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State of the Druid
December 6, 2016
0.9.2
• new groupBy engine (2–5x performance boost)
• ability to disable rollup at ingestion time
• ability to filter on longs
• new encoding options for long-typed columns
• performance improvements for HyperUnique (19–30%) and
DataSketches (up to 80%)
• query cache implementation based on Caffeine
• new lookup extension exposing fine grained caching strategies
• support for reading ORC files
• new aggregators for variance and standard deviation
• download at http://druid.io/downloads.html
0.9.3 (so far)
• 100+ commits since 0.9.2
• target release: mid-Q1 2017
• built-in SQL
• grouping over integer columns (9x improvement on 600M
row TPC-H dataset)
• expression language for aggregations: AVG(ln(x)
• “like” filter optimized for prefixes: foo LIKE "bar%"
• faster indexing with less memory
• kafka indexing service improvements
• many more changes expected by release
Built-in SQL
• Apache Calcite based parser and planner
• handled by the broker
• time series: GROUP BY FLOOR(__time TO HOUR)
• metadata: SELECT * FROM metadata.COLUMNS
• explain: EXPLAIN PLAN FOR …
• distinct count with hyperloglog
• jdbc driver

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State of the Druid, Dec 2016

  • 1. State of the Druid December 6, 2016
  • 2. 0.9.2 • new groupBy engine (2–5x performance boost) • ability to disable rollup at ingestion time • ability to filter on longs • new encoding options for long-typed columns • performance improvements for HyperUnique (19–30%) and DataSketches (up to 80%) • query cache implementation based on Caffeine • new lookup extension exposing fine grained caching strategies • support for reading ORC files • new aggregators for variance and standard deviation • download at http://druid.io/downloads.html
  • 3. 0.9.3 (so far) • 100+ commits since 0.9.2 • target release: mid-Q1 2017 • built-in SQL • grouping over integer columns (9x improvement on 600M row TPC-H dataset) • expression language for aggregations: AVG(ln(x) • “like” filter optimized for prefixes: foo LIKE "bar%" • faster indexing with less memory • kafka indexing service improvements • many more changes expected by release
  • 4. Built-in SQL • Apache Calcite based parser and planner • handled by the broker • time series: GROUP BY FLOOR(__time TO HOUR) • metadata: SELECT * FROM metadata.COLUMNS • explain: EXPLAIN PLAN FOR … • distinct count with hyperloglog • jdbc driver