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[SPARK-51079][PYTHON] Support large variable types in pandas UDF, createDataFrame and toPandas with Arrow - #49790
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@Kimahriman FYI |
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Seems like panads <> Arrow conversion is fixed in their releases |
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cc @ueshin @zhengruifeng too |
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nit: probably we can also fetch all configs via py4j in batch in the future.
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I feel we are using more and more configs, probably we can also combine them into a runner_conf: Dict[str, str] later
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Should be ready to go once it passes |
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Merged to master and branch-4.0. |
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…ateDataFrame and toPandas with Arrow ### What changes were proposed in this pull request? This PR is a retry of #41569 that implements to use large variable types within PySpark everywhere. #39572 implemented the core logic but it only supports large variable types in the bold cases below: - `mapInArrow`: **JVM -> Python -> JVM** - Pandas UDF/Function API: **JVM -> Python** -> JVM - createDataFrame with Arrow: Python -> JVM - toPandas with Arrow: JVM -> Python This PR completes them all. ### Why are the changes needed? To consistently support the large variable types. ### Does this PR introduce _any_ user-facing change? `spark.sql.execution.arrow.useLargeVarTypes` is not released out yet so it doesn't affect any end users. ### How was this patch tested? Existing tests with `spark.sql.execution.arrow.useLargeVarTypes` enabled. Closes #49790 from HyukjinKwon/SPARK-39979-followup2. Authored-by: Hyukjin Kwon <[email protected]> Signed-off-by: Hyukjin Kwon <[email protected]> (cherry picked from commit e2ef5a4) Signed-off-by: Hyukjin Kwon <[email protected]>
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…atibility ### What changes were proposed in this pull request? Previously, [PR](#49790) extends ArrowSerializer class to support large variable types in pandas UDF. This PR makes it more backward-compatible by adding a new constructor for cases where the flag for large variable is irrelevant (default to false). ### Why are the changes needed? To achieve versionless in spark connect, we need to be careful about changing a client API in Scala. For example, the change of an interface like this could affect Scala UDF referring to this class (as it is part of the spark connect client package) but running with an older spark connect client. ### How was this patch tested? N/A. It's a new constructor. Closes #50513 from haiyangsun-db/SPARK-51079. Authored-by: Haiyang Sun <[email protected]> Signed-off-by: Hyukjin Kwon <[email protected]>
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…atibility ### What changes were proposed in this pull request? Previously, [PR](#49790) extends ArrowSerializer class to support large variable types in pandas UDF. This PR makes it more backward-compatible by adding a new constructor for cases where the flag for large variable is irrelevant (default to false). ### Why are the changes needed? To achieve versionless in spark connect, we need to be careful about changing a client API in Scala. For example, the change of an interface like this could affect Scala UDF referring to this class (as it is part of the spark connect client package) but running with an older spark connect client. ### How was this patch tested? N/A. It's a new constructor. Closes #50513 from haiyangsun-db/SPARK-51079. Authored-by: Haiyang Sun <[email protected]> Signed-off-by: Hyukjin Kwon <[email protected]> (cherry picked from commit 75d80c7) Signed-off-by: Hyukjin Kwon <[email protected]>
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…ateDataFrame and toPandas with Arrow ### What changes were proposed in this pull request? This PR is a retry of apache#41569 that implements to use large variable types within PySpark everywhere. apache#39572 implemented the core logic but it only supports large variable types in the bold cases below: - `mapInArrow`: **JVM -> Python -> JVM** - Pandas UDF/Function API: **JVM -> Python** -> JVM - createDataFrame with Arrow: Python -> JVM - toPandas with Arrow: JVM -> Python This PR completes them all. ### Why are the changes needed? To consistently support the large variable types. ### Does this PR introduce _any_ user-facing change? `spark.sql.execution.arrow.useLargeVarTypes` is not released out yet so it doesn't affect any end users. ### How was this patch tested? Existing tests with `spark.sql.execution.arrow.useLargeVarTypes` enabled. Closes apache#49790 from HyukjinKwon/SPARK-39979-followup2. Authored-by: Hyukjin Kwon <[email protected]> Signed-off-by: Hyukjin Kwon <[email protected]> (cherry picked from commit 454111a) Signed-off-by: Hyukjin Kwon <[email protected]>
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…atibility ### What changes were proposed in this pull request? Previously, [PR](apache#49790) extends ArrowSerializer class to support large variable types in pandas UDF. This PR makes it more backward-compatible by adding a new constructor for cases where the flag for large variable is irrelevant (default to false). ### Why are the changes needed? To achieve versionless in spark connect, we need to be careful about changing a client API in Scala. For example, the change of an interface like this could affect Scala UDF referring to this class (as it is part of the spark connect client package) but running with an older spark connect client. ### How was this patch tested? N/A. It's a new constructor. Closes apache#50513 from haiyangsun-db/SPARK-51079. Authored-by: Haiyang Sun <[email protected]> Signed-off-by: Hyukjin Kwon <[email protected]> (cherry picked from commit 0c50c2c) Signed-off-by: Hyukjin Kwon <[email protected]>
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What changes were proposed in this pull request?
This PR is a retry of #41569 that implements to use large variable types within PySpark everywhere.
#39572 implemented the core logic but it only supports large variable types in the bold cases below:
mapInArrow: JVM -> Python -> JVMThis PR completes them all.
Why are the changes needed?
To consistently support the large variable types.
Does this PR introduce any user-facing change?
spark.sql.execution.arrow.useLargeVarTypesis not released out yet so it doesn't affect any end users.How was this patch tested?
Existing tests with
spark.sql.execution.arrow.useLargeVarTypesenabled.