go models/domainlayer/ticket/board.go models/domainlayer/code/repo.go In the plugins/gitlab/impl/impl.go file, there is a GitLab plugin implementation of the above interface, which can be used as a reference. And the plugins/gitlab/api/blueprint_v200.go contains implementation details. The following files contain the models that the relevant implementations depend on for reference: plugins/gitlab/models/project.go plugins/gitlab/models/scope_config.go","s":"the DataSourcePluginBlueprintV200 interface","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"#the-datasourcepluginblueprintv200-interface","p":15786},{"i":15811,"t":"For example Dora, and Refdff plugins belong to the Metric Plugins These plugins are mainly for calculating various metrics, they do not directly contact the data source, so we classify them as Metric Plugins.","s":"Metric Plugins","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"#metric-plugins","p":15786},{"i":15813,"t":"Metric Plugins needs to implement the PluginMetric interface to support project The interface definition for this interface looks like this: type PluginMetric interface { // returns a list of required data entities and expected features. // [{ \"model\": \"cicd_tasks\", \"requiredFields\": {\"column\": \"type\", \"execptedValue\": \"Deployment\"}}, ...] RequiredDataEntities() (data []map[string]interface{}, err errors.Error) // returns if the metric depends on Project for calculation. // Currently, only dora would return true. IsProjectMetric() bool // indicates which plugins must be executed before executing this one. // declare a set of dependencies with this RunAfter() ([]string, errors.Error) // returns an empty pointer of the plugin setting struct. // (no concrete usage at this point) Settings() (p interface{}) } Project needs PluginMetric to know whether a Metric Plugin is dependent on project, and the tables and fields required in its calculation process. In the plugins/dora/impl/impl.go file, there is a Dora plugin implementation of the above interface, which can be used as a sample reference.You can find it by searching the following fields: func (plugin Dora) RequiredDataEntities() (data []map[string]interface{}, err errors.Error) func (plugin Dora) IsProjectMetric() bool func (plugin Dora) RunAfter() ([]string, errors.Error) func (plugin Dora) Settings() interface{}","s":"The PluginMetric Interface","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"#the-pluginmetric-interface","p":15786},{"i":15815,"t":"To dig deeper into developing and utilizing our built-in functions and have a better developer experience, feel free to dive into our godoc reference.","s":"References","u":"/zh/docs/v1.0/DeveloperManuals/Project","h":"#references","p":15786},{"i":15817,"t":"For developers who wish to contribute to or develop based on the Apache DevLake, the pkg.go.dev is a good resource for reference, you can learn the overall structure of the code base or the definition of a specific function.","s":"Source Code References","u":"/zh/docs/v1.0/DeveloperManuals/SourceCodeReference","h":"","p":15816},{"i":15819,"t":"A unit test is a type of software testing that tests individual units or components of a software application. The goal of unit testing is to validate that each unit of the software performs as expected. Unit tests are typically written by developers as they work on the code, to ensure that the code they are writing is correct and behaves as intended. Unit tests are automated and are typically written in the same programming language as the code being tested. They are usually run as part of a continuous integration process, ensuring that changes to the codebase don't break existing functionality. Unit tests are typically small and focus on a specific function or method of the code. They are usually designed to run quickly and in isolation, so that any issues that are discovered can be easily traced back to the specific code that is causing the problem.","s":"Introduction of UnitTest","u":"/zh/docs/v1.0/DeveloperManuals/UnitTest","h":"","p":15818},{"i":15821,"t":"Here are some resources for writing UnitTest for golang: Add a test - The Go Programming Language How To Write Unit Tests in Go | DigitalOcean","s":"How to write UnitTest for golang","u":"/zh/docs/v1.0/DeveloperManuals/UnitTest","h":"","p":15818},{"i":15823,"t":"Source code","s":"Test case examples","u":"/zh/docs/v1.0/DeveloperManuals/UnitTest","h":"","p":15818},{"i":15825,"t":"Library-1 Library-2","s":"Recommended Libraries for writing UnitTest cases","u":"/zh/docs/v1.0/DeveloperManuals/UnitTest","h":"","p":15818},{"i":15827,"t":"Please make sure your public key was included in the https://downloads.apache.org/incubator/devlake/KEYS , if not, please update this file first.","s":"DevLake Release Guide","u":"/zh/docs/v1.0/DeveloperManuals/Release-SOP","h":"","p":15826},{"i":15829,"t":"Clone the svn repository svn co https://dist.apache.org/repos/dist/dev/incubator/devlake Append your public key to the KEYS file cd devlake Check if your public key is in the KEYS file If it does not, create a new GPG key, and then run the following command to see if it was successful. gpg --list-sigs <your name> Append your publick key gpg --armor --export <your name> >> KEYS Upload svn add KEYS svn commit -m \"update KEYS\" svn cp https://dist.apache.org/repos/dist/dev/incubator/devlake/KEYS https://dist.apache.org/repos/dist/release/incubator/devlake/ -m \"update KEYS\" We will use v0.16.0 as an example to demonstrate the release process.","s":"How to update KEYS","u":"/zh/docs/v1.0/DeveloperManuals/Release-SOP","h":"#how-to-update-keys","p":15826},{"i":15831,"t":"https://www.apache.org/legal/release-policy.html https://incubator.apache.org/guides/releasemanagement.html","s":"ASF Release Policy","u":"/zh/docs/v1.0/DeveloperManuals/Release-SOP","h":"#asf-release-policy","p":15826},{"i":15833,"t":"gpg creating and verifying the signature shasum creating and verifying the checksum git checkout and pack the codebase svn uploading the code to the Apache code hosting server","s":"Tools:","u":"/zh/docs/v1.0/DeveloperManuals/Release-SOP","h":"#tools","p":15826},{"i":15835,"t":"Check against the Incubator Release Checklist Create folder releases/lake-v0.16.0 and put the two files docker-compose.yml and env.example in there. Update the file .github/ISSUE_TEMPLATE/bug-report.yml to include the version v0.16.0","s":"Prepare","u":"/zh/docs/v1.0/DeveloperManuals/Release-SOP","h":"#prepare","p":15826},{"i":15837,"t":"Checkout to the branch/commit git clone https://github.com/apache/incubator-devlake.git cd incubator-devlake git checkout b268d53a48edb26d3c9b73b782798703f068f655 Tag the commit and push to origin git tag v0.16.0-rc2 git push origin v0.16.0-rc2 Pack the code git archive --format=tar.gz --output=\"<the-output-dir>/apache-devlake-0.16.0-incubating-src.tar.gz\" --prefix=\"apache-devlake-0.16.0-incubating-src/\" v0.16.0-rc2 Before proceeding to the next step, please make sure your public key was included in the https://downloads.apache.org/incubator/devlake/KEYS Create signature and checksum cd <the-output-dir> gpg -s --armor --output apache-devlake-0.16.0-incubating-src.tar.gz.asc --detach-sig apache-devlake-0.16.0-incubating-src.tar.gz shasum -a 512 apache-devlake-0.16.0-incubating-src.tar.gz > apache-devlake-0.16.0-incubating-src.tar.gz.sha512 Verify signature and checksum gpg --verify apache-devlake-0.16.0-incubating-src.tar.gz.asc apache-devlake-0.16.0-incubating-src.tar.gz shasum -a 512 --check apache-devlake-0.16.0-incubating-src.tar.gz.sha512","s":"Pack","u":"/zh/docs/v1.0/DeveloperManuals/Release-SOP","h":"#pack","p":15826},{"i":15839,"t":"Clone the svn repository svn co https://dist.apache.org/repos/dist/dev/incubator/devlake Copy the files into the svn local directory cd devlake mkdir -p 0.16.0-incubating-rc2 cp <the-output-dir>/apache-devlake-0.16.0-incubating-src.tar.gz* 0.16.0-incubating-rc2/ Upload local files svn add 0.16.0-incubating-rc2 svn commit -m \"add 0.16.0-incubating-rc2\"","s":"Upload","u":"/zh/docs/v1.0/DeveloperManuals/Release-SOP","h":"#upload","p":15826},{"i":15841,"t":"You can check Incubator Release Checklist before voting. Devlake community vote: Start the vote by sending an email to dev@devlake.apache.org Title: [VOTE] Release Apache DevLake (Incubating) v{{.Version}}-rc{{.RC}} Content: Hello everyone, This is a call for vote to release Apache DevLake (Incubating) v{{.Version}}-rc{{.RC}} The release candidates: https://dist.apache.org/repos/dist/dev/incubator/devlake/{{.Version}}-incubating-rc{{.RC}}/ Git tag for the release: https://github.com/apache/incubator-devlake/releases/tag/v{{.Version}}-rc{{.RC}} Keys to verify the Release Candidate: https://downloads.apache.org/incubator/devlake/KEYS How to build: https://devlake.apache.org/docs/DeveloperManuals/DeveloperSetup The vote will be open for at least 72 hours or until the necessary number of votes are reached. If approved we will seek final release approval from the IPMC. Please vote accordingly: [ ] +1 approve [ ] +0 no opinion [ ] -1 disapprove with the reason Thanks {{.YourName}} Announce the vote result: Title: [RESULT][VOTE] Release Apache DevLake (Incubating) v{{.Version}}-rc{{.RC}} Content: Hello everyone, The vote closes now with the following results: {{.TotalVotes}} (+1 binding) votes {{range .Votes}}- {{.Name}}{{end}} I will bring the vote results to general@incubator.apache.org Thanks {{.YourName}} Apache incubator community vote: Start the vote by sending an email to general@incubator.apache.org Title: [VOTE] Release Apache DevLake (Incubating) v{{.Version}}-rc{{.RC}} Content: Hello everyone, This is a call for vote to release Apache DevLake (Incubating) v{{.Version}}-rc{{.RC}}. The Apache DevLake community has voted on and approved a proposal to release Apache DevLake (Incubating) version v{{.Version}}-rc{{.RC}}. Community vote thread: [Community Vote Thread]({{.VoteThreadURL}}) Community vote result thread: [Community Vote Result Thread]({{.VoteResultThreadURL}}) The release candidates: [Release Candidates]({{.RCURL}}) Git tag for the release: [Git Tag]({{.GitTagURL}}) Keys to verify the Release Candidate: https://downloads.apache.org/incubator/devlake/KEYS How to build: https://devlake.apache.org/docs/DeveloperManuals/DeveloperSetup/ The vote will be open for at least 72 hours or until the necessary number of votes are reached. Please vote accordingly: [ ] +1 approve [ ] +0 no opinion [ ] -1 disapprove with the reason Thanks {{.YourName}} Announce the vote result: Title: [RESULT][VOTE] Release Apache DevLake (Incubating) v{{.Version}}-rc{{.RC}} Content: Hello everyone, I am pleased to announce that the vote for Apache DevLake (Incubating) v{{.Version}}-rc{{.RC}} has now concluded. Thank you all for your review and participation in the voting process. The release voting has passed with {{bindingVotes}} binding votes, {{nonBindingVotes}} non-binding vote and no +0 or -1 votes. The individuals who provided binding votes are: {{bindingVotesList}} The non-binding vote{{pluralNonBindingVotes}} is from: {{nonBindingVotesList}} You can find the voting thread at the following link: [Voting Thread](https://lists.apache.org/thread/{{votingThreadURL}}) In closing, I want to express my gratitude to everyone who has offered us help, advice, and guidance throughout this process. We will proceed with completing the remaining tasks. Thank you all once again! Best Regards, {{.YourName}}","s":"Vote","u":"/zh/docs/v1.0/DeveloperManuals/Release-SOP","h":"#vote","p":15826},{"i":15844,"t":"Move the release to the ASF content distribution system svn mv https://dist.apache.org/repos/dist/dev/incubator/devlake/0.16.0-incubating-rc2 https://dist.apache.org/repos/dist/release/incubator/devlake/0.16.0-incubating -m \"transfer packages for 0.16.0-incubating-rc2\" Wait until the directory https://downloads.apache.org/incubator/devlake/0.16.0-incubating/ was created Remove the last release from https://downloads.apache.org/ (according the Apache release policy, this link should be pointing to the current release) svn rm https://dist.apache.org/repos/dist/release/incubator/devlake/0.15.0-incubating -m \"remove 0.15.0-incubating\" Announce release by sending an email to general@incubator.apache.org Title: [[ANNOUNCE] Release Apache Devlake(incubating) {{.Version}}-incubating Content: Hello everyone, The Apache DevLake (Incubating) {{.Version}}-incubating has been released! **Apache DevLake** is an open-source dev data platform that ingests, analyzes, and visualizes the fragmented data from DevOps tools to distill insights for engineering productivity. Download Links: https://downloads.apache.org/incubator/devlake/ Changelogs: - xxx. - xxx. - xxx. Website: https://devlake.apache.org/ Resources: - Issue:https://github.com/apache/incubator-devlake/issues - Mailing list: dev@devlake.apache.org Best Regards, {{.YourName}} ---- Disclaimer: Apache DevLake(incubating) is an effort undergoing incubation at the Apache Software Foundation (ASF), sponsored by the Apache Incubator PMC. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.","s":"Apache","u":"/zh/docs/v1.0/DeveloperManuals/Release-SOP","h":"#apache","p":15826},{"i":15846,"t":"Create tag v0.16.0 and push git checkout v0.16.0-rc2 git tag v0.16.0 git push origin v0.16.0 Open the URL https://github.com/apache/incubator-devlake/releases/, draft a new release, fill in the form and upload two files docker-compose.yml and env.example","s":"GitHub","u":"/zh/docs/v1.0/DeveloperManuals/Release-SOP","h":"#github","p":15826},{"i":15848,"t":"Please refer to the rules when creating a new tag for Apache DevLake alpha: internal testing/preview, i.e. v0.12.0-alpha1 beta: community/customer testing/preview, i.e. v0.12.0-beta1 rc: asf release candidate, i.e. v0.12.0-rc1","s":"Tag Naming Conventions","u":"/zh/docs/v1.0/DeveloperManuals/TagNamingConventions","h":"","p":15847},{"i":15850,"t":"The document explains how you can set up Apache DevLake securely. First of all, there are 4 services included in the deployment: database: mysql is supported, you may use it or any other compatible DBS like cloud-based systems. You should follow the document from the database to make it secure. grafana: You are likely to use it most of the time, browsing built-in dashboards, and creating your own customized metric. grafana supports User Management, please follow the official document to set it up based on your need. devlake: This is the core service for Data Collection and Metric Calculation, all collected/calculated data would be stored to the database, and accessed by the grafana service. devlake itself doesn't support User Management of any kind, so we don't recommend that you expose its port to the outside world. config-ui: A web interface to set up devlake to do the work. You may set up an automated blueprint to collect data. config-ui supports Basic Authentication, by simply set up the Environment Variable ADMIN_USER and ADMIN_PASS for the container. There are commented lines in config-ui.environment section in our docker-compose.yml file for your convenience. In General, we suggest that you reduce the Attack Surface as small as possible.","s":"Security and Authentication","u":"/zh/docs/v1.0/GettingStarted/Authentication","h":"","p":15849},{"i":15852,"t":"database: Remove the ports if you don't need to access the database directly devlake: Remove the ports section. If you want to call the API directly, do it via config-ui/api endpoint. grafana: We have no choice but to expose the ports for people to browse the dashboards. However, you may want to set up the User Management, and a read-only database account for grafana config-ui: Normally, exposing the ports with Basic Authentication is sufficient for Internal Deployment, you may choose to remove the ports and use techniques like k8s port-forwarding or expose-port-when-needed to enhance the security. Keep in mind config-ui is NOT designed to be used by many people, and it shouldn't be. Do NOT grant access if NOT necessary.","s":"Internal Deployment (Recommended)","u":"/zh/docs/v1.0/GettingStarted/Authentication","h":"#internal-deployment-recommended","p":15849},{"i":15854,"t":"THIS IS DANGEROUS, DON'T DO IT. If you insist, here are some suggestions you may follow, please consult Security Advisor before everything: database: Same as above. grafana: Same as above. In addition, set up the HTTPS for the transportation. devlake: Same as above. config-ui: Same as above. In addition, use port-forward if you are using k8s, otherwise, set up HTTPS for the transportation.","s":"Internet Deployment (NOT Recommended)","u":"/zh/docs/v1.0/GettingStarted/Authentication","h":"#internet-deployment-not-recommended","p":15849},{"i":15856,"t":"Security is complicated, all suggestions listed above are based on what we learned so far. Apache Devlake makes no guarantee of any kind, please consult your Security Advisor before applying.","s":"Disclaimer","u":"/zh/docs/v1.0/GettingStarted/Authentication","h":"#disclaimer","p":15849},{"i":15858,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/GettingStarted/Authentication","h":"#troubleshooting","p":15849},{"i":15860,"t":"This document describes everything you need to know about DORA, and implementing this powerful and practical framework in DevLake.","s":"DORA","u":"/zh/docs/v1.0/DORA","h":"","p":15859},{"i":15862,"t":"Created six years ago by a team of researchers, DORA stands for \"DevOps Research & Assessment\" and is the answer to years of research, having examined thousands of teams, seeking a reliable and actionable approach to understanding the performance of software development teams. DORA has since become a standardized framework focused on the stability and velocity of development processes, one that avoids the more controversial aspects of productivity and individual performance measures. There are two key clusters of data inside DORA: Velocity and Stability. The DORA framework is focused on keeping them in context with each other, as a whole, rather than as independent variables, making the data more challenging to misinterpret or abuse. Within velocity are two core metrics: Deployment Frequency: Number of successful deployments to production, how rapidly is your team releasing to users? Lead Time for Changes: How long does it take from commit to the code running in production? This is important, as it reflects how quickly your team can respond to user requirements. Stability is composed of two core metrics: Change Failure Rate: How often are your deployments causing a failure? Median Time to Restore Service (MTTR): How long does it take the team to properly recover from a failure once it is identified? However, MTTR is replaced by Failed Deployment Recovery Time from the 2023 DORA report. This metric measures the finish time of a deployment to the resolution of the incident caused by the deployment. To make DORA even more actionable, there are well-established benchmarks to determine if you are performing at \"Elite\", \"High\", \"Medium\", or \"Low\" levels. Inside DevLake, you will find the benchmarking table available to assess and compare your own projects.","s":"What are DORA metrics?","u":"/zh/docs/v1.0/DORA","h":"#what-are-dora-metrics","p":15859},{"i":15864,"t":"DORA metrics help teams and projects measure and improve software development practices to consistently deliver reliable products, and thus happy users!","s":"Why is DORA important?","u":"/zh/docs/v1.0/DORA","h":"#why-is-dora-important","p":15859},{"i":15866,"t":"DevLake measures DORA metrics at the project level. You can set up DORA metrics in a few steps: Install: via Docker Compose or Helm. Configure and collect data: Create data connections to retrieve the data from various tools such as Jira, GitHub, Jenkins, etc. Configure the DORA-related scope config to define deployments and incidents. Create a DevLake project, and associate the data connections with the project. Collect data to see DORA metrics Report: DevLake provides a built-in DORA dashboard and another dashboard to help you debug DORA. See an example screenshot below or check out our live demo. DevLake now supports Jenkins, GitHub Action, GitLab CI, BitBucket and Azure Pipelines as the data sources for deployments; Jira boards, GitHub issues, TAPD workspaces and Zentao issues as the sources for incidents data; Github/BitBucket/Azure/GitLab repos as the sources for Pull Requests and Commits. If your CI/CD or incident management tools are not listed on the Supported Data Sources page, have no fear! DevLake provides incoming webhooks to push your deployments or incidents to DevLake. The webhook configuration doc can be found here.","s":"How to implement DORA metrics with Apache DevLake?","u":"/zh/docs/v1.0/DORA","h":"#how-to-implement-dora-metrics-with-apache-devlake","p":15859},{"i":15868,"t":"Let us walk through the DORA implementation process for a project team with the following toolchain Source Code Management and Code Review: GitHub Continous Deployments: GitHub Actions & CircleCI Incident management: Jira Calculating DORA metrics requires three key entities: Pull requests, deployments, and incidents. Their exact definitions of course depend on a project's DevOps practice and vary project by project. For the project in this example, let us assume the following definition: Code Changes: All commits and pull requests in GitHub. Deployments: GitHub workflow run whose jobs contain \"deploy\" and \"push-image\" in their names and CircleCI deployments. Incidents: Jira issues whose type is \"DORA Incident\" In the next section, we will demonstrate how to configure DevLake to implement DORA metrics for the aforementioned example project team.","s":"A real-world example","u":"/zh/docs/v1.0/DORA","h":"#a-real-world-example","p":15859},{"i":15870,"t":"1.1 Visit the config-ui at http://localhost:4000. 1.2 Go to the Connections page. Create a Jira connection. 1.3 Add your project's Jira boards. Click the Associate Scope Config icon to configure for DORA metrics. To make it simple, fields with the label are DORA-related configurations for every data source. Via these fields, you can define what \"incidents\" and \"deployments\" are for each data source. This project uses Jira issue type DORA Incident as \"incident\". Please select the DORA Incident for the Incident field. Jira issues belonging to this type will be converted to 'INCIDENT' in DevLake. 1.4 Create a GitHub connection. Add the GitHub repositories. Once added, associate the scope configuration with the repositories. In this project, the GitHub CI jobs 'deploy' and 'push-image' are recognized as deployments. To identify these deployments, please use the pattern '(?i)(deploy|push-image)'. GitHub Workflow runs that match this pattern will be transformed into 'deployments' and recorded in the table 'cicd_deployments' in DevLake. Please note that starting from v0.20, DevLake automatically collects GitHub deployments and converts them into DevLake deployments by default.","s":"Step 1 - Configure GitHub & Jira connections","u":"/zh/docs/v1.0/DORA","h":"#step-1---configure-github--jira-connections","p":15859},{"i":15872,"t":"Using CircleCI as an example, we demonstrate how to actively push data to DevLake using the Webhook approach, in cases where DevLake does not have a plugin specific to that tool to pull data from your data source. Please note that CircleCI will be supported from v0.21. 2.1 Go to the Connections page. Add a webhook called 'CircleCI'. 2.2 Copy the curl command to register deployments to Devlake. This curl command includes a non-expired API key generated automatically. 2.3 Head to your CircleCI's pipelines page in a new tab. Find your deployment pipeline and click Configuration File. 2.4 Paste the curl command to the config.yml. Change the key-values in the payload. See the full payload schema here. version: 2.1 jobs: build: docker: - image: cimg/base:stable steps: - checkout - run: name: \"build\" command: | echo Hello, World! deploy: docker: - image: cimg/base:stable steps: - checkout - run: name: \"deploy\" command: | # The time a deploy started start_time=`date '+%Y-%m-%dT%H:%M:%S%z'` # Some deployment tasks here ... echo Hello, World! # Send the request to DevLake after deploy # The values start with a '$CIRCLE_' are CircleCI's built-in variables curl http://127.0.0.1:4000/api/plugins/webhook/2/deployments -X 'POST' -d \"{ \\\"commit_sha\\\":\\\"$CIRCLE_SHA1\\\", \\\"repo_url\\\":\\\"$CIRCLE_REPOSITORY_URL\\\", \\\"start_time\\\":\\\"$start_time\\\" }\" workflows: build_and_deploy_workflow: jobs: - build - deploy 2.5 Run the CircleCI pipeline. Visit DevLake's DB to check if the deployments have been successfully pushed to DevLake. The deployments will appear in table cicd_deployments and cicd_deployment_commits in DevLake's database.","s":"Step 2 - Collect CircleCI deployments via webhook","u":"/zh/docs/v1.0/DORA","h":"#step-2---collect-circleci-deployments-via-webhook","p":15859},{"i":15874,"t":"Once all the data connections and webhooks have been configured, it is essential to associate them with a DevLake project. This association is necessary to accurately measure DORA metrics. 3.1 Go to the Projects page. Create 'project1' and enable DORA metrics. 3.2 Add Jira and a GitHub connections to this project. Choose the Jira boards and GitHub repos that belong to 'project1'. 3.3 Go to the webhooks tab. Add the existing webhook 'CircleCI' to this project. 3.4 Go to the blueprint status tab, click Collect All to start data collection.","s":"Step 3 - Create a project and collect data","u":"/zh/docs/v1.0/DORA","h":"#step-3---create-a-project-and-collect-data","p":15859},{"i":15876,"t":"With all the data collected, DevLake's DORA dashboard is ready to deliver your DORA metrics and benchmarks. Click the Dashboards on the top right corner. You can find the DORA dashboard within the Grafana instance shipped with DevLake, ready for you to put into action. You can customize the DORA dashboard by editing the underlying SQL query of each panel. For a breakdown of each metric's SQL query, please refer to the corresponding metric docs: Deployment Frequency Lead Time for Changes Median Time to Restore Service Change Failure Rate If you are not familiar with Grafana, please refer to our Grafana doc, or jump into Slack for help. 🎉🎉🎉 Congratulations! You are now a DevOps Hero, with your own DORA dashboard!","s":"Step 4 - View and customize DevLake's DORA dashboard","u":"/zh/docs/v1.0/DORA","h":"#step-4---view-and-customize-devlakes-dora-dashboard","p":15859},{"i":15878,"t":"To create the DORA dashboard with your own toolchain, please look at the configuration tutorial and project organization documentation for more details.","s":"Try it Out","u":"/zh/docs/v1.0/DORA","h":"#try-it-out","p":15859},{"i":15880,"t":"If you run into any problem, please check the DORA debug dashboard, DORA troubleshooting documentation or create an issue on GitHub.","s":"Troubleshooting","u":"/zh/docs/v1.0/DORA","h":"#troubleshooting","p":15859},{"i":15882,"t":"The key steps to deploy and utilize Apache DevLake.","s":"Quick Start","u":"/zh/docs/v1.0/GettingStarted/QuickStart","h":"","p":15881},{"i":15884,"t":"Install DevLake using either Docker Compose or Helm. If you want to upgrade DevLake to a newer version, please refer to the upgrade manuals.","s":"Step 1. Install DevLake","u":"/zh/docs/v1.0/GettingStarted/QuickStart","h":"#step-1-install-devlake","p":15881},{"i":15886,"t":"Configure DevLake via the Config UI. Follow the turorial for configuration instructions. If you specifically want to configure DORA metrics, please refer to the DORA manuals for detailed instructions.","s":"Step 2. Configure DevLake","u":"/zh/docs/v1.0/GettingStarted/QuickStart","h":"#step-2-configure-devlake","p":15881},{"i":15888,"t":"After configuring DevLake and collecting data, access the Config UI and click the \"Dashboards\" button in the top-right corner to view the data in Grafana dashboards. For comprehensive instructions, please refer to the Grafana manuals.","s":"Step 3. View Data in Grafana Dashboards","u":"/zh/docs/v1.0/GettingStarted/QuickStart","h":"#step-3-view-data-in-grafana-dashboards","p":15881},{"i":15891,"t":"Docker v19.03.10+ docker-compose v2.2.3+ (If you have Docker Desktop installed then you already have the Compose plugin installed)","s":"Prerequisites","u":"/zh/docs/v1.0/GettingStarted/DockerComposeSetup","h":"#prerequisites","p":15889},{"i":15893,"t":"Download docker-compose.yml and env.example from the latest release into a folder. Rename env.example to .env. For Mac/Linux users, please run mv env.example .env in the terminal. This file contains the environment variables that the Devlake server will use. Additional ones can be found in the compose file(s). Generate a secure encryption key using a method such as OpenSSL. For example, run the following command to generate a 128-character string consisting of uppercase letters: openssl rand -base64 2000 | tr -dc 'A-Z' | fold -w 128 | head -n 1 Copy the generated string. Set the value of the ENCRYPTION_SECRET environment variable: Method 1: In the docker-compose.yml, set an environment variable ENCRYPTION_SECRET: \"copied string\" Method 2: Alternatively, you can set the ENCRYPTION_SECRET environment variable in the .env file: ENCRYPTION_SECRET=\"copied string\" If you set the ENCRYPTION_SECRET environment variable in both docker-compose.yml and the .env file, the value set in docker-compose.yml takes precedence. Please make sure to keep the ENCRYPTION_SECRET safe as it is used to encrypt sensitive information in the database, such as personal access tokens and passwords. If ENCRYPTION_SECRET is lost, it may not be possible to decrypt this sensitive information. By default, the timezone is UTC. You can change it by adjusting the env variable TZ in docker-compose.yml Run docker-compose up -d if the version of Docker Desktop is too low to use docker compose up -d.","s":"Launch DevLake","u":"/zh/docs/v1.0/GettingStarted/DockerComposeSetup","h":"#launch-devlake","p":15889},{"i":15895,"t":"Visit \"config-ui\" at http://localhost:4000 in your browser to configure DevLake and collect data. Please follow the tutorial \"devlake\" container takes a while to fully boot up. If \"config-ui\" complains about API being unreachable, please wait a few seconds and refresh the page. To view dashboards, click View Dashboards button in the top left corner, or visit localhost:3002 (username: \"admin\", password: \"admin\"). We use Grafana to visualize the DevOps data and build dashboards. For how to customize and provision dashboards, please see our Grafana doc.","s":"Collect data and view dashboards","u":"/zh/docs/v1.0/GettingStarted/DockerComposeSetup","h":"#collect-data-and-view-dashboards","p":15889},{"i":15897,"t":"警告 Back up your Grafana dashboards before upgrading if you have modified/customized any dashboards. You can re-import these dashboards to Grafana after the upgrade. If you are upgrading from DevLake v0.17.x or earlier versions to v0.18.x or later versions, you need to find the ENCODE_KEY value in the .env file of devlake container, and assign the value to ENCRYPTION_SECRET via .env file or environment variable in docker-compose.yml Run docker-compose down to stop services; Download docker-compose.yml and env.example from the latest release. Use the new docker-compose.yml and env.example to replace the old docker-compose.yml and .env; Or if you have modified/customized values in the old files, compare the new files with the old ones, adjust the old files according to the new ones. [Optional] If you are upgrading from earlier versions to v0.18.0+, set the ENCRYPTION_SECRET environment variable in docker-compose.yml or .env file, refer to the second caution. Run docker-compose up -d to start DevLake services.","s":"Upgrade","u":"/zh/docs/v1.0/GettingStarted/DockerComposeSetup","h":"#upgrade","p":15889},{"i":15900,"t":"Yes, please follow the steps below: Comment out the 'mysql' part in docker-compose.yml: mysql: image: mysql:8 volumes: - mysql-storage:/var/lib/mysql restart: always ports: - \"127.0.0.1:3306:3306\" environment: MYSQL_ROOT_PASSWORD: admin MYSQL_DATABASE: lake MYSQL_USER: merico MYSQL_PASSWORD: merico command: --character-set-server=utf8mb4 --collation-server=utf8mb4_bin Comment out the 'mysql' volume in docker-compose.yml: volumes: mysql-storage: Comment out the 'depends_on mysql' part in docker-compose.yml: depends_on: - mysql Provide your managed cloud database connection info in the 'grafana' section in docker-compose.yml: MYSQL_URL: This parameter specifies the URL of your database instance. For example, if your database is hosted on a server with IP address 123.45.67.89 and port number 3306, the URL would be \"123.45.67.89:3306\". MYSQL_DATABASE: This parameter specifies the name of your database. For example, if your database is named \"mydatabase\", you would set this parameter to \"mydatabase\". MYSQL_USER: This parameter specifies the username that you will use to connect to your database. For example, if your username is \"myuser\", you would set this parameter to \"myuser\". MYSQL_PASSWORD: This parameter specifies the password that you will use to connect to your database. For example, if your password is \"mypassword\", you would set this parameter to \"mypassword\". MYSQL_URL: 123.45.67.89:3306 MYSQL_DATABASE: mydatabase MYSQL_USER: myuser MYSQL_PASSWORD: mypassword Set DB_URL to your own DB_URL in .env: DB_URL=\"mysql://YOUR_USER:YOUR_PASSWORD@YOUR_IP:YOUR_PORT/lake?charset=utf8mb4&parseTime=True\" # Don't forget to create db named `lake` in your own db, and set character-set-server=utf8mb4, collation-server=utf8mb4_bin as below # character-set-server=utf8mb4 # collation-server=utf8mb4_bin Final step: docker compose up -d","s":"Can I use a managed cloud database service instead of running database in Docker?","u":"/zh/docs/v1.0/GettingStarted/DockerComposeSetup","h":"#can-i-use-a-managed-cloud-database-service-instead-of-running-database-in-docker","p":15889},{"i":15902,"t":"Yes, please follow the steps below: Comment out the 'grafana' part: grafana: image: mericodev/devlake-dashboard:latest build: context: grafana/ ports: - \"3002:3000\" volumes: - grafana-storage:/var/lib/grafana environment: GF_SERVER_ROOT_URL: \"http://localhost:4000/grafana\" GF_USERS_DEFAULT_THEME: \"light\" MYSQL_URL: mysql:3306 MYSQL_DATABASE: lake MYSQL_USER: merico MYSQL_PASSWORD: merico restart: always depends_on: - mysql Comment out grafana volume: volumes: grafana-storage: Set up the config-ui container to adopt the external Grafana in the docker-compose.yml config-ui: ... environment: USE_EXTERNAL_GRAFANA: \"true\" GRAFANA_ENDPOINT: \"http://grafana.example.com\" ... Please connect your Grafana to DevLake's database by following https://grafana.com/docs/grafana/latest/administration/data-source-management/ If you want to import dashboards, please check https://grafana.com/docs/grafana/latest/dashboards/export-import/. You can find DevLake's self-built Grafana dashboards here (https://github.com/apache/incubator-devlake/tree/main/grafana/dashboards). Final step: docker compose up -d","s":"Can I use an external Grafana instead of running Grafana in Docker?","u":"/zh/docs/v1.0/GettingStarted/DockerComposeSetup","h":"#can-i-use-an-external-grafana-instead-of-running-grafana-in-docker","p":15889},{"i":15904,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/GettingStarted/DockerComposeSetup","h":"#troubleshooting","p":15889},{"i":15907,"t":"Please follow the guide for detailed information: Upgrade via Docker Compose Upgrade via Helm","s":"How to Upgrade to a new version","u":"/zh/docs/v1.0/GettingStarted/Upgrade","h":"#how-to-upgrade-to-a-new-version","p":15905},{"i":15909,"t":"ENCRYPTION_SECRET: It is important to keep the ENCRYPTION_SECRET safe as it is used to encrypt sensitive information in the database, such as personal access tokens and passwords. Losing the ENCRYPTION_SECRET may result in the inability to decrypt this sensitive information. .env file: The .env file is now optional. You can choose to store your variables in the environment instead. Remember to consider important variables such as ENCRYPTION_SECRET and DB_URL. If both the environment variables and the .env file exist, the values in the environment variables will take precedence. However, make sure that these variables are defined in either one of them to avoid any issues with DevLake. Back up your Grafana dashboards before upgrade if you have modified/customized any dashboards. You can re-import these dashboards to Grafana after the upgrade. Upgrade to v0.18.0+: When upgrading from a previous version of DevLake, you need to find the ENCODE_KEY value in the .env file of devlake container, and assign the value to ENCRYPTION_SECRET according to the upgrade steps, see helm setup or docker compose setup. This will ensure that the encryption process continues to work as expected. Upgrade to v0.18.0+: When upgrading from a previous version of DevLake, You may encounter the below error when upgrading because the built-in grafana has been replaced by the official grafana dependency. So you may need to delete the grafana deployment first, please also take care of Note 3 before deleting. Error: UPGRADE FAILED: cannot patch \"devlake-grafana\" with kind Deployment: Deployment.apps \"devlake-grafana\" is invalid: spec.selector: Invalid value: v1.LabelSelector{MatchLabels:map[string]string{\"app.kubernetes.io/instance\":\"devlake\", \"app.kubernetes.io/name\":\"grafana\"}, MatchExpressions:[]v1.LabelSelectorRequirement(nil)}: field is immutable When upgrading via docker-compose.yml, please download the new docker-compose.yml and env.example from release assets to do the upgrade, please refer to docker compose upgrade notes These notes provide guidance on upgrading your Apache DevLake to a newer version. Ensure you follow them carefully to avoid any issues during the upgrade process.","s":"Key points","u":"/zh/docs/v1.0/GettingStarted/Upgrade","h":"#key-points","p":15905},{"i":15912,"t":"Helm >= 3.6.0 Kubernetes >= 1.19.0","s":"Prerequisites","u":"/zh/docs/v1.0/GettingStarted/HelmSetup","h":"#prerequisites","p":15910},{"i":15914,"t":"You can also check https://github.com/apache/incubator-devlake-helm-chart to make contribution​ To install or upgrade to the latest version, please check here​","s":"Quick Start","u":"/zh/docs/v1.0/GettingStarted/HelmSetup","h":"#quick-start","p":15910},{"i":15916,"t":"To install the chart with release name devlake,follow these steps: Generate a secure encryption key using a method such as OpenSSL. For example, run the following command to generate a 128-character string consisting of uppercase letters: openssl rand -base64 2000 | tr -dc 'A-Z' | fold -w 128 | head -n 1 Copy the generated string, set the value of the ENCRYPTION_SECRET environment variable by running the following command: export ENCRYPTION_SECRET=\"copied string\" This command will set the ENCRYPTION_SECRET environment variable to the value of the copied string. Please make sure to keep the ENCRYPTION_SECRET safe as it is used to encrypt sensitive information in the database, such as personal access tokens and passwords. If ENCRYPTION_SECRET is lost, it may not be possible to decrypt this sensitive information. By default, the timezone is UTC. To customize the timezone settings, apply the --set commonEnvs.TZ=\"your timezone\",grafana.env.TZ=\"your timezone\" command. For further guidance, please refer to FAQ. By default, a random password is generated and stored in a Kubernetes Secret for the Grafana admin user. Alternatively, you can explicitly set the password via --set grafana.adminPassword=<your password>. For further guidance, please refer to FAQ. Install the chart by running the following commands: helm repo add devlake https://apache.github.io/incubator-devlake-helm-chart helm repo update helm install devlake devlake/devlake --version=1.0-beta1 --set lake.encryptionSecret.secret=$ENCRYPTION_SECRET And visit your devlake from the node port (32001 by default). http://YOUR-NODE-IP:32001 Tips:​ If you are using minikube inside your mac, please use the following command to forward the port: kubectl port-forward service/devlake-ui 30090:4000 and open another terminal: kubectl port-forward service/devlake-grafana 30091:3000 Then you can visit: config-ui by url http://YOUR-NODE-IP:30090 grafana by url http://YOUR-NODE-IP:30091","s":"Install","u":"/zh/docs/v1.0/GettingStarted/HelmSetup","h":"#install","p":15910},{"i":15918,"t":"警告 Back up your Grafana dashboards before upgrading if you have modified/customized any dashboards. You can re-import these dashboards to Grafana after the upgrade. If you are upgrading from DevLake v0.18.x or later versions: helm repo update helm upgrade devlake devlake/devlake --version=1.0-beta1 If you are upgrading from DevLake v0.17.x or earlier versions to v0.18.x or later versions: Copy the ENCODE_KEY value from /app/config/.env of the lake pod (e.g. devlake-lake-0), and replace the <ENCRYPTION_SECRET> in the upgrade command below. You may encounter the below error when upgrading because the built-in grafana has been replaced by the official grafana dependency. So you may need to delete the grafana deployment first. Error: UPGRADE FAILED: cannot patch \"devlake-grafana\" with kind Deployment: Deployment.apps \"devlake-grafana\" is invalid: spec.selector: Invalid value: v1.LabelSelector{MatchLabels:map[string]string{\"app.kubernetes.io/instance\":\"devlake\", \"app.kubernetes.io/name\":\"grafana\"}, MatchExpressions:[]v1.LabelSelectorRequirement(nil)}: field is immutable helm repo update helm upgrade devlake devlake/devlake --version=1.0-beta1 --set lake.encryptionSecret.secret=<ENCRYPTION_SECRET>","s":"Upgrade","u":"/zh/docs/v1.0/GettingStarted/HelmSetup","h":"#upgrade","p":15910},{"i":15920,"t":"To uninstall/delete the devlake release: helm uninstall devlake","s":"Uninstall","u":"/zh/docs/v1.0/GettingStarted/HelmSetup","h":"#uninstall","p":15910},{"i":15923,"t":"Conditions: IP Address of Kubernetes node: 192.168.0.6 Want to visit devlake with port 30000. ENCRYPTION_SECRET=$(openssl rand -base64 2000 | tr -dc 'A-Z' | fold -w 128 | head -n 1) helm install devlake devlake/devlake --set service.uiPort=30000 --set lake.encryptionSecret.secret=$ENCRYPTION_SECRET After deployed, visit devlake: http://192.168.0.6:30000","s":"Deploy with NodePort","u":"/zh/docs/v1.0/GettingStarted/HelmSetup","h":"#deploy-with-nodeport","p":15910},{"i":15925,"t":"Conditions: I have already configured default ingress for the Kubernetes cluster I want to use http://devlake.example.com for visiting devlake ENCRYPTION_SECRET=$(openssl rand -base64 2000 | tr -dc 'A-Z' | fold -w 128 | head -n 1) helm install devlake devlake/devlake --set \"ingress.enabled=true,ingress.hostname=devlake.example.com\" --set lake.encryptionSecret.secret=$ENCRYPTION_SECRET After deployed, visit devlake: http://devlake.example.com, and grafana at http://devlake.example.com/grafana","s":"Deploy with Ingress","u":"/zh/docs/v1.0/GettingStarted/HelmSetup","h":"#deploy-with-ingress","p":15910},{"i":15927,"t":"Conditions: I have already configured ingress(class: nginx) for the Kubernetes cluster, and the https using 8443 port. I want to use https://devlake-0.example.com:8443 for visiting devlake. The https certificates are generated by letsencrypt.org, and the certificate and key files: cert.pem and key.pem First, create the secret: kubectl create secret tls ssl-certificate --cert cert.pem --key secret.pem Then, deploy the devlake: ENCRYPTION_SECRET=$(openssl rand -base64 2000 | tr -dc 'A-Z' | fold -w 128 | head -n 1) helm install devlake devlake/devlake \\ --set \"ingress.enabled=true,ingress.enableHttps=true,ingress.hostname=devlake-0.example.com\" \\ --set \"ingress.className=nginx,ingress.httpsPort=8443\" \\ --set \"ingress.tlsSecretName=ssl-certificate\" --set lake.encryptionSecret.secret=$ENCRYPTION_SECRET After deployed, visit devlake: https://devlake-0.example.com:8443, and grafana at https://devlake-0.example.com:8443/grafana","s":"Deploy with Ingress (Https)","u":"/zh/docs/v1.0/GettingStarted/HelmSetup","h":"#deploy-with-ingress-https","p":15910},{"i":15929,"t":"Some useful parameters for the chart, you could also check them in values.yaml Parameter Description Default replicaCount Replica Count for devlake, currently not used 1 imageTag The version tag for all images see Values.yaml commonEnvs The common envs for all pods except grafana {TZ: \"UTC\"} mysql.useExternal If use external mysql server, set true false mysql.externalServer External mysql server address 127.0.0.1 mysql.externalPort External mysql server port 3306 mysql.username username for mysql merico mysql.password password for mysql merico mysql.database database for mysql lake mysql.rootPassword root password for mysql admin mysql.storage.type storage type, pvc or hostpath pvc mysql.storage.class storage class for mysql's volume \"\" mysql.storage.size volume size for mysql's data 5Gi mysql.storage.hostPath the host path if mysql.storage.type is hostpath /devlake/mysql/data mysql.image.repository repository for mysql's image mysql mysql.image.tag image tag for mysql's image 8 mysql.image.pullPolicy pullPolicy for mysql's image IfNotPresent mysql.initContainers init containers to run to complete before mysql [] mysql.extraLabels extra labels for mysql's statefulset {} mysql.securityContext pod security context values {} mysql.containerSecurityContext container security context values {} mysql.service.type mysql service type ClusterIP mysql.service.nodePort specify mysql nodeport \"\" grafana dashboard, datasource, etc. settings for grafana, installed by grafana official chart lake.image.repository repository for lake's image apache/devlake lake.image.pullPolicy pullPolicy for lake's image Always lake.port the port of devlake backend 8080 lake.envs initial envs for lake see Values.yaml lake.extraEnvsFromSecret existing secret name of extra envs \"\" lake.encryptionSecret.secretName the k8s secret name for ENCRYPTION_SECRET \"\" lake.encryptionSecret.secret the secret for ENCRYPTION_SECRET \"\" lake.encryptionSecret.autoCreateSecret whether let the helm chart create the secret true lake.extraLabels extra labels for lake's statefulset {} lake.securityContext pod security context values {} lake.containerSecurityContext container security context values {} ui.image.repository repository for ui's image apache/devlake-config-ui ui.image.pullPolicy pullPolicy for ui's image Always ui.basicAuth.enabled If the basic auth in ui is enabled false ui.basicAuth.user The user name for the basic auth \"admin\" ui.basicAuth.password The password for the basic auth \"admin\" ui.basicAuth.autoCreateSecret If let the helm chart create the secret true ui.basicAuth.secretName The basic auth secret name \"\" ui.extraLabels extra labels for ui's statefulset {} ui.securityContext pod security context values {} ui.containerSecurityContext container security context values {} service.type Service type for exposed service NodePort service.uiPort Node port for config ui 32001 service.ingress.enabled If enable ingress false service.ingress.enableHttps If enable https false service.ingress.className Name for ingressClass. leave empty for using default \"\" service.ingress.hostname The hostname/domainname for ingress localhost service.ingress.prefix The prefix for endpoints, currently not used / service.ingress.tlsSecretName The secret name for tls's certificate for https \"\" service.ingress.httpPort The http port for ingress 80 service.ingress.httpsPort The https port for ingress 443 option.database The database type, valids: mysql mysql option.connectionSecretName The database connection details secret name devlake-mysql-auth option.autoCreateSecret If let the helm chart create the secret true","s":"Parameters","u":"/zh/docs/v1.0/GettingStarted/HelmSetup","h":"#parameters","p":15910},{"i":15931,"t":"Can I use a managed Cloud database service instead of running database in docker? Yes, it just set useExternal value to true while you deploy devlake with helm chart. Below we'll use MySQL on AWS RDS as an example. a. (Optional) Create a MySQL instance on AWS RDS following this doc, skip this step if you'd like to use an existing instance b. Proviede below values while install from helm: - `mysql.useExternal`: this should be `true` - `mysql.externalServer`: use your RDS instance's IP address or domain name. - `mysql.externalPort`: use your RDS instance's database port. - `mysql.username`: use your `username` for access RDS instance's DB - `mysql.password`: use your `password` for access RDS instance's DB - `mysql.database`: use your RDS instance's DB name, you may need to create a database first with `CREATE DATABASE <DB name>;` Here is the example: helm repo add devlake https://apache.github.io/incubator-devlake-helm-chart helm repo update ENCRYPTION_SECRET=$(openssl rand -base64 2000 | tr -dc 'A-Z' | fold -w 128 | head -n 1) helm install devlake devlake/devlake \\ --set mysql.useExternal=true \\ --set mysql.externalServer=db.example.com \\ --set mysql.externalPort=3306 \\ --set mysql.username=admin \\ --set mysql.password=password_4_admin \\ --set mysql.database=devlake --set lake.encryptionSecret.secret=$ENCRYPTION_SECRET Can I use a secret to store the database connection details? Yes, to do so, you need to have a secret in your Kubernetes Cluster that contains the following values: MYSQL_USER: The user to connect to your DB. MYSQL_PASSWORD: The password to connect to your DB. MYSQL_DATABASE: The database to connect to your DB. MYSQL_ROOT_PASSWORD: The root password to connect to your DB. DB_URL: mysql://username:password@dbserver:port/database?charset=utf8mb4&parseTime=True The secret name needs to be the same as the value option.connectionSecretName Can I use an external Grafana instead of running built-in Grafana? Yes, the devlake helm chart supports using an external Grafana. You can set the following values while installing from helm: grafana.enabled: this should be false grafana.external.url: use your Grafana's URL, e.g. https://grafana.example.com Here is the example: helm repo add devlake https://apache.github.io/incubator-devlake-helm-chart helm repo update ENCRYPTION_SECRET=$(openssl rand -base64 2000 | tr -dc 'A-Z' | fold -w 128 | head -n 1) helm install devlake devlake/devlake \\ --set grafana.enabled=false \\ --set grafana.external.url=https://grafana.example.com --set lake.encryptionSecret.secret=$ENCRYPTION_SECRET How to set the timezone? If not explicitly set, the default is UTC commonEnvs.TZ: your timezone grafana.env.TZ: your timezone Here is the example: helm repo add devlake https://apache.github.io/incubator-devlake-helm-chart helm repo update ENCRYPTION_SECRET=$(openssl rand -base64 2000 | tr -dc 'A-Z' | fold -w 128 | head -n 1) helm install devlake devlake/devlake \\ --set commonEnvs.TZ=<your timezone> \\ --set grafana.env.TZ=<your timezone> How to set the Grafana admin password? If not explicitly set, a random password will be generated and saved in a Kubernetes Secret grafana.adminPassword: your password Here is the example: helm repo add devlake https://apache.github.io/incubator-devlake-helm-chart helm repo update ENCRYPTION_SECRET=$(openssl rand -base64 2000 | tr -dc 'A-Z' | fold -w 128 | head -n 1) helm install devlake devlake/devlake \\ --set grafana.adminPassword=<your password> \\ --set lake.encryptionSecret.secret=$ENCRYPTION_SECRET","s":"FAQ","u":"/zh/docs/v1.0/GettingStarted/HelmSetup","h":"#faq","p":15910},{"i":15933,"t":"If you run into any problem, please check the Troubleshooting or create an issue","s":"Troubleshooting","u":"/zh/docs/v1.0/GettingStarted/HelmSetup","h":"#troubleshooting","p":15910},{"i":15936,"t":"The accumulated number of added lines of code.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/AddedLinesOfCode","h":"#what-is-this-metric","p":15934},{"i":15938,"t":"identify potential bottlenecks that may affect the output Encourage the team to implement a development model that matches the business requirements; develop excellent coding habits","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/AddedLinesOfCode","h":"#why-is-it-important","p":15934},{"i":15940,"t":"N/A","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/AddedLinesOfCode","h":"#which-dashboards-does-it-exist-in","p":15934},{"i":15942,"t":"This metric is calculated by summing the additions of commits in the given data range. Data Sources Required This metric relies on commits collected from GitHub, GitLab or BitBucket. Data Transformation Required N/A SQL Queries The following SQL shows how to find the added lines of code in specific repositories, eg. 'repo-1' and 'repo-2'. SELECT sum(c.additions) as added_lines_of_code FROM commits c LEFT JOIN repo_commits rc ON c.sha = rc.commit_sha LEFT JOIN repos r ON r.id = rc.repo_id WHERE -- please replace the repo ids with your own, or create a '$repo_id' variable in Grafana r.id in ('repo-1','repo-2') and message not like '%Merge%' and $__timeFilter(c.authored_date) -- the following condition will remove the month with incomplete data and c.authored_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) If you want to measure the monthly trend of added lines of code in the screenshot below, please run the following SQL in Grafana. WITH _commits as( SELECT DATE_ADD(date(authored_date), INTERVAL -DAY(date(authored_date))+1 DAY) as time, sum(additions) as added_lines_of_code FROM commits WHERE message not like '%Merge%' and $__timeFilter(authored_date) -- the following condition will remove the month with incomplete data and authored_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) group by 1 ) SELECT date_format(time,'%M %Y') as month, added_lines_of_code FROM _commits ORDER BY time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/AddedLinesOfCode","h":"#how-is-it-calculated","p":15934},{"i":15944,"t":"From the project/team dimension, observe the accumulated change in added lines to assess the team activity and code growth rate From version cycle dimension, observe the active time distribution of code changes, and evaluate the effectiveness of project development model. From the member dimension, observe the trend and stability of code output of each member, and identify the key points that affect code output by comparison.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/AddedLinesOfCode","h":"#how-to-improve","p":15934},{"i":15947,"t":"The amount of time it takes a bug to fix.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/BugAge","h":"#what-is-this-metric","p":15945},{"i":15949,"t":"Help the team to establish an effective hierarchical response mechanism for bugs. Focus on the resolution of important problems in the backlog. Improve team's and individual's bug fixing efficiency. Identify good/to-be-improved practices that affect bug age age","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/BugAge","h":"#why-is-it-important","p":15945},{"i":15951,"t":"Jira GitHub Weekly Bug Retro","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/BugAge","h":"#which-dashboards-does-it-exist-in","p":15945},{"i":15953,"t":"Similar to requirement lead time, this metric equals resolution_date - created_date of issues in type \"BUG\". Data Sources Required This metric relies on issues collected from Jira, GitHub, or TAPD. Data Transformation Required This metric relies on the 'type-bug' configuration in Jira, GitHub or TAPD's transformation rules while adding/editing a blueprint. This configuration tells DevLake what issues are bugs. SQL Queries The following SQL shows how to find the bug age of a specific bug. -- lead_time_minutes is a pre-calculated field whose value equals 'resolution_date - created_date' SELECT lead_time_minutes/1440 as bug_age_in_days FROM issues WHERE type = 'BUG' If you want to measure the mean bug age in the screenshot below, please run the following SQL in Grafana. with _bugs as( SELECT DATE_ADD(date(i.resolution_date), INTERVAL -DAY(date(i.resolution_date))+1 DAY) as time, AVG(i.lead_time_minutes/1440) as issue_lead_time FROM issues i join board_issues bi on i.id = bi.issue_id join boards b on bi.board_id = b.id WHERE -- $board_id is a variable defined in Grafana's dashboard settings to filter out issues by boards b.id in ($board_id) and i.status = \"DONE\" and i.type = 'BUG' and $__timeFilter(i.resolution_date) -- the following condition will remove the month with incomplete data and i.resolution_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) group by 1 ) SELECT date_format(time,'%M %Y') as month, issue_lead_time as \"Mean Bug Age in Days\" FROM _bugs ORDER BY time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/BugAge","h":"#how-is-it-calculated","p":15945},{"i":15955,"t":"Observe the trend of bug age and locate the key reasons. Compare the age of bugs by severity levels, types (business, functional classification), affected components, etc.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/BugAge","h":"#how-to-improve","p":15945},{"i":15958,"t":"The number of successful builds.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/BuildCount","h":"#what-is-this-metric","p":15956},{"i":15960,"t":"As a process indicator, it reflects the value flow efficiency of upstream production and research links Identify excellent/to-be-improved practices that impact the build, and drive the team to precipitate reusable tools and mechanisms to build infrastructure for fast and high-frequency delivery","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/BuildCount","h":"#why-is-it-important","p":15956},{"i":15962,"t":"Jenkins","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/BuildCount","h":"#which-dashboards-does-it-exist-in","p":15956},{"i":15964,"t":"This metric is calculated by counting the number of successful cicd_pipelines, such as Jenkins builds, GitLab pipelines and GitHub workflow runs in the given data range. Data Sources Required This metric relies on Jenkins builds, GitLab pipelines or GitHub workflow runs. Data Transformation Required N/A SQL Queries The following SQL shows how to find the total number of successful CI builds finished in the given time range. SELECT count(*) FROM cicd_pipelines WHERE result = 'SUCCESS' and $__timeFilter(finished_date) ORDER BY 1 If you want to measure the monthly trend of the successful build count in the screenshot below, please run the following SQL in Grafana. WITH _builds as( SELECT DATE_ADD(date(finished_date), INTERVAL -DAYOFMONTH(date(finished_date))+1 DAY) as time, count(*) as build_count FROM cicd_pipelines WHERE result = \"SUCCESS\" and $__timeFilter(finished_date) -- the following condition will remove the month with incomplete data and finished_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) GROUP BY 1 ) SELECT date_format(time,'%M %Y') as month, build_count as \"Build Count\" FROM _builds ORDER BY time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/BuildCount","h":"#how-is-it-calculated","p":15956},{"i":15966,"t":"From the project dimension, compare the number of builds and success rate by combining the project phase and the complexity of tasks. From the time dimension, analyze the trend of the number of builds and success rate to see if it has improved over time.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/BuildCount","h":"#how-to-improve","p":15956},{"i":15969,"t":"Amount of bugs per 1,000 lines of code.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/BugCountPer1kLinesOfCode","h":"#what-is-this-metric","p":15967},{"i":15971,"t":"Defect drill-down analysis to inform the development of design and code review strategies and to improve the internal QA process Assist teams to locate projects/modules with higher defect severity and density, and clean up technical debts Identify good/to-be-improved practices that affect defect count or defect rate, to reduce the number of future defects","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/BugCountPer1kLinesOfCode","h":"#why-is-it-important","p":15967},{"i":15973,"t":"N/A","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/BugCountPer1kLinesOfCode","h":"#which-dashboards-does-it-exist-in","p":15967},{"i":15975,"t":"The number of bugs divided by the total accumulated lines of code (additions + deletions) in the given data range. Data sources required issues collected from Jira, GitHub or TAPD. commits collected from GitHub, GitLab or BitBucket. Data Transformation Required This metric relies on the 'type-bug' configuration in Jira, GitHub or TAPD's transformation rules while adding/editing a blueprint. This configuration tells DevLake what issues are bugs. SQL Queries If you want to measure the monthly trend of Bugs per 1k lines of code in the screenshot below, please run the following SQL in Grafana. with _line_of_code as ( select DATE_ADD(date(authored_date), INTERVAL -DAY(date(authored_date))+1 DAY) as time, sum(additions + deletions) as line_count from commits where message not like 'Merge%' and $__timeFilter(authored_date) group by 1 ), _bug_count as( select DATE_ADD(date(created_date), INTERVAL -DAY(date(created_date))+1 DAY) as time, count(*) as bug_count from issues i where type = 'Bug' and $__timeFilter(created_date) group by 1 ), _bug_count_per_1k_loc as( select loc.time, 1.0 * bc.bug_count / loc.line_count * 1000 as bug_count_per_1k_loc from _line_of_code loc left join _bug_count bc on bc.time = loc.time where bc.bug_count is not null and loc.line_count is not null and loc.line_count != 0 ) select date_format(time,'%M %Y') as month, bug_count_per_1k_loc as 'Bug Count per 1000 Lines of Code' from _bug_count_per_1k_loc order by time;","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/BugCountPer1kLinesOfCode","h":"#how-is-it-calculated","p":15967},{"i":15977,"t":"From the project or team dimension, observe the statistics on the total number of defects, the distribution of the number of defects in each severity level/type/owner, the cumulative trend of defects, and the change trend of the defect rate in thousands of lines, etc. From version cycle dimension, observe the statistics on the cumulative trend of the number of defects/defect rate, which can be used to determine whether the growth rate of defects is slowing down, showing a flat convergence trend, and is an important reference for judging the stability of software version quality From the time dimension, analyze the trend of the number of test defects, defect rate to locate the key items/key points Evaluate whether the software quality and test plan are reasonable by referring to CMMI standard values","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/BugCountPer1kLinesOfCode","h":"#how-to-improve","p":15967},{"i":15980,"t":"The ratio of successful builds to all builds.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/BuildSuccessRate","h":"#what-is-this-metric","p":15978},{"i":15982,"t":"As a process indicator, it reflects the value flow efficiency of upstream production and research links Identify excellent/to-be-improved practices that impact the build, and drive the team to precipitate reusable tools and mechanisms to build infrastructure for fast and high-frequency delivery","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/BuildSuccessRate","h":"#why-is-it-important","p":15978},{"i":15984,"t":"Jenkins","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/BuildSuccessRate","h":"#which-dashboards-does-it-exist-in","p":15978},{"i":15986,"t":"The number of successful builds divided by the total number of builds in the given data range. Data Sources Required This metric relies on Jenkins builds, GitLab pipelines or GitHub workflow runs. Data Transformation Required N/A SQL Queries The following SQL shows how to find the success rate of CI builds finished in the given time range. SELECT 1.0 * sum(case when result = 'SUCCESS' then 1 else 0 end)/ count(*) as \"Build Success Rate\" FROM cicd_pipelines WHERE $__timeFilter(finished_date) ORDER BY 1 If you want to measure the distribution of CI build result like the donut chart below, please run the following SQL in Grafana. SELECT result, count(*) as build_count FROM cicd_pipelines WHERE $__timeFilter(finished_date) and id like \"%jenkins%\" and name in ($job_id) -- the following condition will remove the month with incomplete data and finished_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) GROUP BY 1 ORDER BY 2 DESC If you want to measure the mean build success rate per month in the screenshot below, please run the following SQL in Grafana. WITH _build_success_rate as( SELECT DATE_ADD(date(finished_date), INTERVAL -DAYOFMONTH(date(finished_date))+1 DAY) as time, result FROM cicd_pipelines WHERE $__timeFilter(finished_date) -- the following condition will remove the month with incomplete data and finished_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) ) SELECT date_format(time,'%M %Y') as month, 1.0 * sum(case when result = 'SUCCESS' then 1 else 0 end)/ count(*) as \"Build Success Rate\" FROM _build_success_rate GROUP BY 1 ORDER BY 1","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/BuildSuccessRate","h":"#how-is-it-calculated","p":15978},{"i":15988,"t":"From the project dimension, compare the number of builds and success rate by combining the project phase and the complexity of tasks. From the time dimension, analyze the trend of the number of builds and success rate to see if it has improved over time.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/BuildSuccessRate","h":"#how-to-improve","p":15978},{"i":15991,"t":"The duration of successful builds.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/BuildDuration","h":"#what-is-this-metric","p":15989},{"i":15993,"t":"As a process indicator, it reflects the value flow efficiency of upstream production and research links Identify excellent/to-be-improved practices that impact the build, and drive the team to precipitate reusable tools and mechanisms to build infrastructure for fast and high-frequency delivery","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/BuildDuration","h":"#why-is-it-important","p":15989},{"i":15995,"t":"Jenkins","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/BuildDuration","h":"#which-dashboards-does-it-exist-in","p":15989},{"i":15997,"t":"This metric is calculated by getting the duration of successful cicd_pipelines, such as Jenkins builds, GitLab pipelines and GitHub workflow runs in the given data range. Data Sources Required This metric relies on Jenkins builds, GitLab pipelines or GitHub workflow runs. Data Transformation Required N/A SQL Queries The following SQL shows how to find the mean duration of successful CI builds finished in the given time range. SELECT avg(duration_sec/60) as duration_in_minutes FROM cicd_pipelines WHERE result = 'SUCCESS' and $__timeFilter(finished_date) ORDER BY 1 If you want to measure the mean duration of builds in the screenshot below, please run the following SQL in Grafana. WITH _builds as( SELECT DATE_ADD(date(finished_date), INTERVAL -DAYOFMONTH(date(finished_date))+1 DAY) as time, avg(duration_sec) as mean_duration_sec FROM cicd_pipelines WHERE $__timeFilter(finished_date) and id like \"%jenkins%\" and name in ($job_id) -- the following condition will remove the month with incomplete data and finished_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) GROUP BY 1 ) SELECT date_format(time,'%M %Y') as month, mean_duration_sec/60 as mean_duration_minutes FROM _builds ORDER BY time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/BuildDuration","h":"#how-is-it-calculated","p":15989},{"i":15999,"t":"From the project dimension, compare the number of builds and success rate by combining the project phase and the complexity of tasks. From the time dimension, analyze the trend of the number of builds and success rate to see if it has improved over time.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/BuildDuration","h":"#how-to-improve","p":15989},{"i":16002,"t":"The number of commit authors who have committed code.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/CommitAuthorCount","h":"#what-is-this-metric","p":16000},{"i":16004,"t":"Take inventory of project/team R&D resource inputs, assess input-output ratio, and rationalize resource deployment.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/CommitAuthorCount","h":"#why-is-it-important","p":16000},{"i":16006,"t":"N/A","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/CommitAuthorCount","h":"#which-dashboards-does-it-exist-in","p":16000},{"i":16008,"t":"This metric is calculated by counting the number of commit authors in the given data range. Data Sources Required This metric relies on commits collected from GitHub, GitLab or BitBucket. Data Transformation Required N/A SQL Queries The following SQL shows how to find the commit author count in specific repositories, eg. 'repo-1' and 'repo-2'. SELECT count(distinct c.author_id) FROM commits c LEFT JOIN repo_commits rc ON c.sha = rc.commit_sha LEFT JOIN repos r ON r.id = rc.repo_id WHERE -- please replace the repo ids with your own, or create a '$repo_id' variable in Grafana r.id in ('repo-1', 'repo-2') and message not like '%Merge%' and $__timeFilter(c.authored_date) -- the following condition will remove the month with incomplete data and c.authored_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH)","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/CommitAuthorCount","h":"#how-is-it-calculated","p":16000},{"i":16010,"t":"As a secondary indicator, this helps assess the labor cost of participating in coding.