6c5a43106a
* SQL: Morph QueryMakerFactory into SqlEngine. Groundwork for introducing an indexing-service-task-based SQL engine under the umbrella of #12262. Also includes some other changes related to improving error behavior. Main changes: 1) Elevate the QueryMakerFactory interface (an extension point that allows customization of how queries are made) into SqlEngine. SQL engines can influence planner behavior through EngineFeatures, and can fully control the mechanics of query execution using QueryMakers. 2) Remove the server-wide QueryMakerFactory choice, in favor of the choice being made by the SQL entrypoint. The indexing-service-task-based SQL engine would be associated with its own entrypoint, like /druid/v2/sql/task. Other changes: 1) Adjust DruidPlanner to try either DRUID or BINDABLE convention based on analysis of the planned rels; never try both. In particular, we no longer try BINDABLE when DRUID fails. This simplifies the logic and improves error messages. 2) Adjust error message "Cannot build plan for query" to omit the SQL query text. Useful because the text can be quite long, which makes it easy to miss the text about the problem. 3) Add a feature to block context parameters used internally by the SQL planner from being supplied by end users. 4) Add a feature to enable adding row signature to the context for Scan queries. This is useful in building the task-based engine. 5) Add saffron.properties file that turns off sets and graphviz dumps in "cannot plan" errors. Significantly reduces log spam on the Broker. * Fixes from CI. * Changes from review. * Can vectorize, now that join-to-filter is on by default. * Checkstyle! And variable renames! * Remove throws from test. |
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README.md
Website | Documentation | Developer Mailing List | User Mailing List | Slack | Twitter | Download
Apache Druid
Druid is a high performance real-time analytics database. Druid's main value add is to reduce time to insight and action.
Druid is designed for workflows where fast queries and ingest really matter. Druid excels at powering UIs, running operational (ad-hoc) queries, or handling high concurrency. Consider Druid as an open source alternative to data warehouses for a variety of use cases. The design documentation explains the key concepts.
Getting started
You can get started with Druid with our local or Docker quickstart.
Druid provides a rich set of APIs (via HTTP and JDBC) for loading, managing, and querying your data. You can also interact with Druid via the built-in console (shown below).
Load data
Load streaming and batch data using a point-and-click wizard to guide you through ingestion setup. Monitor one off tasks and ingestion supervisors.
Manage the cluster
Manage your cluster with ease. Get a view of your datasources, segments, ingestion tasks, and services from one convenient location. All powered by SQL systems tables, allowing you to see the underlying query for each view.
Issue queries
Use the built-in query workbench to prototype DruidSQL and native queries or connect one of the many tools that help you make the most out of Druid.
Documentation
You can find the documentation for the latest Druid release on the project website.
If you would like to contribute documentation, please do so under
/docs in this repository and submit a pull request.
Community
Community support is available on the druid-user mailing list, which is hosted at Google Groups.
Development discussions occur on dev@druid.apache.org, which you can subscribe to by emailing dev-subscribe@druid.apache.org.
Chat with Druid committers and users in real-time on the Apache Druid Slack channel. Please use this invitation link to join and invite others.
Building from source
Please note that JDK 8 or JDK 11 is required to build Druid.
See the latest build guide for instructions on building Apache Druid from source.
Contributing
Please follow the community guidelines for contributing.
For instructions on setting up IntelliJ dev/intellij-setup.md