7a3bd89a85
* perf: introduce benchmark for StringDimensionIndexer jdk11 -- Benchmark Mode Cnt Score Error Units StringDimensionIndexerProcessBenchmark.parallelReadWrite avgt 10 30471.552 ± 456.716 us/op StringDimensionIndexerProcessBenchmark.parallelReadWrite:parallelReader avgt 10 18069.863 ± 327.923 us/op StringDimensionIndexerProcessBenchmark.parallelReadWrite:parallelWriter avgt 10 67676.617 ± 2351.311 us/op StringDimensionIndexerProcessBenchmark.soloReader avgt 10 1048.079 ± 1.120 us/op StringDimensionIndexerProcessBenchmark.soloWriter avgt 10 4629.769 ± 29.353 us/op * perf: switch DimensionDictionary to StampedLock jdk11 - Benchmark Mode Cnt Score Error Units StringDimensionIndexerProcessBenchmark.parallelReadWrite avgt 10 37958.372 ± 1685.206 us/op StringDimensionIndexerProcessBenchmark.parallelReadWrite:parallelReader avgt 10 31192.232 ± 2755.365 us/op StringDimensionIndexerProcessBenchmark.parallelReadWrite:parallelWriter avgt 10 58256.791 ± 1998.220 us/op StringDimensionIndexerProcessBenchmark.soloReader avgt 10 1079.440 ± 1.753 us/op StringDimensionIndexerProcessBenchmark.soloWriter avgt 10 4585.690 ± 13.225 us/op * perf: use optimistic locking in DimensionDictionary jdk11 - Benchmark Mode Cnt Score Error Units StringDimensionIndexerProcessBenchmark.parallelReadWrite avgt 10 6212.366 ± 162.684 us/op StringDimensionIndexerProcessBenchmark.parallelReadWrite:parallelReader avgt 10 1807.235 ± 109.339 us/op StringDimensionIndexerProcessBenchmark.parallelReadWrite:parallelWriter avgt 10 19427.759 ± 611.692 us/op StringDimensionIndexerProcessBenchmark.soloReader avgt 10 194.370 ± 1.050 us/op StringDimensionIndexerProcessBenchmark.soloWriter avgt 10 2871.423 ± 14.426 us/op * perf: refactor DimensionDictionary null handling to need less locks jdk11 - Benchmark Mode Cnt Score Error Units StringDimensionIndexerProcessBenchmark.parallelReadWrite avgt 10 6591.619 ± 470.497 us/op StringDimensionIndexerProcessBenchmark.parallelReadWrite:parallelReader avgt 10 1387.338 ± 144.587 us/op StringDimensionIndexerProcessBenchmark.parallelReadWrite:parallelWriter avgt 10 22204.462 ± 1620.806 us/op StringDimensionIndexerProcessBenchmark.soloReader avgt 10 204.911 ± 0.459 us/op StringDimensionIndexerProcessBenchmark.soloWriter avgt 10 2935.376 ± 12.639 us/op * perf: refactor DimensionDictionary add handling to do a little less work jdk11 - Benchmark Mode Cnt Score Error Units StringDimensionIndexerProcessBenchmark.parallelReadWrite avgt 10 2914.859 ± 22.519 us/op StringDimensionIndexerProcessBenchmark.parallelReadWrite:parallelReader avgt 10 508.010 ± 14.675 us/op StringDimensionIndexerProcessBenchmark.parallelReadWrite:parallelWriter avgt 10 10135.408 ± 82.745 us/op StringDimensionIndexerProcessBenchmark.soloReader avgt 10 205.415 ± 0.158 us/op StringDimensionIndexerProcessBenchmark.soloWriter avgt 10 3098.743 ± 23.603 us/op |
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README.md
Website | Twitter | Download | Get Started | Documentation | Community | Build | Contribute | License
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 web 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
See the latest documentation for the documentation for the current official release. If you need information on a previous release, you can browse previous releases documentation.
Make documentation and tutorials updates in /docs
using MarkDown and contribute them using a pull request.
Community
Visit the official project community page to read about getting involved in contributing to Apache Druid, and how we help one another use and operate Druid.
- Druid users can find help in the
druid-user
mailing list on Google Groups, and have more technical conversations in#troubleshooting
on Slack. - Druid development discussions take place in the
druid-dev
mailing list (dev@druid.apache.org). Subscribe by emailing dev-subscribe@druid.apache.org. For live conversations, join the#dev
channel on Slack.
Check out the official community page for details of how to join the community Slack channels.
Find articles written by community members and a calendar of upcoming events on the project site - contribute your own events and articles by submitting a PR in the apache/druid-website-src
repository.
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