Apache Druid: a high performance real-time analytics database.
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Adding task slot count metrics to Druid Overlord (#10379)
* Adding more worker metrics to Druid Overlord

* Changing the nomenclature from worker to peon as that represents the metrics that we want to monitor better

* Few more instance of worker usage replaced with peon

* Modifying the peon idle count logic to only use eligible workers available capacity

* Changing the naming to task slot count instead of peon

* Adding some unit test coverage for the new test runner apis

* Addressing Review Comments

* Modifying the TaskSlotCountStatsProvider apis so that overlords which are not leader do not emit these metrics

* Fixing the spelling issue in the docs

* Setting the annotation Nullable on the TaskSlotCountStatsProvider methods
2020-09-28 23:50:38 -07:00
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cloud bump version to 0.20.0-SNAPSHOT (#10124) 2020-07-06 15:08:32 -07:00
codestyle Add "offset" parameter to the Scan query. (#10233) 2020-08-13 14:56:24 -07:00
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docs Adding task slot count metrics to Druid Overlord (#10379) 2020-09-28 23:50:38 -07:00
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extensions-contrib Adding task slot count metrics to Druid Overlord (#10379) 2020-09-28 23:50:38 -07:00
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indexing-hadoop Store hash partition function in dataSegment and allow segment pruning only when hash partition function is provided (#10288) 2020-09-24 16:32:56 -07:00
indexing-service Adding task slot count metrics to Druid Overlord (#10379) 2020-09-28 23:50:38 -07:00
integration-tests Store hash partition function in dataSegment and allow segment pruning only when hash partition function is provided (#10288) 2020-09-24 16:32:56 -07:00
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sql add vectorizeVirtualColumns query context parameter (#10432) 2020-09-28 18:48:34 -07:00
web-console Web console: Display compaction status (#10438) 2020-09-28 22:19:28 -07:00
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README.md

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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.

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

data loader Kafka

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

management

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

query view combo

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 #druid channel in the Apache Slack team. Please use this invitation link to join the ASF Slack, and once joined, go into the #druid channel.

Building from source

Please note that JDK 8 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

License

Apache License, Version 2.0