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author | Sean Zhong <seanzhong@databricks.com> | 2016-09-01 16:31:13 +0800 |
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committer | Wenchen Fan <wenchen@databricks.com> | 2016-09-01 16:31:13 +0800 |
commit | a18c169fd050e71fdb07b153ae0fa5c410d8de27 (patch) | |
tree | be693af0f087329fd457ad0c238ffb2a3a9f8bab /sql/hive/src/test | |
parent | 536fa911c181958d84f14156f7d57ef5fd68df48 (diff) | |
download | spark-a18c169fd050e71fdb07b153ae0fa5c410d8de27.tar.gz spark-a18c169fd050e71fdb07b153ae0fa5c410d8de27.tar.bz2 spark-a18c169fd050e71fdb07b153ae0fa5c410d8de27.zip |
[SPARK-16283][SQL] Implements percentile_approx aggregation function which supports partial aggregation.
## What changes were proposed in this pull request?
This PR implements aggregation function `percentile_approx`. Function `percentile_approx` returns the approximate percentile(s) of a column at the given percentage(s). A percentile is a watermark value below which a given percentage of the column values fall. For example, the percentile of column `col` at percentage 50% is the median value of column `col`.
### Syntax:
```
# Returns percentile at a given percentage value. The approximation error can be reduced by increasing parameter accuracy, at the cost of memory.
percentile_approx(col, percentage [, accuracy])
# Returns percentile value array at given percentage value array
percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy])
```
### Features:
1. This function supports partial aggregation.
2. The memory consumption is bounded. The larger `accuracy` parameter we choose, we smaller error we get. The default accuracy value is 10000, to match with Hive default setting. Choose a smaller value for smaller memory footprint.
3. This function supports window function aggregation.
### Example usages:
```
## Returns the 25th percentile value, with default accuracy
SELECT percentile_approx(col, 0.25) FROM table
## Returns an array of percentile value (25th, 50th, 75th), with default accuracy
SELECT percentile_approx(col, array(0.25, 0.5, 0.75)) FROM table
## Returns 25th percentile value, with custom accuracy value 100, larger accuracy parameter yields smaller approximation error
SELECT percentile_approx(col, 0.25, 100) FROM table
## Returns the 25th, and 50th percentile values, with custom accuracy value 100
SELECT percentile_approx(col, array(0.25, 0.5), 100) FROM table
```
### NOTE:
1. The `percentile_approx` implementation is different from Hive, so the result returned on same query maybe slightly different with Hive. This implementation uses `QuantileSummaries` as the underlying probabilistic data structure, and mainly follows paper `Space-efficient Online Computation of Quantile Summaries` by Greenwald, Michael and Khanna, Sanjeev. (http://dx.doi.org/10.1145/375663.375670)`
2. The current implementation of `QuantileSummaries` doesn't support automatic compression. This PR has a rule to do compression automatically at the caller side, but it may not be optimal.
## How was this patch tested?
Unit test, and Sql query test.
## Acknowledgement
1. This PR's work in based on lw-lin's PR https://github.com/apache/spark/pull/14298, with improvements like supporting partial aggregation, fixing out of memory issue.
Author: Sean Zhong <seanzhong@databricks.com>
Closes #14868 from clockfly/appro_percentile_try_2.
Diffstat (limited to 'sql/hive/src/test')
-rw-r--r-- | sql/hive/src/test/scala/org/apache/spark/sql/catalyst/ExpressionToSQLSuite.scala | 5 |
1 files changed, 5 insertions, 0 deletions
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/catalyst/ExpressionToSQLSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/catalyst/ExpressionToSQLSuite.scala index b4eb50e331..fdd02821df 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/catalyst/ExpressionToSQLSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/catalyst/ExpressionToSQLSuite.scala @@ -155,6 +155,11 @@ class ExpressionToSQLSuite extends SQLBuilderTest with SQLTestUtils { test("aggregate functions") { checkSqlGeneration("SELECT approx_count_distinct(value) FROM t1 GROUP BY key") + checkSqlGeneration("SELECT percentile_approx(value, 0.25) FROM t1 GROUP BY key") + checkSqlGeneration("SELECT percentile_approx(value, array(0.25, 0.75)) FROM t1 GROUP BY key") + checkSqlGeneration("SELECT percentile_approx(value, 0.25, 100) FROM t1 GROUP BY key") + checkSqlGeneration( + "SELECT percentile_approx(value, array(0.25, 0.75), 100) FROM t1 GROUP BY key") checkSqlGeneration("SELECT avg(value) FROM t1 GROUP BY key") checkSqlGeneration("SELECT corr(value, key) FROM t1 GROUP BY key") checkSqlGeneration("SELECT count(value) FROM t1 GROUP BY key") |