[ https://issues.apache.org/jira/browse/SPARK-42775?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Chenhao Li updated SPARK-42775: ------------------------------- Description: In the {{approx_percentile}} expression, Spark casts decimal to double to update the aggregation state ([ApproximatePercentile.scala#L181|https://github.com/apache/spark/blob/933dc0c42f0caf74aaa077fd4f2c2e7208452b9b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala#L181]) and casts the result double back to decimal ([ApproximatePercentile.scala#L206|https://github.com/apache/spark/blob/933dc0c42f0caf74aaa077fd4f2c2e7208452b9b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala#L206]). The precision loss in the casts can make the result decimal out of its precision range. This can lead to the following counter-intuitive results: {code:sql} spark-sql> select approx_percentile(col, 0.5) from values (9999999999999999999) as tab(col); NULL spark-sql> select approx_percentile(col, 0.5) is null from values (9999999999999999999) as tab(col); false spark-sql> select cast(approx_percentile(col, 0.5) as string) from values (9999999999999999999) as tab(col); 10000000000000000000 spark-sql> desc select approx_percentile(col, 0.5) from values (9999999999999999999) as tab(col); approx_percentile(col, 0.5, 10000) decimal(19,0) {code} The result is actually not null, so the second query returns false. The first query returns null because the result cannot fit into {{{}decimal(19, 0){}}}. A suggested fix is to use `Decimal.changePrecision` here to ensure the result fits, and really returns a null or throws an exception when the result doesn't fit. was: In the `approx_percentile` expression, Spark casts decimal to double to update the aggregation state ([ApproximatePercentile.scala#L181|https://github.com/apache/spark/blob/933dc0c42f0caf74aaa077fd4f2c2e7208452b9b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala#L181]) and casts the result double back to decimal ([ApproximatePercentile.scala#L206|https://github.com/apache/spark/blob/933dc0c42f0caf74aaa077fd4f2c2e7208452b9b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala#L206]). The precision loss in the casts can make the result decimal out of its precision range. This can lead to the following counter-intuitive results: {code:sql} spark-sql> select approx_percentile(col, 0.5) from values (9999999999999999999) as tab(col); NULL spark-sql> select approx_percentile(col, 0.5) is null from values (9999999999999999999) as tab(col); false spark-sql> select cast(approx_percentile(col, 0.5) as string) from values (9999999999999999999) as tab(col); 10000000000000000000 spark-sql> desc select approx_percentile(col, 0.5) from values (9999999999999999999) as tab(col); approx_percentile(col, 0.5, 10000) decimal(19,0) {code} The result is actually not null, so the second query returns false. The first query returns null because the result cannot fit into {{decimal(19, 0)}}. A suggested fix is to use `Decimal.changePrecision` here to ensure the result fits, and really returns a null or throws an exception when the result doesn't fit. > approx_percentile produces wrong results for large decimals. > ------------------------------------------------------------ > > Key: SPARK-42775 > URL: https://issues.apache.org/jira/browse/SPARK-42775 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 2.1.0, 2.2.0, 2.3.0, 2.4.0, 3.0.0, 3.1.0, 3.2.0, 3.3.0, > 3.4.0 > Reporter: Chenhao Li > Priority: Major > > In the {{approx_percentile}} expression, Spark casts decimal to double to > update the aggregation state > ([ApproximatePercentile.scala#L181|https://github.com/apache/spark/blob/933dc0c42f0caf74aaa077fd4f2c2e7208452b9b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala#L181]) > and casts the result double back to decimal > ([ApproximatePercentile.scala#L206|https://github.com/apache/spark/blob/933dc0c42f0caf74aaa077fd4f2c2e7208452b9b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala#L206]). > The precision loss in the casts can make the result decimal out of its > precision range. This can lead to the following counter-intuitive results: > {code:sql} > spark-sql> select approx_percentile(col, 0.5) from values > (9999999999999999999) as tab(col); > NULL > spark-sql> select approx_percentile(col, 0.5) is null from values > (9999999999999999999) as tab(col); > false > spark-sql> select cast(approx_percentile(col, 0.5) as string) from values > (9999999999999999999) as tab(col); > 10000000000000000000 > spark-sql> desc select approx_percentile(col, 0.5) from values > (9999999999999999999) as tab(col); > approx_percentile(col, 0.5, 10000) decimal(19,0) > {code} > The result is actually not null, so the second query returns false. The first > query returns null because the result cannot fit into {{{}decimal(19, 0){}}}. > A suggested fix is to use `Decimal.changePrecision` here to ensure the result > fits, and really returns a null or throws an exception when the result > doesn't fit. -- This message was sent by Atlassian Jira (v8.20.10#820010) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org