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/CommitAuthorCount","h":"#how-to-improve","p":16000},{"i":16013,"t":"The percentage of changes that were made to a code that then resulted in incidents, rollbacks, or any type of production failure.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/CFR","h":"#what-is-this-metric","p":16011},{"i":16015,"t":"Unlike Deployment Frequency and Lead Time for Changes that measure the throughput, Change Failure Rate measures the stability and quality of software delivery. A low CFR reflects a bad end-user experience as the production failure is relatively high.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/CFR","h":"#why-is-it-important","p":16011},{"i":16017,"t":"DORA dashboard. See live demo.","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/CFR","h":"#which-dashboards-does-it-exist-in","p":16011},{"i":16019,"t":"The number of deployments affected by incidents/total number of deployments. For example, if there are five deployments and two deployments cause one or more incidents, that is a 40% change failure rate. When there are multiple deployments triggered by one pipeline, tools like GitLab and BitBucket will generate more than one deployment. In these cases, DevLake will consider these deployments as ONE deployment and use the last deployment's finished date as the deployment finished date. Below are the 2023 DORA benchmarks for different development teams from Google's report. However, it's difficult to tell which group a team falls into when the team's change failure rate is between 15% and 64%. Therefore, DevLake provides its own benchmarks to address this problem: Groups Benchmarks DevLake Benchmarks Elite performers 5% (0, 5%] High performers 10% (5%, 10%] Medium performers 15% (10%, 15%] Low performers 64% (15%, 100%] Click to expand or collapse 2021 DORA benchmarks Groups Benchmarks DevLake Benchmarks Elite performers 0%-15% (0, 15%] High performers 16%-30% (16%, 20%] Medium performers 16%-30% (21%, 30%] Low performers 16%-30% (30%, 100%] Source: 2021 Accelerate State of DevOps, Google Data Sources Required Deployments from Jenkins, GitLab CI, GitHub Action, BitBucket Pipelines, or Webhook, etc. Incidents from Jira issues, GitHub issues, TAPD issues, PagerDuty Incidents, etc. Transformation Rules Required Define deployment and incident in data transformations while configuring the blueprint of a project to let DevLake know what CI/issue records can be regarded as deployments or incidents. SQL Queries If you want to measure the monthly trend of Change Failure Rate, run the following SQL in Grafana. -- Metric 4: change failure rate per month with _deployments as ( -- When deploying multiple commits in one pipeline, GitLab and BitBucket may generate more than one deployment. However, DevLake consider these deployments as ONE production deployment and use the last one's finished_date as the finished date. SELECT cdc.cicd_deployment_id as deployment_id, max(cdc.finished_date) as deployment_finished_date FROM cicd_deployment_commits cdc JOIN project_mapping pm on cdc.cicd_scope_id = pm.row_id and pm.`table` = 'cicd_scopes' WHERE pm.project_name in (${project:sqlstring}+'') and cdc.result = 'SUCCESS' and cdc.environment = 'PRODUCTION' GROUP BY 1 HAVING $__timeFilter(max(cdc.finished_date)) ), _failure_caused_by_deployments as ( -- calculate the number of incidents caused by each deployment SELECT d.deployment_id, d.deployment_finished_date, count(distinct case when i.type = 'INCIDENT' then d.deployment_id else null end) as has_incident FROM _deployments d left join project_issue_metrics pim on d.deployment_id = pim.deployment_id left join issues i on pim.id = i.id GROUP BY 1,2 ), _change_failure_rate_for_each_month as ( SELECT date_format(deployment_finished_date,'%y/%m') as month, case when count(deployment_id) is null then null else sum(has_incident)/count(deployment_id) end as change_failure_rate FROM _failure_caused_by_deployments GROUP BY 1 ) SELECT cm.month, cfr.change_failure_rate FROM calendar_months cm LEFT JOIN _change_failure_rate_for_each_month cfr on cm.month = cfr.month WHERE $__timeFilter(cm.month_timestamp) If you want to measure in which category your team falls, run the following SQL in Grafana. -- Metric 3: change failure rate with _deployments as ( -- When deploying multiple commits in one pipeline, GitLab and BitBucket may generate more than one deployment. However, DevLake consider these deployments as ONE production deployment and use the last one's finished_date as the finished date. SELECT cdc.cicd_deployment_id as deployment_id, max(cdc.finished_date) as deployment_finished_date FROM cicd_deployment_commits cdc JOIN project_mapping pm on cdc.cicd_scope_id = pm.row_id and pm.`table` = 'cicd_scopes' WHERE pm.project_name in (${project}) and cdc.result = 'SUCCESS' and cdc.environment = 'PRODUCTION' GROUP BY 1 HAVING $__timeFilter(max(cdc.finished_date)) ), _failure_caused_by_deployments as ( -- calculate the number of incidents caused by each deployment SELECT d.deployment_id, d.deployment_finished_date, count(distinct case when i.type = 'INCIDENT' then d.deployment_id else null end) as has_incident FROM _deployments d left join project_issue_metrics pim on d.deployment_id = pim.deployment_id left join issues i on pim.id = i.id GROUP BY 1,2 ), _change_failure_rate as ( SELECT case when count(deployment_id) is null then null else sum(has_incident)/count(deployment_id) end as change_failure_rate FROM _failure_caused_by_deployments ), _is_collected_data as( SELECT CASE WHEN COUNT(i.id) = 0 AND COUNT(cdc.id) = 0 THEN 'No All' WHEN COUNT(i.id) = 0 THEN 'No Incidents' WHEN COUNT(cdc.id) = 0 THEN 'No Deployments' END AS is_collected FROM (SELECT 1) AS dummy LEFT JOIN issues i ON i.type = 'INCIDENT' LEFT JOIN cicd_deployment_commits cdc ON 1=1 ) SELECT CASE WHEN ('$dora_report') = '2023' THEN CASE WHEN is_collected = \"No All\" THEN \"N/A. Please check if you have collected deployments/incidents.\" WHEN is_collected = \"No Incidents\" THEN \"N/A. Please check if you have collected incidents.\" WHEN is_collected = \"No Deployments\" THEN \"N/A. Please check if you have collected deployments.\" WHEN change_failure_rate <= .05 THEN CONCAT(round(change_failure_rate*100,1), \"%(elite)\") WHEN change_failure_rate <= .10 THEN CONCAT(round(change_failure_rate*100,1), \"%(high)\") WHEN change_failure_rate <= .15 THEN CONCAT(round(change_failure_rate*100,1), \"%(medium)\") WHEN change_failure_rate > .15 THEN CONCAT(round(change_failure_rate*100,1), \"%(low)\") ELSE \"N/A. Please check if you have collected deployments/incidents.\" END WHEN ('$dora_report') = '2021' THEN CASE WHEN is_collected = \"No All\" THEN \"N/A. Please check if you have collected deployments/incidents.\" WHEN is_collected = \"No Incidents\" THEN \"N/A. Please check if you have collected incidents.\" WHEN is_collected = \"No Deployments\" THEN \"N/A. Please check if you have collected deployments.\" WHEN change_failure_rate <= .15 THEN CONCAT(round(change_failure_rate*100,1), \"%(elite)\") WHEN change_failure_rate <= .20 THEN CONCAT(round(change_failure_rate*100,1), \"%(high)\") WHEN change_failure_rate <= .30 THEN CONCAT(round(change_failure_rate*100,1), \"%(medium)\") WHEN change_failure_rate > .30 THEN CONCAT(round(change_failure_rate*100,1), \"%(low)\") ELSE \"N/A. Please check if you have collected deployments/incidents.\" END ELSE 'Invalid dora report' END AS change_failure_rate FROM _change_failure_rate, _is_collected_data","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/CFR","h":"#how-is-it-calculated","p":16011},{"i":16021,"t":"Add unit tests for all new feature \"Shift left\", start QA early and introduce more automated tests Enforce code review if it's not strictly executed","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/CFR","h":"#how-to-improve","p":16011},{"i":16024,"t":"This metric is the number of duplicated blocks of lines. This metric is collected from SonarQube, check this doc for detailed definition.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/CQDuplicatedBlocks","h":"#what-is-this-metric","p":16022},{"i":16026,"t":"Duplicated Blocks is a code metric that measures the amount of duplicated code in a project. Duplicated blocks are sequences of code that are identical or nearly identical to each other, and can occur within a single file or across multiple files. Duplicated code can make the codebase harder to maintain, increase the risk of bugs and errors, and make it more difficult to understand and modify the code. Identifying and removing duplicated code can improve the maintainability, reliability, and readability of the codebase, and reduce the risk of introducing bugs or errors in the future.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/CQDuplicatedBlocks","h":"#why-is-it-important","p":16022},{"i":16028,"t":"SonarQube","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/CQDuplicatedBlocks","h":"#which-dashboards-does-it-exist-in","p":16022},{"i":16030,"t":"This metric is calculated by counting the total number of duplicated_blocks. Data Sources Required This metric relies on file metrics collected from SonarQube. Data Transformation Required N/A SQL Queries The following SQL shows how to find duplicated blocks of lines in specific projects, eg. 'project1' and 'project2'. SELECT sum(duplicated_blocks) FROM cq_file_metrics WHERE project_key in ('project1', 'project2')","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/CQDuplicatedBlocks","h":"#how-is-it-calculated","p":16022},{"i":16033,"t":"This metric is the number of files involved in duplications. This metric is collected from SonarQube, check this doc for detailed definition.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/CQDuplicatedLines","h":"#what-is-this-metric","p":16031},{"i":16035,"t":"Duplicated lines are individual lines of code that are identical or nearly identical to each other, and can occur within a single file or across multiple files. Duplicated code can make the codebase harder to maintain, increase the risk of bugs and errors, and make it more difficult to understand and modify the code. Identifying and removing duplicated code can improve the maintainability, reliability, and readability of the codebase, and reduce the risk of introducing bugs or errors in the future.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/CQDuplicatedLines","h":"#why-is-it-important","p":16031},{"i":16037,"t":"SonarQube","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/CQDuplicatedLines","h":"#which-dashboards-does-it-exist-in","p":16031},{"i":16039,"t":"This SQL query calculates the percentage of duplicated lines in a project, as well as the total number of lines in the project. The sum(duplicated_lines) represents the total number of duplicated lines in the project, while sum(num_of_lines) represents the total number of lines of code. These two values are divided and multiplied by 100 to get the percentage of duplicated lines in the project. Data Sources Required This metric relies on file metrics collected from SonarQube. Data Transformation Required N/A SQL Queries The following SQL shows how to find the number of files involved in duplications in specific projects, eg. 'project1' and 'project2'. SELECT CONCAT(ROUND(sum(duplicated_lines) / sum(num_of_lines) * 100, 1), '% ', 'Duplications on ', ROUND(sum(ncloc) / 1000, 0),'k Lines') FROM cq_file_metrics WHERE project_key in ('project1', 'project2')","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/CQDuplicatedLines","h":"#how-is-it-calculated","p":16031},{"i":16042,"t":"This metric is a the total number of issues found in a project by SonarQube. It includes various types of issues such as bugs, vulnerabilities, code smells, and security hotspots. This metric is collected from SonarQube, check this doc for detailed definition.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/CQIssueCount","h":"#what-is-this-metric","p":16040},{"i":16044,"t":"Issue provides information about potential problems or issues in the code. Issues can include bugs, vulnerabilities, and code smells, which can all affect the maintainability, reliability, and security of the codebase. By identifying and addressing issues, developers can improve the quality of their code and reduce technical debt. Additionally, tracking issues over time can help to identify trends and measure progress in improving code quality.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/CQIssueCount","h":"#why-is-it-important","p":16040},{"i":16046,"t":"SonarQube","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/CQIssueCount","h":"#which-dashboards-does-it-exist-in","p":16040},{"i":16048,"t":"This metric is calculated by counting the total number of cq_issues. Data Sources Required This metric relies on issues collected from SonarQube. Data Transformation Required N/A SQL Queries The following SQL shows how to find issues in specific projects, eg. 'project1' and 'project2'. To get the issue count of 'BUG', please add the condition type = BUG SELECT count(*) as 'Bugs' FROM cq_issues WHERE $__timeFilter(created_date) and project_key in ('project1', 'project2') and `type` = 'BUG' ORDER BY created_date To get the issue count of 'VULNERABILITY', please add the condition type = VULNERABILITY SELECT count(*) as 'Vulnerabilities' FROM cq_issues WHERE $__timeFilter(created_date) and project_key in ('project1', 'project2') and `type` = 'VULNERABILITY' ORDER BY created_date To get the issue count of 'HOTSPOTS', please add the condition type = HOTSPOTS SELECT COUNT(IF(status = 'TO_REVIEW', 1, NULL)) AS 'Security Hotspots' FROM cq_issues WHERE $__timeFilter(created_date) and project_key in ('project1', 'project2') and `type` = 'HOTSPOTS' ORDER BY created_date SELECT CONCAT(ROUND(COUNT(IF(status != 'TO_REVIEW', 1, NULL)) / COUNT(*) * 100, 2), '%') AS 'Reviewed' FROM cq_issues WHERE $__timeFilter(created_date) and project_key in ('project1', 'project2') and `type` = 'HOTSPOTS' ORDER BY created_date To get the issue count of 'CODE_SMELL', please add the condition type = CODE_SMELL SELECT COUNT(if(type = 'CODE_SMELL', 1, null)) as 'Code Smells' FROM cq_issues WHERE $__timeFilter(created_date) and project_key in ('project1', 'project2')","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/CQIssueCount","h":"#how-is-it-calculated","p":16040},{"i":16051,"t":"The number of commits created.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/CommitCount","h":"#what-is-this-metric","p":16049},{"i":16053,"t":"Identify potential bottlenecks that may affect output Encourage R&D practices of small step submissions and develop excellent coding habits","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/CommitCount","h":"#why-is-it-important","p":16049},{"i":16055,"t":"GitHub Release Quality and Contribution Analysis Demo-Is this month more productive than last? Demo-Commit Count by Author","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/CommitCount","h":"#which-dashboards-does-it-exist-in","p":16049},{"i":16057,"t":"This metric is calculated by counting the number of commits in the given data range. Data Sources Required This metric relies on commits collected from GitHub, GitLab or BitBucket. Data Transformation Required N/A SQL Queries The following SQL shows how to find commits in specific repositories, eg. 'repo-1' and 'repo-2'. SELECT r.id, c.* FROM commits c LEFT JOIN repo_commits rc ON c.sha = rc.commit_sha LEFT JOIN repos r ON r.id = rc.repo_id WHERE -- please replace the repo ids with your own, or create a '$repo_id' variable in Grafana r.id in ('repo-1','repo-2') and message not like '%Merge%' and $__timeFilter(c.authored_date) -- the following condition will remove the month with incomplete data and c.authored_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) If you want to measure the monthly trend of commit count in the screenshot below, please run the following SQL in Grafana. with _commits as( SELECT DATE_ADD(date(c.authored_date), INTERVAL -DAY(date(c.authored_date))+1 DAY) as time, count(c.sha) as commit_count FROM commits c LEFT JOIN repo_commits rc ON c.sha = rc.commit_sha LEFT JOIN repos r ON r.id = rc.repo_id WHERE -- please replace the repo ids with your own, or create a '$repo_id' variable in Grafana r.id in ($repo_id) and message not like '%Merge%' and $__timeFilter(c.authored_date) -- the following condition will remove the month with incomplete data and c.authored_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) group by 1 ) SELECT date_format(time,'%M %Y') as month, commit_count as \"Commit Count\" FROM _commits ORDER BY time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/CommitCount","h":"#how-is-it-calculated","p":16049},{"i":16059,"t":"Identify the main reasons for the unusual number of commits and the possible impact on the number of commits through comparison Evaluate whether the number of commits is reasonable in conjunction with more microscopic workload metrics (e.g. lines of code/code equivalents)","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/CommitCount","h":"#how-to-improve","p":16049},{"i":16062,"t":"This metric is the number of unit tests that have been executed against the code. This metric is collected from SonarQube, check this doc for detailed definition.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/CQTest","h":"#what-is-this-metric","p":16060},{"i":16064,"t":"Test is an indicator used to indicate the test coverage of the code. This means that SonarQube checks that your code contains enough test cases to cover the various paths and branches in your code. This metric can help you understand the extent of your code test coverage, thereby determining your code quality and stability.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/CQTest","h":"#why-is-it-important","p":16060},{"i":16066,"t":"SonarQube","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/CQTest","h":"#which-dashboards-does-it-exist-in","p":16060},{"i":16068,"t":"This SQL query retrieves the test coverage percentage for the lines to cover in a project from the cq_file_metrics table in SonarQube. The query calculates the percentage by subtracting the number of uncovered lines from the total number of lines to cover, then dividing the result by the total number of lines to cover, and multiplying by 100. The result is rounded to one decimal point and displayed as a percentage. Additionally, the query also includes a message that shows the total number of lines to cover in thousands. Data Sources Required This metric relies on file_metrics collected from SonarQube. Data Transformation Required N/A SQL Queries The following SQL shows how to find unit tests that have been executed against the code in specific projects, eg. 'project1' and 'project2'. SELECT CONCAT(ROUND((sum(lines_to_cover) - sum(uncovered_lines)) / sum(lines_to_cover) * 100, 1), '% ', 'Coverage on ', ROUND(sum(lines_to_cover) / 1000, 0),'k Lines to cover') FROM cq_file_metrics WHERE project_key in ('project1', 'project2')","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/CQTest","h":"#how-is-it-calculated","p":16060},{"i":16071,"t":"The accumulated number of deleted lines of code.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/DeletedLinesOfCode","h":"#what-is-this-metric","p":16069},{"i":16073,"t":"identify potential bottlenecks that may affect the output Encourage the team to implement a development model that matches the business requirements; develop excellent coding habits","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/DeletedLinesOfCode","h":"#why-is-it-important","p":16069},{"i":16075,"t":"N/A","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/DeletedLinesOfCode","h":"#which-dashboards-does-it-exist-in","p":16069},{"i":16077,"t":"This metric is calculated by summing the deletions of commits in the given data range. Data Sources Required This metric relies on commits collected from GitHub, GitLab or BitBucket. Data Transformation Required N/A SQL Queries The following SQL shows how to find the deleted lines of code in specific repositories, eg. 'repo-1' and 'repo-2'. SELECT sum(c.deletions) as deleted_lines_of_code FROM commits c LEFT JOIN repo_commits rc ON c.sha = rc.commit_sha LEFT JOIN repos r ON r.id = rc.repo_id WHERE -- please replace the repo ids with your own, or create a '$repo_id' variable in Grafana r.id in ('repo-1','repo-2') and message not like '%Merge%' and $__timeFilter(c.authored_date) -- the following condition will remove the month with incomplete data and c.authored_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) If you want to measure the monthly trend of deleted lines of code in the screenshot below, please run the following SQL in Grafana. with _commits as( SELECT DATE_ADD(date(authored_date), INTERVAL -DAY(date(authored_date))+1 DAY) as time, sum(deletions) as deleted_lines_of_code FROM commits WHERE message not like '%Merge%' and $__timeFilter(authored_date) -- the following condition will remove the month with incomplete data and authored_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) group by 1 ) SELECT date_format(time,'%M %Y') as month, deleted_lines_of_code FROM _commits ORDER BY time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/DeletedLinesOfCode","h":"#how-is-it-calculated","p":16069},{"i":16079,"t":"From the project/team dimension, observe the accumulated change in Added lines to assess the team activity and code growth rate From version cycle dimension, observe the active time distribution of code changes, and evaluate the effectiveness of project development model. From the member dimension, observe the trend and stability of code output of each member, and identify the key points that affect code output by comparison.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/DeletedLinesOfCode","h":"#how-to-improve","p":16069},{"i":16082,"t":"This metric is a measure of effort to fix all code smells. This metric is collected from SonarQube, check this doc for detailed definition.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/CQMaintainability-Debt","h":"#what-is-this-metric","p":16080},{"i":16084,"t":"It helps developers and project managers understand the costs and risks associated with maintaining the codebase. High levels of technical debt can lead to more bugs, slower development cycles, and higher maintenance costs over time. By monitoring technical debt and working to reduce it, developers can ensure that their code is easier to maintain, more reliable, and more scalable.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/CQMaintainability-Debt","h":"#why-is-it-important","p":16080},{"i":16086,"t":"SonarQube","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/CQMaintainability-Debt","h":"#which-dashboards-does-it-exist-in","p":16080},{"i":16088,"t":"This SQL query calculates the total technical debt in days for all issues in the SonarQube instance. check this doc for detailed info. Data Sources Required This metric relies on issues collected from SonarQube. Data Transformation Required N/A SQL Queries The following SQL shows how to find effort to fix all code smells in specific projects, eg. 'project1' and 'project2'. SELECT CONCAT(ROUND(SUM(debt) / (8 * 60), 0), ' days') AS 'Debt' FROM cq_issues WHERE $__timeFilter(created_date) project_key in ('project1', 'project2')","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/CQMaintainability-Debt","h":"#how-is-it-calculated","p":16080},{"i":16091,"t":"How often an organization deploys code to production or release it to end users. Below is a picture showing the definition of DevLake deployments.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/DeploymentFrequency","h":"#what-is-this-metric","p":16089},{"i":16093,"t":"Deployment frequency reflects the efficiency of a team's deployment. A team that deploys more frequently can deliver the product faster and users' feature requirements can be met faster.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/DeploymentFrequency","h":"#why-is-it-important","p":16089},{"i":16095,"t":"DORA dashboard. See live demo.","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/DeploymentFrequency","h":"#which-dashboards-does-it-exist-in","p":16089},{"i":16097,"t":"Deployment frequency is calculated based on the number of deployment days, not the number of deployments, e.g., daily, weekly, monthly, yearly. When there are multiple deployments triggered by one pipeline, tools like GitLab and BitBucket will generate more than one deployment. In these cases, DevLake will consider these deployments as ONE deployment and use the last deployment's finished date as the deployment finished date. Below are the 2023 DORA benchmarks for different development teams from Google's report. However, it's difficult to tell which group a team falls into when the team's deployment frequency is between once per week and once per month. Therefore, DevLake provides its own benchmarks to address this problem: Groups Benchmarks DevLake Benchmarks The Criteria of DevLake Benchmarks Elite performers On-demand (multiple deploys per day) On-demand Median Number of Deployment Days per Week >= 7 High performers Between once per day and once per week Between once per day and once per week Median Number of Deployment Days per Week >= 1 Medium performers Between once per week and once per month Between once per week and once per month Median Number of Deployment Days per Month >= 1 Low performers Between once per week and once per month Fewer than once per month Median Number of Deployment Days per Month < 1 Source: 2023 Accelerate State of DevOps, Google Click to expand or collapse 2021 DORA benchmarks Groups Benchmarks DevLake Benchmarks The Criteria of DevLake Benchmarks Elite performers On-demand (multiple deploys per day) On-demand Median Number of Deployment Days per Week >= 7 High performers Between once per week and once per month Between once per day and once per month Median Number of Deployment Days per Month >= 1 Medium performers Between once per month and once every 6 months Between once per month and once every 6 months Median Number of Deployment Days per six Months >= 1 Low performers Fewer than once per six months Fewer than once per six months Median Number of Deployment Days per six Months < 1 Source: 2021 Accelerate State of DevOps, Google Data Sources Required Deployments from Jenkins, GitLab CI, GitHub Action, BitBucket Pipelines, Webhook, etc. Transformation Rules Required Define deployment in data transformations while configuring the blueprint of a project to let DevLake know what CI records can be regarded as deployments. SQL Queries DevLake deployments can be found in table cicd_deployment_commits. If you want to measure the monthly trend of deployment count as the picture shown below, run the following SQL in Grafana. -- Metric 1: Number of deployments per month with _deployments as( -- When deploying multiple commits in one pipeline, GitLab and BitBucket may generate more than one deployment. However, DevLake consider these deployments as ONE production deployment and use the last one's finished_date as the finished date. SELECT date_format(deployment_finished_date,'%y/%m') as month, count(cicd_deployment_id) as deployment_count FROM ( SELECT cdc.cicd_deployment_id, max(cdc.finished_date) as deployment_finished_date FROM cicd_deployment_commits cdc JOIN project_mapping pm on cdc.cicd_scope_id = pm.row_id and pm.`table` = 'cicd_scopes' WHERE pm.project_name in (${project:sqlstring}+'') and cdc.result = 'SUCCESS' and cdc.environment = 'PRODUCTION' GROUP BY 1 HAVING $__timeFilter(max(cdc.finished_date)) ) _production_deployments GROUP BY 1 ) SELECT cm.month, case when d.deployment_count is null then 0 else d.deployment_count end as deployment_count FROM calendar_months cm LEFT JOIN _deployments d on cm.month = d.month WHERE $__timeFilter(cm.month_timestamp) If you want to measure in which category your team falls as in the picture shown below, run the following SQL in Grafana. Unlike monthly deployments which are based on the number of deployments, the metric below is based on deployment days. -- Metric 1: Deployment Frequency with last_few_calendar_months as( -- construct the last few calendar months within the selected time period in the top-right corner SELECT CAST((SYSDATE()-INTERVAL (H+T+U) DAY) AS date) day FROM ( SELECT 0 H UNION ALL SELECT 100 UNION ALL SELECT 200 UNION ALL SELECT 300 ) H CROSS JOIN ( SELECT 0 T UNION ALL SELECT 10 UNION ALL SELECT 20 UNION ALL SELECT 30 UNION ALL SELECT 40 UNION ALL SELECT 50 UNION ALL SELECT 60 UNION ALL SELECT 70 UNION ALL SELECT 80 UNION ALL SELECT 90 ) T CROSS JOIN ( SELECT 0 U UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5 UNION ALL SELECT 6 UNION ALL SELECT 7 UNION ALL SELECT 8 UNION ALL SELECT 9 ) U WHERE (SYSDATE()-INTERVAL (H+T+U) DAY) > $__timeFrom() ), _production_deployment_days as( -- When deploying multiple commits in one pipeline, GitLab and BitBucket may generate more than one deployment. However, DevLake consider these deployments as ONE production deployment and use the last one's finished_date as the finished date. SELECT cdc.cicd_deployment_id as deployment_id, max(DATE(cdc.finished_date)) as day FROM cicd_deployment_commits cdc JOIN project_mapping pm on cdc.cicd_scope_id = pm.row_id and pm.`table` = 'cicd_scopes' WHERE pm.project_name in (${project}) and cdc.result = 'SUCCESS' and cdc.environment = 'PRODUCTION' GROUP BY 1 ), _days_weekly_deploy as( -- calculate the number of deployment days every week SELECT date(DATE_ADD(last_few_calendar_months.day, INTERVAL -WEEKDAY(last_few_calendar_months.day) DAY)) as week, MAX(if(_production_deployment_days.day is not null, 1, null)) as weeks_deployed, COUNT(distinct _production_deployment_days.day) as days_deployed FROM last_few_calendar_months LEFT JOIN _production_deployment_days ON _production_deployment_days.day = last_few_calendar_months.day GROUP BY week ), _days_monthly_deploy as( -- calculate the number of deployment days every month SELECT date(DATE_ADD(last_few_calendar_months.day, INTERVAL -DAY(last_few_calendar_months.day)+1 DAY)) as month, MAX(if(_production_deployment_days.day is not null, 1, null)) as months_deployed, COUNT(distinct _production_deployment_days.day) as days_deployed FROM last_few_calendar_months LEFT JOIN _production_deployment_days ON _production_deployment_days.day = last_few_calendar_months.day GROUP BY month ), _days_six_months_deploy AS ( SELECT month, SUM(days_deployed) OVER ( ORDER BY month ROWS BETWEEN 5 PRECEDING AND CURRENT ROW ) AS days_deployed_per_six_months, COUNT(months_deployed) OVER ( ORDER BY month ROWS BETWEEN 5 PRECEDING AND CURRENT ROW ) AS months_deployed_count, ROW_NUMBER() OVER ( PARTITION BY DATE_FORMAT(month, '%Y-%m') DIV 6 ORDER BY month DESC ) AS rn FROM _days_monthly_deploy ), _median_number_of_deployment_days_per_week_ranks as( SELECT *, percent_rank() over(order by days_deployed) as ranks FROM _days_weekly_deploy ), _median_number_of_deployment_days_per_week as( SELECT max(days_deployed) as median_number_of_deployment_days_per_week FROM _median_number_of_deployment_days_per_week_ranks WHERE ranks <= 0.5 ), _median_number_of_deployment_days_per_month_ranks as( SELECT *, percent_rank() over(order by days_deployed) as ranks FROM _days_monthly_deploy ), _median_number_of_deployment_days_per_month as( SELECT max(days_deployed) as median_number_of_deployment_days_per_month FROM _median_number_of_deployment_days_per_month_ranks WHERE ranks <= 0.5 ), _days_per_six_months_deploy_by_filter AS ( SELECT month, days_deployed_per_six_months, months_deployed_count FROM _days_six_months_deploy WHERE rn%6 = 1 ), _median_number_of_deployment_days_per_six_months_ranks as( SELECT *, percent_rank() over(order by days_deployed_per_six_months) as ranks FROM _days_per_six_months_deploy_by_filter ), _median_number_of_deployment_days_per_six_months as( SELECT min(days_deployed_per_six_months) as median_number_of_deployment_days_per_six_months, min(months_deployed_count) as is_collected FROM _median_number_of_deployment_days_per_six_months_ranks WHERE ranks >= 0.5 ) SELECT CASE WHEN ('$dora_report') = '2023' THEN CASE WHEN median_number_of_deployment_days_per_week >= 7 THEN CONCAT(median_number_of_deployment_days_per_week, ' deployment days per week(elite)') WHEN median_number_of_deployment_days_per_week >= 1 THEN CONCAT(median_number_of_deployment_days_per_week, ' deployment days per week(high)') WHEN median_number_of_deployment_days_per_month >= 1 THEN CONCAT(median_number_of_deployment_days_per_month, ' deployment days per month(medium)') WHEN median_number_of_deployment_days_per_month < 1 and is_collected is not null THEN CONCAT(median_number_of_deployment_days_per_month, ' deployment days per month(low)') ELSE \"N/A. Please check if you have collected deployments.\" END WHEN ('$dora_report') = '2021' THEN CASE WHEN median_number_of_deployment_days_per_week >= 7 THEN CONCAT(median_number_of_deployment_days_per_week, ' deployment days per week(elite)') WHEN median_number_of_deployment_days_per_month >= 1 THEN CONCAT(median_number_of_deployment_days_per_month, ' deployment days per month(high)') WHEN median_number_of_deployment_days_persix_months >= 1 THEN CONCAT(median_number_of_deployment_days_per_six_months, ' deployment days per six months(medium)') WHEN median_number_of_deployment_days_per_six_months < 1 and is_collected is not null THEN CONCAT(median_number_of_deployment_days_per_six_months, ' deployment days per six months(low)') ELSE \"N/A. Please check if you have collected deployments.\" END ELSE 'Invalid dora report' END AS 'Deployment Frequency' FROM _median_number_of_deployment_days_per_week, _median_number_of_deployment_days_per_month, _median_number_of_deployment_days_per_six_months","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/DeploymentFrequency","h":"#how-is-it-calculated","p":16089},{"i":16099,"t":"Trunk development. Work in small batches and often merge their work into shared trunks. Integrate CI/CD tools for automated deployment Improve automated test coverage","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/DeploymentFrequency","h":"#how-to-improve","p":16089},{"i":16102,"t":"The time of changes that were made to a code that then resulted in incidents, rollbacks, or any type of production failure.