Flink’s Table & SQL API makes it possible to work with queries written in the SQL language, but these queries need to be embedded within a table program that is written in either Java or Scala. Moreover, these programs need to be packaged with a build tool before being submitted to a cluster. This more or less limits the usage of Flink to Java/Scala programmers.
The SQL Client aims to provide an easy way of writing, debugging, and submitting table programs to a Flink cluster without a single line of Java or Scala code. The SQL Client CLI allows for retrieving and visualizing real-time results from the running distributed application on the command line.
Attention The SQL Client is in an early developement phase. Even though the application is not production-ready yet, it can be a quite useful tool for prototyping and playing around with Flink SQL. In the future, the community plans to extend its functionality by providing a REST-based SQL Client Gateway.
This section describes how to setup and run your first Flink SQL program from the command-line.
The SQL Client is bundled in the regular Flink distribution and thus runnable out-of-the-box. It requires only a running Flink cluster where table programs can be executed. For more information about setting up a Flink cluster see the [Cluster & Deployment]({{ site.baseurl }}/ops/deployment/cluster_setup.html) part. If you simply want to try out the SQL Client, you can also start a local cluster with one worker using the following command:
{% highlight bash %} ./bin/start-cluster.sh {% endhighlight %}
The SQL Client scripts are also located in the binary directory of Flink. In the future, a user will have two possiblities of starting the SQL Client CLI either by starting an embedded standalone process or by connecting to a remote SQL Client Gateway. At the moment only the embedded mode is supported. You can start the CLI by calling:
{% highlight bash %} ./bin/sql-client.sh embedded {% endhighlight %}
By default, the SQL Client will read its configuration from the environment file located in ./conf/sql-client-defaults.yaml. See the configuration part for more information about the structure of environment files.
Once the CLI has been started, you can use the HELP command to list all available SQL statements. For validating your setup and cluster connection, you can enter your first SQL query and press the Enter key to execute it:
{% highlight sql %} SELECT ‘Hello World’; {% endhighlight %}
This query requires no table source and produces a single row result. The CLI will retrieve results from the cluster and visualize them. You can close the result view by pressing the Q key.
The CLI supports two execution modes (batch or streaming) for queries. Which you can specify in the configuration file
In streaming execution mode, the CLI supports two modes for maintaining and visualizing results.
The table mode materializes results in memory and visualizes them in a regular, paginated table representation. It can be enabled by executing the following command in the CLI:
{% highlight text %} SET execution.result-mode=table; {% endhighlight %}
The changelog mode does not materialize results and visualizes the result stream that is produced by a continuous query consisting of insertions (+) and retractions (-).
{% highlight text %} SET execution.result-mode=changelog; {% endhighlight %}
You can use the following query to see both result modes in action:
{% highlight sql %} SELECT name, COUNT(*) AS cnt FROM (VALUES (‘Bob’), (‘Alice’), (‘Greg’), (‘Bob’)) AS NameTable(name) GROUP BY name; {% endhighlight %}
This query performs a bounded word count example.
In changelog mode, the visualized changelog should be similar to:
{% highlight text %}
In table mode, the visualized result table is continuously updated until the table program ends with:
{% highlight text %} Bob, 2 Alice, 1 Greg, 1 {% endhighlight %}
The configuration section explains how to read from table sources and configure other table program properties.
Currently Flink has a preliminary supports for SQL language(include DDL, Query and DML features). See [SQL]({{ site.baseurl }}/dev/table/sql.html)
SQL CLI provides a CREATE TABLE command to replace the table definitions in YAML configuation file. SQL users pass a SQL DDL description text to SQL CLI, it will be passed into table objects and registered to the tableEnvironment, then following SQL Querys can access these tables directly (we would reuse the tableEnvironment in a session).
Attention We strongly suggest everyone use DDL syntax to define a Table, and use DML to query or update the Table.
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The SQL Client can be started with the following optional CLI commands. They are discussed in detail in the subsequent paragraphs.
{% highlight text %} ./bin/sql-client.sh embedded --help
Mode “embedded” submits Flink jobs from the local machine.
Syntax: embedded [OPTIONS] “embedded” mode options: -d,--defaults The environment properties with which every new session is initialized. Properties might be overwritten by session properties. -e,--environment The environment properties to be imported into the session. It might overwrite default environment properties. -h,--help Show the help message with descriptions of all options. -j,--jar A JAR file to be imported into the session. The file might contain user-defined classes needed for the execution of statements such as functions, table sources, or sinks. Can be used multiple times. -l,--library A JAR file directory with which every new session is initialized. The files might contain user-defined classes needed for the execution of statements such as functions, table sources, or sinks. Can be used multiple times. -s,--session The identifier for a session. ‘default’ is the default identifier. {% endhighlight %}
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A SQL query needs a configuration environment in which it is executed. The so-called environment files define available table sources and sinks, catalogs, user-defined functions, and other properties required for execution and deployment.
