2014-06-01 20:27:05 -04:00
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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import com.typesafe.tools.mima.core._
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2015-03-20 14:43:57 -04:00
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import com.typesafe.tools.mima.core.ProblemFilters._
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2014-06-01 20:27:05 -04:00
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/**
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* Additional excludes for checking of Spark's binary compatibility.
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*
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2016-03-16 02:25:31 -04:00
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* This acts as an official audit of cases where we excluded other classes. Please use the narrowest
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2014-06-01 20:27:05 -04:00
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* possible exclude here. MIMA will usually tell you what exclude to use, e.g.:
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*
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* ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.rdd.RDD.take")
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*
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* It is also possible to exclude Spark classes and packages. This should be used sparingly:
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*
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* MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap")
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2016-01-03 19:58:01 -05:00
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*
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* For a new Spark version, please update MimaBuild.scala to reflect the previous version.
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2014-06-01 20:27:05 -04:00
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*/
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object MimaExcludes {
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2016-04-09 03:00:39 -04:00
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2021-07-06 08:16:07 -04:00
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// Exclude rules for 3.3.x from 3.2.0 after 3.2.0 release
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2021-07-02 16:47:36 -04:00
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lazy val v33excludes = v32excludes ++ Seq(
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)
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2021-07-06 08:16:07 -04:00
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// Exclude rules for 3.2.x from 3.1.1
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lazy val v32excludes = Seq(
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// Spark Internals
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ProblemFilters.exclude[Problem]("org.apache.spark.rpc.*"),
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ProblemFilters.exclude[Problem]("org.spark-project.jetty.*"),
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ProblemFilters.exclude[Problem]("org.spark_project.jetty.*"),
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ProblemFilters.exclude[Problem]("org.sparkproject.jetty.*"),
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ProblemFilters.exclude[Problem]("org.apache.spark.internal.*"),
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ProblemFilters.exclude[Problem]("org.apache.spark.unused.*"),
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ProblemFilters.exclude[Problem]("org.apache.spark.unsafe.*"),
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ProblemFilters.exclude[Problem]("org.apache.spark.memory.*"),
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ProblemFilters.exclude[Problem]("org.apache.spark.util.collection.unsafe.*"),
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ProblemFilters.exclude[Problem]("org.apache.spark.sql.catalyst.*"),
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ProblemFilters.exclude[Problem]("org.apache.spark.sql.execution.*"),
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ProblemFilters.exclude[Problem]("org.apache.spark.sql.internal.*"),
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// [SPARK-34848][CORE] Add duration to TaskMetricDistributions
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.status.api.v1.TaskMetricDistributions.this"),
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// [SPARK-34488][CORE] Support task Metrics Distributions and executor Metrics Distributions
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// in the REST API call for a specified stage
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ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.status.api.v1.StageData.this"),
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// [SPARK-35896] Include more granular metrics for stateful operators in StreamingQueryProgress
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.sql.streaming.StateOperatorProgress.this"),
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(problem: Problem) => problem match {
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case MissingClassProblem(cls) => !cls.fullName.startsWith("org.sparkproject.jpmml") &&
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!cls.fullName.startsWith("org.sparkproject.dmg.pmml")
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case _ => true
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},
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2020-12-22 03:23:56 -05:00
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// [SPARK-33808][SQL] DataSource V2: Build logical writes in the optimizer
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[SPARK-33955][SS] Add latest offsets to source progress
### What changes were proposed in this pull request?
This patch proposes to add latest offset to source progress for streaming queries.
### Why are the changes needed?
Currently we record start and end offsets per source in streaming process. Latest offset is an important information for streaming process but the progress lacks of this info. We can use it to track the process lag and adjust streaming queries. We should add latest offset to source progress.
### Does this PR introduce _any_ user-facing change?
Yes, for new metric about latest source offset in source progress.
### How was this patch tested?
Unit test. Manually test in Spark cluster:
```
"description" : "KafkaV2[Subscribe[page_view_events]]",
"startOffset" : {
"page_view_events" : {
"2" : 582370921,
"4" : 391910836,
"1" : 631009201,
"3" : 406601346,
"0" : 195799112
}
},
"endOffset" : {
"page_view_events" : {
"2" : 583764414,
"4" : 392338002,
"1" : 632183480,
"3" : 407101489,
"0" : 197304028
}
},
"latestOffset" : {
"page_view_events" : {
"2" : 589852545,
"4" : 394204277,
"1" : 637313869,
"3" : 409286602,
"0" : 203878962
}
},
"numInputRows" : 4999997,
"inputRowsPerSecond" : 29287.70501405811,
```
Closes #30988 from viirya/latest-offset.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2021-01-03 04:31:38 -05:00
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ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.connector.write.V1WriteBuilder"),
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// [SPARK-33955] Add latest offsets to source progress
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[SPARK-34862][SQL] Support nested column in ORC vectorized reader
### What changes were proposed in this pull request?
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 false (i.e. disabling 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. We plan to enable it by default on 3.3. Leave it disable in 3.2 in case for any unknown bugs.
### Why are the changes needed?
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?
No.
### How was this patch tested?
Added one simple test in `OrcSourceSuite.scala` to verify correctness.
Definitely need more unit tests and add benchmark here, but I want to first collect feedback before crafting more tests.
Closes #31958 from c21/orc-vector.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
2021-04-02 02:10:34 -04:00
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.sql.streaming.SourceProgress.this"),
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// [SPARK-34862][SQL] Support nested column in ORC vectorized reader
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getBoolean"),
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getByte"),
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getShort"),
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getInt"),
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getLong"),
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getFloat"),
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getDouble"),
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getDecimal"),
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getUTF8String"),
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getBinary"),
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getArray"),
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getMap"),
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2021-04-28 23:46:24 -04:00
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ProblemFilters.exclude[DirectAbstractMethodProblem]("org.apache.spark.sql.vectorized.ColumnVector.getChild"),
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// [SPARK-35135][CORE] Turn WritablePartitionedIterator from trait into a default implementation class
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2021-06-17 02:29:33 -04:00
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ProblemFilters.exclude[IncompatibleTemplateDefProblem]("org.apache.spark.util.collection.WritablePartitionedIterator"),
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// [SPARK-35757][CORE] Add bitwise AND operation and functionality for intersecting bloom filters
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2021-07-17 01:23:14 -04:00
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ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.util.sketch.BloomFilter.intersectInPlace"),
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// [SPARK-35276][CORE] Calculate checksum for shuffle data and write as checksum file
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.shuffle.sort.io.LocalDiskShuffleMapOutputWriter.commitAllPartitions"),
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.shuffle.sort.io.LocalDiskSingleSpillMapOutputWriter.transferMapSpillFile"),
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.shuffle.api.ShuffleMapOutputWriter.commitAllPartitions"),
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.shuffle.api.SingleSpillShuffleMapOutputWriter.transferMapSpillFile"),
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ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.shuffle.api.SingleSpillShuffleMapOutputWriter.transferMapSpillFile"),
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ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.shuffle.api.ShuffleMapOutputWriter.commitAllPartitions")
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2020-12-04 17:10:42 -05:00
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)
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2016-07-11 01:05:16 -04:00
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def excludes(version: String) = version match {
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2021-07-02 16:47:36 -04:00
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case v if v.startsWith("3.3") => v33excludes
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2020-12-04 17:10:42 -05:00
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case v if v.startsWith("3.2") => v32excludes
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2015-09-07 16:42:30 -04:00
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case _ => Seq()
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}
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2015-10-22 12:46:30 -04:00
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}
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