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/FailedDeploymentRecoveryTime","h":"#what-is-this-metric","p":16100},{"i":16104,"t":"This metric is crucial in evaluating the resilience and efficiency of a team's deployment process. A shorter recovery time indicates a team's ability to swiftly detect issues, troubleshoot them, and restore the system to a functional state, minimizing downtime and impact on end-users.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/FailedDeploymentRecoveryTime","h":"#why-is-it-important","p":16100},{"i":16106,"t":"DORA dashboard. See live demo.","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/FailedDeploymentRecoveryTime","h":"#which-dashboards-does-it-exist-in","p":16100},{"i":16108,"t":"The time from deployment to the incident corresponding to deployment is resolved. For example, if a deployment finishes at 10:00 AM and causes an incident at 10:20. Then, the incident gets resolved at 11:00 AM. The failed deployment recovery time is one hour. Below are the 2023 DORA benchmarks for different development teams from Google's report. However, it's difficult to tell which group a team falls into when the team's failed deployment recovery time is between one week and six months. Therefore, DevLake provides its own benchmarks to address this problem: Groups Benchmarks DevLake Benchmarks Elite performers Less than one hour Less than one hour High performers Less than one day Less than one day Medium performers Between one day and one week Between one day and one week Low performers More than six months More than one week Source: 2023 Accelerate State of DevOps, Google Data Sources Required Deployments from Jenkins, GitLab CI, GitHub Action, BitBucket Pipelines, Webhook, etc. Incidents from Jira issues, GitHub issues, TAPD issues, PagerDuty Incidents, Webhook, etc. Transformation Rules Required Define deployment and incident in data transformations while configuring the blueprint of a project to let DevLake know what CI/issue records can be regarded as deployments or incidents. SQL Queries If you want to measure the monthly trend of the Failed Deployment Recovery Time as the picture shown below, run the following SQL in Grafana. -- ***** 2023 report ***** -- -- Metric 4: Failed deployment recovery time with _deployments as ( SELECT cdc.cicd_deployment_id as deployment_id, max(cdc.finished_date) as deployment_finished_date FROM cicd_deployment_commits cdc JOIN project_mapping pm on cdc.cicd_scope_id = pm.row_id and pm.`table` = 'cicd_scopes' WHERE pm.project_name in ($project) and cdc.result = 'SUCCESS' and cdc.environment = 'PRODUCTION' GROUP BY 1 HAVING $__timeFilter(max(cdc.finished_date)) ), _incidents_for_deployments as ( SELECT i.id as incident_id, i.created_date as incident_create_date, i.resolution_date as incident_resolution_date, fd.deployment_id as caused_by_deployment, fd.deployment_finished_date, date_format(fd.deployment_finished_date,'%y/%m') as deployment_finished_month FROM issues i left join project_issue_metrics pim on i.id = pim.id join _deployments fd on pim.deployment_id = fd.deployment_id WHERE i.type = 'INCIDENT' and $__timeFilter(i.resolution_date) ), _recovery_time_ranks as ( SELECT *, percent_rank() over(PARTITION BY deployment_finished_month order by TIMESTAMPDIFF(MINUTE, deployment_finished_date, incident_resolution_date)) as ranks FROM _incidents_for_deployments ), _median_recovery_time as ( SELECT deployment_finished_month, max(TIMESTAMPDIFF(MINUTE, deployment_finished_date, incident_resolution_date)) as median_recovery_time FROM _recovery_time_ranks WHERE ranks <= 0.5 GROUP BY deployment_finished_month ), _metric_recovery_time_2023_report as ( SELECT cm.month, case when m.median_recovery_time is null then 0 else m.median_recovery_time/60 end as median_recovery_time_in_hour FROM calendar_months cm LEFT JOIN _median_recovery_time m on cm.month = m.deployment_finished_month WHERE $__timeFilter(cm.month_timestamp) ) SELECT cm.month, CASE WHEN '${dora_report}' = '2023' THEN mrt.median_recovery_time_in_hour END AS '${title_value} In Hours' FROM calendar_months cm LEFT JOIN _metric_recovery_time_2023_report mrt ON cm.month = mrt.month WHERE $__timeFilter(cm.month_timestamp) If you want to measure in which category your team falls into as in the picture shown below, run the following SQL in Grafana. -- ***** 2023 report ***** -- -- Metric 4: Failed deployment recovery time with _deployments as ( SELECT cdc.cicd_deployment_id as deployment_id, max(cdc.finished_date) as deployment_finished_date FROM cicd_deployment_commits cdc JOIN project_mapping pm on cdc.cicd_scope_id = pm.row_id and pm.`table` = 'cicd_scopes' WHERE pm.project_name in ($project) and cdc.result = 'SUCCESS' and cdc.environment = 'PRODUCTION' GROUP BY 1 HAVING $__timeFilter(max(cdc.finished_date)) ), _incidents_for_deployments as ( SELECT i.id as incident_id, i.created_date as incident_create_date, i.resolution_date as incident_resolution_date, fd.deployment_id as caused_by_deployment, fd.deployment_finished_date, date_format(fd.deployment_finished_date,'%y/%m') as deployment_finished_month FROM issues i left join project_issue_metrics pim on i.id = pim.id join _deployments fd on pim.deployment_id = fd.deployment_id WHERE i.type = 'INCIDENT' and $__timeFilter(i.resolution_date) ), _recovery_time_ranks as ( SELECT *, percent_rank() over(order by TIMESTAMPDIFF(MINUTE, deployment_finished_date, incident_resolution_date)) as ranks FROM _incidents_for_deployments ), _median_recovery_time as ( SELECT max(TIMESTAMPDIFF(MINUTE, deployment_finished_date, incident_resolution_date)) as median_recovery_time FROM _recovery_time_ranks WHERE ranks <= 0.5 ), _metric_recovery_time_2023_report as( SELECT CASE WHEN ('$dora_report') = '2023' THEN CASE WHEN median_recovery_time < 60 THEN CONCAT(round(median_recovery_time/60,1), \"(elite)\") WHEN median_recovery_time < 24 * 60 THEN CONCAT(round(median_recovery_time/60,1), \"(high)\") WHEN median_recovery_time < 7 * 24 * 60 THEN CONCAT(round(median_recovery_time/60,1), \"(medium)\") WHEN median_recovery_time >= 7 * 24 * 60 THEN CONCAT(round(median_recovery_time/60,1), \"(low)\") ELSE \"N/A. Please check if you have collected incidents.\" END END AS median_recovery_time FROM _median_recovery_time ) SELECT median_recovery_time AS median_time_in_hour FROM _metric_recovery_time_2023_report WHERE ('$dora_report') = '2023'","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/FailedDeploymentRecoveryTime","h":"#how-is-it-calculated","p":16100},{"i":16110,"t":"Add unit tests for all new feature \"Shift left\", start QA early and introduce more automated tests Enforce code review if it is not strictly executed Improve your user support workflow to cope with incidents more efficiently","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/FailedDeploymentRecoveryTime","h":"#how-to-improve","p":16100},{"i":16113,"t":"The amount of time it takes an incident to fix.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/IncidentAge","h":"#what-is-this-metric","p":16111},{"i":16115,"t":"Help the team to establish an effective hierarchical response mechanism for incidents. Focus on the resolution of important problems in the backlog. Improve team's and individual's incident fixing efficiency. Identify good/to-be-improved practices that affect incident age","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/IncidentAge","h":"#why-is-it-important","p":16111},{"i":16117,"t":"Jira GitHub","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/IncidentAge","h":"#which-dashboards-does-it-exist-in","p":16111},{"i":16119,"t":"Similar to requirement lead time, this metric equals resolution_date - created_date of issues in type \"INCIDENT\". Data Sources Required This metric relies on issues collected from Jira, GitHub, TAPD, or PagerDuty. Transformation Rules Required This metric relies on the 'type-incident' configuration in Jira, GitHub or TAPD's transformation rules while adding/editing a blueprint. This configuration tells DevLake what issues are incidents. SQL Queries The following SQL shows how to find the incident age of a specific incident. -- lead_time_minutes is a pre-calculated field whose value equals 'resolution_date - created_date' SELECT lead_time_minutes/1440 as incident_age_in_days FROM issues WHERE type = 'INCIDENT' If you want to measure the mean incident age in the screenshot below, please run the following SQL in Grafana. with _incidents as( SELECT DATE_ADD(date(i.resolution_date), INTERVAL -DAY(date(i.resolution_date))+1 DAY) as time, AVG(i.lead_time_minutes/1440) as issue_lead_time FROM issues i join board_issues bi on i.id = bi.issue_id join boards b on bi.board_id = b.id WHERE -- $board_id is a variable defined in Grafana's dashboard settings to filter out issues by boards b.id in ($board_id) and i.status = \"DONE\" and i.type = 'INCIDENT' and $__timeFilter(i.resolution_date) -- the following condition will remove the month with incomplete data and i.resolution_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) group by 1 ) SELECT date_format(time,'%M %Y') as month, issue_lead_time as \"Mean Incident Age in Days\" FROM _incidents ORDER BY time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/IncidentAge","h":"#how-is-it-calculated","p":16111},{"i":16121,"t":"Observe the trend of incident age and locate the key reasons. Compare the age of incidents by severity levels, types (business, functional classification), affected components, etc.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/IncidentAge","h":"#how-to-improve","p":16111},{"i":16124,"t":"Amount of incidents per 1,000 lines of code.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/IncidentCountPer1kLinesOfCode","h":"#what-is-this-metric","p":16122},{"i":16126,"t":"Defect drill-down analysis to inform the development of design and code review strategies and to improve the internal QA process Assist teams to locate projects/modules with higher defect severity and density, and clean up technical debts Identify good/to-be-improved practices that affect defect count or defect rate, to reduce the number of future defects","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/IncidentCountPer1kLinesOfCode","h":"#why-is-it-important","p":16122},{"i":16128,"t":"N/A","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/IncidentCountPer1kLinesOfCode","h":"#which-dashboards-does-it-exist-in","p":16122},{"i":16130,"t":"The number of incidents divided by total accumulated lines of code (additions + deletions) in the given data range. Data Sources Required issues collected from Jira, GitHub, TAPD or PagerDuty. commits collected from GitHub, GitLab or BitBucket. Data Transformation Required This metric relies on the 'type-incident' configuration in Jira, GitHub or TAPD's transformation rules while adding/editing a blueprint. This configuration tells DevLake what issues are incidents. SQL Queries If you want to measure the monthly trend of Incidents per 1k lines of code in the screenshot below, please run the following SQL in Grafana. with _line_of_code as ( select DATE_ADD(date(authored_date), INTERVAL -DAY(date(authored_date))+1 DAY) as time, sum(additions + deletions) as line_count from commits where message not like 'Merge%' and $__timeFilter(authored_date) group by 1 ), _incident_count as( select DATE_ADD(date(created_date), INTERVAL -DAY(date(created_date))+1 DAY) as time, count(*) as incident_count from issues i where type = 'INCIDENT' and $__timeFilter(created_date) group by 1 ), _incident_count_per_1k_loc as( select loc.time, 1.0 * ic.incident_count / loc.line_count * 1000 as incident_count_per_1k_loc from _line_of_code loc left join _incident_count ic on ic.time = loc.time where ic.incident_count is not null and loc.line_count is not null and loc.line_count != 0 ) select date_format(time,'%M %Y') as month, incident_count_per_1k_loc as 'Incident Count per 1000 Lines of Code' from _incident_count_per_1k_loc order by time;","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/IncidentCountPer1kLinesOfCode","h":"#how-is-it-calculated","p":16122},{"i":16132,"t":"From the project or team dimension, observe the statistics on the total number of defects, the distribution of the number of defects in each severity level/type/owner, the cumulative trend of defects, and the change trend of the defect rate in thousands of lines, etc. From version cycle dimension, observe the statistics on the cumulative trend of the number of defects/defect rate, which can be used to determine whether the growth rate of defects is slowing down, showing a flat convergence trend, and is an important reference for judging the stability of software version quality From the time dimension, analyze the trend of the number of test defects, defect rate to locate the key items/key points Evaluate whether the software quality and test plan are reasonable by referring to CMMI standard values","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/IncidentCountPer1kLinesOfCode","h":"#how-to-improve","p":16122},{"i":16135,"t":"The time it takes from the first commit until a PR is issued.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/PRCodingTime","h":"#what-is-this-metric","p":16133},{"i":16137,"t":"It is recommended that you keep every task on a workable and manageable scale for a reasonably short amount of coding time. The average coding time of most engineering teams is around 3-4 days.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/PRCodingTime","h":"#why-is-it-important","p":16133},{"i":16139,"t":"Engineering Throughput and Cycle Time Engineering Throughput and Cycle Time - Team View","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/PRCodingTime","h":"#which-dashboards-does-it-exist-in","p":16133},{"i":16141,"t":"Data Sources Required This metric relies on PRs/MRs collected from GitHub, GitLab, BitBucket, Gitee or other code review tools. Data Transformation Required N/A SQL Queries The following SQL shows how to find the coding time of a specific PR. DevLake pre-calculates the metric and stores it in table.project_pr_metrics. SELECT pr_coding_time/60 as 'PR Coding Time(h)' FROM project_pr_metrics If you want to measure the monthly trend of PR coding time in the screenshot below, please run the following SQL in Grafana. SELECT DATE_ADD(date(pr.created_date), INTERVAL -DAY(date(pr.created_date))+1 DAY) as time, avg(ppm.pr_coding_time)/60 as 'PR Coding Time(h)' FROM pull_requests pr JOIN project_pr_metrics ppm ON pr.id = ppm.id GROUP BY 1 ORDER BY 1","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/PRCodingTime","h":"#how-is-it-calculated","p":16133},{"i":16143,"t":"Divide coding tasks into workable and manageable pieces.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/PRCodingTime","h":"#how-to-improve","p":16133},{"i":16146,"t":"The time to restore service after service incidents, rollbacks, or any type of production failure happened.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/MTTR","h":"#what-is-this-metric","p":16144},{"i":16148,"t":"This metric is essential to measure the disaster control capability of your team and the robustness of the software.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/MTTR","h":"#why-is-it-important","p":16144},{"i":16150,"t":"DORA dashboard. See live demo.","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/MTTR","h":"#which-dashboards-does-it-exist-in","p":16144},{"i":16152,"t":"MTTR = Total incident age (in hours)/number of incidents. If you have three incidents that happened in the given data range, one lasting 1 hour, one lasting 2 hours and one lasting 3 hours. Your MTTR will be: (1 + 2 + 3) / 3 = 2 hours. Below are the 2023 DORA benchmarks for different development teams from Google's report. However, it's difficult to tell which group a team falls into when the team's median time to restore service is between one week and six months. Therefore, DevLake provides its own benchmarks to address this problem: Groups Benchmarks DevLake Benchmarks Elite performers Less than one hour Less than one hour High performers Less than one day Less than one day Medium performers Between one day and one week Between one day and one week Low performers More than six months More than one week Click to expand or collapse 2021 DORA benchmarks Groups Benchmarks DevLake Benchmarks Elite performers Less than one hour Less than one hour High performers Less than one day Less than one day Medium performers Between one day and one week Between one day and one week Low performers More than six months More than one week Source: 2021 Accelerate State of DevOps, Google Data Sources Required Incidents from Jira issues, GitHub issues, TAPD issues, PagerDuty Incidents, etc. Transformation Rules Required Define incident in data transformations while configuring the blueprint of a project to let DevLake know what CI/issue records can be regarded as deployments or incidents. SQL Queries If you want to measure the monthly trend of the Median Time to Restore Service as the picture shown below, run the following SQL in Grafana. -- Metric 3: median time to restore service - MTTR -- Metric 3: median time to restore service - MTTR with _incidents as ( -- get the number of incidents created each month SELECT distinct i.id, date_format(i.created_date,'%y/%m') as month, cast(lead_time_minutes as signed) as lead_time_minutes FROM issues i join board_issues bi on i.id = bi.issue_id join boards b on bi.board_id = b.id join project_mapping pm on b.id = pm.row_id and pm.`table` = 'boards' WHERE pm.project_name in (${project:sqlstring}+'') and i.type = 'INCIDENT' and i.lead_time_minutes is not null ), _find_median_mttr_each_month_ranks as( SELECT *, percent_rank() over(PARTITION BY month order by lead_time_minutes) as ranks FROM _incidents ), _mttr as( SELECT month, max(lead_time_minutes) as median_time_to_resolve FROM _find_median_mttr_each_month_ranks WHERE ranks <= 0.5 GROUP BY month ) SELECT cm.month, case when m.median_time_to_resolve is null then 0 else m.median_time_to_resolve/60 end as median_time_to_resolve_in_hour FROM calendar_months cm LEFT JOIN _mttr m on cm.month = m.month WHERE $__timeFilter(cm.month_timestamp) If you want to measure in which category your team falls into as in the picture shown below, run the following SQL in Grafana. -- ***** 2023 report ***** -- -- Metric 4: Failed deployment recovery time with _incidents as ( -- get the incidents created within the selected time period in the top-right corner SELECT distinct i.id, cast(lead_time_minutes as signed) as lead_time_minutes FROM issues i join board_issues bi on i.id = bi.issue_id join boards b on bi.board_id = b.id join project_mapping pm on b.id = pm.row_id and pm.`table` = 'boards' WHERE pm.project_name in (${project}) and i.type = 'INCIDENT' and $__timeFilter(i.created_date) ), _median_mttr_ranks as( SELECT *, percent_rank() over(order by lead_time_minutes) as ranks FROM _incidents ), _median_mttr as( SELECT max(lead_time_minutes) as median_time_to_resolve FROM _median_mttr_ranks WHERE ranks <= 0.5 ), _metric_mttr_2021_report as( SELECT CASE WHEN ('$dora_report') = '2021' THEN CASE WHEN median_time_to_resolve < 60 THEN CONCAT(round(median_time_to_resolve/60,1), \"(elite)\") WHEN median_time_to_resolve < 24 * 60 THEN CONCAT(round(median_time_to_resolve/60,1), \"(high)\") WHEN median_time_to_resolve < 7 * 24 * 60 THEN CONCAT(round(median_time_to_resolve/60,1), \"(medium)\") WHEN median_time_to_resolve >= 7 * 24 * 60 THEN CONCAT(round(median_time_to_resolve/60,1), \"(low)\") ELSE \"N/A. Please check if you have collected incidents.\" END END AS median_time_to_resolve FROM _median_mttr ) SELECT median_time_to_resolve AS median_time_to_resolve FROM _metric_mttr_2021_report WHERE ('$dora_report') = '2021'","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/MTTR","h":"#how-is-it-calculated","p":16144},{"i":16154,"t":"Use automated tools to quickly report failure Prioritize recovery when a failure happens Establish a go-to action plan to respond to failures immediately Reduce the deployment time for failure-fixing","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/MTTR","h":"#how-to-improve","p":16144},{"i":16157,"t":"The number of pull requests (eg. GitHub PRs, Bitbucket PRs, GitLab MRs) created.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/PRCount","h":"#what-is-this-metric","p":16155},{"i":16159,"t":"Code review metrics are process indicators to provide quick feedback on developers' code quality Promote the team to establish a unified coding specification and standardize the code review criteria Identify modules with low-quality risks in advance, optimize practices, and precipitate into reusable knowledge and tools to avoid technical debt accumulation","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/PRCount","h":"#why-is-it-important","p":16155},{"i":16161,"t":"GitHub GitLab Weekly Community Retro Engineering Throughput and Cycle Time Engineering Throughput and Cycle Time - Team View","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/PRCount","h":"#which-dashboards-does-it-exist-in","p":16155},{"i":16163,"t":"This metric is calculated by counting the number of PRs in the given data range. Data Sources Required This metric relies on PRs/MRs collected from GitHub, GitLab, BitBucket, Gitee or other code review tools. Data Transformation Required N/A SQL Queries The following SQL shows how to find PRs created in specific repositories and given time range. select count(*) as pull_request_count from pull_requests pr where -- $__timeFilter will take Grafana's time range $__timeFilter(created_date) -- please replace the repo ids with your own, or create a '$repo_id' variable in Grafana and base_repo_id in ('repo_1', 'repo_2') -- remove PRs submitted by bots, comment it out if you don't need it and author_name not rlike '^robot-|-robot$|\\\\[bot\\\\]|-bot$|-ci$|-testing$' If you want to measure the monthly trend of PR count in the screenshot below, please run the following SQL in Grafana. WITH _prs as( SELECT DATE_ADD(date(created_date), INTERVAL -DAY(date(created_date))+1 DAY) as time, count(*) as pr_count FROM pull_requests WHERE -- please replace the repo ids with your own, or create a '$repo_id' variable in Grafana base_repo_id in ('repo_1', 'repo_2') and $__timeFilter(created_date) -- the following condition will remove the month with incomplete data and created_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) -- remove PRs submitted by bots, comment it out if you don't need it and author_name not rlike '^robot-|-robot$|\\\\[bot\\\\]|-bot$|-ci$|-testing$' GROUP BY 1 ) SELECT date_format(time,'%M %Y') as month, pr_count as \"Pull Request Count\" FROM _prs ORDER BY time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/PRCount","h":"#how-is-it-calculated","p":16155},{"i":16165,"t":"From the developer dimension, we evaluate the code quality of developers by combining the task complexity with the metrics related to the number of review passes and review rounds. From the reviewer dimension, we observe the reviewer's review style by taking into account the task complexity, the number of passes and the number of review rounds. From the project/team dimension, we combine the project phase and team task complexity to aggregate the metrics related to the number of review passes and review rounds, and identify the modules with abnormal code review process and possible quality risks.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/PRCount","h":"#how-to-improve","p":16155},{"i":16168,"t":"The median amount of time for a code change to be deployed into production.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/LeadTimeForChanges","h":"#what-is-this-metric","p":16166},{"i":16170,"t":"This metric measures the time it takes to a code change to the production environment and reflects the speed of software delivery. A lower average change preparation time means that your team is efficient at coding and deploying your project.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/LeadTimeForChanges","h":"#why-is-it-important","p":16166},{"i":16172,"t":"DORA dashboard. See live demo.","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/LeadTimeForChanges","h":"#which-dashboards-does-it-exist-in","p":16166},{"i":16174,"t":"This metric is quite similar to PR Cycle Time. The difference is that 'Lead Time for Changes' uses a different method to filter PRs. Find the PRs' associated deployment commits whose finished_date falls into the time range that users select. Find the associated pull requests of the commits diff between two consecutive successful deployment commits in the production environment. Calculate the PRs' median cycle time. This will be the Median Lead Time for Changes. PR cycle time is pre-calculated by the dora plugin during every data collection. You can find it in pr_cycle_time in table.project_pr_metrics of DevLake's database. Below are the 2023 DORA benchmarks for different development teams from Google's report. However, it's difficult to tell which group a team falls into when the team's median lead time for changes is between one week and one month. Therefore, DevLake provides its own benchmarks to address this problem: Groups Benchmarks DevLake Benchmarks Elite performers Less than one day Less than one day High performers Between one day and one week Between one day and one week Medium performers Between one week and one month Between one week and one month Low performers Between one week and one month More than one month Source: 2023 Accelerate State of DevOps, Google Click to expand or collapse 2021 DORA benchmarks Groups Benchmarks DevLake Benchmarks Elite performers Less than one hour Less than one hour High performers Between one day and one week Less than one week Medium performers Between one month and six months Between one week and six months Low performers More than six months More than six months Source: 2021 Accelerate State of DevOps, Google Data Sources Required Deployments from Jenkins, GitLab CI, GitHub Action, BitBucket Pipelines, Webhook, etc. Pull Requests from GitHub PRs, GitLab MRs, BitBucket PRs, Azure DevOps PRs, etc. Transformation Rules Required Define deployment in data transformations while configuring the blueprint of a project to let DevLake know what CI records can be regarded as deployments. SQL Queries If you want to measure the monthly trend of median lead time for changes as the picture shown below, run the following SQL in Grafana. -- Metric 2: median change lead time per month with _pr_stats as ( -- get the cycle time of PRs deployed by the deployments finished each month SELECT distinct pr.id, date_format(cdc.finished_date,'%y/%m') as month, ppm.pr_cycle_time FROM pull_requests pr join project_pr_metrics ppm on ppm.id = pr.id join project_mapping pm on pr.base_repo_id = pm.row_id and pm.`table` = 'repos' join cicd_deployment_commits cdc on ppm.deployment_commit_id = cdc.id WHERE pm.project_name in (${project:sqlstring}+'') and pr.merged_date is not null and ppm.pr_cycle_time is not null and $__timeFilter(cdc.finished_date) ), _find_median_clt_each_month_ranks as( SELECT *, percent_rank() over(PARTITION BY month order by pr_cycle_time) as ranks FROM _pr_stats ), _clt as( SELECT month, max(pr_cycle_time) as median_change_lead_time FROM _find_median_clt_each_month_ranks WHERE ranks <= 0.5 group by month ) SELECT cm.month, case when _clt.median_change_lead_time is null then 0 else _clt.median_change_lead_time/60 end as median_change_lead_time_in_hour FROM calendar_months cm LEFT JOIN _clt on cm.month = _clt.month WHERE $__timeFilter(cm.month_timestamp) If you want to measure in which category your team falls as in the picture shown below, run the following SQL in Grafana. -- Metric 2: median lead time for changes with _pr_stats as ( -- get the cycle time of PRs deployed by the deployments finished in the selected period SELECT distinct pr.id, ppm.pr_cycle_time FROM pull_requests pr join project_pr_metrics ppm on ppm.id = pr.id join project_mapping pm on pr.base_repo_id = pm.row_id and pm.`table` = 'repos' join cicd_deployment_commits cdc on ppm.deployment_commit_id = cdc.id WHERE pm.project_name in (${project}) and pr.merged_date is not null and ppm.pr_cycle_time is not null and $__timeFilter(cdc.finished_date) ), _median_change_lead_time_ranks as( SELECT *, percent_rank() over(order by pr_cycle_time) as ranks FROM _pr_stats ), _median_change_lead_time as( -- use median PR cycle time as the median change lead time SELECT max(pr_cycle_time) as median_change_lead_time FROM _median_change_lead_time_ranks WHERE ranks <= 0.5 ) SELECT CASE WHEN ('$dora_report') = '2023' THEN CASE WHEN median_change_lead_time < 24 * 60 THEN CONCAT(round(median_change_lead_time/60,1), \"(elite)\") WHEN median_change_lead_time < 7 * 24 * 60 THEN CONCAT(round(median_change_lead_time/60,1), \"(high)\") WHEN median_change_lead_time < 30 * 24 * 60 THEN CONCAT(round(median_change_lead_time/60,1), \"(medium)\") WHEN median_change_lead_time >= 30 * 24 * 60 THEN CONCAT(round(median_change_lead_time/60,1), \"(low)\") ELSE \"N/A. Please check if you have collected deployments/pull_requests.\" END WHEN ('$dora_report') = '2021' THEN CASE WHEN median_change_lead_time < 60 THEN CONCAT(round(median_change_lead_time/60,1), \"(elite)\") WHEN median_change_lead_time < 7 * 24 * 60 THEN CONCAT(round(median_change_lead_time/60,1), \"(high)\") WHEN median_change_lead_time < 180 * 24 * 60 THEN CONCAT(round(median_change_lead_time/60,1), \"(medium)\") WHEN median_change_lead_time >= 180 * 24 * 60 THEN CONCAT(round(median_change_lead_time/60,1), \"(low)\") ELSE \"N/A. Please check if you have collected deployments/pull_requests.\" END ELSE 'Invalid dora report' END AS median_change_lead_time FROM _median_change_lead_time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/LeadTimeForChanges","h":"#how-is-it-calculated","p":16166},{"i":16176,"t":"Break requirements into smaller, more manageable deliverables Optimize the code review process \"Shift left\", start QA early and introduce more automated tests Integrate CI/CD tools to automate the deployment process","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/LeadTimeForChanges","h":"#how-to-improve","p":16166},{"i":16179,"t":"PR Cycle Time is the sum of PR Coding Time, PR Time-to-Merge and PR Deploy Time. It is the total time from the first commit to when the PR is deployed. The reason why we use PR Time-to-Merge rather than PR Pickup Time + PR Review Time is that a merged PR may not have any review. In this case, PR Pickup Time and PR Review Time will be NULL, while PR Time-to-Merge is not.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/PRCycleTime","h":"#what-is-this-metric","p":16177},{"i":16181,"t":"PR Cycle Time indicates the overall velocity of the delivery progress in terms of PR.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/PRCycleTime","h":"#why-is-it-important","p":16177},{"i":16183,"t":"Engineering Throughput and Cycle Time Engineering Throughput and Cycle Time - Team View","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/PRCycleTime","h":"#which-dashboards-does-it-exist-in","p":16177},{"i":16185,"t":"You can define deployment based on your actual practice. For a full list of deployment's definitions that DevLake support, please refer to Deployment Frequency. Data Sources Required This metric relies on PRs/MRs collected from GitHub, GitLab, BitBucket, Gitee or other code review tools. Data Transformation Required N/A SQL Queries The following SQL shows how to find the cycle time of a specific PR. DevLake pre-calculates the metric and stores it in table.project_pr_metrics. SELECT pr_cycle_time/60 as 'PR Cycle Time(h)' FROM project_pr_metrics If you want to measure the monthly trend of PR cycle time in the screenshot below, please run the following SQL in Grafana. SELECT DATE_ADD(date(pr.created_date), INTERVAL -DAY(date(pr.created_date))+1 DAY) as time, avg(ppm.pr_cycle_time)/60 as 'PR Cycle Time(h)' FROM pull_requests pr JOIN project_pr_metrics ppm ON pr.id = ppm.id GROUP BY 1 ORDER BY 1","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/PRCycleTime","h":"#how-is-it-calculated","p":16177},{"i":16187,"t":"Divide coding tasks into workable and manageable pieces; Use DevLake's dashboards to monitor your delivery progress; Have a habit to check for hanging PRs regularly; Set up alerts for your communication tools (e.g. Slack, Lark) when new PRs are issued; Use automated tests for the initial work; Reduce PR size; Analyze the causes for long reviews.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/PRCycleTime","h":"#how-to-improve","p":16177},{"i":16190,"t":"The time it takes from when a PR is merged to when it is deployed.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/PRDeployTime","h":"#what-is-this-metric","p":16188},{"i":16192,"t":"Based on historical data, establish a baseline of the delivery capacity of a single iteration to improve the organization and planning of R&D resources. Evaluate whether the delivery capacity matches the business phase and demand scale. Identify key bottlenecks and reasonably allocate resources.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/PRDeployTime","h":"#why-is-it-important","p":16188},{"i":16194,"t":"Engineering Throughput and Cycle Time Engineering Throughput and Cycle Time - Team View","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/PRDeployTime","h":"#which-dashboards-does-it-exist-in","p":16188},{"i":16196,"t":"PR deploy time is calculated by subtracting a PR's deployed_date and merged_date. Hence, we should associate PR/MRs with deployments. Here are the detailed steps: Find the commits diff between two consecutive deployments by deployments' commit_sha under the same scope and environment (in terms of TESTING/STAGING/PRODUCTION), for example, we will get commit-2 and commit-3 by calculating commits_diff between deployment-1 and deployment-2, which means commit-2 and commit-3 are deployed by deployment-2 Connect PR/MR and commits_diff through merge_commit or pr_commit, for example, we get pr-3 connected to commit-3 Now we can get pr-3's deploy time by finish_time of deployment-2 minus merge_time of pr-3. Data Transformation Required This metric relies on two sources: PR/MRs collected from GitHub or GitLab by enabling \"Code Review\" under the Data Entities section. Deployments collected in one of the following ways:: Open APIs of Jenkins, GitLab, GitHub, etc by enabling \"CICD\" under the Data Entities section. Webhook for general CI tools. Releases and PR/MRs from GitHub, GitLab APIs, etc. SQL Queries The following SQL shows how to find the deploy time of a specific PR. DevLake pre-calculates the metric and stores it in table.project_pr_metrics. SELECT pr_deploy_time/60 as 'PR Deploy Time(h)' FROM project_pr_metrics If you want to measure the monthly trend of PR deploy time in the screenshot below, please run the following SQL in Grafana. SELECT DATE_ADD(date(pr.created_date), INTERVAL -DAY(date(pr.created_date))+1 DAY) as time, avg(ppm.pr_deploy_time)/60 as 'PR Deploy Time(h)' FROM pull_requests pr JOIN project_pr_metrics ppm ON pr.id = ppm.id GROUP BY 1 ORDER BY 1","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/PRDeployTime","h":"#how-is-it-calculated","p":16188},{"i":16198,"t":"N/A","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/PRDeployTime","h":"#how-to-improve","p":16188},{"i":16201,"t":"The ratio of PRs/MRs that get merged.