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author | Sean Zhong <seanzhong@databricks.com> | 2016-08-25 16:36:16 -0700 |
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committer | Yin Huai <yhuai@databricks.com> | 2016-08-25 16:36:16 -0700 |
commit | d96d1515638da20b594f7bfe3cfdb50088f25a04 (patch) | |
tree | 69e7803b4f49d0ed03073795843eb95d8f63529f /sql/catalyst/src/test | |
parent | 9b5a1d1d53bc4412de3cbc86dc819b0c213229a8 (diff) | |
download | spark-d96d1515638da20b594f7bfe3cfdb50088f25a04.tar.gz spark-d96d1515638da20b594f7bfe3cfdb50088f25a04.tar.bz2 spark-d96d1515638da20b594f7bfe3cfdb50088f25a04.zip |
[SPARK-17187][SQL] Supports using arbitrary Java object as internal aggregation buffer object
## What changes were proposed in this pull request?
This PR introduces an abstract class `TypedImperativeAggregate` so that an aggregation function of TypedImperativeAggregate can use **arbitrary** user-defined Java object as intermediate aggregation buffer object.
**This has advantages like:**
1. It now can support larger category of aggregation functions. For example, it will be much easier to implement aggregation function `percentile_approx`, which has a complex aggregation buffer definition.
2. It can be used to avoid doing serialization/de-serialization for every call of `update` or `merge` when converting domain specific aggregation object to internal Spark-Sql storage format.
3. It is easier to integrate with other existing monoid libraries like algebird, and supports more aggregation functions with high performance.
Please see `org.apache.spark.sql.TypedImperativeAggregateSuite.TypedMaxAggregate` to find an example of how to defined a `TypedImperativeAggregate` aggregation function.
Please see Java doc of `TypedImperativeAggregate` and Jira ticket SPARK-17187 for more information.
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes #14753 from clockfly/object_aggregation_buffer_try_2.
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