fcb68e0f5d
## What changes were proposed in this pull request? This PR improves performance of operations with `range()` by changing Java code generated by Catalyst. This PR is inspired by the [blog article](https://databricks.com/blog/2017/02/16/processing-trillion-rows-per-second-single-machine-can-nested-loop-joins-fast.html). This PR changes generated code in the following two points. 1. Replace a while-loop with long instance variables a for-loop with int local varibles 2. Suppress generation of `shouldStop()` method if this method is unnecessary (e.g. `append()` is not generated). These points facilitates compiler optimizations in a JIT compiler by feeding the simplified Java code into the JIT compiler. The performance is improved by 7.6x. Benchmark program: ```java val N = 1 << 29 val iters = 2 val benchmark = new Benchmark("range.count", N * iters) benchmark.addCase(s"with this PR") { i => var n = 0 var len = 0 while (n < iters) { len += sparkSession.range(N).selectExpr("count(id)").collect.length n += 1 } } benchmark.run ``` Performance result without this PR ``` OpenJDK 64-Bit Server VM 1.8.0_111-8u111-b14-2ubuntu0.16.04.2-b14 on Linux 4.4.0-47-generic Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz range.count: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ w/o this PR 1349 / 1356 796.2 1.3 1.0X ``` Performance result with this PR ``` OpenJDK 64-Bit Server VM 1.8.0_111-8u111-b14-2ubuntu0.16.04.2-b14 on Linux 4.4.0-47-generic Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz range.count: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ with this PR 177 / 271 6065.3 0.2 1.0X ``` Here is a comparison between generated code w/o and with this PR. Only the method ```agg_doAggregateWithoutKey``` is changed. Generated code without this PR ```java /* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator { /* 006 */ private Object[] references; /* 007 */ private scala.collection.Iterator[] inputs; /* 008 */ private boolean agg_initAgg; /* 009 */ private boolean agg_bufIsNull; /* 010 */ private long agg_bufValue; /* 011 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numOutputRows; /* 012 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numGeneratedRows; /* 013 */ private boolean range_initRange; /* 014 */ private long range_number; /* 015 */ private TaskContext range_taskContext; /* 016 */ private InputMetrics range_inputMetrics; /* 017 */ private long range_batchEnd; /* 018 */ private long range_numElementsTodo; /* 019 */ private scala.collection.Iterator range_input; /* 020 */ private UnsafeRow range_result; /* 021 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder range_holder; /* 022 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter range_rowWriter; /* 023 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_numOutputRows; /* 024 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_aggTime; /* 025 */ private UnsafeRow agg_result; /* 026 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder agg_holder; /* 027 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter agg_rowWriter; /* 028 */ /* 029 */ public GeneratedIterator(Object[] references) { /* 030 */ this.references = references; /* 031 */ } /* 032 */ /* 033 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 034 */ partitionIndex = index; /* 035 */ this.inputs = inputs; /* 036 */ agg_initAgg = false; /* 037 */ /* 038 */ this.range_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[0]; /* 039 */ this.range_numGeneratedRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[1]; /* 040 */ range_initRange = false; /* 041 */ range_number = 0L; /* 042 */ range_taskContext = TaskContext.get(); /* 043 */ range_inputMetrics = range_taskContext.taskMetrics().inputMetrics(); /* 044 */ range_batchEnd = 0; /* 045 */ range_numElementsTodo = 0L; /* 046 */ range_input = inputs[0]; /* 047 */ range_result = new UnsafeRow(1); /* 048 */ this.range_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(range_result, 0); /* 049 */ this.range_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(range_holder, 1); /* 050 */ this.agg_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[2]; /* 051 */ this.agg_aggTime = (org.apache.spark.sql.execution.metric.SQLMetric) references[3]; /* 052 */ agg_result = new UnsafeRow(1); /* 053 */ this.agg_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(agg_result, 0); /* 054 */ this.agg_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(agg_holder, 1); /* 055 */ /* 056 */ } /* 057 */ /* 058 */ private void agg_doAggregateWithoutKey() throws java.io.IOException { /* 059 */ // initialize aggregation buffer /* 060 */ agg_bufIsNull = false; /* 061 */ agg_bufValue = 0L; /* 062 */ /* 063 */ // initialize Range /* 064 */ if (!range_initRange) { /* 065 */ range_initRange = true; /* 066 */ initRange(partitionIndex); /* 067 */ } /* 068 */ /* 069 */ while (true) { /* 070 */ while (range_number != range_batchEnd) { /* 071 */ long range_value = range_number; /* 072 */ range_number += 1L; /* 073 */ /* 074 */ // do aggregate /* 075 */ // common sub-expressions /* 076 */ /* 077 */ // evaluate aggregate function /* 078 */ boolean agg_isNull1 = false; /* 079 */ /* 080 */ long agg_value1 = -1L; /* 081 */ agg_value1 = agg_bufValue + 1L; /* 082 */ // update aggregation buffer /* 083 */ agg_bufIsNull = false; /* 084 */ agg_bufValue = agg_value1; /* 085 */ /* 086 */ if (shouldStop()) return; /* 087 */ } /* 088 */ /* 089 */ if (range_taskContext.isInterrupted()) { /* 090 */ throw new TaskKilledException(); /* 091 */ } /* 092 */ /* 093 */ long range_nextBatchTodo; /* 094 */ if (range_numElementsTodo > 1000L) { /* 095 */ range_nextBatchTodo = 1000L; /* 096 */ range_numElementsTodo -= 1000L; /* 097 */ } else { /* 098 */ range_nextBatchTodo = range_numElementsTodo; /* 099 */ range_numElementsTodo = 0; /* 100 */ if (range_nextBatchTodo == 0) break; /* 101 */ } /* 102 */ range_numOutputRows.add(range_nextBatchTodo); /* 103 */ range_inputMetrics.incRecordsRead(range_nextBatchTodo); /* 104 */ /* 105 */ range_batchEnd += range_nextBatchTodo * 1L; /* 106 */ } /* 107 */ /* 108 */ } /* 109 */ /* 110 */ private void initRange(int idx) { /* 111 */ java.math.BigInteger index = java.math.BigInteger.valueOf(idx); /* 112 */ java.math.BigInteger numSlice = java.math.BigInteger.valueOf(2L); /* 113 */ java.math.BigInteger numElement = java.math.BigInteger.valueOf(10000L); /* 114 */ java.math.BigInteger step = java.math.BigInteger.valueOf(1L); /* 115 */ java.math.BigInteger start = java.math.BigInteger.valueOf(0L); /* 117 */ /* 118 */ java.math.BigInteger st = index.multiply(numElement).divide(numSlice).multiply(step).add(start); /* 119 */ if (st.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) { /* 120 */ range_number = Long.MAX_VALUE; /* 121 */ } else if (st.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) { /* 122 */ range_number = Long.MIN_VALUE; /* 123 */ } else { /* 124 */ range_number = st.longValue(); /* 125 */ } /* 126 */ range_batchEnd = range_number; /* 127 */ /* 128 */ java.math.BigInteger end = index.add(java.math.BigInteger.ONE).multiply(numElement).divide(numSlice) /* 129 */ .multiply(step).add(start); /* 130 */ if (end.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) { /* 131 */ partitionEnd = Long.MAX_VALUE; /* 132 */ } else if (end.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) { /* 133 */ partitionEnd = Long.MIN_VALUE; /* 134 */ } else { /* 135 */ partitionEnd = end.longValue(); /* 136 */ } /* 137 */ /* 138 */ java.math.BigInteger startToEnd = java.math.BigInteger.valueOf(partitionEnd).subtract( /* 139 */ java.math.BigInteger.valueOf(range_number)); /* 140 */ range_numElementsTodo = startToEnd.divide(step).longValue(); /* 141 */ if (range_numElementsTodo < 0) { /* 142 */ range_numElementsTodo = 0; /* 143 */ } else if (startToEnd.remainder(step).compareTo(java.math.BigInteger.valueOf(0L)) != 0) { /* 144 */ range_numElementsTodo++; /* 145 */ } /* 146 */ } /* 147 */ /* 148 */ protected void processNext() throws java.io.IOException { /* 149 */ while (!agg_initAgg) { /* 150 */ agg_initAgg = true; /* 151 */ long agg_beforeAgg = System.nanoTime(); /* 152 */ agg_doAggregateWithoutKey(); /* 153 */ agg_aggTime.add((System.nanoTime() - agg_beforeAgg) / 1000000); /* 154 */ /* 155 */ // output the result /* 156 */ /* 157 */ agg_numOutputRows.add(1); /* 158 */ agg_rowWriter.zeroOutNullBytes(); /* 159 */ /* 160 */ if (agg_bufIsNull) { /* 161 */ agg_rowWriter.setNullAt(0); /* 162 */ } else { /* 163 */ agg_rowWriter.write(0, agg_bufValue); /* 164 */ } /* 165 */ append(agg_result); /* 166 */ } /* 167 */ } /* 168 */ } ``` Generated code with this PR ```java /* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator { /* 006 */ private Object[] references; /* 007 */ private scala.collection.Iterator[] inputs; /* 008 */ private boolean agg_initAgg; /* 009 */ private boolean agg_bufIsNull; /* 010 */ private long agg_bufValue; /* 011 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numOutputRows; /* 012 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numGeneratedRows; /* 013 */ private boolean range_initRange; /* 014 */ private long range_number; /* 015 */ private TaskContext range_taskContext; /* 016 */ private InputMetrics range_inputMetrics; /* 017 */ private long range_batchEnd; /* 018 */ private long range_numElementsTodo; /* 019 */ private scala.collection.Iterator range_input; /* 020 */ private UnsafeRow range_result; /* 021 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder range_holder; /* 022 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter range_rowWriter; /* 023 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_numOutputRows; /* 024 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_aggTime; /* 