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/PRMergeRate","h":"#what-is-this-metric","p":16199},{"i":16203,"t":"Code review metrics are process indicators to provide quick feedback on developers' code quality Promote the team to establish a unified coding specification and standardize the code review criteria Identify modules with low-quality risks in advance, optimize practices, and precipitate into reusable knowledge and tools to avoid technical debt accumulation","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/PRMergeRate","h":"#why-is-it-important","p":16199},{"i":16205,"t":"GitHub GitLab Weekly Community Retro Engineering Throughput and Cycle Time Engineering Throughput and Cycle Time - Team View","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/PRMergeRate","h":"#which-dashboards-does-it-exist-in","p":16199},{"i":16207,"t":"The number of merged PRs divided by the number of all PRs in the given data range. Data Sources Required This metric relies on PRs/MRs collected from GitHub, GitLab, BitBucket, Gitee or other code review tools. Data Transformation Required N/A SQL Queries The following SQL shows how to find the PR merged rate in specific repositories and given time range, eg. 'repo-1' and 'repo-2'. select count(distinct case when merged_date is not null then id else null end)/count(*) as pr_merged_rate from pull_requests pr where -- $__timeFilter will take Grafana's time range $__timeFilter(created_date) -- please replace the repo ids with your own, or create a '$repo_id' variable in Grafana and base_repo_id in ('repo_1', 'repo_2') -- remove PRs submitted by bots, comment it out if you don't need it and author_name not rlike '^robot-|-robot$|\\\\[bot\\\\]|-bot$|-ci$|-testing$' If you want to measure the monthly trend of PR merge rate in the screenshot below, please run the following SQL in Grafana. SELECT DATE_ADD(date(created_date), INTERVAL -DAYOFMONTH(date(created_date))+1 DAY) as time, count(distinct case when merged_date is not null then id else null end)/count(*) as pr_merged_rate FROM pull_requests WHERE $__timeFilter(created_date) -- please replace the repo ids with your own, or create a '$repo_id' variable in Grafana and base_repo_id in ('repo_1', 'repo_2') -- remove PRs submitted by bots, comment it out if you don't need it and author_name not rlike '^robot-|-robot$|\\\\[bot\\\\]|-bot$|-ci$|-testing$' GROUP BY 1 If you want to measure the monthly trend of PR status distribution, please run the following SQL in Grafana. SELECT DATE_ADD(date(created_date), INTERVAL -DAYOFMONTH(date(created_date))+1 DAY) as time, count(distinct case when status != 'closed' then id else null end) as \"PR: Open\", count(distinct case when status = 'closed' and merged_date is null then id else null end) as \"PR: Closed without merging\", count(distinct case when status = 'closed' and merged_date is not null then id else null end) as \"PR: Closed and merged\" FROM pull_requests WHERE $__timeFilter(created_date) and created_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) and base_repo_id in ('repo_1', 'repo_2') -- remove PRs submitted by bots, comment it out if you don't need it and author_name not rlike '^robot-|-robot$|\\\\[bot\\\\]|-bot$|-ci$|-testing$' GROUP BY 1","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/PRMergeRate","h":"#how-is-it-calculated","p":16199},{"i":16209,"t":"From the developer dimension, we evaluate the code quality of developers by combining the task complexity with the metrics related to the number of review passes and review rounds. From the reviewer dimension, we observe the reviewer's review style by taking into account the task complexity, the number of passes and the number of review rounds. From the project/team dimension, we combine the project phase and team task complexity to aggregate the metrics related to the number of review passes and review rounds, and identify the modules with abnormal code review process and possible quality risks.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/PRMergeRate","h":"#how-to-improve","p":16199},{"i":16212,"t":"The time it takes from when a PR is issued until the first comment is added to that PR.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/PRPickupTime","h":"#what-is-this-metric","p":16210},{"i":16214,"t":"PR Pickup Time shows how engaged your team is in collaborative work by identifying the delay in picking up PRs.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/PRPickupTime","h":"#why-is-it-important","p":16210},{"i":16216,"t":"Engineering Throughput and Cycle Time Engineering Throughput and Cycle Time - Team View","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/PRPickupTime","h":"#which-dashboards-does-it-exist-in","p":16210},{"i":16218,"t":"Data Sources Required This metric relies on PRs/MRs collected from GitHub, GitLab, BitBucket, Gitee or other code review tools. Data Transformation Required N/A SQL Queries The following SQL shows how to find the pickup time of a specific PR. DevLake pre-calculates the metric and stores it in table.project_pr_metrics. SELECT pr_pickup_time/60 as 'PR Pickup Time(h)' FROM project_pr_metrics If you want to measure the monthly trend of PR pickup time in the screenshot below, please run the following SQL in Grafana. SELECT DATE_ADD(date(pr.created_date), INTERVAL -DAY(date(pr.created_date))+1 DAY) as time, avg(ppm.pr_pickup_time)/60 as 'PR Pickup Time(h)' FROM pull_requests pr JOIN project_pr_metrics ppm ON pr.id = ppm.id GROUP BY 1 ORDER BY 1","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/PRPickupTime","h":"#how-is-it-calculated","p":16210},{"i":16220,"t":"Use DevLake's dashboard to monitor your delivery progress; Have a habit to check for hanging PRs regularly; Set up alerts for your communication tools (e.g. Slack, Lark) when new PRs are issued.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/PRPickupTime","h":"#how-to-improve","p":16210},{"i":16223,"t":"The average number of comments of PRs in the selected time range.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/PRReviewDepth","h":"#what-is-this-metric","p":16221},{"i":16225,"t":"PR Review Depth (in Comments per RR) is related to the quality of code review, indicating how thorough your team reviews PRs.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/PRReviewDepth","h":"#why-is-it-important","p":16221},{"i":16227,"t":"Engineering Throughput and Cycle Time Engineering Throughput and Cycle Time - Team View","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/PRReviewDepth","h":"#which-dashboards-does-it-exist-in","p":16221},{"i":16229,"t":"This metric is calculated by counting the total number of PR comments divided by the total number of PRs in the selected time range. Data Sources Required This metric relies on PRs/MRs collected from GitHub, GitLab, BitBucket, Gitee or other code review tools. Data Transformation Required N/A SQL Queries If you want to measure the monthly trend of PR review time in the screenshot below, please run the following SQL in Grafana. SELECT DATE_ADD(date(pr.created_date), INTERVAL -$interval(date(pr.created_date))+1 DAY) as time, count(distinct prc.id)/count(pr.id) as \"PR Review Depth\" FROM pull_requests pr left join pull_request_comments prc on pr.id = prc.pull_request_id WHERE $__timeFilter(pr.created_date) and pr.base_repo_id in ($repo_id) and pr.merged_date is not null GROUP BY 1","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/PRReviewDepth","h":"#how-is-it-calculated","p":16221},{"i":16231,"t":"Encourage multiple reviewers to review a PR; Review Depth is an indicator for generally how thorough your PRs are reviewed, but it does not mean the deeper the better. In some cases, spending an excessive amount of resources on reviewing PRs is also not recommended.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/PRReviewDepth","h":"#how-to-improve","p":16221},{"i":16234,"t":"The time it takes to complete a code review of a PR before it gets merged.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/PRReviewTime","h":"#what-is-this-metric","p":16232},{"i":16236,"t":"Code review should be conducted almost in real-time and usually take less than two days. Abnormally long PR Review Time may indicate one or more of the following problems: The PR size is too large that makes it difficult to review. The team is too busy to review code.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/PRReviewTime","h":"#why-is-it-important","p":16232},{"i":16238,"t":"Engineering Throughput and Cycle Time Engineering Throughput and Cycle Time - Team View","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/PRReviewTime","h":"#which-dashboards-does-it-exist-in","p":16232},{"i":16240,"t":"This metric is the time frame between when the first comment is added to a PR, to when the PR is merged. Data Sources Required This metric relies on PRs/MRs collected from GitHub, GitLab, BitBucket, Gitee or other code review tools. Data Transformation Required N/A SQL Queries The following SQL shows how to find the review time of a specific PR. DevLake pre-calculates the metric and stores it in table.project_pr_metrics. SELECT pr_review_time/60 as 'PR Review Time(h)' FROM project_pr_metrics If you want to measure the monthly trend of PR review time in the screenshot below, please run the following SQL in Grafana. SELECT DATE_ADD(date(pr.created_date), INTERVAL -DAY(date(pr.created_date))+1 DAY) as time, avg(ppm.pr_review_time)/60 as 'PR Review Time(h)' FROM pull_requests pr JOIN project_pr_metrics ppm ON pr.id = ppm.id GROUP BY 1 ORDER BY 1","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/PRReviewTime","h":"#how-is-it-calculated","p":16232},{"i":16242,"t":"Use DevLake's dashboards to monitor your delivery progress; Use automated tests for the initial work; Reduce PR size; Analyze the causes for long reviews.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/PRReviewTime","h":"#how-to-improve","p":16232},{"i":16245,"t":"The average code changes (in Lines of Code) of PRs in the selected time range.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/PRSize","h":"#what-is-this-metric","p":16243},{"i":16247,"t":"Small PRs can reduce risks of introducing new bugs and increase code review quality, as problems may often be hidden in big chuncks of code and difficult to identify.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/PRSize","h":"#why-is-it-important","p":16243},{"i":16249,"t":"Engineering Throughput and Cycle Time Engineering Throughput and Cycle Time - Team View","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/PRSize","h":"#which-dashboards-does-it-exist-in","p":16243},{"i":16251,"t":"This metric is calculated by counting the total number of code changes (in LOC) divided by the total number of PRs in the selected time range. Data Sources Required This metric relies on PRs/MRs collected from GitHub, GitLab, BitBucket, Gitee or other code review tools. Data Transformation Required N/A SQL Queries If you want to measure the monthly trend of PR review time in the screenshot below, please run the following SQL in Grafana. with _pr_commits_data as( SELECT DATE_ADD(date(pr.created_date), INTERVAL -$interval(date(pr.created_date))+1 DAY) as time, pr.id as pr_id, prc.commit_sha, sum(c.additions)+sum(c.deletions) as loc FROM pull_requests pr left join pull_request_commits prc on pr.id = prc.pull_request_id left join commits c on prc.commit_sha = c.sha WHERE $__timeFilter(pr.created_date) and pr.base_repo_id in ($repo_id) group by 1,2,3 ) SELECT time, sum(loc)/count(distinct pr_id) as 'PR Size' FROM _pr_commits_data GROUP BY 1","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/PRSize","h":"#how-is-it-calculated","p":16243},{"i":16253,"t":"Divide coding tasks into workable and manageable pieces; Encourage developers to submit small PRs and only keep related changes in the same PR.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/PRSize","h":"#how-to-improve","p":16243},{"i":16256,"t":"The number of delivered requirements or features.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/RequirementCount","h":"#what-is-this-metric","p":16254},{"i":16258,"t":"Based on historical data, establish a baseline of the delivery capacity of a single iteration to improve the organization and planning of R&D resources. Evaluate whether the delivery capacity matches the business phase and demand scale. Identify key bottlenecks and reasonably allocate resources.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/RequirementCount","h":"#why-is-it-important","p":16254},{"i":16260,"t":"Jira GitHub","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/RequirementCount","h":"#which-dashboards-does-it-exist-in","p":16254},{"i":16262,"t":"This metric is calculated by counting the number of delivered issues in type \"REQUIREMENT\" in the given data range. Data Sources Required This metric relies on the issues collected from Jira, GitHub, or TAPD. Data Transformation Required This metric relies on the 'type-requirement' configuration in Jira, GitHub or TAPD's transformation rules while adding/editing a blueprint. This configuration tells DevLake what issues are requirements. SQL Queries The following SQL shows how to find the total count of requirements in specific boards, eg. 'board-1' and 'board-2'. select count(*) as \"Requirement Count\" from issues i join board_issues bi on i.id = bi.issue_id where i.type = 'REQUIREMENT' and i.status = 'DONE' -- please replace the board ids with your own, or create a '$board_id' variable in Grafana and bi.board_id in ('board-1','board-2') and $__timeFilter(i.created_date) If you want to see the monthly trend of requirement count in the screenshot below, please run the following SQL SELECT DATE_ADD(date(i.created_date), INTERVAL -DAYOFMONTH(date(i.created_date))+1 DAY) as time, count(distinct case when status != 'DONE' then i.id else null end) as \"Number of Open Requirements\", count(distinct case when status = 'DONE' then i.id else null end) as \"Number of Delivered Requirements\" FROM issues i join board_issues bi on i.id = bi.issue_id join boards b on bi.board_id = b.id where i.type = 'REQUIREMENT' and $__timeFilter(i.created_date) -- please replace the board ids with your own, or create a '$board_id' variable in Grafana and bi.board_id in ($board_id) group by 1","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/RequirementCount","h":"#how-is-it-calculated","p":16254},{"i":16264,"t":"Analyze the number of requirements and delivery rate of different time cycles to find the stability and trend of the development process. Analyze and compare the number of requirements delivered and delivery rate of each project/team, and compare the scale of requirements of different projects. Based on historical data, establish a baseline of the delivery capacity of a single iteration (optimistic, probable and pessimistic values) to provide a reference for iteration estimation. Drill down to analyze the number and percentage of requirements in different phases of SDLC. Analyze rationality and identify the requirements stuck in the backlog.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/RequirementCount","h":"#how-to-improve","p":16254},{"i":16267,"t":"The ratio of delivered requirements to all requirements.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/RequirementDeliveryRate","h":"#what-is-this-metric","p":16265},{"i":16269,"t":"Based on historical data, establish a baseline of the delivery capacity of a single iteration to improve the organization and planning of R&D resources. Evaluate whether the delivery capacity matches the business phase and demand scale. Identify key bottlenecks and reasonably allocate resources.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/RequirementDeliveryRate","h":"#why-is-it-important","p":16265},{"i":16271,"t":"Jira GitHub","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/RequirementDeliveryRate","h":"#which-dashboards-does-it-exist-in","p":16265},{"i":16273,"t":"The number of delivered requirements divided by the total number of requirements in the given data range. Data Sources Required This metric relies on the issues collected from Jira, GitHub, or TAPD. Data Transformation Required This metric relies on the 'type-requirement' configuration in Jira, GitHub or TAPD's transformation rules while adding/editing a blueprint. This configuration tells DevLake what issues are requirements. SQL Queries The following SQL shows how to find the requirement delivery rate in specific boards, eg. 'board-1' and 'board-2'. WITH _requirements as( SELECT count(distinct i.id) as total_count, count(distinct case when i.status = 'DONE' then i.id else null end) as delivered_count FROM issues i join board_issues bi on i.id = bi.issue_id WHERE i.type = 'REQUIREMENT' and $__timeFilter(i.created_date) -- please replace the board ids with your own, or create a '$board_id' variable in Grafana and bi.board_id in ('board_1', 'board_2') ) SELECT now() as time, 1.0 * delivered_count/total_count as requirement_delivery_rate FROM _requirements If you want to measure the monthly trend of requirement delivery rate in the screenshot below, please run the following SQL in Grafana. WITH _requirements as( SELECT DATE_ADD(date(i.created_date), INTERVAL -DAYOFMONTH(date(i.created_date))+1 DAY) as time, 1.0 * count(distinct case when i.status = 'DONE' then i.id else null end)/count(distinct i.id) as delivered_rate FROM issues i join board_issues bi on i.id = bi.issue_id WHERE i.type = 'REQUIREMENT' and $__timeFilter(i.created_date) -- please replace the board ids with your own, or create a '$board_id' variable in Grafana and bi.board_id in ($board_id) GROUP BY 1 ) SELECT time, delivered_rate FROM _requirements ORDER BY time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/RequirementDeliveryRate","h":"#how-is-it-calculated","p":16265},{"i":16275,"t":"Analyze the number of requirements and delivery rate of different time cycles to find the stability and trend of the development process. Analyze and compare the number of requirements delivered and delivery rate of each project/team, and compare the scale of requirements of different projects. Based on historical data, establish a baseline of the delivery capacity of a single iteration (optimistic, probable and pessimistic values) to provide a reference for iteration estimation. Drill down to analyze the number and percentage of requirements in different phases of SDLC. Analyze rationality and identify the requirements stuck in the backlog.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/RequirementDeliveryRate","h":"#how-to-improve","p":16265},{"i":16278,"t":"The time it takes from when a PR is issued to when it is merged. Essentially, PR Time to Merge = PR Pickup Time + PR Review Time.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/PRTimeToMerge","h":"#what-is-this-metric","p":16276},{"i":16280,"t":"The delay of reviewing and waiting to review PRs has large impact on delivery speed, while reasonably short PR Time to Merge can indicate frictionless teamwork. Improving on this metric is the key to reduce PR cycle time.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/PRTimeToMerge","h":"#why-is-it-important","p":16276},{"i":16282,"t":"GitHub Weekly Community Retro","s":"Which dashboard(s) does it exist in?","u":"/zh/docs/v1.0/Metrics/PRTimeToMerge","h":"#which-dashboards-does-it-exist-in","p":16276},{"i":16284,"t":"Data Sources Required This metric relies on PRs/MRs collected from GitHub, GitLab, BitBucket, Gitee or other code review tools. Data Transformation Required N/A SQL Queries The following SQL shows how to find the mean time to merge of PRs in specific repositories and given time range. SELECT avg(TIMESTAMPDIFF(Minute,created_date,merged_date)/1440) FROM pull_requests WHERE -- $__timeFilter will take Grafana's time range $__timeFilter(created_date) -- please replace the repo ids with your own, or create a '$repo_id' variable in Grafana and base_repo_id in ('repo_1', 'repo_2') and merged_date is not null If you want to measure the monthly trend of PR time to merge in the screenshot below, please run the following SQL in Grafana. with _prs as( SELECT DATE_ADD(date(created_date), INTERVAL -DAY(date(created_date))+1 DAY) as time, avg(TIMESTAMPDIFF(Minute,created_date,merged_date)/1440) as time_to_merge FROM pull_requests WHERE $__timeFilter(created_date) -- the following condition will remove the month with incomplete data and created_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) -- please replace the repo ids with your own, or create a '$repo_id' variable in Grafana and base_repo_id in ('repo_1', 'repo_2') GROUP BY 1 ) SELECT date_format(time,'%M %Y') as month, time_to_merge as \"Time to Merge\" FROM _prs ORDER BY time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/PRTimeToMerge","h":"#how-is-it-calculated","p":16276},{"i":16286,"t":"Use DevLake's dashboards to monitor your delivery progress; Have a habit to check for hanging PRs regularly; Set up alerts for your communication tools (e.g. Slack, Lark) when new PRs are issued; Reduce PR size; Analyze the causes for long reviews.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/PRTimeToMerge","h":"#how-to-improve","p":16276},{"i":16289,"t":"The average number of story points per requirement.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/RequirementGranularity","h":"#what-is-this-metric","p":16287},{"i":16291,"t":"Promote product teams to split requirements carefully, improve requirements quality, help developers understand requirements clearly, deliver efficiently and with high quality, and improve the project management capability of the team. Establish a data-supported workload estimation model to help R&D teams calibrate their estimation methods and more accurately assess the granularity of requirements, which is useful to achieve better issue planning in project management.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/RequirementGranularity","h":"#why-is-it-important","p":16287},{"i":16293,"t":"Jira GitHub","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/RequirementGranularity","h":"#which-dashboards-does-it-exist-in","p":16287},{"i":16295,"t":"The average story points of issues in type \"REQUIREMENT\" in the given data range. Data Sources Required This metric relies on issues collected from Jira, GitHub, or TAPD. Data Transformation Required This metric relies on the 'type-requirement' configuration in Jira, GitHub or TAPD's transformation rules while adding/editing a blueprint. This configuration tells DevLake what issues are requirements. Besides, if you are importing Jira issues, you also need to configure the field of 'story_points' in the transformation.","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/RequirementGranularity","h":"#how-is-it-calculated","p":16287},{"i":16297,"t":"Analyze the story points/requirement lead time of requirements to evaluate whether the ticket size, ie. requirement complexity is optimal. Compare the estimated requirement granularity with the actual situation and evaluate whether the difference is reasonable by combining more microscopic workload metrics (e.g. lines of code/code equivalents)","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/RequirementGranularity","h":"#how-to-improve","p":16287},{"i":16300,"t":"DevLake Components A DevLake installation typically consists of the following components: Config UI: A handy user interface to create, trigger, and debug Blueprints. A Blueprint specifies the where (data connection), what (data scope), how (transformation rule), and when (sync frequency) of a data pipeline. API Server: The main programmatic interface of DevLake. Runner: The runner does all the heavy-lifting for executing tasks. In the default DevLake installation, it runs within the API Server, but DevLake provides a temporal-based runner (beta) for production environments. Database: The database stores both DevLake's metadata and user data collected by data pipelines. DevLake supports MySQL and PostgreSQL as of v0.11. Plugins: Plugins enable DevLake to collect and analyze dev data from any DevOps tools with an accessible API. DevLake community is actively adding plugins for popular DevOps tools, but if your preferred tool is not covered yet, feel free to open a GitHub issue to let us know or check out our doc on how to build a new plugin by yourself. Dashboards: Dashboards deliver data and insights to DevLake users. A dashboard is simply a collection of SQL queries along with corresponding visualization configurations. DevLake's official dashboard tool is Grafana and pre-built dashboards are shipped in Grafana's JSON format. Users are welcome to swap for their own choice of dashboard/BI tool if desired.","s":"Overview","u":"/zh/docs/v1.0/Overview/Architecture","h":"#overview","p":16298},{"i":16302,"t":"A typical plugin's dataflow is illustrated below: The Raw layer stores the API responses from data sources (DevOps tools) in JSON. This saves developers' time if the raw data is to be transformed differently later on. Please note that communicating with data sources' APIs is usually the most time-consuming step. The Tool layer extracts raw data from JSONs into a relational schema that's easier to consume by analytical tasks. Each DevOps tool would have a schema that's tailored to its data structure, hence the name, the Tool layer. The Domain layer attempts to build a layer of abstraction on top of the Tool layer so that analytics logics can be re-used across different tools. For example, GitHub's Pull Request (PR) and GitLab's Merge Request (MR) are similar entities. They each have their own table name and schema in the Tool layer, but they're consolidated into a single entity in the Domain layer, so that developers only need to implement metrics like Cycle Time and Code Review Rounds once against the domain layer schema.","s":"Dataflow","u":"/zh/docs/v1.0/Overview/Architecture","h":"#dataflow","p":16298},{"i":16304,"t":"Extensible: DevLake's plugin system allows users to integrate with any DevOps tool. DevLake also provides a dbt plugin that enables users to define their own data transformation and analysis workflows. Portable: DevLake has a modular design and provides multiple options for each module. Users of different setups can freely choose the right configuration for themselves. Robust: DevLake provides an SDK to help plugins efficiently and reliably collect data from data sources while respecting their API rate limits and constraints.","s":"Principles","u":"/zh/docs/v1.0/Overview/Architecture","h":"#principles","p":16298},{"i":16307,"t":"The amount of time it takes a requirement to deliver.","s":"What is this metric?","u":"/zh/docs/v1.0/Metrics/RequirementLeadTime","h":"#what-is-this-metric","p":16305},{"i":16309,"t":"Analyze key projects and critical points, identify good/to-be-improved practices that affect requirement lead time, and reduce the risk of delays Focus on the end-to-end velocity of value delivery process; coordinate different parts of R&D to avoid efficiency shafts; make targeted improvements to bottlenecks.","s":"Why is it important?","u":"/zh/docs/v1.0/Metrics/RequirementLeadTime","h":"#why-is-it-important","p":16305},{"i":16311,"t":"Jira GitHub Community Experience","s":"Which dashboard(s) does it exist in","u":"/zh/docs/v1.0/Metrics/RequirementLeadTime","h":"#which-dashboards-does-it-exist-in","p":16305},{"i":16313,"t":"This metric equals resolution_date - created_date of issues in type \"REQUIREMENT\". Data Sources Required This metric relies on issues collected from Jira, GitHub, or TAPD. Data Transformation Required This metric relies on the 'type-requirement' configuration in Jira, GitHub or TAPD's transformation rules while adding/editing a blueprint. This configuration tells DevLake what issues are requirements. SQL Queries The following SQL shows how to find the lead time of a specific requirement. -- lead_time_minutes is a pre-calculated field whose value equals 'resolution_date - created_date' SELECT lead_time_minutes/1440 as requirement_lead_time_in_days FROM issues WHERE type = 'REQUIREMENT' If you want to measure the mean requirement lead time in the screenshot below, please run the following SQL in Grafana. with _issues as( SELECT DATE_ADD(date(i.resolution_date), INTERVAL -DAY(date(i.resolution_date))+1 DAY) as time, AVG(i.lead_time_minutes/1440) as issue_lead_time FROM issues i join board_issues bi on i.id = bi.issue_id join boards b on bi.board_id = b.id WHERE -- $board_id is a variable defined in Grafana's dashboard settings to filter out issues by boards b.id in ($board_id) and i.type = 'REQUIREMENT' and i.status = \"DONE\" and $__timeFilter(i.resolution_date) -- the following condition will remove the month with incomplete data and i.resolution_date >= DATE_ADD(DATE_ADD($__timeFrom(), INTERVAL -DAY($__timeFrom())+1 DAY), INTERVAL +1 MONTH) group by 1 ) SELECT date_format(time,'%M %Y') as month, issue_lead_time as \"Mean Requirement Lead Time in Days\" FROM _issues ORDER BY time","s":"How is it calculated?","u":"/zh/docs/v1.0/Metrics/RequirementLeadTime","h":"#how-is-it-calculated","p":16305},{"i":16315,"t":"Analyze the trend of requirement lead time to observe if it has improved over time. Compare the requirement lead time of each project/team to identify key projects with abnormal lead time. Drill down to analyze a requirement's staying time in different phases of SDLC. Analyze the bottleneck of delivery velocity and improve the workflow.","s":"How to improve?","u":"/zh/docs/v1.0/Metrics/RequirementLeadTime","h":"#how-to-improve","p":16305},{"i":16318,"t":"Apache DevLake (Incubating) is an open-source dev data platform that ingests, analyzes, and visualizes the fragmented data from DevOps tools to extract insights for engineering excellence, developer experience, and community growth. Apache DevLake is designed for developer teams looking to make better sense of their development process and to bring a more data-driven approach to their own practices. You can ask Apache DevLake many questions regarding your development process. Just connect and query.","s":"What is Apache DevLake?","u":"/zh/docs/v1.0/Overview/Introduction","h":"#what-is-apache-devlake","p":16316},{"i":16320,"t":"Unified data integration: Bring together DevOps data from across the Software Development Life Cycle (SDLC) with our standard data model. Out-of-the-box insights: Access key engineering metrics through intuitive, use-case driven dashboards. Customizable: Extend DevLake to align with your unique needs, adding data sources, metrics, and dashboards as required. Industry standards implementation: Use DevLake to apply recognized DORA metrics to optimize DevOps performance. Create a thriving culture: DevLake is centered on healthy practices that may help teams adopt and build a practical data-driven culture.","s":"What can be accomplished with DevLake?","u":"/zh/docs/v1.0/Overview/Introduction","h":"#what-can-be-accomplished-with-devlake","p":16316},{"i":16323,"t":"To implement a proof of concept for Apache DevLake tailored to your specific use cases, you can install it on your local machines using Docker Compose. The detailed instructions for this setup can be found in the Docker Compose setup documentation. Alternatively, if your infrastructure is powered by Kubernetes, you can explore the Helm setup option. The Helm setup documentation provides guidance on deploying and configuring Apache DevLake using Helm.","s":"1. Setting-up DevLake","u":"/zh/docs/v1.0/Overview/Introduction","h":"#1-setting-up-devlake","p":16316},{"i":16325,"t":"Once Installed, you can start configuring DevLake with supported data sources like GitHub, GitLab, Jira, Jenkins, BitBucket, Azure DevOps, SonarQube, PagerDuty, TAPD, ZenTao, Teambition, and we are extending our support to many other tools, feel free to check out the roadmap. However, if your CI/CD tool is not currently supported by DevLake, you can utilize the webhooks feature. The Webhooks feature allows you to actively push data to DevLake when there is not a specific plugin available for your DevOps tool.","s":"2. Configuring Data Source","u":"/zh/docs/v1.0/Overview/Introduction","h":"#2-configuring-data-source","p":16316},{"i":16327,"t":"Once you have connected a data source to Apache DevLake, you can create a \"Project\" to ensure that you are all set for execution. The process of setting up a project in DevLake typically involves four steps:","s":"3. Creating your Project","u":"/zh/docs/v1.0/Overview/Introduction","h":"#3-creating-your-project","p":16316},{"i":16329,"t":"After configuring your project in DevLake, you can access pre-built dashboards in Grafana. These dashboards provide visualizations and insights for various metrics related to software development. To customize the dashboards according to your specific goals and requirements, you can tweak them using Grafana's features. Additionally, if you prefer to create your own dashboards, you have the option to use SQL queries to fetch the necessary data from DevLake referring to the domain layer data schema and SQL examples in the metrics documentation.","s":"4. Checking the Dashboards and Metrics","u":"/zh/docs/v1.0/Overview/Introduction","h":"#4-checking-the-dashboards-and-metrics","p":16316},{"i":16331,"t":"Last updated: Nov 9, 2023","s":"Key Concepts","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"","p":16330},{"i":16333,"t":"The following terms are arranged in the order of their appearance in the actual user workflow in the config UI.","s":"In Config UI","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#in-config-ui","p":16330},{"i":16335,"t":"A data source is a specific DevOps tool from which you wish to sync your data, such as GitHub, GitLab, Jira and Jenkins. Typically, DevLake uses one data plugin to pull data for a single data source. For example, the jira plugin is used to fetch data from Jira. However, there are cases