[SPARK-26580][SQL] remove Scala 2.11 hack for Scala UDF #23498
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cloud-fan
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Jan 9, 2019
docs/sql-migration-guide-upgrade.md
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| - Since Spark 3.0, JSON datasource and JSON function `schema_of_json` infer TimestampType from string values if they match to the pattern defined by the JSON option `timestampFormat`. Set JSON option `inferTimestamp` to `false` to disable such type inferring. | ||
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| - In Spark version 2.4 and earlier, if `org.apache.spark.sql.functions.udf(Any, DataType)` gets a Scala closure with primitive-type argument, the returned UDF will return null if the input values is null. Since Spark 3.0, the UDF will return the default value of the Java type if the input value is null. For example, `val f = udf((x: Int) => x, IntegerType)`, `f($"x")` will return null in Spark 2.4 and earlier if column `x` is null, and return 0 in Spark 3.0. |
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this migration guide should have been added when we switch to Scala 2.12.
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should this say this is because of Scala 2.12?
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Yea, I agree with this change. |
srowen
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Jan 9, 2019
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Test build #100962 has finished for PR 23498 at commit
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Test build #101014 has finished for PR 23498 at commit
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retest this please |
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Test build #101015 has finished for PR 23498 at commit
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Test build #4501 has finished for PR 23498 at commit
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retest this please |
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Test build #101046 has finished for PR 23498 at commit
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thanks, merging to master! |
jackylee-ch
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Feb 18, 2019
## What changes were proposed in this pull request? In apache#22732 , we tried our best to keep the behavior of Scala UDF unchanged in Spark 2.4. However, since Spark 3.0, Scala 2.12 is the default. The trick that was used to keep the behavior unchanged doesn't work with Scala 2.12. This PR proposes to remove the Scala 2.11 hack, as it's not useful. ## How was this patch tested? existing tests. Closes apache#23498 from cloud-fan/udf. Authored-by: Wenchen Fan <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
cloud-fan
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Feb 21, 2020
…F by default ### What changes were proposed in this pull request? This PR proposes to throw exception by default when user use untyped UDF(a.k.a `org.apache.spark.sql.functions.udf(AnyRef, DataType)`). And user could still use it by setting `spark.sql.legacy.useUnTypedUdf.enabled` to `true`. ### Why are the changes needed? According to #23498, since Spark 3.0, the untyped UDF will return the default value of the Java type if the input value is null. For example, `val f = udf((x: Int) => x, IntegerType)`, `f($"x")` will return 0 in Spark 3.0 but null in Spark 2.4. And the behavior change is introduced due to Spark3.0 is built with Scala 2.12 by default. As a result, this might change data silently and may cause correctness issue if user still expect `null` in some cases. Thus, we'd better to encourage user to use typed UDF to avoid this problem. ### Does this PR introduce any user-facing change? Yeah. User will hit exception now when use untyped UDF. ### How was this patch tested? Added test and updated some tests. Closes #27488 from Ngone51/spark_26580_followup. Lead-authored-by: yi.wu <[email protected]> Co-authored-by: wuyi <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
cloud-fan
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Feb 21, 2020
…F by default ### What changes were proposed in this pull request? This PR proposes to throw exception by default when user use untyped UDF(a.k.a `org.apache.spark.sql.functions.udf(AnyRef, DataType)`). And user could still use it by setting `spark.sql.legacy.useUnTypedUdf.enabled` to `true`. ### Why are the changes needed? According to #23498, since Spark 3.0, the untyped UDF will return the default value of the Java type if the input value is null. For example, `val f = udf((x: Int) => x, IntegerType)`, `f($"x")` will return 0 in Spark 3.0 but null in Spark 2.4. And the behavior change is introduced due to Spark3.0 is built with Scala 2.12 by default. As a result, this might change data silently and may cause correctness issue if user still expect `null` in some cases. Thus, we'd better to encourage user to use typed UDF to avoid this problem. ### Does this PR introduce any user-facing change? Yeah. User will hit exception now when use untyped UDF. ### How was this patch tested? Added test and updated some tests. Closes #27488 from Ngone51/spark_26580_followup. Lead-authored-by: yi.wu <[email protected]> Co-authored-by: wuyi <[email protected]> Signed-off-by: Wenchen Fan <[email protected]> (cherry picked from commit 82ce475) Signed-off-by: Wenchen Fan <[email protected]>
sjincho
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Apr 15, 2020
…F by default ### What changes were proposed in this pull request? This PR proposes to throw exception by default when user use untyped UDF(a.k.a `org.apache.spark.sql.functions.udf(AnyRef, DataType)`). And user could still use it by setting `spark.sql.legacy.useUnTypedUdf.enabled` to `true`. ### Why are the changes needed? According to apache#23498, since Spark 3.0, the untyped UDF will return the default value of the Java type if the input value is null. For example, `val f = udf((x: Int) => x, IntegerType)`, `f($"x")` will return 0 in Spark 3.0 but null in Spark 2.4. And the behavior change is introduced due to Spark3.0 is built with Scala 2.12 by default. As a result, this might change data silently and may cause correctness issue if user still expect `null` in some cases. Thus, we'd better to encourage user to use typed UDF to avoid this problem. ### Does this PR introduce any user-facing change? Yeah. User will hit exception now when use untyped UDF. ### How was this patch tested? Added test and updated some tests. Closes apache#27488 from Ngone51/spark_26580_followup. Lead-authored-by: yi.wu <[email protected]> Co-authored-by: wuyi <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
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What changes were proposed in this pull request?
In #22732 , we tried our best to keep the behavior of Scala UDF unchanged in Spark 2.4.
However, since Spark 3.0, Scala 2.12 is the default. The trick that was used to keep the behavior unchanged doesn't work with Scala 2.12.
This PR proposes to remove the Scala 2.11 hack, as it's not useful.
How was this patch tested?
existing tests.