c21 opened a new pull request #31958: URL: https://github.com/apache/spark/pull/31958
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If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> 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? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> 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? <!-- Note that it means *any* user-facing change including all aspects such as the documentation fix. If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If possible, please also clarify if this is a user-facing change compared to the released Spark versions or within the unreleased branches such as master. If no, write 'No'. --> ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> -- This is an automated message from the Apache Git Service. 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