ed47a1e1a9
Code changes: - In the lookup-based extractionFns, inherit injective property from the lookup itself if not specified. Doc changes: - Add a "Query execution" section to the lookups doc explaining how injective lookups and their optimizations work. - Remove scary warnings against using registeredLookup extractionFns. They are necessary and important since they work with filters and function cascades -- two things that the dimension specs do not do. They deserve to be first class citizens. - Move the "registeredLookup" fn above the "lookup" fn. It's probably more commonly used, so the docs read better this way.
Druid
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments.
Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
Druid can load both streaming and batch data and integrates with Samza, Kafka, Storm, Spark, and Hadoop.
License
More Information
More information about Druid can be found on http://www.druid.io.
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/content in this repository and submit a pull request.
Getting Started
You can get started with Druid with our quickstart.
Reporting Issues
If you find any bugs, please file a GitHub issue.
Community
Community support is available on the druid-user mailing list(druid-user@googlegroups.com).
Development discussions occur on the druid-development list(druid-development@googlegroups.com).
We also have a couple people hanging out on IRC in #druid-dev on
irc.freenode.net.
Contributing
Please follow the guidelines listed here.