5b6727f319
In the majority of cases, this improves performance. There's only one case I'm aware of where this may be a net negative: for time_floor(__time, <period>) where there are many repeated __time values. In nonvectorized processing, SingleLongInputCachingExpressionColumnValueSelector implements an optimization to avoid computing the time_floor function on every row. There is no such optimization in vectorized processing. IMO, we shouldn't mention this in the docs. Rationale: It's too fiddly of a thing: it's not guaranteed that nonvectorized processing will be faster due to the optimization, because it would have to overcome the inherent speed advantage of vectorization. So it'd always require testing to determine the best setting for a specific dataset. It would be bad if users disabled vectorization thinking it would speed up their queries, and it actually slowed them down. And even if users do their own testing, at some point in the future we'll implement the optimization for vectorized processing too, and it's likely that users that explicitly disabled vectorization will continue to have it disabled. I'd like to avoid this outcome by encouraging all users to enable vectorization at all times. Really advanced users would be following development activity anyway, and can read this issue |
||
|---|---|---|
| .github | ||
| .idea | ||
| benchmarks | ||
| cloud | ||
| codestyle | ||
| core | ||
| dev | ||
| distribution | ||
| docs | ||
| examples | ||
| extendedset | ||
| extensions-contrib | ||
| extensions-core | ||
| helm/druid | ||
| hll | ||
| hooks | ||
| indexing-hadoop | ||
| indexing-service | ||
| integration-tests | ||
| 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 | ||
| 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.
For instructions on building Druid from source, see docs/development/build.md
Contributing
Please follow the community guidelines for contributing.
For instructions on setting up IntelliJ dev/intellij-setup.md