c21 opened a new pull request #31958:
URL: https://github.com/apache/spark/pull/31958
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### What changes were proposed in this pull request?
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This PR is to support nested column type in Spark ORC vectorized reader.
Currently ORC vectorized reader [does not support nested column type (struct,
array and
map)](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcFileFormat.scala#L138).
We implemented nested column vectorized reader for FB-ORC in our internal fork
of Spark. We are seeing performance improvement compared to non-vectorized
reader when reading nested columns. In addition, this can also help improve the
non-nested column performance when reading non-nested and nested columns
together in one query.
Before this PR:
* `OrcColumnVector` is the implementation class for Spark's `ColumnVector`
to wrap Hive's/ORC's `ColumnVector` to read `AtomicType` data.
After this PR:
* `OrcColumnVector` is an abstract class to keep interface being shared
between multiple implementation class of orc column vectors, namely
`OrcAtomicColumnVector` (for `AtomicType`), `OrcArrayColumnVector` (for
`ArrayType`), `OrcMapColumnVector` (for `MapType`), `OrcStructColumnVector`
(for `StructType`). So the original logic to read `AtomicType` data is moved
from `OrcColumnVector` to `OrcAtomicColumnVector`. The abstract class of
`OrcColumnVector` is needed here because of supporting nested column (i.e.
nested column vectors).
* A utility method `OrcColumnVectorUtils.toOrcColumnVector` is added to
create Spark's `OrcColumnVector` from Hive's/ORC's `ColumnVector`.
* A new user-facing config
`spark.sql.orc.enableNestedColumnVectorizedReader` is added to control
enabling/disabling vectorized reader for nested columns. The default value is
true (i.e. enabling by default). For certain tables having deep nested columns,
vectorized reader might take too much memory for each sub-column vectors,
compared to non-vectorized reader. So providing a config here to work around
OOM for query reading wide and deep nested columns if any.
### Why are the changes needed?
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Improve query performance when reading nested columns from ORC file format.
Tested with locally adding a small benchmark in `OrcReadBenchmark.scala`.
Seeing more than 1x run time improvement.
```
Running benchmark: SQL Nested Column Scan
Running case: Native ORC MR
Stopped after 2 iterations, 37850 ms
Running case: Native ORC Vectorized (Enabled Nested Column)
Stopped after 2 iterations, 15892 ms
Running case: Native ORC Vectorized (Disabled Nested Column)
Stopped after 2 iterations, 37954 ms
Running case: Hive built-in ORC
Stopped after 2 iterations, 35118 ms
Java HotSpot(TM) 64-Bit Server VM 1.8.0_181-b13 on Mac OS X 10.15.7
Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz
SQL Nested Column Scan: Best Time(ms) Avg Time(ms)
Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------------
Native ORC MR 18706 18925
310 0.1 17839.6 1.0X
Native ORC Vectorized (Enabled Nested Column) 7625 7946
455 0.1 7271.6 2.5X
Native ORC Vectorized (Disabled Nested Column) 18415 18977
796 0.1 17561.5 1.0X
Hive built-in ORC 17469 17559
127 0.1 16660.1 1.1X
```
Benchmark:
```
nestedColumnScanBenchmark(1024 * 1024)
def nestedColumnScanBenchmark(values: Int): Unit = {
val benchmark = new Benchmark(s"SQL Nested Column Scan", values, output
= output)
withTempPath { dir =>
withTempTable("t1", "nativeOrcTable", "hiveOrcTable") {
import spark.implicits._
spark.range(values).map(_ => Random.nextLong).map { x =>
val arrayOfStructColumn = (0 until 5).map(i => (x + i, s"$x" * 5))
val mapOfStructColumn = Map(
s"$x" -> (x * 0.1, (x, s"$x" * 100)),
(s"$x" * 2) -> (x * 0.2, (x, s"$x" * 200)),
(s"$x" * 3) -> (x * 0.3, (x, s"$x" * 300)))
(arrayOfStructColumn, mapOfStructColumn)
}.toDF("col1", "col2")
.createOrReplaceTempView("t1")
prepareTable(dir, spark.sql(s"SELECT * FROM t1"))
benchmark.addCase("Native ORC MR") { _ =>
withSQLConf(SQLConf.ORC_VECTORIZED_READER_ENABLED.key -> "false") {
spark.sql("SELECT SUM(SIZE(col1)), SUM(SIZE(col2)) FROM
nativeOrcTable").noop()
}
}
benchmark.addCase("Native ORC Vectorized (Enabled Nested Column)") {
_ =>
spark.sql("SELECT SUM(SIZE(col1)), SUM(SIZE(col2)) FROM
nativeOrcTable").noop()
}
benchmark.addCase("Native ORC Vectorized (Disabled Nested Column)")
{ _ =>
withSQLConf(SQLConf.ORC_VECTORIZED_READER_NESTED_COLUMN_ENABLED.key -> "false")
{
spark.sql("SELECT SUM(SIZE(col1)), SUM(SIZE(col2)) FROM
nativeOrcTable").noop()
}
}
benchmark.addCase("Hive built-in ORC") { _ =>
spark.sql("SELECT SUM(SIZE(col1)), SUM(SIZE(col2)) FROM
hiveOrcTable").noop()
}
benchmark.run()
}
}
}
```
### Does this PR introduce _any_ user-facing change?
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