Every environment file is a regular YAML file. An example of such a file is presented below.
{% highlight yaml %}
tables:
execution: type: streaming time-characteristic: event-time parallelism: 1 max-parallelism: 16 min-idle-state-retention: 0 max-idle-state-retention: 0 result-mode: table
deployment: response-timeout: 5000
catalogs:
This configuration:
MyTableName that reads from a CSV file,table result mode.HiveCatalog (type: hive) named with their own default databasesDepending on the use case, a configuration can be split into multiple files. Therefore, environment files can be created for general purposes (defaults environment file using --defaults) as well as on a per-session basis (session environment file using --environment). Every CLI session is initialized with the default properties followed by the session properties. For example, the defaults environment file could specify all table sources that should be available for querying in every session whereas the session environment file only declares a specific state retention time and parallelism. Both default and session environment files can be passed when starting the CLI application. If no default environment file has been specified, the SQL Client searches for ./conf/sql-client-defaults.yaml in Flink's configuration directory.
Attention Properties that have been set within a CLI session (e.g. using the SET command) have highest precedence:
{% highlight text %} CLI commands > session environment file > defaults environment file {% endhighlight %}
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The SQL Client does not require to setup a Java project using Maven or SBT. Instead, you can pass the dependencies as regular JAR files that get submitted to the cluster. You can either specify each JAR file separately (using --jar) or define entire library directories (using --library). For connectors to external systems (such as Apache Kafka) and corresponding data formats (such as JSON), Flink provides ready-to-use JAR bundles. These JAR files are suffixed with sql-jar and can be downloaded for each release from the Maven central repository.
{% if site.is_stable %}
| Name | Version | Download |
|---|---|---|
| Filesystem | Built-in | |
| Apache Kafka | 0.11 | Download |
| Name | Download |
|---|---|
| CSV | Built-in |
| JSON | Download |
{% endif %}
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Sources are defined using a set of YAML properties. Similar to a SQL CREATE TABLE statement you define the name of the table, the final schema of the table, connector, and a data format if necessary. Additionally, you have to specify its type (source, sink, or both).
{% highlight yaml %} name: MyTable # required: string representing the table name type: source # required: currently only ‘source’ is supported schema: ... # required: final table schema connector: ... # required: connector configuration format: ... # optional: format that depends on the connector {% endhighlight %}
Attention Not every combination of connector and format is supported. Internally, your YAML file is translated into a set of string-based properties by which the SQL Client tries to resolve a matching table source. If a table source can be resolved also depends on the JAR files available in the classpath.
The following example shows an environment file that defines a table source reading JSON data from Apache Kafka. All properties are explained in the following subsections.
{% highlight yaml %} tables:
The resulting schema of the TaxiRide table contains most of the fields of the JSON schema. Furthermore, it adds a rowtime attribute rowTime and processing-time attribute procTime. Both connector and format allow to define a property version (which is currently version 1) for future backwards compatibility.
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The schema allows for describing the final appearance of a table. It specifies the final name, final type, and the origin of a field. The origin of a field might be important if the name of the field should differ from the input format. For instance, a field name&field should reference nameField from an Avro format.
{% highlight yaml %} schema:
For each field, the following properties can be used:
{% highlight yaml %} name: ... # required: final name of the field type: ... # required: final type of the field represented as a type string proctime: ... # optional: boolean flag whether this field should be a processing-time attribute rowtime: ... # optional: wether this field should be a event-time attribute from: ... # optional: original field in the input that is referenced/aliased by this field {% endhighlight %}
The following type strings are supported for being defined in an environment file:
{% highlight yaml %} VARCHAR BOOLEAN TINYINT SMALLINT INT BIGINT FLOAT DOUBLE DECIMAL DATE TIME TIMESTAMP ROW(fieldtype, ...) # unnamed row; e.g. ROW(VARCHAR, INT) that is mapped to Flink‘s RowTypeInfo # with indexed fields names f0, f1, ... ROW(fieldname fieldtype, ...) # named row; e.g., ROW(myField VARCHAR, myOtherField INT) that # is mapped to Flink’s RowTypeInfo POJO(class) # e.g., POJO(org.mycompany.MyPojoClass) that is mapped to Flink‘s PojoTypeInfo ANY(class) # e.g., ANY(org.mycompany.MyClass) that is mapped to Flink’s GenericTypeInfo ANY(class, serialized) # used for type information that is not supported by Flink's Table & SQL API {% endhighlight %}
In order to control the event-time behavior for table sources, the SQL Client provides predefined timestamp extractors and watermark strategies. For more information about time handling in Flink and especially event-time, we recommend the general event-time section.
