75d9e5e7a7
* * Add DimensionSelector.idLookup() and nameLookupPossibleInAdvance() to allow better inspection of features DimensionSelectors supports, and safer code working with DimensionSelectors in BaseTopNAlgorithm, BaseFilteredDimensionSpec, DimensionSelectorUtils; * Add PredicateFilteringDimensionSelector, to make BaseFilteredDimensionSpec to be able to decorate DimensionSelectors with unknown cardinality; * Add DimensionSelector.makeValueMatcher() (two kinds) for DimensionSelector-side specifics-aware optimization of ValueMatchers; * Optimize getRow() in BaseFilteredDimensionSpec's DimensionSelector, StringDimensionIndexer's DimensionSelector and SingleScanTimeDimSelector; * Use two static singletons, TrueValueMatcher and FalseValueMatcher, instead of BooleanValueMatcher; * Add NullStringObjectColumnSelector singleton and use it in MapVirtualColumn * Rename DimensionSelectorUtils.makeNonDictionaryEncodedIndexedIntsBasedValueMatcher to makeNonDictionaryEncodedRowBasedValueMatcher * Make ArrayBasedIndexedInts constructor private, replace it's usages with of() static factory method * Cache baseIdLookup in ForwardingFilteredDimensionSelector * Fix a bug in DimensionSelectorUtils.makeRowBasedValueMatcher(selector, predicate, matchNull) * Employ precomputed BitSet optimization in DimensionSelector.makeValueMatcher(value, matchNull) when lookupId() is not available, but cardinality is known and lookupName() is available * Doc fixes * Addressed comments * Fix * Fix * Adjust javadoc of DimensionSelector.nameLookupPossibleInAdvance() for SingleScanTimeDimSelector * throw UnsupportedOperationException instead of IAE in BaseTopNAlgorithm |
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|---|---|---|
| api | ||
| aws-common | ||
| benchmarks | ||
| bytebuffer-collections | ||
| codestyle | ||
| common | ||
| distribution | ||
| docs | ||
| examples | ||
| extendedset | ||
| extensions-contrib | ||
| extensions-core | ||
| indexing-hadoop | ||
| indexing-service | ||
| integration-tests | ||
| java-util | ||
| processing | ||
| publications | ||
| server | ||
| services | ||
| sql | ||
| .gitignore | ||
| .travis.yml | ||
| CONTRIBUTING.md | ||
| DruidCorporateCLA.pdf | ||
| DruidIndividualCLA.pdf | ||
| LICENSE | ||
| NOTICE | ||
| README.md | ||
| druid_intellij_formatting.xml | ||
| eclipse.importorder | ||
| eclipse_formatting.xml | ||
| pom.xml | ||
| upload.sh | ||
README.md
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.