* SQL: Add resolution parameter to quantile agg, rename to APPROX_QUANTILE.
* Fix bug with re-use of filtered approximate histogram aggregators.
Also add APPROX_QUANTILE tests for filtering and running on complex columns.
Includes some slight refactoring to allow tests to make DruidTables that
include complex columns.
* Remove unused import
This simplifies DruidSemiJoin, which no longer needs to add aggregation back
in. It also allows some more kinds of queries to plan properly, like the one
added in "testTopNFilterJoin".
* SQL: Ditch CalciteConnection layer and add DruidMeta, extension aggregators.
Switched from CalciteConnection to Planner, bringing benefits:
- CalciteConnection's JDBC interface no longer sits between the SQL server
(HTTP/Avatica) and Druid's query layer. Instead, the SQL servers can use
Druid Sequence objects directly, reducing overhead in the query return path.
- Implemented our own Planner-based Avatica Meta, letting us control
connection timeouts and connection / statement limits. The previous
CalciteConnection-based implementation didn't have any limits or timeouts.
- The Planner interface lets us override the operator table, opening up
SQL language extensions. This patch includes two: APPROX_COUNT_DISTINCT
in core, and a QUANTILE aggregator in the druid-histogram extension.
Also:
- Added INFORMATION_SCHEMA metadata schema.
- Added tests for Unicode literals and escapes.
* Verify statement is actually open before closing it.
* More detailed INFORMATION_SCHEMA docs.
* SQL support for nested groupBys.
Allows, for example, doing exact count distinct by writing:
SELECT COUNT(*) FROM (SELECT DISTINCT col FROM druid.foo)
Contrast with approximate count distinct, which is:
SELECT COUNT(DISTINCT col) FROM druid.foo
* Add deeply-nested groupBy docs, tests, and maxQueryCount config.
* Extract magic constants into statics.
* Rework rules to put preconditions in the "matches" method.
Jonathan Wei
Gian Merlino
DaimonPl
Roman Leventov