where DevLake may use multiple data plugins for a single data source. This approach is employed to enhance the synchronization speed and provide other advantages. For instance, when retrieving data from GitHub or GitLab, aside from the github and gitlab plugins, the gitextractor is also used to fetch data. In these cases, DevLake still recognizes GitHub or GitLab as a single data source.","s":"Data Source","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#data-source","p":16330},{"i":16337,"t":"A data connection is a specific instance of a data source. It stores the necessary access information, such as the endpoint URL and authentication token, to establish a connection to that data source. A single data source can have one or more data connections associated with it. This allows you to connect to and retrieve data from different instances or installations of the same data source. To set up a new data connection, it is recommended to use the 'Data Connections' page in DevLake. This page provides a convenient interface for adding and configuring data connections. Once a data connection is set up, you can later associate it with a DevLake project.","s":"Data Connection","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#data-connection","p":16330},{"i":16339,"t":"A data scope is the top-level 'container' in a data source. For example, a data scope for Jira is a Jira board, for TAPD is a TAPD workspace, for GitHub/GitLab/BitBucket is a repo, for Jenkins is a Jenkins job, etc. You can add multiple data scopes to a data connection to determine which data to collect. Data scopes vary for different data sources.","s":"Data Scope","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#data-scope","p":16330},{"i":16341,"t":"A scope config refers to the configuration of a data scope. It defines the specific data entities to be collected and the transformations to be applied to that data. Each data scope can have at most one scope config associated with it; while a scope config can be shared among multiple data scopes under the same data connection. A scope config consists of two parts: Data Entities and Transformations. Data Entities​ Data entities refer to the specific data fields that are collected from different data domains. Check the supported data entities of each data source. Data entities are categorized into six data domains in DevLake: Issue Tracking, Source Code Management, Code Review, CI/CD, Code Quality, and Cross-Domain. When setting up the scope config of a GitHub data connection, you have the flexibility to choose which specific data entities you want to collect. if you only want to collect 'repos', 'commits', and 'pull requests' while excluding 'issues' and 'workflow runs', you need to check the 'Source Code Management' and 'Code Review' domains, and uncheck 'Issue Tracking' or 'CI/CD' domains. Transformations​ Transformations are configurations for users to customize how DevLake transforms raw API responses to the domain layer data. Although configuring transformation rules is not mandatory, certain pre-built dashboards, such as DORA and Weekly Bug Retro require them to display the metrics accurately. If you leave the rules blank or have not configured them correctly, only a few data source dashboards will be displayed as expected. You can find the required transformations in the 'Dashboard Introduction' panel in each pre-built dashboard.","s":"Scope Config","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#scope-config","p":16330},{"i":16343,"t":"On a high level, a DevLake project can be viewed as a real-world project or product line. It represents a specific initiative or endeavor within the software development domain. On a lower level, a DevLake project is a way of organizing and grouping data from different domains. DevLake uses various data scopes, such as repos, boards, cicd_scopes, and cq_projects as the 'container' to associate different types of data to a specific project. A project has a blueprint for data collection and metric computation. DevLake measures DORA metrics at the project level. Each project has a set of DORA metrics. For example, if a user associates 'Jenkins Job A' and 'Jira board B' with project M, only the 'deployments' from 'Jenkins Job A' and the 'incidents' from 'Jira board B' will be considered when calculating the Change Failure Rate metric for project M.","s":"Project","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#project","p":16330},{"i":16345,"t":"A blueprint serves as the plan to synchronize data from data sources into the DevLake platform. Creating a blueprint consists of four steps: Adding data connections: You can add one or more data connections to a blueprint, depending on the data sources you want to sync with DevLake. Each data connection represents a specific data source, such as GitHub or Jira. Setting up the data scope: When adding a data connection, you can choose to collect all or part of the configured data scopes of the data connection. Setting up the sync policy: You can specify the sync frequency and the time range for data collection. The relationship between 'Blueprint', 'Project' and 'Data Connection' is explained as follows: A blueprint will be automatically created by along the creation with a DevLake project. Each blueprint can have multiple data connections. Each data connection can have multiple data scopes. Each set of data scope only consists of one GitHub/GitLab project or Jira board, along with their corresponding data entities. Each set of data scope can only have one set of scope config.","s":"Blueprint","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#blueprint","p":16330},{"i":16347,"t":"Typically, the following terms do not appear in the regular mode of the Config UI, but can be very useful if you use DevLake's APIs or the advanced mode of Config UI.","s":"APIs and Config UI Advanced Mode","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#apis-and-config-ui-advanced-mode","p":16330},{"i":16349,"t":"A data plugin is a specific module that syncs or transforms data. There are two types of data plugins: Data Collection Plugins and Data Transformation Plugins. Data Collection Plugins pull data from one or more data sources. DevLake supports 8 data plugins in this category: ae, feishu, gitextractor, github, gitlab, jenkins, jira and tapd. Data Transformation Plugins transform the data pulled by other Data Collection Plugins. refdiff is currently the only plugin in this category. Although the names of the data plugins are not displayed in the regular mode of DevLake Configuration UI, they can be used directly in JSON in the Advanced Mode. For detailed information about the relationship between data sources and data plugins, please refer to Supported Data Sources.","s":"Data Plugins","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#data-plugins","p":16330},{"i":16351,"t":"A pipeline is an orchestration of tasks of data collection, extraction, conversion and enrichment, defined in the DevLake API. A pipeline is composed of one or multiple stages that are executed in a sequential order. Any error occurring during the execution of any stage, task or subtask will cause the immediate fail of the pipeline. The composition of a pipeline is explained as follows: Notice: You can manually orchestrate the pipeline in Configuration UI Advanced Mode and the DevLake API; whereas in Configuration UI regular mode, an optimized pipeline orchestration will be automatically generated for you.","s":"Pipelines","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#pipelines","p":16330},{"i":16353,"t":"A stages is a collection of tasks performed by data plugins. Stages are executed in a sequential order in a pipeline.","s":"Stages","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#stages","p":16330},{"i":16355,"t":"A task is a collection of subtasks that perform any of the collection, extraction, conversion and enrichment jobs of a particular data plugin. Tasks are executed in a parallel order in any stages.","s":"Tasks","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#tasks","p":16330},{"i":16357,"t":"A subtask is the minimal work unit in a pipeline that performs in any of the four roles: Collectors, Extractors, Converters and Enrichers. Subtasks are executed in sequential orders. Collectors: Collect raw data from data sources, normally via DevLake API and stored into raw data table Extractors: Extract data from raw data tables to tool layer tables Converters: Convert data from tool layer tables into domain layer tables Enrichers: Enrich data from one domain to other domains. For instance, the Fourier Transformation can examine issue_changelog to show time distribution of an issue on every assignee.","s":"Subtasks","u":"/zh/docs/v1.0/Overview/KeyConcepts","h":"#subtasks","p":16330},{"i":16359,"t":"For users/developers who wish to interact with the Apache DevLake by using the RESTful APIs, the Swagger Document would very useful for you. The devlake docker image has it packaged, you may access it from: If you are using the devlake container alone without config-ui: http://<DEVLAKE_CONTIANER_HOST>:<PORT>/swagger/index.html or http://<CONFIG_UI_CONTIANER_HOST>:<PORT>/api/swagger/index.html","s":"API References","u":"/zh/docs/v1.0/Overview/References","h":"","p":16358},{"i":16362,"t":"DevLake has joined the Apache Incubator and is aiming to become a top-level project. To achieve this goal, the Apache DevLake (Incubating) community will continue to make efforts in helping development teams to analyze and improve their engineering productivity. In this roadmap, we have summarized three major goals followed by the feature breakdown to invite the broader community to join us and grow together. As a dev data analysis application, discover and implement 3 (or even more!) usage scenarios: A collection of metrics to track the contribution, quality and growth of open-source projects DORA metrics for DevOps engineers To be decided (let us know if you have any suggestions!) As dev data infrastructure, provide robust data collection modules, customizable data models, and data extensibility. Design better user experience for end-users and contributors.","s":"Goals","u":"/zh/docs/v1.0/Overview/Roadmap","h":"#goals","p":16360},{"i":16364,"t":"Apache DevLake is currently under rapid development. You are more than welcome to use the following table to explore your intereted features and make contributions. We deeply appreciate the collective effort of our community to make this project possible! Category Features More data sources across different DevOps domains (Goal No.1 & 2). See existing data sources Issue Tracking: Jira (cloud) #886 (Done) Jira (server/data center) #1687 (Done) GitHub Issues #407 (Done) GitLab Issues #715 (Done) TAPD #560 (Beta) Zentao #2961 (Beta) Teambition #1882 (Beta) Trello #1881 (Pending) Ones #1884 (Pending) Source Code Management: GitHub (Done) GitLab (Done) BitBucket #2100 (Supported from v0.16) Azure DevOps #2604 (Supported from v0.16) Gitee #1883 (WIP) Coder #3447 (Pending) Code Review: GitHub PRs (Done) GitLab MRs (Done) BitBucket PRs #2100 (Supported from v0.16) Azure DevOps PRs #2604 (Supported from v0.16) Phabricator (Pending) Gerrit (Pending) CI/CD: GitHub Action #2584 (Done) GitLab CI #2583 (Done) Jenkins #2637 (Done) Bamboo CI #3322 (WIP) Argo CI #2585 (Pending) Argo CD #5207 (Pending) TeamCity (Pending) Code Quality: SonarQube #2305 (Supported from v0.16) Coverity (Pending) Incident Management: PagerDuty #3498 (WIP) ServiceNow #4234 (Planned) QA: Selenium (Pending) Junit (Pending) JMeter (Pending) Cucumber Test (Pending) Documentation: Google Docs (Pending) Lark Docs (Pending) Tencent Docs (Pending) Calendar: Lark Calendar #261 (Done) Google Calendar (Pending) Zoom Calendar (Pending) Tencent Calendar (Pending) OSS Community Metrics: GitHub stars, clones, watches #3721 (WIP) Instant Messaging: Slack (Pending) Discord (Pending) Lark messages (Pending) Improved data collection, data models and data extensibility (Goal No.2) Data Collection: Complete the logging system Implement a good error handling mechanism during data collection Data Models: Introduce DBT to allow users to create and modify the domain layer schema. #1479 (Done) Design the data models for 6 new domains, please refers to the data models of the tools under each domain (see the cell above): Code Quality (WIP) Testing Calendar Documentation OSS Community Metrics (WIP) Instant messaging Polish the data models for existing domains: Issue/Task Management, Source Code Management, Code Review and CI/CD. Data Extensibility: Enhance the performance of data application under large-scaled usage scenarios Support OLAP databases for more flexible data storage options Better user experience (Goal No.3) For new users: Iterate on a clearer step-by-step guide to improve the pre-configuration experience. Provide a new Config UI to reduce frictions for data configuration #1700 (Done) Showcase dashboard live demos to let users explore and learn about the dashboards. #1784 (Done) For returning users: Provide detailed guides to help users customize Grafana dashboards. Work on the documentation for advanced features in the Config UI, such as the usage of Advanced Mode and replacements of old auth tokens for data connections. For contributors: Add more guide to set up DevLake in different operating system. Provide clearer docs for contributors to get on board easier. Add Swagger to document API #292 (Done) More docs about raw/tool/domain data models","s":"Feature Breakdown","u":"/zh/docs/v1.0/Overview/Roadmap","h":"#feature-breakdown","p":16360},{"i":16366,"t":"A roadmap is only useful when it captures real user needs. We are glad to hear from you if you have specific use cases, feedback, or ideas. You can submit an issue to let us know! Also, if you plan to work (or are already working) on a new or existing feature, tell us, so that we can update the roadmap accordingly. We are happy to share knowledge and context to help your feature land successfully.","s":"How to Influence the Roadmap","u":"/zh/docs/v1.0/Overview/Roadmap","h":"#how-to-influence-the-roadmap","p":16360},{"i":16369,"t":"Apache DevLake(incubating) supports the following data sources. The data from each data source is collected with one or more plugins. Detailed plugin docs can be found here. Data Source Domain(s) Supported Versions Config UI Availability Triggered Plugins Collection Mode GitHub Source Code Management, Code Review, Issue Tracking, CI/CD (GitHub Actions) Cloud Available github, gitextractor Incremental Sync GitLab Source Code Management, Code Review, Issue Tracking, CI/CD (GitLab CI) Cloud, Community Edition 11+ Available gitlab, gitextractor Full Refresh, Incremental Sync(for issues,MRs) Jira Issue Tracking Cloud, Server/Data Center 7.x, 8.x Available jira Full Refresh, Incremental Sync(for issues and related) Jenkins CI/CD 2.263.x+ Available jenkins Incremental Sync BitBucket (Beta) Source Code Management, Code Review Cloud Advanced Mode Available bitbucket, gitextractor Full Refresh TAPD (Beta) Issue Tracking Cloud Advanced Mode Available tapd Full Refresh, Incremental Sync(for stories, bugs, tasks) Teambition (Beta) Issue Tracking Cloud Advanced Mode Available teambition Full Refresh Zentao (Beta) Issue Tracking v17.x, v18.x Advanced Mode Available zentao Full Refresh Gitee (WIP) Source Code Management, Code Review, Issue Tracking Cloud Not Available gitee, gitextractor Full Refresh, Incremental Sync(for issues,MRs) PagerDuty Issue Tracking Cloud Available pagerduty Incremental Sync Opsgenie Issue Tracking Cloud Available opsgenie Full Refresh (for users,teams), Incremental Sync (for issues) Feishu (WIP) Calendar Cloud Not Available feishu Full Refresh AE Source Code Management On-prem Not Available ae Full Refresh Sonarqube CODE QUALITY SonarQube v8.x, v9.x Available sonarqube Full Refresh Bamboo CI(WIP) CI/CD v6.8.1, Server Not Available bamboo Full Refresh Azure Devops (Beta) CI/CD, Source Code Management, Code Review Cloud Available azuredevops, gitextractor Full Refresh CircleCI CI/CD Cloud Available circleci Full Refresh","s":"Data Sources and Data Plugins","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#data-sources-and-data-plugins","p":16367},{"i":16371,"t":"This table shows the entities collected by each plugin. Domain layer entities in this table are consistent with the entities here. ✅ : Collect by default. 💪 : Collect not by default. You need to add the corresponding subtasks to collect these entities in the advanced mode. Domain Layer Entities ae dora gitextractor incoming webhook github gitlab jenkins jira refdiff tapd sonarqube bamboo azuredevops opsgenie circleci accounts ✅ ✅ ✅ ✅ ✅ ✅ board_issues ✅ ✅ ✅ ✅ ✅ board_repos ✅ ✅ board_sprints ✅ ✅ ✅ boards ✅ ✅ ✅ ✅ ✅ cicd_pipeline_commits ✅ ✅ ✅ ✅ ✅ ✅ ✅ cicd_pipelines ✅ ✅ ✅ ✅ ✅ ✅ ✅ cicd_scopes ✅ ✅ ✅ ✅ ✅ ✅ ✅ cicd_tasks ✅ 💪 ✅ ✅ ✅ ✅ ✅ ✅ commit_file_components ✅ commit_files ✅ commit_line_change ✅ commit_parents ✅ commits ✅ ✅ 💪 💪 commits_diffs ✅ components finished_commits_diffs issue_changelogs ✅ ✅ issue_comments ✅ ✅ issue_commits ✅ ✅ issue_labels ✅ ✅ ✅ issue_repo_commits ✅ issue_worklogs ✅ ✅ issues ✅ ✅ ✅ ✅ project_issue_metrics ✅ ✅ ✅ ✅ ✅ project_mapping ✅ ✅ ✅ ✅ ✅ ✅ project_metrics ✅ ✅ ✅ ✅ ✅ ✅ project_pr_metrics ✅ ✅ ✅ ✅ project ✅ ✅ ✅ ✅ ✅ ✅ ✅ pull_request_comments ✅ ✅ pull_request_commits ✅ ✅ ✅ pull_request_issues ✅ pull_request_labels ✅ ✅ pull_requests ✅ ✅ ✅ ref_commits ✅ refs ✅ ✅ refs_issues_diffs ✅ ref_pr_cherry_picks ✅ repo_commits ✅ 💪 💪 repo_snapshot ✅ repos ✅ ✅ sprint_issues ✅ ✅ ✅ sprints ✅ ✅ ✅ team_users teams ✅ user_account users ✅ ✅ ✅ cq_projects ✅ cq_issues ✅ cq_issue_code_blocks ✅ cq_file_metrics ✅","s":"Data Collection Scope By Each Plugin","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#data-collection-scope-by-each-plugin","p":16367},{"i":16373,"t":"bold: means it may collect slowly. *bold*: means it may collect very slowly.","s":"Data Sync Policy","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#data-sync-policy","p":16367},{"i":16375,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectStatusMeta 1 - - CollectProjectsMeta <10 ❌ - CollectIssueTypesMeta <10 ❌ - CollectIssuesMeta <10^4 ✅ ✅ CollectIssueChangelogsMeta 1000~10^5 ✅ ✅ CollectAccountsMeta <10^3 ❌ ❌ CollectWorklogsMeta 1000~10^5 ✅ ✅ CollectRemotelinksMeta 1000~10^5 ✅ ✅ CollectSprintsMeta <100 ❌ ❌ CollectEpicsMeta <100 ❌ ✅","s":"Jira","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#jira","p":16367},{"i":16377,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectApiBuildsMeta ≈100 ❌ ❌ CollectApiStagesMeta ≈10^4 ❌ ✅","s":"Jenkins","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#jenkins","p":16367},{"i":16379,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectApiIssuesMeta <10^4 ✅ ✅ CollectApiMergeRequestsMeta <10^3 ✅ ✅ CollectApiMrNotesMeta <10^5 ❌ ✅ CollectApiMrCommitsMeta <10^5 ❌ ✅ CollectApiPipelinesMeta <10^4 ✅ ❌ CollectApiJobsMeta <10^5 ❌ ✅","s":"GitLab","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#gitlab","p":16367},{"i":16381,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? --------------------------------- Common ----------------------- CollectMilestonesMeta ≈10 ✅ ❌ CollectRunsMeta <10^4 ✅ ✅ CollectApiCommentsMeta 400 (max page that GitHub supports) ✅ ✅ CollectApiEventsMeta 400 (max page that GitHub supports) ❌ ❌ CollectApiPullRequestReviewsMeta <10^5 ✅ ✅ --------------------------------- Graphql Only (Default) ----------------------- CollectIssueMeta ≈10^4 ❌ ✅ CollectPrMeta ≈10^3 ❌ ✅ CollectCheckRunMeta <10^4 ❌ ✅ CollectAccountMeta ≈10^2 ❌ - --------------------------------- Restful Only (Not by Default) ----------------------- CollectApiIssuesMeta ≈10^4 ✅ ❌ CollectApiPullRequestsMeta ≈10^2 ❌ ❌ CollectApiPullRequestCommitsMeta ≈10^4 ✅ ✅ CollectApiPrReviewCommentsMeta ≈10^4 ✅ ✅ CollectAccountsMeta ≈10^4 ❌ ❌ CollectAccountOrgMeta ≈10^4 ❌ ❌ CollectJobsMeta <10^6 ❌ ✅ CollectApiCommitsMeta Not enabled - - CollectApiCommitStatsMeta Not enabled - -","s":"Github","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#github","p":16367},{"i":16383,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectMeetingTopUserItemMeta ≈10^3 ❌ ✅","s":"Feishu","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#feishu","p":16367},{"i":16385,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectApiRepoMeta 1 ❌ ❌ CollectApiPullRequestsMeta ≈10^3 ❌ ❌ CollectApiIssuesMeta ≈10^4 ❌ ❌ CollectApiPrCommentsMeta ≈10^5 ❌ ❌ *CollectApiIssueCommentsMeta* ≈10^6 ❌ ❌ CollectApiPipelinesMeta <10^4 ❌ ❌ CollectApiDeploymentsMeta <10^2 ❌ ❌","s":"Bitbucket","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#bitbucket","p":16367},{"i":16387,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectApiBranchesMeta 1 ❌ ❌ CollectApiPullRequestsMeta ≈10^3 ❌ ❌ CollectApiPrActivitiesMeta ≈10^4 ❌ ❌ CollectApiPrCommentsMeta ≈10^5 ❌ ❌ *CollectApiIssueCommentsMeta* ≈10^6 ❌ ❌ CollectApiCommitsMeta <10^4 ❌ ❌ CollectApiPrCommitsMeta <10^2 ❌ ❌","s":"Bitbucket Server/Data Center","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#bitbucket-serverdata-center","p":16367},{"i":16389,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectApiRepoMeta 1 ❌ ❌ CollectApiPullRequestsMeta ≈10^3 ✅ ❌ CollectApiIssuesMeta ≈10^4 ✅ ❌ CollectCommitsMeta? ≈10^4 ✅ ❌ CollectApiPrCommentsMeta ≈10^5 ❌ ❌ *CollectApiIssueCommentsMeta* ≈10^6 ✅ ❌ CollectApiPullRequestCommitsMeta ≈10^5 ❌ ❌ CollectApiPullRequestReviewsMeta ≈10^5 ❌ ❌ *CollectApiCommitStatsMeta* ≈10^6 (Not enable) ❌ ❌","s":"Gitee","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#gitee","p":16367},{"i":16391,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectAccounts <10^4 ❌ ❌ CollectIssues <10^4 ❌ ❌ CollectHotspots <10^4 ❌ ❌ CollectFilemetrics <10^4 ❌ ❌ CollectAdditionalFilemetrics <10^4 ❌ ❌","s":"SonarQube","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#sonarqube","p":16367},{"i":16393,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectPlanMeta <10^4 ❌ ❌ CollectJobMeta <10^5 ❌ ❌ CollectPlanBuildMeta <10^6 ❌ ❌ CollectJobBuildMeta <10^6 ❌ ❌ CollectDeployMeta 1 ❌ ❌","s":"Bamboo","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#bamboo","p":16367},{"i":16395,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectExecutionMeta <10^3 ❌ ❌ CollectStoryMeta <10^4 ❌ ❌ CollectBugMeta <10^4 ❌ ❌ CollectTaskMeta <10^4 ❌ ❌ CollectAccountMeta ≈10^2 ❌ ❌ CollectDepartmentMeta ≈10^2 ❌ ❌","s":"Zentao","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#zentao","p":16367},{"i":16397,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectWorkitemTypesMeta ≈10 - - CollectStoryCustomFieldsMeta ≈10 - - CollectTaskCustomFieldsMeta ≈10 - - CollectBugCustomFieldsMeta ≈10 - - CollectStoryCategoriesMeta ≈10 - - CollectStoryStatusMeta ≈10 - - CollectStoryStatusLastStepMeta ≈10 - - CollectBugStatusMeta ≈10 - - CollectBugStatusLastStepMeta ≈10 - - CollectAccountsMeta ≈10^3 ❌ ❌ CollectIterationMeta ≈10^4 ✅ ✅ CollectStoryMeta ≈10^4 ✅ ✅ CollectBugMeta ≈10^4 ✅ ✅ CollectTaskMeta ≈10^4 ✅ ✅ CollectBugChangelogMeta ≈10^6 ✅ ✅ CollectStoryChangelogMeta ≈10^6 ✅ ✅ CollectTaskChangelogMeta ≈10^6 ✅ ✅ CollectWorklogMeta ≈10^6 ✅ ✅ CollectBugCommitMeta ≈10^6 ✅ ✅ CollectStoryCommitMeta ≈10^6 ✅ ✅ CollectTaskCommitMeta ≈10^6 ✅ ✅ CollectStoryBugMeta ≈10^6 ✅ ✅","s":"Tapd","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#tapd","p":16367},{"i":16399,"t":"Subtask Name Estimated Max Number of Request Does It support Incremental Collection? Does It Support Time Filter? CollectBuilds <10^3 ❌ ❌ CollectJobs <10^4 ❌ ❌ CollectPullRequests <10^3 ❌ ❌ CollectPullRequestCommits <10^4 ❌ ❌","s":"Azure Devops","u":"/zh/docs/v1.0/Overview/SupportedDataSources","h":"#azure-devops","p":16367},{"i":16402,"t":"This plugin collects Azure DevOps data through Azure DevOps REST API.","s":"Summary","u":"/zh/docs/v1.0/Plugins/azuredevops","h":"#summary","p":16400},{"i":16404,"t":"Available for Azure DevOps Cloud. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/azuredevops","h":"#supported-versions","p":16400},{"i":16406,"t":"Check out the Azure DevOps entities collected by this plugin.","s":"Entities","u":"/zh/docs/v1.0/Plugins/azuredevops","h":"#entities","p":16400},{"i":16408,"t":"Right now, this plugin supports only full refresh. Check out the [data refresh policy](/zh/docs/v1.0/Overview/SupportedDataSources#Azure DevOps) of this plugin.","s":"Data Refresh Policy","u":"/zh/docs/v1.0/Plugins/azuredevops","h":"#data-refresh-policy","p":16400},{"i":16410,"t":"Metrics that can be calculated based on the data collected from Azure DevOps: Commit Count Commit Author Count Added Lines of Code Deleted Lines of Code Build Count Build Duration Build Success Rate DORA - Deployment Frequency DORA - Lead Time for Changes DORA - Median Time to Restore Service DORA - Change Failure Rate","s":"Metrics","u":"/zh/docs/v1.0/Plugins/azuredevops","h":"#metrics","p":16400},{"i":16412,"t":"Configuring Azure DevOps via config-ui.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/azuredevops","h":"#configuration","p":16400},{"i":16414,"t":"You can trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"MY PIPELINE\", \"plan\": [ [ { \"plugin\": \"azuredevops\", \"options\": { \"connectionId\": 1, \"scopeId\": \"orgname/reponame\", \"transformationRules\": { \"deploymentPattern\": \"\", \"productionPattern\": \"\" } } } ] ] } '","s":"API Sample Request","u":"/zh/docs/v1.0/Plugins/azuredevops","h":"#api-sample-request","p":16400},{"i":16417,"t":"This plugin collects Bamboo's CI data through API. It then computes and visualizes various DevOps metrics from the Bamboo data, which helps tech leads, QA and DevOps engineers, and project managers to answer questions such as: What is the deployment frequency of your team? How long does it take for your codes to get deployed?","s":"Summary","u":"/zh/docs/v1.0/Plugins/bamboo","h":"#summary","p":16415},{"i":16419,"t":"Only Bamboo v6.8.1+ is supported as of v0.20. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/bamboo","h":"#supported-versions","p":16415},{"i":16421,"t":"Check out the Bamboo entities collected by this plugin.","s":"Entities","u":"/zh/docs/v1.0/Plugins/bamboo","h":"#entities","p":16415},{"i":16423,"t":"Check out the data refresh policy of this plugin.","s":"Data Refresh Policy","u":"/zh/docs/v1.0/Plugins/bamboo","h":"#data-refresh-policy","p":16415},{"i":16425,"t":"Metrics that can be calculated based on the data collected from Bamboo: DORA - Deployment Frequency DORA - Lead Time for Changes DORA - Median Time to Restore Service DORA - Change Failure Rate","s":"Metrics","u":"/zh/docs/v1.0/Plugins/bamboo","h":"#metrics","p":16415},{"i":16427,"t":"You can trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"project1-BLUEPRINT\", \"blueprintId\": 1, \"plan\": [ [ { \"plugin\": \"bamboo\", \"options\": { \"connectionId\": 1, \"key\": \"TEST\", \"transformationRules\":{ \"deploymentPattern\":\"\", \"productionPattern\":\"\", } } } ] ] } '","s":"API Sample Request","u":"/zh/docs/v1.0/Plugins/bamboo","h":"#api-sample-request","p":16415},{"i":16429,"t":"references","s":"References","u":"/zh/docs/v1.0/Plugins/bamboo","h":"#references","p":16415},{"i":16432,"t":"This plugin collects various entities from Bitbucket, including pull requests, issues, comments, pipelines, git commits, and etc. As of v0.14.2, bitbucket plugin can only be invoked through DevLake API. Its support in Config-UI is WIP.","s":"Summary","u":"/zh/docs/v1.0/Plugins/bitbucket","h":"#summary","p":16430},{"i":16434,"t":"Available for BitBucket Cloud. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/bitbucket","h":"#supported-versions","p":16430},{"i":16436,"t":"Check out the BitBucket entities collected by this plugin.","s":"Entities","u":"/zh/docs/v1.0/Plugins/bitbucket","h":"#entities","p":16430},{"i":16438,"t":"Metrics that can be calculated based on the data collected from bitbucket: Commit Count Commit Author Count Added Lines of Code Deleted Lines of Code PR Count PR Time To Merge PR Merge Rate PR Review Depth PR Size Build Count Build Duration Build Success Rate","s":"Metrics","u":"/zh/docs/v1.0/Plugins/bitbucket","h":"#metrics","p":16430},{"i":16440,"t":"Configuring Bitbucket via Config UI","s":"Configuration","u":"/zh/docs/v1.0/Plugins/bitbucket","h":"#configuration","p":16430},{"i":16442,"t":"Note: Please replace the http://localhost:8080 in the sample requests with your actual DevLake API endpoint. For how to view DevLake API's swagger documentation, please refer to the \"Using DevLake API\" section of Developer Setup. You can trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"project1\", \"plan\": [ [ { \"plugin\": \"bitbucket\", \"options\": { \"connectionId\": 1, \"fullName\": \"likyh/likyhphp\", \"transformationRules\":{ \"deploymentPattern\":\"\", \"productionPattern\":\"\", \"issueStatusTodo\":\"new,open\", \"issueStatusInProgress\":\"\", \"issueStatusDone\":\"resolved,closed\", \"issueStatusOther\":\"on hold,wontfix,duplicate,invalid\" } } } ] ] } '","s":"API Sample Request","u":"/zh/docs/v1.0/Plugins/bitbucket","h":"#api-sample-request","p":16430},{"i":16444,"t":"references","s":"References","u":"/zh/docs/v1.0/Plugins/bitbucket","h":"#references","p":16430},{"i":16447,"t":"This plugin collects various entities from CircleCI, including accounts, jobs, workflows, pipelines and projects.","s":"Summary","u":"/zh/docs/v1.0/Plugins/circleci","h":"#summary","p":16445},{"i":16449,"t":"Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/circleci","h":"#supported-versions","p":16445},{"i":16451,"t":"Check out the CircleCI entities collected by this plugin.","s":"Entities","u":"/zh/docs/v1.0/Plugins/circleci","h":"#entities","p":16445},{"i":16453,"t":"Metrics that can be calculated based on the data collected from CircleCI: Build Count Build Duration Build Success Rate DORA - Deployment Frequency DORA - Lead Time for Changes DORA - Median Time to Restore Service DORA - Change Failure Rate","s":"Metrics","u":"/zh/docs/v1.0/Plugins/circleci","h":"#metrics","p":16445},{"i":16455,"t":"Configuring CircleCI via Config UI","s":"Configuration","u":"/zh/docs/v1.0/Plugins/circleci","h":"#configuration","p":16445},{"i":16457,"t":"Note: Please replace the http://localhost:8080 in the sample requests with your actual DevLake API endpoint. For how to view DevLake API's swagger documentation, please refer to the \"Using DevLake API\" section of Developer Setup. You can trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"project1\", \"plan\": [ [ { \"plugin\": \"circleci\", \"options\": { \"connectionId\": 1, \"fullName\": \"likyh/likyhphp\", \"transformationRules\":{ \"deploymentPattern\":\"\", \"productionPattern\":\"\", \"issueStatusTodo\":\"new,open\", \"issueStatusInProgress\":\"\", \"issueStatusDone\":\"resolved,closed\", \"issueStatusOther\":\"on hold,wontfix,duplicate,invalid\" } } } ] ] } '","s":"API Sample Request","u":"/zh/docs/v1.0/Plugins/circleci","h":"#api-sample-request","p":16445},{"i":16459,"t":"references","s":"References","u":"/zh/docs/v1.0/Plugins/circleci","h":"#references","p":16445},{"i":16462,"t":"This plugin collects various entities from Bitbucket Data Center, including pull requests, issues, comments, pipelines, git commits, and etc. As of v0.20.0, bitbucket_server plugin can only be invoked through DevLake API. Its support in Config-UI is WIP.","s":"Summary","u":"/zh/docs/v1.0/Plugins/bitbucket_server","h":"#summary","p":16460},{"i":16464,"t":"Available for BitBucket Data Center 8.16 and BitBucket Data Center and Server 7.21. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/bitbucket_server","h":"#supported-versions","p":16460},{"i":16466,"t":"Check out the BitBucket entities collected by this plugin.","s":"Entities","u":"/zh/docs/v1.0/Plugins/bitbucket_server","h":"#entities","p":16460},{"i":16468,"t":"Metrics that can be calculated based on the data collected from bitbucket: Commit Count Commit Author Count Added Lines of Code Deleted Lines of Code PR Count PR Time To Merge PR Merge Rate PR Review Depth PR Size","s":"Metrics","u":"/zh/docs/v1.0/Plugins/bitbucket_server","h":"#metrics","p":16460},{"i":16470,"t":"Configuring Bitbucket via Config UI","s":"Configuration","u":"/zh/docs/v1.0/Plugins/bitbucket_server","h":"#configuration","p":16460},{"i":16472,"t":"Note: Please replace the http://localhost:8080 in the sample requests with your actual DevLake API endpoint. For how to view DevLake API's swagger documentation, please refer to the \"Using DevLake API\" section of Developer Setup. You can trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"project1\", \"plan\": [ [ { \"plugin\": \"bitbucket\", \"options\": { \"connectionId\": 1, \"fullName\": \"likyh/likyhphp\", \"transformationRules\":{ \"deploymentPattern\":\"\", \"productionPattern\":\"\", \"issueStatusTodo\":\"new,open\", \"issueStatusInProgress\":\"\", \"issueStatusDone\":\"resolved,closed\", \"issueStatusOther\":\"on hold,wontfix,duplicate,invalid\" } } } ] ] } '","s":"API Sample Request","u":"/zh/docs/v1.0/Plugins/bitbucket_server","h":"#api-sample-request","p":16460},{"i":16474,"t":"references","s":"References","u":"/zh/docs/v1.0/Plugins/bitbucket_server","h":"#references","p":16460},{"i":16477,"t":"This plugin provides users the ability to: Add/delete columns in domain layer tables Insert values to certain columns with data extracted from some raw layer tables Import data from CSV files(only issues and issue_commits two tables are supported) NOTE: The names of columns added via this plugin must start with the prefix x_ For now, only the following five types were supported: varchar(255) text bigint float timestamp array","s":"Summary","u":"/zh/docs/v1.0/Plugins/customize","h":"#summary","p":16475},{"i":16480,"t":"To extract data, switch to Advanced Mode on the first step of creating a Blueprint and paste a JSON config as the following: The example below demonstrates how to extract status name from the table _raw_jira_api_issues: For non-array types: Extract the status name from the _raw_jira_api_issues table and assign it to the x_test column in the issues table. For array types: Extract the status name from the _raw_jira_api_issues table, and create a new issue_custom_array_fields table containing issue_id, field_id, and value columns. This table has a one-to-many relationship with the issues table. issue_id is the id corresponding to the issue, x_test corresponds to the field_id column, and the value of x_test corresponds to the value column. We leverage the package https://github.com/tidwall/gjson to extract value from the JSON. For the extraction syntax, please refer to this docs table: domain layer table name rawDataTable: raw layer table, from which we extract values by json path rawDataParams: the filter to select records from the raw layer table (The value should be a string not an object) mapping: the extraction rule; the key is the extension field name; the value is json path [ [ { \"plugin\":\"customize\", \"options\":{ \"transformationRules\":[ { \"table\":\"issues\", \"rawDataTable\":\"_raw_jira_api_issues\", \"rawDataParams\":\"{\\\"ConnectionId\\\":1,\\\"BoardId\\\":8}\", \"mapping\":{ \"x_test\":\"fields.status.name\" } } ] } } ] ] You can also trigger data extraction by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"extract fields\", \"plan\": [ [ { \"plugin\": \"customize\", \"options\": { \"transformationRules\": [ { \"table\": \"issues\", \"rawDataTable\": \"_raw_jira_api_issues\", \"rawDataParams\": \"{\\\"ConnectionId\\\":1,\\\"BoardId\\\":8}\", \"mapping\": { \"x_test\": \"fields.status.name\" } } ] } } ] ] } '","s":"Trigger Data Extraction","u":"/zh/docs/v1.0/Plugins/customize","h":"#trigger-data-extraction","p":16475},{"i":16482,"t":"Get all columns of the table issues GET /plugins/customize/issues/fields NOTE some fields are omitted in the following example response [ { \"columnName\": \"id\", \"displayName\": \"\", \"dataType\": \"varchar(255)\", \"description\": \"\" }, { \"columnName\": \"created_at\", \"displayName\": \"\", \"dataType\": \"datetime(3)\", \"description\": \"\" }, { \"columnName\": \"x_time\", \"displayName\": \"time\", \"dataType\": \"timestamp\", \"description\": \"test for time\" }, { \"columnName\": \"x_int\", \"displayName\": \"bigint\", \"dataType\": \"bigint\", \"description\": \"test for int\" }, { \"columnName\": \"x_float\", \"displayName\": \"float\", \"dataType\": \"float\", \"description\": \"test for float\" }, { \"columnName\": \"x_text\", \"displayName\": \"text\", \"dataType\": \"text\", \"description\": \"test for text\" }, { \"columnName\": \"x_varchar\", \"displayName\": \"varchar\", \"dataType\": \"varchar(255)\", \"description\": \"test for varchar\" } ]","s":"List Columns","u":"/zh/docs/v1.0/Plugins/customize","h":"#list-columns","p":16475},{"i":16484,"t":"Create a new column x_abc with datatype varchar(255) for the table issues. The value of columnName must start with x_ and consist of no more than 50 alphanumerics and underscores. The value of field dataType must be one of the following 5 types: varchar(255) text bigint float timestamp POST /plugins/customize/issues/fields { \"columnName\": \"x_abc\", \"displayName\": \"ABC\", \"dataType\": \"varchar(255)\", \"description\": \"test field\" }","s":"Create a Customized Column","u":"/zh/docs/v1.0/Plugins/customize","h":"#create-a-customized-column","p":16475},{"i":16486,"t":"Drop the column x_text of the table issues DELETE /plugins/customize/issues/fields/x_test","s":"Drop A Column","u":"/zh/docs/v1.0/Plugins/customize","h":"#drop-a-column","p":16475},{"i":16488,"t":"POST /plugins/customize/csvfiles/issues.csv The HTTP Content-Type must be multipart/form-data, and the form should have three fields: file: The CSV file boardId: It will be written to the id field of the boards table, the board_id field of board_issues, and the _raw_data_params field of issues boardName: It will be written to the name field of the boards table Upload a CSV file and import it to the issues table via this API. There should be no extra fields in the file except the labels field, and if the field value is NULL, it should be NULL in the CSV instead of the empty string. DevLake will parse the CSV file and store it in the issues table, where the labels are stored in the issue_labels table. If the boardId does not appear, a new record will be created in the boards table. The board_issues table will be updated at the same time as the import. The following is an issues.CSV file sample: id _raw_data_params url icon_url issue_key title description epic_key type status original_status story_point resolution_date created_date updated_date parent_issue_id priority original_estimate_minutes time_spent_minutes time_remaining_minutes creator_id creator_name assignee_id assignee_name severity component lead_time_minutes original_project original_type x_int x_time x_varchar x_float labels bitbucket:BitbucketIssue:1:1 board789 https://api.bitbucket.org/2.0/repositories/thenicetgp/lake/issues/1 1 issue test bitbucket issues test for devlake issue TODO new 0 NULL 2022-07-17 07:15:55.959+00:00 2022-07-17 09:11:42.656+00:00 major 0 0 0 bitbucket:BitbucketAccount:1:62abf394192edb006fa0e8cf tgp bitbucket:BitbucketAccount:1:62abf394192edb006fa0e8cf tgp NULL NULL NULL 10 2022-09-15 15:27:56 world 8 NULL bitbucket:BitbucketIssue:1:10 board789 https://api.bitbucket.org/2.0/repositories/thenicetgp/lake/issues/10 10 issue test007 issue test007 issue TODO new 0 NULL 2022-08-12 13:43:00.783+00:00 2022-08-12 13:43:00.783+00:00 trivial 0 0 0 bitbucket:BitbucketAccount:1:62abf394192edb006fa0e8cf tgp bitbucket:BitbucketAccount:1:62abf394192edb006fa0e8cf tgp NULL NULL NULL 30 2022-09-15 15:27:56 abc 2456790 hello worlds bitbucket:BitbucketIssue:1:13 board789 https://api.bitbucket.org/2.0/repositories/thenicetgp/lake/issues/13 13 issue test010 issue test010 issue TODO new 0 NULL 2022-08-12 13:44:46.508+00:00 2022-08-12 13:44:46.508+00:00 critical 0 0 0 bitbucket:BitbucketAccount:1:62abf394192edb006fa0e8cf tgp NULL NULL NULL 1 2022-09-15 15:27:56 NULL 0.00014 NULL bitbucket:BitbucketIssue:1:14 board789 https://api.bitbucket.org/2.0/repositories/thenicetgp/lake/issues/14 14 issue test011 issue test011 issue TODO new 0 NULL 2022-08-12 13:45:12.810+00:00 2022-08-12 13:45:12.810+00:00 blocker 0 0 0 bitbucket:BitbucketAccount:1:62abf394192edb006fa0e8cf tgp bitbucket:BitbucketAccount:1:62abf394192edb006fa0e8cf tgp NULL NULL NULL 41534568464351 2022-09-15 15:27:56 NULL NULL label1,label2,label3","s":"Upload issues.csv file","u":"/zh/docs/v1.0/Plugins/customize","h":"#upload-issuescsv-file","p":16475},{"i":16490,"t":"POST /plugins/customize/csvfiles/issue_commits.csv The Content-Type should be multipart/form-data, and the form should have two fields: file: The CSV file boardId: It will be written to the _raw_data_params field of issue_commits The following is an issue_commits.CSV file sample: issue_id commit_sha jira:JiraIssue:1:10063 8748a066cbaf67b15e86f2c636f9931347e987cf jira:JiraIssue:1:10064 e6bde456807818c5c78d7b265964d6d48b653af6 jira:JiraIssue:1:10065 8f91020bcf684c6ad07adfafa3d8a2f826686c42 jira:JiraIssue:1:10066 0dfe2e9ed88ad4e27f825d9b67d4d56ac983c5ef jira:JiraIssue:1:10145 07aa2ebed68e286dc51a7e0082031196a6135f74 jira:JiraIssue:1:10145 d70d6687e06304d9b6e0cb32b3f8c0f0928400f7 jira:JiraIssue:1:10159 d28785ff09229ac9e3c6734f0c97466ab00eb4da jira:JiraIssue:1:10202 0ab12c4d4064003602edceed900d1456b6209894 jira:JiraIssue:1:10203 980e9fe7bc3e22a0409f7241a024eaf9c53680dd","s":"Upload issue_commits.csv file","u":"/zh/docs/v1.0/Plugins/customize","h":"#upload-issue_commitscsv-file","p":16475},{"i":16493,"t":"This plugin collects Feishu meeting data through Feishu Openapi.","s":"Summary","u":"/zh/docs/v1.0/Plugins/feishu","h":"#summary","p":16491},{"i":16495,"t":"Will be available for all versions. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/feishu","h":"#supported-versions","p":16491},{"i":16497,"t":"In order to fully use this plugin, you need to get app_id and app_secret from a Feishu administrator (for help on App info, please see official Feishu Docs). A connection should be created before you can collect any data. Currently, this plugin supports creating connection by requesting the connections API: curl 'http://localhost:8080/plugins/feishu/connections' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"feishu\", \"endpoint\": \"https://open.feishu.cn/open-apis/vc/v1/\", \"proxy\": \"http://localhost:1080\", \"rateLimitPerHour\": 20000, \"appId\": \"<YOUR_APP_ID>\", \"appSecret\": \"<YOUR_APP_SECRET>\" } '","s":"Configuration","u":"/zh/docs/v1.0/Plugins/feishu","h":"#configuration","p":16491},{"i":16499,"t":"To collect data, select Advanced Mode on the Create Pipeline Run page and paste a JSON config like the following: [ [ { \"plugin\": \"feishu\", \"options\": { \"connectionId\": 1, \"numOfDaysToCollect\" : 80 } } ] ] numOfDaysToCollect: The number of days you want to collect rateLimitPerSecond: The number of requests to send(Maximum is 8) You can also trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"feishu 20211126\", \"plan\": [[{ \"plugin\": \"feishu\", \"options\": { \"connectionId\": 1, \"numOfDaysToCollect\" : 80 } }]] } '","s":"Collect data from Feishu","u":"/zh/docs/v1.0/Plugins/feishu","h":"#collect-data-from-feishu","p":16491},{"i":16502,"t":"dbt (data build tool) enables analytics engineers to transform data in their warehouses by simply writing select statements. dbt handles turning these select statements into tables and views. dbt does the T in ELT (Extract, Load, Transform) processes – it doesn’t extract or load data, but it’s extremely good at transforming data that’s already loaded into your warehouse.","s":"Summary","u":"/zh/docs/v1.0/Plugins/dbt","h":"#summary","p":16500},{"i":16504,"t":"If you plan to use this product, you need to install some environments first. Required Packages to Install​ python3.7+ dbt-mysql Commands to run or create in your terminal and the dbt project​ pip install dbt-mysql dbt init demoapp (demoapp is project name) create your SQL transformations and data models","s":"User setup","u":"/zh/docs/v1.0/Plugins/dbt","h":"#user-setup","p":16500},{"i":16506,"t":"Use the Raw JSON API to manually initiate a run using cURL or graphical API tool such as Postman. POST the following request to the DevLake API Endpoint. [ [ { \"plugin\": \"dbt\", \"options\": { \"projectPath\": \"/Users/abeizn/demoapp\", \"projectName\": \"demoapp\", \"projectTarget\": \"dev\", \"selectedModels\": [\"my_first_dbt_model\",\"my_second_dbt_model\"], \"projectVars\": { \"demokey1\": \"demovalue1\", \"demokey2\": \"demovalue2\" } } } ] ] projectPath: the absolute path of the dbt project. (required) projectName: the name of the dbt project. (required) projectTarget: this is the default target your dbt project will use. (optional) selectedModels: a model is a select statement. Models are defined in .sql files, and typically in your models directory. (required) And selectedModels accepts one or more arguments. Each argument can be one of: a package name, runs all models in your project, example: example a model name, runs a specific model, example: my_fisrt_dbt_model a fully-qualified path to a directory of models. projectVars: variables to parametrize dbt models. (optional) example: select * from events where event_type = '{{ var(\"event_type\") }}' To execute this SQL query in your model, you need set a value for event_type.","s":"Convert Data By DBT","u":"/zh/docs/v1.0/Plugins/dbt","h":"#convert-data-by-dbt","p":16500},{"i":16508,"t":"Learn more about dbt in the docs Check out Discourse for commonly asked questions and answers","s":"Resources:","u":"/zh/docs/v1.0/Plugins/dbt","h":"#resources","p":16500},{"i":16511,"t":"This plugin collects Gitee data through Gitee Openapi.","s":"Summary","u":"/zh/docs/v1.0/Plugins/gitee","h":"#summary","p":16509},{"i":16513,"t":"Will be available for Gitee Cloud. Check this doc for more details.","s":"Supported  Versions","u":"/zh/docs/v1.0/Plugins/gitee","h":"#supported--versions","p":16509},{"i":16515,"t":"In order to fully use this plugin, you need to get the token on the Gitee website. A connection should be created before you can collect any data. Currently, this plugin supports creating connection by requesting the connections API: curl 'http://localhost:8080/plugins/gitee/connections' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"gitee\", \"endpoint\": \"https://gitee.com/api/v5/\", \"proxy\": \"http://localhost:1080\", \"rateLimitPerHour\": 20000, \"token\": \"<YOUR_TOKEN>\" } '","s":"Configuration","u":"/zh/docs/v1.0/Plugins/gitee","h":"#configuration","p":16509},{"i":16517,"t":"In order to collect data, you have to compose a JSON looks like following one, and send it by selecting Advanced Mode on Create Pipeline Run page: Configure-UI Mode [ [ { \"plugin\": \"gitee\", \"options\": { \"connectionId\": 1, \"repo\": \"lake\", \"owner\": \"merico-dev\" } } ] ] and if you want to perform certain subtasks. [ [ { \"plugin\": \"gitee\", \"subtasks\": [\"collectXXX\", \"extractXXX\", \"convertXXX\"], \"options\": { \"connectionId\": 1, \"repo\": \"lake\", \"owner\": \"merico-dev\" } } ] ] Curl Mode: You can also trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"gitee 20211126\", \"plan\": [[{ \"plugin\": \"gitee\", \"options\": { \"connectionId\": 1, \"repo\": \"lake\", \"owner\": \"merico-dev\" } }]] } ' and if you want to perform certain subtasks. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"gitee 20211126\", \"plan\": [[{ \"plugin\": \"gitee\", \"subtasks\": [\"collectXXX\", \"extractXXX\", \"convertXXX\"], \"options\": { \"connectionId\": 1, \"repo\": \"lake\", \"owner\": \"merico-dev\" } }]] } '","s":"Collect data from Gitee","u":"/zh/docs/v1.0/Plugins/gitee","h":"#collect-data-from-gitee","p":16509},{"i":16520,"t":"This plugin extracts commits and references from a remote or local git repository. It then saves the data into the database or csv files.","s":"Summary","u":"/zh/docs/v1.0/Plugins/gitextractor","h":"#summary","p":16518},{"i":16522,"t":"Use the Git repo extractor to retrieve data about commits and branches from your repository. Use the GitHub plugin to retrieve data about Github issues and PRs from your repository. NOTE: you can run only one issue collection stage as described in the Github Plugin README. Use the RefDiff plugin to calculate version diff, which will be stored in refs_commits table. Note: If you do not want to collect commit files, you can bypass this step by setting the SKIP_COMMIT_FILES=true in the .env file. This will prevent the plugin from collecting commit file data.","s":"Steps to make this plugin work","u":"/zh/docs/v1.0/Plugins/gitextractor","h":"#steps-to-make-this-plugin-work","p":16518},{"i":16524,"t":"curl --location --request POST 'localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"git repo extractor\", \"plan\": [ [ { \"Plugin\": \"gitextractor\", \"Options\": { \"url\": \"https://github.com/merico-dev/lake.git\", \"repoId\": \"github:GithubRepo:384111310\" } } ] ] } ' url: the location of the git repository. It should start with http/https for a remote git repository and with / for a local one. repoId: column id of repos. Note : For GitHub, to find the repo id run $(\"meta[name=octolytics-dimension-repository_id]\").getAttribute('content') in browser console. proxy: optional, http proxy, e.g. http://your-proxy-server.com:1080. user: optional, for cloning private repository using HTTP/HTTPS password: optional, for cloning private repository using HTTP/HTTPS privateKey: optional, for SSH cloning, base64 encoded PEM file passphrase: optional, passphrase for the private key","s":"Sample Request","u":"/zh/docs/v1.0/Plugins/gitextractor","h":"#sample-request","p":16518},{"i":16526,"t":"You call also run this plugin in a standalone mode without any DevLake service running using the following command: go run plugins/gitextractor/main.go -url https://github.com/merico-dev/lake.git -id github:GithubRepo:384111310 -db \"merico:merico@tcp(127.0.0.1:3306)/lake?charset=utf8mb4&parseTime=True\" For more options (e.g., saving to a csv file instead of a db), please read plugins/gitextractor/main.go.","s":"Standalone Mode","u":"/zh/docs/v1.0/Plugins/gitextractor","h":"#standalone-mode","p":16518},{"i":16528,"t":"This plugin depends on libgit2, you need to install version 1.3.0 to run and debug this plugin on your local machine.","s":"Development","u":"/zh/docs/v1.0/Plugins/gitextractor","h":"#development","p":16518},{"i":16530,"t":"1. require cmake [ubuntu] apt install cmake -y [centos] yum install cmake -y 2. compiling git clone -b v1.3.0 https://github.com/libgit2/libgit2.git && cd libgit2 mkdir build && cd build && cmake .. make && make install 3.PKG_CONFIG and LD_LIBRARY_PATH [centos] export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib64:/usr/local/lib64/pkgconfig export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib64 [ubuntu] export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib:/usr/local/lib/pkgconfig export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib Troubleshooting (Linux)​ Q: # pkg-config --cflags -- libgit2 Package libgit2 was not found in the pkg-config search path. Perhaps you should add the directory containing libgit2.pc to the PKG_CONFIG_PATH environment variable No package 'libgit2' found pkg-config: exit status 1 A: Make sure your pkg config path covers the installation: if your libgit2.pc in /usr/local/lib64/pkgconfig(like centos) export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib64:/usr/local/lib64/pkgconfig else if your libgit2.pc in /usr/local/lib/pkgconfig(like ubuntu) export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib:/usr/local/lib/pkgconfig else consider install pkgconfig or rebuild the libgit2","s":"Linux","u":"/zh/docs/v1.0/Plugins/gitextractor","h":"#linux","p":16518},{"i":16532,"t":"NOTE: Do NOT install libgit2 via MacPorts or homebrew, install from source instead. brew install cmake git clone https://github.com/libgit2/libgit2.git cd libgit2 git checkout v1.3.0 mkdir build cd build cmake .. make make install Troubleshooting (MacOS)​ Q: I got an error saying: pkg-config: exec: \"pkg-config\": executable file not found in $PATH A: Make sure you have pkg-config installed: brew install pkg-config Make sure your pkg config path covers the installation: export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib:/usr/local/lib/pkgconfig","s":"MacOS","u":"/zh/docs/v1.0/Plugins/gitextractor","h":"#macos","p":16518},{"i":16535,"t":"This plugin collects GitHub data through REST API and GraphQL API. It then computes and visualizes various DevOps metrics from the GitHub data, which helps tech leads, QA and DevOps engineers, and project managers to answer questions such as: Is this month more productive than last? How fast do we respond to customer requirements? Was our quality improved or not?","s":"Summary","u":"/zh/docs/v1.0/Plugins/github","h":"#summary","p":16533},{"i":16537,"t":"Available for GitHub Cloud. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/github","h":"#supported-versions","p":16533},{"i":16539,"t":"Check out the GitHub entities collected by this plugin.","s":"Entities","u":"/zh/docs/v1.0/Plugins/github","h":"#entities","p":16533},{"i":16541,"t":"Check out the data refresh policy of this plugin.","s":"Data Refresh Policy","u":"/zh/docs/v1.0/Plugins/github","h":"#data-refresh-policy","p":16533},{"i":16543,"t":"Metrics that can be calculated based on the data collected from GitHub: Requirement Count Requirement Lead Time Requirement Delivery Rate Requirement Granularity Bug Age Bug Count per 1k Lines of Code Incident Age Incident Count per 1k Lines of Code Commit Count Commit Author Count Added Lines of Code Deleted Lines of Code PR Count PR Cycle Time PR Coding Time PR Pickup Time PR Review Time PR Deploy Time PR Time To Merge PR Merge Rate PR Review Depth PR Size Build Count Build Duration Build Success Rate DORA - Deployment Frequency DORA - Lead Time for Changes DORA - Median Time to Restore Service DORA - Change Failure Rate","s":"Metrics","u":"/zh/docs/v1.0/Plugins/github","h":"#metrics","p":16533},{"i":16545,"t":"Configuring GitHub via Config UI Configuring GitHub via Config UI's advanced mode.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/github","h":"#configuration","p":16533},{"i":16547,"t":"You can trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"project1-BLUEPRINT\", \"blueprintId\": 1, \"plan\": [ [ { \"plugin\": \"github\", \"options\": { \"connectionId\": 1, \"scopeId\": \"384111310\", \"transformationRules\":{ \"deploymentPattern\":\"\", \"productionPattern\":\"\", \"issueComponent\":\"\", \"issuePriority\":\"(high|medium|low)$\", \"issueSeverity\":\"\", \"issueTypeBug\":\"(bug)$\", \"issueTypeIncident\":\"\", \"issueTypeRequirement\":\"(feature|feature-request)$\", \"prBodyClosePattern\":\"\", \"prComponent\":\"\", \"prType\":\"\" } } } ] ] } ' or curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"project1-BLUEPRINT\", \"blueprintId\": 1, \"plan\": [ [ { \"plugin\": \"github\", \"options\": { \"connectionId\": 1, \"owner\": \"apache\", \"repo\": \"incubator-devlake\", \"transformationRules\":{ \"deploymentPattern\":\"\", \"productionPattern\":\"\", \"issueComponent\":\"\", \"issuePriority\":\"(high|medium|low)$\", \"issueSeverity\":\"\", \"issueTypeBug\":\"(bug)$\", \"issueTypeIncident\":\"\", \"issueTypeRequirement\":\"(feature|feature-request)$\", \"prBodyClosePattern\":\"\", \"prComponent\":\"\", \"prType\":\"\" } } } ] ] } '","s":"API Sample Request","u":"/zh/docs/v1.0/Plugins/github","h":"#api-sample-request","p":16533},{"i":16549,"t":"references","s":"References","u":"/zh/docs/v1.0/Plugins/github","h":"#references","p":16533},{"i":16552,"t":"This plugin collects Jenkins data through Remote Access API. It then computes and visualizes various DevOps metrics from the Jenkins data, which helps tech leads and DevOps engineers to answer questions such as: What is the deployment frequency of your team? What is the build success rate? How long does it take for a code change to be deployed into production?","s":"Summary","u":"/zh/docs/v1.0/Plugins/jenkins","h":"#summary","p":16550},{"i":16554,"t":"Available for Jenkins v2.263.x+. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/jenkins","h":"#supported-versions","p":16550},{"i":16556,"t":"Please note that it is important to avoid rerunning Jenkins builds in place, and instead ensure that each rerun has a unique build number. This is because rerunning builds with the same build number can lead to inconsistencies in the data collected by the Jenkins plugin.","s":"Note","u":"/zh/docs/v1.0/Plugins/jenkins","h":"#note","p":16550},{"i":16558,"t":"Check out the Jenkins entities collected by this plugin.","s":"Entities","u":"/zh/docs/v1.0/Plugins/jenkins","h":"#entities","p":16550},{"i":16560,"t":"Check out the data refresh policy of this plugin.","s":"Data Refresh Policy","u":"/zh/docs/v1.0/Plugins/jenkins","h":"#data-refresh-policy","p":16550},{"i":16562,"t":"Metrics that can be calculated based on the data collected from Jenkins: Build Count Build Duration Build Success Rate DORA - Deployment Frequency DORA - Lead Time for Changes DORA - Median Time to Restore Service DORA - Change Failure Rate","s":"Metrics","u":"/zh/docs/v1.0/Plugins/jenkins","h":"#metrics","p":16550},{"i":16564,"t":"Configuring Jenkins via Config UI Configuring Jenkins via Config UI's advanced mode.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/jenkins","h":"#configuration","p":16550},{"i":16566,"t":"You can trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"project1-BLUEPRINT\", \"blueprintId\": 1, \"plan\": [ [ { \"plugin\": \"jenkins\", \"options\": { \"connectionId\": 1, \"scopeId\": \"auto_deploy\", \"transformationRules\":{ \"deploymentPattern\":\"\", \"productionPattern\":\"\" } } } ] ] } ' or curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"project1-BLUEPRINT\", \"blueprintId\": 2, \"plan\": [ [ { \"plugin\": \"jenkins\", \"options\": { \"connectionId\": 1, \"jobFullName\": \"auto_deploy\", \"transformationRules\":{ \"deploymentPattern\":\"\", \"productionPattern\":\"\" } } } ] ] } '","s":"API Sample Request","u":"/zh/docs/v1.0/Plugins/jenkins","h":"#api-sample-request","p":16550},{"i":16568,"t":"references","s":"References","u":"/zh/docs/v1.0/Plugins/jenkins","h":"#references","p":16550},{"i":16571,"t":"This plugin collects Jira data through Jira REST API. It then computes and visualizes various engineering metrics from the Jira data.","s":"Summary","u":"/zh/docs/v1.0/Plugins/jira","h":"#summary","p":16569},{"i":16573,"t":"Available for Jira Cloud, Sever/Data Center 7.x, 8.x. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/jira","h":"#supported-versions","p":16569},{"i":16575,"t":"Check out the Jira entities collected by this plugin.","s":"Entities","u":"/zh/docs/v1.0/Plugins/jira","h":"#entities","p":16569},{"i":16577,"t":"Check out the data refresh policy of this plugin.","s":"Data Refresh Policy","u":"/zh/docs/v1.0/Plugins/jira","h":"#data-refresh-policy","p":16569},{"i":16579,"t":"Metrics that can be calculated based on the data collected from Jira: Requirement Count Requirement Lead Time Requirement Delivery Rate Requirement Granularity Bug Age Bug Count per 1k Lines of Code Incident Age Incident Count per 1k Lines of Code","s":"Metrics","u":"/zh/docs/v1.0/Plugins/jira","h":"#metrics","p":16569},{"i":16581,"t":"Configuring Jira via config-ui. Configuring Jira via Config UI's advanced mode.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/jira","h":"#configuration","p":16569},{"i":16583,"t":"You can trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"MY PIPELINE\", \"plan\": [ [ { \"plugin\": \"jira\", \"options\": { \"connectionId\": 1, \"boardId\": 8, \"transformationRules\": { \"epicKeyField\": \"\", \"storyPointField\": \"\", \"remotelinkCommitShaPattern\": \"\", \"typeMappings\": { \"10040\": { \"standardType\": \"Incident\", \"statusMappings\": null } } } } } ] ] } '","s":"API Sample Request","u":"/zh/docs/v1.0/Plugins/jira","h":"#api-sample-request","p":16569},{"i":16586,"t":"This plugin collects GitLab data through API. It then computes and visualizes various DevOps metrics from the GitLab data, which helps tech leads, QA and DevOps engineers, and project managers to answer questions such as: How long does it take for your codes to get merged? How much time is spent on code review? How long does it take for your codes to get merged? How much time is spent on code review?","s":"Summary","u":"/zh/docs/v1.0/Plugins/gitlab","h":"#summary","p":16584},{"i":16588,"t":"Available for GitLab Cloud, Community Edition 11+. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/gitlab","h":"#supported-versions","p":16584},{"i":16590,"t":"Check out the GitLab entities collected by this plugin.","s":"Entities","u":"/zh/docs/v1.0/Plugins/gitlab","h":"#entities","p":16584},{"i":16592,"t":"Check out the data refresh policy of this plugin.","s":"Data Refresh Policy","u":"/zh/docs/v1.0/Plugins/gitlab","h":"#data-refresh-policy","p":16584},{"i":16594,"t":"Metrics that can be calculated based on the data collected from GitLab: Commit Count Commit Author Count Added Lines of Code Deleted Lines of Code PR Count PR Cycle Time PR Coding Time PR Pickup Time PR Review Time PR Deploy Time PR Time To Merge PR Merge Rate PR Review Depth PR Size Build Count Build Duration Build Success Rate DORA - Deployment Frequency DORA - Lead Time for Changes DORA - Median Time to Restore Service DORA - Change Failure Rate","s":"Metrics","u":"/zh/docs/v1.0/Plugins/gitlab","h":"#metrics","p":16584},{"i":16596,"t":"Configuring GitLab via config-ui. Configuring GitLab via Config UI's advanced mode.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/gitlab","h":"#configuration","p":16584},{"i":16598,"t":"You can trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"project1-BLUEPRINT\", \"blueprintId\": 1, \"plan\": [ [ { \"plugin\": \"gitlab\", \"options\": { \"connectionId\": 1, \"projectId\": 33728042, \"transformationRules\":{ \"deploymentPattern\":\"\", \"productionPattern\":\"\", \"issueComponent\":\"\", \"issuePriority\":\"(high|medium|low)$\", \"issueSeverity\":\"\", \"issueTypeBug\":\"(bug)$\", \"issueTypeIncident\":\"\", \"issueTypeRequirement\":\"(feature|feature-request)$\", \"prBodyClosePattern\":\"\", \"prComponent\":\"\", \"prType\":\"\" } } } ] ] } '","s":"API Sample Request","u":"/zh/docs/v1.0/Plugins/gitlab","h":"#api-sample-request","p":16584},{"i":16600,"t":"references","s":"References","u":"/zh/docs/v1.0/Plugins/gitlab","h":"#references","p":16584},{"i":16603,"t":"This plugin collects Opsgenie incident data, users and teams, and uses them to compute incident-type DORA metrics. Namely, Median time to restore service Change failure rate. Incident Age Incident Count Per 1k Lines of Code","s":"Summary","u":"/zh/docs/v1.0/Plugins/opsgenie","h":"#summary","p":16601},{"i":16605,"t":"Available for Atlassian Opsgenie Cloud, for both US or EU instances. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/opsgenie","h":"#supported-versions","p":16601},{"i":16607,"t":"Configure Opsgenie via Config UI. See instructions here. Configure Opsgenie via Config UI's advanced mode.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/opsgenie","h":"#configuration","p":16601},{"i":16610,"t":"This plugin collects all incidents from PagerDuty, and uses them to compute incident-type DORA metrics. Namely, Median time to restore service Change failure rate. Incident Age Incident Count Per 1k Lines of Code","s":"Summary","u":"/zh/docs/v1.0/Plugins/pagerduty","h":"#summary","p":16608},{"i":16612,"t":"Available for PagerDuty Cloud. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/pagerduty","h":"#supported-versions","p":16608},{"i":16614,"t":"Configure PagerDuty via Config UI. See instructions here. Configure PagerDuty via Config UI's advanced mode.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/pagerduty","h":"#configuration","p":16608},{"i":16617,"t":"RefDiff is a plugin that performs calculation tasks and has 2 main purposes. Calculate the difference in commits between releases/tags to analyze the amount of code in each release Calculate the difference in commits between deployments to calculate DORA metrics And the output of RefDiff is stored in the table commits_diffs, finished_commits_diffs, ref_commits.","s":"Summary","u":"/zh/docs/v1.0/Plugins/refdiff","h":"#summary","p":16615},{"i":16619,"t":"You need to run gitextractor before the refdiff plugin. The gitextractor plugin should create records in the refs table in your database before this plugin can be run.","s":"Important Note","u":"/zh/docs/v1.0/Plugins/refdiff","h":"#important-note","p":16615},{"i":16621,"t":"This is an enrichment plugin based on the domain layer data, no configuration is needed.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/refdiff","h":"#configuration","p":16615},{"i":16623,"t":"To trigger the enrichment, you need to insert a new task into your pipeline. Make sure commits and refs are collected into your database, refs table should contain records like following: id ref_type github:GithubRepo:1:384111310:refs/tags/0.3.5 TAG github:GithubRepo:1:384111310:refs/tags/0.3.6 TAG github:GithubRepo:1:384111310:refs/tags/0.5.0 TAG github:GithubRepo:1:384111310:refs/tags/v0.0.1 TAG github:GithubRepo:1:384111310:refs/tags/v0.2.0 TAG github:GithubRepo:1:384111310:refs/tags/v0.3.0 TAG github:GithubRepo:1:384111310:refs/tags/v0.4.0 TAG github:GithubRepo:1:384111310:refs/tags/v0.6.0 TAG github:GithubRepo:1:384111310:refs/tags/v0.6.1 TAG If you want to run calculatePrCherryPick, please configure GITHUB_PR_TITLE_PATTERN in .env, you can check the example in .env.example(we have a default value, please make sure your pattern is disclosed by single quotes '') And then, trigger a pipeline like the following format: curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"test-refdiff\", \"plan\": [ [ { \"plugin\": \"refdiff\", \"options\": { \"repoId\": \"github:GithubRepo:1:384111310\", \"pairs\": [ { \"newRef\": \"refs/tags/v0.6.0\", \"oldRef\": \"refs/tags/0.5.0\" }, { \"newRef\": \"refs/tags/0.5.0\", \"oldRef\": \"refs/tags/0.4.0\" } ], \"tasks\": [ \"calculateCommitsDiff\", \"calculateIssuesDiff\", \"calculatePrCherryPick\", ] } } ] ] }' Or if you preferred calculating latest releases curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"test-refdiff\", \"plan\": [ [ { \"plugin\": \"refdiff\", \"options\": { \"repoId\": \"github:GithubRepo:1:384111310\", \"tagsPattern\": \"v\\d+\\.\\d+.\\d+\", \"tagsLimit\": 10, \"tagsOrder\": \"reverse semver\", \"tasks\": [ \"calculateCommitsDiff\", \"calculateIssuesDiff\", \"calculatePrCherryPick\", ] } } ] ] }'","s":"How to use refdiff","u":"/zh/docs/v1.0/Plugins/refdiff","h":"#how-to-use-refdiff","p":16615},{"i":16625,"t":"RefDiff can be called by the DORA plugin to support the calculation of DORA metrics. RefDiff has a subtask called 'calculateProjectDeploymentCommitsDiff'. This subtask takes the project_name from task options to calculate the commits diff between two consecutive deployments in this project. That is to say, refdiff will generate the relationship between deployed commit(s) and the deployment in which these commits get deployed. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"test-refdiff-dora\", \"plan\": [ [ { \"plugin\": \"refdiff\", \"options\": { \"projectName\": \"project_name_1\", \"tasks\": [ \"calculateProjectDeploymentCommitsDiff\" ] } } ] ] }'","s":"How to use refdiff in DORA","u":"/zh/docs/v1.0/Plugins/refdiff","h":"#how-to-use-refdiff-in-dora","p":16615},{"i":16627,"t":"This plugin depends on libgit2, you need to install version 1.3.0 in order to run and debug this plugin on your local machine. Click here for a brief guide.","s":"Development","u":"/zh/docs/v1.0/Plugins/refdiff","h":"#development","p":16615},{"i":16630,"t":"This plugin collects TAPD data through its REST APIs. TAPD is an issue-tracking tool similar to Jira.","s":"Summary","u":"/zh/docs/v1.0/Plugins/tapd","h":"#summary","p":16628},{"i":16632,"t":"Advanced mode available for Tapd Cloud. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/tapd","h":"#supported-versions","p":16628},{"i":16634,"t":"Metrics that can be calculated based on the data collected from Tapd: Requirement Count Requirement Lead Time Requirement Delivery Rate Bug Age Incident Age","s":"Metrics","u":"/zh/docs/v1.0/Plugins/tapd","h":"#metrics","p":16628},{"i":16636,"t":"Configuring Tapd via config-ui. Configuring Tapd via Config UI's advanced mode.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/tapd","h":"#configuration","p":16628},{"i":16639,"t":"This plugin collects Teambition data through its REST APIs. Teambition is an issue-tracking tool similar to Trello.","s":"Summary","u":"/zh/docs/v1.0/Plugins/teambition","h":"#summary","p":16637},{"i":16641,"t":"Available for Teambition Cloud. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/teambition","h":"#supported-versions","p":16637},{"i":16643,"t":"Metrics that can be calculated based on the data collected from Teambition: Requirement Count Requirement Lead Time Requirement Delivery Rate Bug Age Incident Age","s":"Metrics","u":"/zh/docs/v1.0/Plugins/teambition","h":"#metrics","p":16637},{"i":16645,"t":"Configuring Teambition via config-ui. Configuring Teambition via Config UI's advanced mode.