The following timestamp extractors are supported:
{% highlight yaml %}
rowtime: timestamps: type: from-field from: ... # required: original field name in the input
rowtime: timestamps: type: from-source {% endhighlight %}
The following watermark strategies are supported:
{% highlight yaml %}
rowtime: watermarks: type: periodic-ascending
rowtime: watermarks: type: periodic-bounded delay: ... # required: delay in milliseconds
rowtime: watermarks: type: from-source {% endhighlight %}
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Flink provides a set of connectors that can be defined in the environment file.
Attention Currently, connectors can only be used as table sources not sinks.
The filesystem connector allows for reading from a local or distributed filesystem. A filesystem can be defined as:
{% highlight yaml %} connector: type: filesystem path: “file:///path/to/whatever” # required {% endhighlight %}
Currently, only files with CSV format can be read from a filesystem. The filesystem connector is included in Flink and does not require an additional JAR file.
The Kafka connector allows for reading from a Apache Kafka topic. It can be defined as follows:
{% highlight yaml %} connector: type: kafka version: 0.11 # required: valid connector versions are “0.8”, “0.9”, “0.10”, and “0.11” topic: ... # required: topic name from which the table is read startup-mode: ... # optional: valid modes are “earliest-offset”, “latest-offset”, # “group-offsets”, or “specific-offsets” specific-offsets: # optional: used in case of startup mode with specific offsets - partition: 0 offset: 42 - partition: 1 offset: 300 properties: # optional: connector specific properties - key: zookeeper.connect value: localhost:2181 - key: bootstrap.servers value: localhost:9092 - key: group.id value: testGroup {% endhighlight %}
Make sure to download the Kafka SQL JAR file and pass it to the SQL Client.
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Flink provides a set of formats that can be defined in the environment file.
The CSV format allows to read comma-separated rows. Currently, this is only supported for the filesystem connector.
{% highlight yaml %} format: type: csv fields: # required: format fields - name: field1 type: VARCHAR - name: field2 type: TIMESTAMP field-delimiter: “,” # optional: string delimiter “,” by default line-delimiter: “\n” # optional: string delimiter “\n” by default quote-character: ‘"’ # optional: single character for string values, empty by default comment-prefix: ‘#’ # optional: string to indicate comments, empty by default ignore-first-line: false # optional: boolean flag to ignore the first line, by default it is not skipped ignore-parse-errors: true # optional: skip records with parse error instead to fail by default {% endhighlight %}
The CSV format is included in Flink and does not require an additional JAR file.
The JSON format allows to read JSON data that corresponds to a given format schema. The format schema can be defined either as a Flink type string, as a JSON schema, or derived from the desired table schema. A type string enables a more SQL-like definition and mapping to the corresponding SQL data types. The JSON schema allows for more complex and nested structures.
If the format schema is equal to the table schema, the schema can also be automatically derived. This allows for defining schema information only once. The names, types, and field order of the format are determined by the table's schema. Time attributes are ignored. A from definition in the table schema is interpreted as a field renaming in the format.
{% highlight yaml %} format: type: json fail-on-missing-field: true # optional: flag whether to fail if a field is missing or not
schema: “ROW(lon FLOAT, rideTime TIMESTAMP)”
json-schema: > { type: ‘object’, properties: { lon: { type: ‘number’ }, rideTime: { type: ‘string’, format: ‘date-time’ } } }
derive-schema: true {% endhighlight %}
Currently, Flink supports only a subset of the JSON schema specification draft-07. Union types (as well as allOf, anyOf, not) are not supported yet. oneOf and arrays of types are only supported for specifying nullability.
Simple references that link to a common definition in the document are supported as shown in the more complex example below:
{% highlight json %} { “definitions”: { “address”: { “type”: “object”, “properties”: { “street_address”: { “type”: “string” }, “city”: { “type”: “string” }, “state”: { “type”: “string” } }, “required”: [ “street_address”, “city”, “state” ] } }, “type”: “object”, “properties”: { “billing_address”: { “$ref”: “#/definitions/address” }, “shipping_address”: { “$ref”: “#/definitions/address” }, “optional_address”: { “oneOf”: [ { “type”: “null” }, { “$ref”: “#/definitions/address” } ] } } } {% endhighlight %}
Make sure to download the JSON SQL JAR file and pass it to the SQL Client.
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Catalogs can be defined as a set of yaml properties and are automatically registered to the environment upon starting SQL Cli.
{% highlight yaml %} catalogs:
Currently Flink supports two types of catalog - FlinkInMemoryCatalog and HiveCatalog.
For more information about integration with Hive metastore, see [Catalogs]({{ site.baseurl }}/dev/table/catalog.html) and [Hive Compatibility]({{ site.baseurl }}/dev/batch/hive_compatibility.html)
The current SQL Client implementation is in a very early development stage and might change in the future as part of the bigger Flink Improvement Proposal 24 (FLIP-24). Feel free to join the discussion and open issue about bugs and features that you find useful.
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