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Set OrcConf.INCLUDE_COLUMNS for ORC reading #4933

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merged 6 commits into from
Mar 19, 2022
Merged

Set OrcConf.INCLUDE_COLUMNS for ORC reading #4933

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8ff19fd
set_INCLUDE_COLUMNS_for_ORC_reading
sperlingxx Mar 11, 2022
61a4f4a
Merge remote-tracking branch 'origin/branch-22.04' into add_orc_conf_…
sperlingxx Mar 16, 2022
315c99c
update
sperlingxx Mar 16, 2022
f819fcf
Revert "update"
sperlingxx Mar 17, 2022
2ec24c4
revert
sperlingxx Mar 17, 2022
22ae09d
update
sperlingxx Mar 18, 2022
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6 changes: 6 additions & 0 deletions sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOrcScanBase.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -803,6 +803,12 @@ private case class GpuOrcFileFilterHandler(
val requestedMapping = if (canPruneCols) {
None
} else {
// Following SPARK-35783, set requested columns as OrcConf. This setting may not make
// any difference. Just in case it might be important for the ORC methods called by us,
// either today or in the future.
val includeColumns = requestedColIds.filter(_ != -1).sorted.mkString(",")
conf.set(OrcConf.INCLUDE_COLUMNS.getAttribute, includeColumns)
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Curious why this is conditional on !canPruneCols? The equivalent Spark code setting this is not similarly conditional. We're calling buildOrcReader in either case which will examine the INCLUDE_COLUMNS setting, so it seems prudent to set it whether or not we can prune, minimally to help keep this code in sync with the Spark version.

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Hi @jlowe, following your question, I found that we should add this conf on the opposite conditional branch, which canPruneCols is TRUE. Because canPruneCols represents whether trusts ORC to prune columns or do it by ourselves.

@wbo4958 please correct me if I am wrong. Thank you!

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I also simplified the requestedColumnIds method.

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I also simplified the requestedColumnIds method.

Please revert this change. The code was intentionally trying to be similar to the Apache Spark version. The same is true for the code calling this function.

I found that we should add this conf on the opposite conditional branch, which canPruneCols is TRUE. Because canPruneCols represents whether trusts ORC to prune columns or do it by ourselves.

IMO we need to do what Spark is doing with respect to when and how it sets up INCLUDE_COLUMNS. Looking at the Spark code, it is always setting INCLUDE_COLUMNS as long as requestedColPruneInfo is non-empty. We should do the same.

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Hi @jlowe, I reverted it. But I found a bigger problem. It seems that the setting of OrcConf.INCLUDE_COLUMNS is NOT compatible with the schema pruning applied in checkSchemaCompatibility, which leads to the failure when constructing SchemaEvolution: https://github.com/NVIDIA/spark-rapids/blob/branch-22.04/sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOrcScanBase.scala#L951.

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checkSchemaCompatibility also is derived from Apache Spark, so does it's version too have issues with INCLUDE_COLUMNS when pruning is applied?

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@jlowe I failed to find checkSchemaCompatibility or similiar things in Apache Spark. Alternatively, I made some change on the checkSchemaCompatibility. Let it prune include status array by the field ID of pruned fields from read schema, which takes place simultaneously with the prune of read schema.


Some(requestedColIds)
}
val fullSchema = StructType(dataSchema ++ partitionSchema)
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