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/teambition","h":"#configuration","p":16637},{"i":16648,"t":"This plugin collects Trello data through Trello's rest api.","s":"Summary","u":"/zh/docs/v1.0/Plugins/trello","h":"#summary","p":16646},{"i":16650,"t":"In order to fully use this plugin, you will need to get apikey and token on the Trello website. A connection should be created before you can collect any data. Currently, this plugin supports creating connection by requesting connections API: curl 'http://localhost:8080/plugins/trello/connections' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"trello\", \"endpoint\": \"https://api.trello.com/\", \"rateLimitPerHour\": 20000, \"appId\": \"<YOUR_APIKEY>\", \"secretKey\": \"<YOUR_TOKEN>\" } '","s":"Configuration","u":"/zh/docs/v1.0/Plugins/trello","h":"#configuration","p":16646},{"i":16652,"t":"You can make the following request to get all the boards. curl 'http://localhost:8080/plugins/trello/connections/<CONNECTION_ID>/proxy/rest/1/members/me/boards?fields=name,id' In order to collect data, you have to compose a JSON looks like following one, and send it by selecting Advanced Mode on Create Pipeline Run page: Configure-UI Mode [ [ { \"plugin\": \"trello\", \"options\": { \"connectionId\": <CONNECTION_ID>, \"boardId\": \"<BOARD_ID>\" } } ] ] and if you want to perform certain subtasks. [ [ { \"plugin\": \"trello\", \"subtasks\": [\"collectXXX\", \"extractXXX\", \"convertXXX\"], \"options\": { \"connectionId\": <CONNECTION_ID>, \"boardId\": \"<BOARD_ID>\" } } ] ] Curl Mode: In order to collect data, you have to make a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\":\"MY PIPELINE\", \"plan\":[ [ { \"plugin\":\"trello\", \"options\":{ \"connectionId\":<CONNECTION_ID>, \"boardId\":\"<BOARD_ID>\" } } ] ] } ' You can make the following request to get all the boards. curl 'http://localhost:8080/plugins/trello/connections/<CONNECTION_ID>/proxy/rest/1/members/me/boards?fields=name,id'","s":"Collect data from Trello","u":"/zh/docs/v1.0/Plugins/trello","h":"#collect-data-from-trello","p":16646},{"i":16655,"t":"This plugin collects SonarQube data through its REST APIs. SonarQube is a tool for static code analysis and code quality management. It can help you discover potential problems and defects in your code, and provide suggestions and solutions. When collecting issues under a certain SonarQube project, divide the issues into smaller groups to collect, so as not to obtain 10,000 issues at once and exceed the result limit of SonarQube APIs themselves. Specific rules include: First, classify the issues according to \"severity\", \"status\", and \"type\" and collect them separately. Secondly, issues are classified according to the time fields \"createdBefore\" and \"createdAfter\" and collected separately. Finally, the issues are classified according to \"the file to which the issue belongs\" and collected separately.","s":"Summary","u":"/zh/docs/v1.0/Plugins/sonarqube","h":"#summary","p":16653},{"i":16657,"t":"Available for SonarQube Server v8.x, v9.x. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/sonarqube","h":"#supported-versions","p":16653},{"i":16659,"t":"Check out the SonarQube entities collected by this plugin.","s":"Entities","u":"/zh/docs/v1.0/Plugins/sonarqube","h":"#entities","p":16653},{"i":16661,"t":"Check out the data refresh policy of this plugin.","s":"Data Refresh Policy","u":"/zh/docs/v1.0/Plugins/sonarqube","h":"#data-refresh-policy","p":16653},{"i":16663,"t":"Most of SonarQube metrics are collected and can be found in DevLake's SonarQube dashboard. Code Quality Issue Count Code Quality Test Code Quality Maintainability-Debt Code Quality Duplicated Blocks Code Quality Duplicated Lines","s":"Metrics","u":"/zh/docs/v1.0/Plugins/sonarqube","h":"#metrics","p":16653},{"i":16665,"t":"Configuring SonarQube via config-ui. Configuring SonarQube via Config UI's advanced mode.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/sonarqube","h":"#configuration","p":16653},{"i":16667,"t":"You can trigger data collection by making a POST request to /pipelines. curl 'http://localhost:8080/pipelines' \\ --header 'Content-Type: application/json' \\ --data-raw ' { \"name\": \"project1-BLUEPRINT\", \"blueprintId\": 1, \"plan\": [ [ { \"plugin\": \"sonarqube\", \"options\": { \"connectionId\": 1, \"projectKey\": \"testDevLake\" } } ] ] } '","s":"API Sample Request","u":"/zh/docs/v1.0/Plugins/sonarqube","h":"#api-sample-request","p":16653},{"i":16669,"t":"references","s":"References","u":"/zh/docs/v1.0/Plugins/sonarqube","h":"#references","p":16653},{"i":16672,"t":"This plugin collects Zentao data through its REST APIs. Zentao is an issue-tracking tool similar to Jira.","s":"Summary","u":"/zh/docs/v1.0/Plugins/zentao","h":"#summary","p":16670},{"i":16674,"t":"Advanced mode vailable for Zentao OSS v18.3, Enterprise v8.2, Flagship v4.3. Check this doc for more details.","s":"Supported Versions","u":"/zh/docs/v1.0/Plugins/zentao","h":"#supported-versions","p":16670},{"i":16676,"t":"Metrics that can be calculated based on the data collected from Zentao: Requirement Count Requirement Lead Time Requirement Delivery Rate Bug Age Incident Age","s":"Metrics","u":"/zh/docs/v1.0/Plugins/zentao","h":"#metrics","p":16670},{"i":16678,"t":"Configuring Zentao via config-ui. Configuring Zentao via Config UI's advanced mode.","s":"Configuration","u":"/zh/docs/v1.0/Plugins/zentao","h":"#configuration","p":16670},{"i":16681,"t":"Incoming Webhooks are your solution to bring data to Apache DevLake when there isn't a specific plugin ready for your DevOps tool. An Incoming Webhook allows users to actively push data to DevLake. When you create an Incoming Webhook within DevLake, DevLake generates a unique URL. You can then post JSON payloads to this URL to push data directly to your DevLake instance. In v0.14+, users can push \"incidents\" and \"deployments\" required by DORA metrics to DevLake via Incoming Webhooks. Webhooks are meant to be used at the lowest level that you want to relate incidents with deployments. For example, if you want to relate incidents at the individual service level, you will need a webhook per service. If you wish to relate incidents at the product level, you will need a webhook for the product. This is because incidents on a project will be related to the last deployment on the project with a timestamp that is before the incident's timestamp. This is true regardless of the source of incidents or deployments. Note: If you post incidents using webhook due to your tool not being supported but your deployments are collected via plugins automatically, you need to re-collect data for deployments for the posted incidents to get mapped to deployments based on timestamps. This is required for Change Failure Rate (DORA) metric to show up correctly for the project. Diagram of the relationship between incidents and deployments:","s":"Summary","u":"/zh/docs/v1.0/Plugins/webhook","h":"#summary","p":16679},{"i":16683,"t":"Check out the Incoming Webhooks entities collected by this plugin.","s":"Entities","u":"/zh/docs/v1.0/Plugins/webhook","h":"#entities","p":16679},{"i":16685,"t":"Metrics that can be calculated based on the data collected from Incoming Webhooks: Requirement Delivery Rate Requirement Granularity Bug Age Bug Count per 1k Lines of Code Incident Age Incident Count per 1k Lines of Code DORA - Deployment Frequency DORA - Lead Time for Changes DORA - Median Time to Restore Service DORA - Change Failure Rate","s":"Metrics","u":"/zh/docs/v1.0/Plugins/webhook","h":"#metrics","p":16679},{"i":16687,"t":"Configuring Incoming Webhooks via Config UI","s":"Configuration","u":"/zh/docs/v1.0/Plugins/webhook","h":"#configuration","p":16679},{"i":16690,"t":"If you want to collect deployment data from your system, you can use the incoming webhooks for deployment. Payload Schema​ You can copy the generated deployment curl commands to your CI/CD script to post deployments to Apache DevLake. Below is the detailed payload schema: Key Required Notes pipeline_id ✔️ Yes This will be the unique id of table cicd_deployments. This key was optional before v1.0.0-beta8. environment ✖️ No the environment this deployment happens. For example, PRODUCTION STAGING TESTING DEVELOPMENT. The default value is PRODUCTION repo_url ✔️ Yes the repo URL of the deployment commit If there is a row in the domain layer table repos where repos.url equals repo_url, the repoId will be filled with repos.id. repo_id ✖️ No related Domain Layer repos.id No default value. name ✖️ No deployment name. The default value is \"deployment for request.commit_sha\" display_title ✖️ No deployment display_title. No default value. ref_name ✖️ No related branch/tag No default value. commit_sha ✔️ Yes the sha of the deployment commit commit_msg ✖️ No the sha of the deployment commit message create_time ✖️ No Time. E.g. 2020-01-01T12:00:00+00:00 No default value. start_time ✔️ Yes Time. E.g. 2020-01-01T12:00:00+00:00 No default value. end_time ✖️ No Time. E.g. 2020-01-01T12:00:00+00:00 The default value is the time when DevLake receives the POST request. result ✖️ No deployment result, one of the values : SUCCESS, FAILURE, ABORT, MANUAL, The default value is SUCCESS. deployment_commits ✖ No Allow deployment webhook to push deployments to multiple repos in one request, includes display_title,repo_url,commit_sha,commit_msg,name,ref_name. Register a Deployment - Sample API Calls​ To deploy on a single repository, use the following command: curl <devlake-host>/api/rest/plugins/webhook/1/deployments -X 'POST' -d '{ \"pipeline_id\": \"required-pipeline-id\", \"environment\":\"PRODUCTION\", \"repo_url\":\"https://github.com/apache/incubator-devlake/\", \"repo_id\": \"optional-repo-id\", \"display_title\":\"optional-custom-deploy-display-title\", \"name\": \"optional-deployment-name. If you do not post a name, DevLake will generate one for you.\", \"ref_name\": \"optional-release-v0.17\", \"commit_sha\":\"015e3d3b480e417aede5a1293bd61de9b0fd051d\", \"commit_msg\":\"optional-commit-message\", \"create_time\":\"2020-01-01T11:00:00+00:00\", \"start_time\":\"2020-01-01T12:00:00+00:00\", \"end_time\":\"2020-01-02T13:00:00+00:00\", \"result\": \"FAILURE\" }' To deploy across multiple repositories (refer to the discussion), use the following command: curl <devlake-host>/api/rest/plugins/webhook/1/deployments -X 'POST' -d '{ \"pipeline_id\": \"required-pipeline-id\", \"environment\":\"PRODUCTION\", \"repo_id\": \"optional-repo-id\", \"display_title\":\"optional-custom-deploy-display-title\", \"name\": \"optional-deployment-name. If you do not post a name, DevLake will generate one for you.\", \"create_time\":\"2020-01-01T11:00:00+00:00\", \"start_time\":\"2020-01-01T12:00:00+00:00\", \"end_time\":\"2020-01-02T13:00:00+00:00\", \"result\": \"FAILURE\", \"deployment_commits\":[ { \"display_title\":\"optional-custom-deployment-commit-display-title-1\", \"repo_url\":\"repo-1\", \"name\":\"optional, if null, it will be deployment for {commit_sha}\", \"ref_name\": \"optional-release-v0.17\", \"commit_sha\":\"c1\", \"commit_msg\":\"optional-msg-1\" }, { \"display_title\":\"optional-custom-deployment-commit-display-title-2\", \"repo_url\":\"repo-2\", \"name\":\"optional, if null, it will be deployment for {commit_sha}\", \"ref_name\": \"optional-release-v0.17\", \"commit_sha\":\"c2\", \"commit_msg\":\"optional-msg-2\" } ] }' If you have set a username/password for Config UI, you'll need to add them to the curl command to register a deployment: curl <devlake-host>/api/rest/plugins/webhook/1/deployments -X 'POST' -u 'username:password' -d '{ \"pipeline_id\": \"required-pipeline-id\", \"deployment_commits\":[ { \"commit_sha\":\"the sha of deployment commit1\", \"repo_url\":\"the repo URL of the deployment commit\" } ], \"start_time\":\"2020-01-01T12:00:00+00:00\", \"end_time\":\"2020-01-02T12:00:00+00:00\" }' A real-world example - Push CircleCI deployments to DevLake​ The following demo shows how to post \"deployments\" to DevLake from CircleCI. In this example, the CircleCI job 'deploy' is used to manage deployments. version: 2.1 jobs: build: docker: - image: cimg/base:stable steps: - checkout - run: name: \"build\" command: | echo Hello, World! deploy: docker: - image: cimg/base:stable steps: - checkout - run: name: \"deploy\" command: | # The time a deploy started start_time=`date '+%Y-%m-%dT%H:%M:%S%z'` # Some deployment tasks here ... echo Hello, World! # Send the request to DevLake after deploy # The values start with a '$CIRCLE_' are CircleCI's built-in variables curl <devlake-host>/api/rest/plugins/webhook/1/deployments -X 'POST' -d \"{ \\\"pipeline_id\\\": \\\"$PIPLINE_ID\\\", \\\"commit_sha\\\":\\\"$CIRCLE_SHA1\\\", \\\"repo_url\\\":\\\"$CIRCLE_REPOSITORY_URL\\\", \\\"start_time\\\":\\\"$start_time\\\" }\" workflows: build_and_deploy_workflow: jobs: - build - deploy","s":"Deployment","u":"/zh/docs/v1.0/Plugins/webhook","h":"#deployment","p":16679},{"i":16692,"t":"If you want to collect issue or incident data from your system, you can use the two webhooks for issues. Register Issues - Update or Create Issues​ POST <devlake-host>/api/rest/plugins/webhook/1/issues needs to be called when an issue or incident is created. The body should be a JSON and include columns as follows: Keyname Required Notes url ✖️ No issue's URL issue_key ✔️ Yes issue's key, needs to be unique in a connection title ✔️ Yes description ✖️ No epic_key ✖️ No in which epic. type ✖️ No type, such as bug/incident/epic/... status ✔️ Yes issue's status. Must be one of TODO DONE IN_PROGRESS original_status ✔️ Yes status in your system, such as created/open/closed/... story_point ✖️ No resolution_date ✖️ No date, Format should be 2020-01-01T12:00:00+00:00 created_date ✔️ Yes date, Format should be 2020-01-01T12:00:00+00:00 updated_date ✖️ No date, Format should be 2020-01-01T12:00:00+00:00 lead_time_minutes ✖️ No how long from this issue accepted to develop parent_issue_key ✖️ No priority ✖️ No original_estimate_minutes ✖️ No time_spent_minutes ✖️ No time_remaining_minutes ✖️ No creator_id ✖️ No the user id of the creator creator_name ✖️ No the user name of the creator, it will just be used to display assignee_id ✖️ No assignee_name ✖️ No severity ✖️ No component ✖️ No which component is this issue in. More information about these columns at DomainLayerIssueTracking. Register Issues - Close Issues (Optional)​ POST <devlake-host>/api/rest/plugins/webhook/1/issue/:issueId/close needs to be called when an issue or incident is closed. Replace :issueId with specific strings and keep the body empty. Register Issues - Sample API Calls​ Sample CURL for creating an incident: curl <devlake-host>/api/rest/plugins/webhook/1/issues -X 'POST' -d '{ \"issue_key\":\"DLK-1234\", \"title\":\"a feature from DLK\", \"description\":\"\", \"url\":\"\", \"type\":\"INCIDENT\", \"status\":\"TODO\", \"created_date\":\"2020-01-01T12:00:00+00:00\", \"updated_date\":\"2020-01-01T12:00:00+00:00\", \"priority\":\"\", \"severity\":\"\", \"creator_id\":\"user1131\", \"creator_name\":\"Nick name 1\", \"assignee_id\":\"user1132\", \"assignee_name\":\"Nick name 2\" }' Sample CURL for Issue Closing: curl <devlake-host>/api/rest/plugins/webhook/1/issue/DLK-1234/close -X 'POST'","s":"Incident / Issue","u":"/zh/docs/v1.0/Plugins/webhook","h":"#incident--issue","p":16679},{"i":16694,"t":"references","s":"References","u":"/zh/docs/v1.0/Plugins/webhook","h":"#references","p":16679},{"i":16697,"t":"Error code An example Causes Solutions 429 subtask collectAPiPipelines ended unexpectedly caused: Error waiting for async Collector execution caused by: retry exceeded 3 times calling projects/{projectId}/pipelines {429} This error exmaple is caused by GitLab's Pipeline APIs. These APIs are implemented via Cloudflare, which is different from other GitLab entities. Two ways: - Enable fixed rate limit in the GitLab connection, lower the API rates to 2,000. If it works, you can try increase the rates to accerlerate. This solution also applies to other plugins that return the 429 while collecting data, such as GitHub, TAPD, etc. - Upgrade to v0.15.x 403 error: preparing task data for gitextractor caused by: unexpected http status code: 403 This is usually caused by the permission of your tokens. For example, if you're using an un-supported auth method, or using a token without ticking permissions to certain entities you want to collect. Find the supported authentication methods and token permissions that should be selected in the corresponding plugin's Config UI manuals, for example, configuring GitHub 1406 subtask extractApiBuilds ended unexpectedly caused by: error adding the result to batch caused by: Error 1406: Data too long for column 'full_display_name' at row 138. See bug #4053 This is usually thrown by MySQL because a certain value is too long A work-around is to manually change the field length to varchar(255) or longer in MySQL. Also, please put up a bug to let us know.","s":"Common Error Code while collecting/processing data","u":"/zh/docs/v1.0/Troubleshooting/Configuration","h":"#common-error-code-while-collectingprocessing-data","p":16695},{"i":16699,"t":"There might be two problems when trying to collect data from a private GitLab server with a self-signed certificate: \"Test Connection\" error. This can be solved by setting the environment variable IN_SECURE_SKIP_VERIFY=true for the devlake container \"GitExtractor\" fails to clone the repository due to certificate verification, sadly, neither gogit nor git2go we are using supports insecure HTTPS. A better approach would be adding your root CA to the devlake container: Mount your rootCA.crt into the devlake container Add a command node to install the mounted certificate Here is an example of the `docker-compose`` installation, the idea applies to other installation methods. devlake: image: apache/devlake:v... ... volumes: ... - /path/to/your/rootCA.crt:/usr/local/share/ca-certificates/rootCA.crt command: [ \"sh\", \"-c\", \"update-ca-certificates; lake\" ] ...","s":"Failed to collect data from the server with a self-signed certificate","u":"/zh/docs/v1.0/Troubleshooting/Configuration","h":"#failed-to-collect-data-from-the-server-with-a-self-signed-certificate","p":16695},{"i":16701,"t":"See bug #3719 This bug happens occasionally in v0.14.x and previous versions. It is fixed by changing the docker base image. Please upgrade to v0.15.x to get it fixed if you encounter it.","s":"GitExtractor task failed in a GitHub/GitLab/BitBucket blueprint","u":"/zh/docs/v1.0/Troubleshooting/Configuration","h":"#gitextractor-task-failed-in-a-githubgitlabbitbucket-blueprint","p":16695},{"i":16703,"t":"We have had a couple of reports suggesting MySQL InnoDB would fail with the message. Error 1206: The total number of locks exceeds the lock table size · Issue #3849 · apache/incubator-devlake [Bug][GitLab] gitlab collectApiJobs task failed for mysql locks error · Issue #3653 · apache/incubator-devlake The cause of the problem is: Before Apache DevLake data collection starts, it must purge expired data in the database. MySQL InnoDB Engine would create locks in memory for the records being deleted. When deleting huge amounts of records, the memory bursts, hence the error. You are likely to see the error when dealing with a huge repository or board. For MySQL, you can solve it by increasing the innodb_buffer_pool_size to a higher value. Here is an example of the docker-compose installation, the idea applies to other installation methods. mysql: image: mysql:8..... ... # add the follow line to the mysql container command: --innodb-buffer-pool-size=200M","s":"Pipeline failed with \"The total number of locks exceeds the lock table size\"","u":"/zh/docs/v1.0/Troubleshooting/Configuration","h":"#pipeline-failed-with-the-total-number-of-locks-exceeds-the-lock-table-size","p":16695},{"i":16705,"t":"See issue #6038 If you're having trouble adding data scopes to a GitHub connection because the repositories keep loading, there are a few things you can check: Make sure your access token has the necessary permissions. If your account is protected by organization SAML enforcement, make sure you've authorized the token using SSO. For more details about authenticating with SAML single sign-on, see here: https://docs.github.com/en/enterprise-cloud@latest/authentication/authenticating-with-saml-single-sign-on.","s":"GitHub repositories keep loading when adding data scopes","u":"/zh/docs/v1.0/Troubleshooting/Configuration","h":"#github-repositories-keep-loading-when-adding-data-scopes","p":16695},{"i":16707,"t":"Sorry for the inconvenience, please help us improve by creating an issue","s":"None of them solve your problem?","u":"/zh/docs/v1.0/Troubleshooting/Configuration","h":"#none-of-them-solve-your-problem","p":16695},{"i":16710,"t":"The cause is the devlake container trying to decrypt data in the database with the wrong key. Please check the GettingStarted/Upgrade doc for more detail.","s":"panic: invalid encKey error","u":"/zh/docs/v1.0/Troubleshooting/Installation","h":"#panic-invalid-enckey-error","p":16708},{"i":16712,"t":"One of the known root causes of the phonomenon is the Dynatrace agent being injected into the container. Excluding the namespace from the Dynatrace deployments should fix the problem. Check #5612 if you needed more detail.","s":"Go Panic in OpenShift Kubernetes","u":"/zh/docs/v1.0/Troubleshooting/Installation","h":"#go-panic-in-openshift-kubernetes","p":16708},{"i":16714,"t":"Sorry for the inconvenience, please help us improve by creating an issue","s":"None of them solve your problem?","u":"/zh/docs/v1.0/Troubleshooting/Installation","h":"#none-of-them-solve-your-problem","p":16708},{"i":16717,"t":"This section may help if Median Time to Restore Service (MTTR) or Change Failure Rate (CFR) do not appear on the dashboards or you want to learn more about how these issue-based metrics are built.","s":"Debugging DORA Issue Metrics","u":"/zh/docs/v1.0/Troubleshooting/Dashboard","h":"#debugging-dora-issue-metrics","p":16715},{"i":16719,"t":"Starting from DevLake v0.18 this dashboard can be found near the DORA dashboard. Also, it can be accessed by a direct link in the Dashboard Instruction panel in the DORA dashboard. This dashboard is a step-by-step guide to check which step went wrong for all 4 of the DORA metrics. The sections are: Check \"Deployment Frequency\" Check \"Median Lead Time for Changes\" Check \"Change Failure Rate\" & \"Median Time to Restore Service","s":"DORA Validation Dashboard","u":"/zh/docs/v1.0/Troubleshooting/Dashboard","h":"#dora-validation-dashboard","p":16715},{"i":16721,"t":"Each chart has a hidden button in the top-right corner to access the context menu. In that menu, click Edit to open a more detailed view with the script that tells how exactly the data is queried.","s":"SQL scripts behind Grafana charts","u":"/zh/docs/v1.0/Troubleshooting/Dashboard","h":"#sql-scripts-behind-grafana-charts","p":16715},{"i":16723,"t":"Following Entity-Relationship diagrams below represent how the data is mapped and used for each of the 4 DORA metrics. They are based on the SQL queries for each of the charts. Legend: Blue box: user data source, be it deployments, pull requests from the source code, or issues White box: a table or entity used by DevLake Connections: lines that tell how the tables are mapped, also specify which fields are used. The project_mapping is responsible for mapping deployments, pull requests from the source code, or issues. To do so, it must be filtered using either table = 'cicd_scopes', table = 'repos', or table = 'boards' when connecting to another table.","s":"How issues and deployments are associated to projects","u":"/zh/docs/v1.0/Troubleshooting/Dashboard","h":"#how-issues-and-deployments-are-associated-to-projects","p":16715},{"i":16725,"t":"DevLake knows to which project an issue or a deployment belongs only by segregation between the webhooks. I.e. each project should have its own webhook. A webhook used by multiple projects means that all the issues or deployments published by that webhook will be replicated among those projects, as they belong to both of them. WIP","s":"If you use webhooks","u":"/zh/docs/v1.0/Troubleshooting/Dashboard","h":"#if-you-use-webhooks","p":16715},{"i":16727,"t":"Sorry for the inconvenience, please help us improve by creating an issue","s":"None of them solve your problem?","u":"/zh/docs/v1.0/Troubleshooting/Dashboard","h":"#none-of-them-solve-your-problem","p":16715},{"i":16730,"t":"DevLake is designed to collect data by first deleting the existing data and then inserting new data. While this approach ensures that the latest data is always available, it leads to a rapid increase in MySQL disk consumption. This growth is primarily caused by the large size of the binary logs generated after each data collection cycle.","s":"How to manage the quickly increasing MySQL disk consumption?","u":"/zh/docs/v1.0/Troubleshooting/MySqlIssue","h":"#how-to-manage-the-quickly-increasing-mysql-disk-consumption","p":16728},{"i":16732,"t":"Because we want to ensure that the latest data is always available. If we don't delete the existing data, some old data which has been deleted from the previous step will still be available in the DevLake database.","s":"Why dose DevLake choose to delete the existing data and then insert new data?","u":"/zh/docs/v1.0/Troubleshooting/MySqlIssue","h":"#why-dose-devlake-choose-to-delete-the-existing-data-and-then-insert-new-data","p":16728},{"i":16734,"t":"Connect to your MySQL server using the MySQL client or any other database management tool such as PhpMyAdmin, MySQL Workbench, etc. Run the following command to check the current status of your binary log files: SHOW BINARY LOGS; This will display a list of all the binary log files that are currently available on your MySQL server. Determine the last binary log file that you want to keep. This is the file that you want to retain for any future point-in-time recovery or replication purposes. Run the following command to purge all binary logs that are older than the binary log file that you want to retain: PURGE BINARY LOGS BEFORE 'DATE' ; You need to provide the specific date and time up to which you want to purge the binary logs. The date and time should be formatted as a string in the 'YYYY-MM-DD hh:mm:ss' format. For example, if you want to purge all binary logs before March 22, 2023, 15:30:00, you would replace DATE with '2023-03-22 15:30:00', like this: PURGE BINARY LOGS BEFORE '2023-03-22 15:30:00' ; After running the command, MySQL will delete all binary log files that are older than the specified file. You can verify that the purge was successful by running the SHOW BINARY LOGS; command again. Note: Keep in mind that deleting old binary log files can affect point-in-time recovery and replication capabilities, so it's important to only delete files that are no longer needed. Additionally, it's recommended to take a backup before deleting any binary log files in case you need to restore to a point before the binary logs were purged.","s":"How to purge old binary logs?","u":"/zh/docs/v1.0/Troubleshooting/MySqlIssue","h":"#how-to-purge-old-binary-logs","p":16728},{"i":16736,"t":"Connect to your MySQL server using the MySQL client or any other database management tool such as PhpMyAdmin, MySQL Workbench, etc. Run the following command to set the expire_logs_days global variable to the number of days that you want to keep binary logs for: SET GLOBAL expire_logs_days = 1;","s":"How to \"automate\" the purge of old binary logs?","u":"/zh/docs/v1.0/Troubleshooting/MySqlIssue","h":"#how-to-automate-the-purge-of-old-binary-logs","p":16728},{"i":16738,"t":"To skip bin logs, you can set the skip-log-bin configuration option directly in the docker-compose.yaml file using the command option. Here's an example of how to do this: services: mysql: image: mysql:8 volumes: - mysql-storage:/var/lib/mysql restart: always ports: - \"127.0.0.1:3306:3306\" environment: MYSQL_ROOT_PASSWORD: admin MYSQL_DATABASE: lake MYSQL_USER: merico MYSQL_PASSWORD: merico command: --character-set-server=utf8mb4 --collation-server=utf8mb4_bin --skip-log-bin After making the changes, you can restart the MySQL container using the following command: docker-compose restart mysql","s":"How to skip bin logs?","u":"/zh/docs/v1.0/Troubleshooting/MySqlIssue","h":"#how-to-skip-bin-logs","p":16728},{"i":16740,"t":"Skip-verifying the SSL certificate: you can add &tls=skip-verify to DB_URL variable, for example: DB_URL=mysql://merico:merico@localhost:3306/lake?charset=utf8mb4&parseTime=True&loc=UTC&tls=skip-verify Verify the SSL certificate: you can add &tls=custom&ca-cert=/path/to/your/ca-certificate.crt to DB_URL variable, for example: DB_URL=mysql://merico:merico@lake.mysql.database.azure.com:3306/lake?charset=utf8mb4&parseTime=True&tls=custom&ca-cert=/path/to/your/DigiCertGlobalRootCA.crt.pem Note: in Python plugins, it will fallback to the skip-verify policy.","s":"How to connect mysql server which requires SSL?","u":"/zh/docs/v1.0/Troubleshooting/MySqlIssue","h":"#how-to-connect-mysql-server-which-requires-ssl","p":16728},{"i":16742,"t":"When users have large repositories with a substantial number of commit files to collect, it can lead to the following issues: 1. The time to collect Git data is too long 2. Errors occurred while writing data to the DevLake database To address this, you can bypass this step by setting the SKIP_COMMIT_FILES=true in the .env file.","s":"How to skip \"subtask collectGitCommits ended unexpectedly, deadlock found when trying to get lock\"?","u":"/zh/docs/v1.0/Troubleshooting/MySqlIssue","h":"#how-to-skip-subtask-collectgitcommits-ended-unexpectedly-deadlock-found-when-trying-to-get-lock","p":16728}],"index":{"version":"2.3.9","fields":["t"],"fieldVectors":[["t/15124",[0,8.121,1,5.83,2,8.847,3,4.966,4,5.126,5,7.642,6,6.999,7,8.121,8,3.54,9,2.112,10,6.221,11,3.473,12,2.559,13,5.619]],["t/15126",[3,4.052,4,3.738,8,4.276,9,2.093,14,6.452,15,6.452,16,5.104,17,3.642,18,2.152,19,4.252,20,1.605,21,4.433,22,5.573,23,3.597,24,4.098,25,5.573,26,3.442,27,3.792,28,4.172,29,2.058,30,5.475,31,3.058,32,3.597,33,1.756,34,6.452,35,6.452,36,4.172,37,4.433,38,3.553,39,2.724,40,8.048,41,3.689,42,5.104,43,6.452,44,6.452,45,3.903,46,4.652]],["t/15128",[3,3.527,4,4.423,9,1.822,20,1.715,26,2.996,47,3.471,48,1.369,49,7.006,50,6.038,51,2.559,52,7.633,53,4.848,54,3.976,55,3.587,56,5.367,57,2.113,58,6.593,59,2.924,60,5.657,61,2.077,62,4.549,63,2.759,64,3.714,65,4.848,66,2.906,67,2.978,68,6.284]],["t/15130",[3,4.714,6,7.439,8,1.468,9,1.381,11,1.441,12,1.062,13,4.55,17,2.071,18,1.224,20,0.551,30,2.291,69,1.571,70,3.67,71,3.67,72,3.67,73,3.67,74,3.67,75,4.534,76,2.156,77,1.012,78,2.291,79,2.126,80,2.72,81,1.63,82,3.368,83,3.67,84,3.17,85,2.373,86,4.422,87,7.165,88,1.754,89,3.368,90,2.646,91,1.507,92,3.67,93,3.67,94,2.804,95,3.67,96,2.098,97,3.67,98,2.72,99,2.903,100,3.021,101,3.67,102,3.67,103,3.67,104,1.854,105,1.724,106,1.696,107,1.975,108,2.804,109,1.63,110,2.521,111,3.67,112,1.932,113,3.368,114,3.67,115,1.283,116,1.786,117,3.368,118,3.368,119,3.67,120,3.67,121,3.67,122,3.67,123,3.67,124,7.786,125,1.874,126,1.682,127,3.67,128,3.67,129,3.67,130,7.165,131,5.787,132,3.67,133,8.631,134,7.165,135,3.67,136,3.67,137,3.67,138,3.67,139,3.67,140,3.67,141,3.67,142,3.67,143,5.787,144,3.67,145,3.17,146,3.67,147,3.67,148,3.67,149,3.67,150,3.67,151,1.892,152,3.67,153,3.67,154,3.67,155,3.67,156,3.67,157,3.67,158,3.67,159,3.67,160,3.67,161,3.67,162,3.67,163,3.67,164,1.231]],["t/15132",[11,3.574,18,2.276,20,1.368,27,2.597,55,3.205,56,4.796,59,2.613,62,4.065,63,2.465,65,4.332,109,4.044,164,3.435,165,5.213,166,6.261,167,3.757,168,4.918,169,4.918,170,6.956,171,2.712,172,2.145,173,2.629,174,3.078,175,4.796,176,3.952,177,6.261,178,3.03,179,5.616,180,2.566,181,5.396,182,4.686,183,5.892,184,3.9,185,5.056,186,5.892,187,6.261,188,3.414,189,6.821]],["t/15134",[3,3.344,9,2.259,10,5.087,13,4.595,17,4.084,18,2.414,20,1.087,26,2.84,75,4.033,151,3.73,172,2.275,190,6.641,191,5.724,192,7.236,193,5.724,194,6.25,195,4.865,196,6.25,197,4.865,198,5.529,199,3.656,200,6.824,201,5.217,202,7.236,203,4.679,204,4.865,205,5.724,206,6.641,207,7.236,208,7.236,209,7.236,210,7.236,211,7.236,212,7.236]],["t/15137",[3,3.661,9,1.891,19,6.612,20,1.507,21,5.443,27,3.017,48,1.421,65,5.032,171,3.15,172,2.491,173,3.053,174,3.575,178,3.52,213,3.603,214,4.003,215,6.843,216,6.843,217,6.523,218,7.923,219,7.272,220,4.591,221,2.399,222,4.363,223,3.254]],["t/15140",[32,3.625,33,1.769,81,2.889,105,3.055,125,2.662,224,2.286,225,6.502,226,4.969,227,5.968,228,5.968,229,6.051,230,6.502,231,4.372,232,8.814,233,2.207,234,5.616,235,4.969,236,6.502,237,2.337,238,8.09,239,6.502,240,8.814,241,3.67,242,6.972,243,2.889,244,6.502,245,5.616,246,6.502,247,6.502,248,6.502,249,6.502,250,4.13,251,1.669,252,3.499,253,2.506,254,3.718]],["t/15142",[20,0.792,39,3.212,54,2.745,62,5.321,64,2.564,105,2.476,125,2.677,224,3.646,226,4.027,251,1.352,255,3.575,256,8.05,257,6.264,258,4.75,259,4.552,260,4.339,261,3.237,262,5.321,263,2.618,264,3.212,265,5.814,266,5.014,267,4.75,268,4.7,269,2.774,270,5.27,271,4.339,272,5.486,273,1.96,274,5.27,275,4.027,276,2.836,277,2.938,278,5.27,279,5.27,280,3.8,281,4.837,282,6.983,283,6.983,284,2.179,285,5.27,286,1.472,287,3.237,288,2.974,289,4.837,290,4.169]],["t/15145",[220,5.126,251,2.271,265,6.761,267,5.524,268,4.657,277,4.932,291,8.847,292,4.608,293,6.999,294,8.847,295,6.761,296,4.305,297,4.229,298,6.999,299,3.684]],["t/15147",[20,0.703,48,0.839,54,2.437,108,3.575,125,1.92,172,1.471,178,2.078,224,3.248,226,3.575,227,4.294,229,6.654,237,1.681,245,7.978,255,3.276,256,3.852,267,2.921,268,2.463,273,1.74,277,2.608,280,8.271,281,7.65,286,1.306,295,3.575,300,2.83,301,4.679,302,3.146,303,4.679,304,4.679,305,4.679,306,4.679,307,3.701,308,3.214,309,11.269,310,3.373,311,4.679,312,4.679,313,4.679,314,2.063,315,1.73,316,2.047,317,3.29,318,2.874,319,2.608,320,2.518,321,1.935,322,4.679,323,2.49,324,4.679,325,3.575,326,2.921,327,4.679,328,4.679,329,4.679,330,4.679,331,4.679,332,4.679,333,2.128,334,4.679,335,4.679,336,4.679]],["t/15149",[20,0.616,28,2.653,39,2.665,69,1.757,77,1.131,125,2.6,199,4.365,213,2.871,251,2.217,264,1.732,265,3.135,293,3.246,296,1.997,314,3.392,337,4.103,338,3.378,339,2.759,340,4.585,341,1.26,342,3.041,343,4.103,344,6.313,345,4.245,346,4.103,347,6.313,348,6.313,349,4.804,350,6.313,351,9.848,352,4.103,353,4.103,354,2.703,355,6.313,356,5.794,357,4.103,358,8.639,359,4.103,360,6.313,361,5.794,362,4.103,363,8.639,364,6.313,365,4.103,366,6.313,367,6.313,368,5.794,369,6.313,370,3.221,371,4.103,372,4.103,373,2.759,374,3.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