caa00aa086
* json_value adjustments changes: * native json_value expression now has optional 3rd argument to specify type, which will cast all values to the specified type * rework how JSON_VALUE is wired up in SQL. Now we are using a custom convertlet to translate JSON_VALUE(... RETURNING type) into dedicated JSON_VALUE_BIGINT, JSON_VALUE_DOUBLE, JSON_VALUE_VARCHAR, JSON_VALUE_ANY instead of using the calcite StandardConvertletTable that wraps JSON_VALUE_ANY in a CAST, so that we preserve the typing of JSON_VALUE to pass down to the native expression as the 3rd argument * fix json_value_any to be usable by humans too, coverage * fix bug * checkstyle * checkstyle * review stuff * validate that options to json_value are the supported options rather than ignore them * remove more legacy undocumented functions |
||
|---|---|---|
| .github | ||
| .idea | ||
| benchmarks | ||
| cloud | ||
| codestyle | ||
| core | ||
| dev | ||
| distribution | ||
| docs | ||
| examples | ||
| extendedset | ||
| extensions-contrib | ||
| extensions-core | ||
| hll | ||
| hooks | ||
| indexing-hadoop | ||
| indexing-service | ||
| integration-tests | ||
| integration-tests-ex | ||
| licenses | ||
| processing | ||
| publications | ||
| server | ||
| services | ||
| sql | ||
| web-console | ||
| website | ||
| .asf.yaml | ||
| .backportrc.json | ||
| .codecov.yml | ||
| .dockerignore | ||
| .gitignore | ||
| .lgtm.yml | ||
| .travis.yml | ||
| CONTRIBUTING.md | ||
| LABELS | ||
| LICENSE | ||
| NOTICE | ||
| README.md | ||
| README.template | ||
| check_test_suite.py | ||
| check_test_suite_test.py | ||
| it.sh | ||
| licenses.yaml | ||
| owasp-dependency-check-suppressions.xml | ||
| pom.xml | ||
| upload.sh | ||
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