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| <h1>Frequently Asked Questions</h1> |
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| <h3 id="is-druid-a-data-warehouse-when-should-i-use-druid-over-redshift-bigquery-snowflake">Is Druid a data warehouse? When should I use Druid over Redshift/BigQuery/Snowflake?</h3> |
| |
| <p>Apache Druid (incubating) is a new type of database to power real-time analytic workloads for |
| event-driven data, and isn’t a traditional data warehouse. Although Druid |
| incorporates architecture ideas from data warehouses such as column-oriented |
| storage, Druid also incorporates designs from search systems and timeseries |
| databases. Druid's architecture is designed to handle many use cases that |
| traditional data warehouses cannot.</p> |
| |
| <p>Druid offers the following advantages over traditional data warehouses:</p> |
| |
| <ul> |
| <li>Low latency streaming ingest, and direct integration with messages buses such as |
| Apache Kafka.</li> |
| <li>Time-based partitioning, which enables performant time-based |
| queries.</li> |
| <li>Fast search and filter, for fast ad-hoc slice and dice.</li> |
| <li>Minimal schema design, and native support for semi-structured and nested data.</li> |
| </ul> |
| |
| <p>Consider using Druid over a data warehouse if you have streaming data, and |
| require low-latency ingest as well as low-latency queries. Also consider Druid |
| if you need ad-hoc analytics. Druid is great for slice and dice and drill |
| downs. Druid is also often used over a data warehouse to power interactive |
| applications, where support for high concurrency queries is required.</p> |
| |
| <h3 id="is-druid-a-sql-on-hadoop-solution-when-should-i-use-druid-over-presto-hive">Is Druid a SQL-on-Hadoop solution? When should I use Druid over Presto/Hive?</h3> |
| |
| <p>Druid supports SQL and can load data from Hadoop, but is not a SQL-on-Hadoop |
| system. Modern SQL-on-Hadoop solutions are used for the same use cases as data |
| warehouses, except they are designed for architectures where compute and |
| storage are separated systems, and data is loaded from storage into the compute |
| layer as needed by queries.</p> |
| |
| <p>The previous section on Druid vs data warehouses also applies to Druid versus |
| SQL-on-Hadoop solutions.</p> |
| |
| <h3 id="is-druid-a-log-aggregation-log-search-system-when-should-i-use-druid-over-elastic-splunk">Is Druid a log aggregation/log search system? When should I use Druid over Elastic/Splunk?</h3> |
| |
| <p>Druid uses inverted indexes (in particular, compressed bitmaps) for fast searching and filtering, but it is not generally considered a search system. |
| While Druid contains many features commonly found in search systems, such as the ability to stream in structured and semi-structured data and the ability to search and filter the data, Druid isn’t commonly used to ingest text logs and run full text search queries over the text logs. |
| However, Druid is often used to ingest and analyze semi-structured data such as JSON.</p> |
| |
| <p>Druid at its core is an analytics engine and as such, it can support numerical aggregations, groupBys (including multi-dimensional groupBys), and other analytic workloads faster and more efficiently than search systems.</p> |
| |
| <h3 id="is-druid-a-timeseries-database-when-should-i-use-druid-over-influxdb-opentsdb-prometheus">Is Druid a timeseries database? When should I use Druid over InfluxDB/OpenTSDB/Prometheus?</h3> |
| |
| <p>Druid does share some characteristics with timeseries databases, but also |
| combines ideas from analytic databases and search systems. Like in timeseries |
| databases, Druid is optimized for data where a timestamp is present. Druid |
| partitions data by time, and queries that include a time filter will be |
| significantly faster than those that do not. Aggregating metrics and filtering |
| on dimensions (which are roughly equivalent to TSDBs' tags) are also very fast when a |
| time filter is present. However, because Druid incorporates many architectural designs |
| from analytics databases and search systems, it can significantly |
| outperformance TSDBs when grouping, searching, and filtering on tags that are |
| not time, or when computing complex metrics such as histograms and quantiles.</p> |
| |
| <h3 id="how-is-druid-deployed">How is Druid deployed?</h3> |
| |
| <p>Druid can be deployed on commodity hardware in any *NIX based environment. |
| A Druid cluster consists of several different processes, each designed to do a small set of things very well (ingestion, querying, coordination, etc). |
| Many of these processes can be co-located and deployed together on the same hardware as described <a href="/docs/latest/tutorials/quickstart">here</a>.</p> |
| |
| <p>Druid was initially created in the cloud, and runs well in AWS, GCP, Azure, and other cloud environments.</p> |
| |
| <h3 id="where-does-druid-fit-in-my-existing-hadoop-based-data-stack">Where does Druid fit in my existing Hadoop-based data stack?</h3> |
| |
| <p>Druid typically connects to a source of raw data such as a message bus such as Apache Kafka, or a filesystem such as HDFS. |
| Druid ingests an optimized, column-oriented, indexed copy of your data and serves analytics workloads on top of it.</p> |
| |
| <p>A common streaming data oriented setup involving Druid looks like this: |
| Raw data → Kafka → Stream processor (optional, typically for ETL) → Kafka (optional) → Druid → Application/user</p> |
| |
| <p>A common batch/static file oriented setup involving Druid looks like this: |
| Raw data → Kafka (optional) → HDFS → ETL process (optional) → Druid → Application/user</p> |
| |
| <p>The same Druid cluster can serve both the streaming and batch path.</p> |
| |
| <h3 id="is-druid-in-memory">Is Druid in-memory?</h3> |
| |
| <p>The earliest iterations of Druid didn’t allow for data to be paged in from |
| and out to disk, so it was often called an “in-memory” database. As Druid |
| evolved, this limitation was removed. To provide a balance between hardware |
| cost and query performance, Druid leverages memory-mapping to page data between |
| disk and memory and extend the amount of data a single node can load up to the |
| size of its disks.</p> |
| |
| <p>Individual Historicals can be configured with the maximum amount of data |
| they should be given. Coupled with the Coordinator’s ability to assign data to |
| different “tiers” based on different query requirements, Druid is essentially a |
| system that can be configured across a wide spectrum of performance |
| requirements. All data can be in memory and processed, or data can be heavily |
| over-committed compared to the amount of memory available. Druid can also |
| support complex configurations, such as configuring the most recent month of |
| data in memory, while everything else is over-committed.</p> |
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