025 */ private UnsafeRow agg_result; /* 026 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder agg_holder; /* 027 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter agg_rowWriter; /* 028 */ /* 029 */ public GeneratedIterator(Object[] references) { /* 030 */ this.references = references; /* 031 */ } /* 032 */ /* 033 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 034 */ partitionIndex = index; /* 035 */ this.inputs = inputs; /* 036 */ agg_initAgg = false; /* 037 */ /* 038 */ this.range_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[0]; /* 039 */ this.range_numGeneratedRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[1]; /* 040 */ range_initRange = false; /* 041 */ range_number = 0L; /* 042 */ range_taskContext = TaskContext.get(); /* 043 */ range_inputMetrics = range_taskContext.taskMetrics().inputMetrics(); /* 044 */ range_batchEnd = 0; /* 045 */ range_numElementsTodo = 0L; /* 046 */ range_input = inputs[0]; /* 047 */ range_result = new UnsafeRow(1); /* 048 */ this.range_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(range_result, 0); /* 049 */ this.range_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(range_holder, 1); /* 050 */ this.agg_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[2]; /* 051 */ this.agg_aggTime = (org.apache.spark.sql.execution.metric.SQLMetric) references[3]; /* 052 */ agg_result = new UnsafeRow(1); /* 053 */ this.agg_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(agg_result, 0); /* 054 */ this.agg_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(agg_holder, 1); /* 055 */ /* 056 */ } /* 057 */ /* 058 */ private void agg_doAggregateWithoutKey() throws java.io.IOException { /* 059 */ // initialize aggregation buffer /* 060 */ agg_bufIsNull = false; /* 061 */ agg_bufValue = 0L; /* 062 */ /* 063 */ // initialize Range /* 064 */ if (!range_initRange) { /* 065 */ range_initRange = true; /* 066 */ initRange(partitionIndex); /* 067 */ } /* 068 */ /* 069 */ while (true) { /* 070 */ long range_range = range_batchEnd - range_number; /* 071 */ if (range_range != 0L) { /* 072 */ int range_localEnd = (int)(range_range / 1L); /* 073 */ for (int range_localIdx = 0; range_localIdx < range_localEnd; range_localIdx++) { /* 074 */ long range_value = ((long)range_localIdx * 1L) + range_number; /* 075 */ /* 076 */ // do aggregate /* 077 */ // common sub-expressions /* 078 */ /* 079 */ // evaluate aggregate function /* 080 */ boolean agg_isNull1 = false; /* 081 */ /* 082 */ long agg_value1 = -1L; /* 083 */ agg_value1 = agg_bufValue + 1L; /* 084 */ // update aggregation buffer /* 085 */ agg_bufIsNull = false; /* 086 */ agg_bufValue = agg_value1; /* 087 */ /* 088 */ // shouldStop check is eliminated /* 089 */ } /* 090 */ range_number = range_batchEnd; /* 091 */ } /* 092 */ /* 093 */ if (range_taskContext.isInterrupted()) { /* 094 */ throw new TaskKilledException(); /* 095 */ } /* 096 */ /* 097 */ long range_nextBatchTodo; /* 098 */ if (range_numElementsTodo > 1000L) { /* 099 */ range_nextBatchTodo = 1000L; /* 100 */ range_numElementsTodo -= 1000L; /* 101 */ } else { /* 102 */ range_nextBatchTodo = range_numElementsTodo; /* 103 */ range_numElementsTodo = 0; /* 104 */ if (range_nextBatchTodo == 0) break; /* 105 */ } /* 106 */ range_numOutputRows.add(range_nextBatchTodo); /* 107 */ range_inputMetrics.incRecordsRead(range_nextBatchTodo); /* 108 */ /* 109 */ range_batchEnd += range_nextBatchTodo * 1L; /* 110 */ } /* 111 */ /* 112 */ } /* 113 */ /* 114 */ private void initRange(int idx) { /* 115 */ java.math.BigInteger index = java.math.BigInteger.valueOf(idx); /* 116 */ java.math.BigInteger numSlice = java.math.BigInteger.valueOf(2L); /* 117 */ java.math.BigInteger numElement = java.math.BigInteger.valueOf(10000L); /* 118 */ java.math.BigInteger step = java.math.BigInteger.valueOf(1L); /* 119 */ java.math.BigInteger start = java.math.BigInteger.valueOf(0L); /* 120 */ long partitionEnd; /* 121 */ /* 122 */ java.math.BigInteger st = index.multiply(numElement).divide(numSlice).multiply(step).add(start); /* 123 */ if (st.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) { /* 124 */ range_number = Long.MAX_VALUE; /* 125 */ } else if (st.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) { /* 126 */ range_number = Long.MIN_VALUE; /* 127 */ } else { /* 128 */ range_number = st.longValue(); /* 129 */ } /* 130 */ range_batchEnd = range_number; /* 131 */ /* 132 */ java.math.BigInteger end = index.add(java.math.BigInteger.ONE).multiply(numElement).divide(numSlice) /* 133 */ .multiply(step).add(start); /* 134 */ if (end.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) { /* 135 */ partitionEnd = Long.MAX_VALUE; /* 136 */ } else if (end.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) { /* 137 */ partitionEnd = Long.MIN_VALUE; /* 138 */ } else { /* 139 */ partitionEnd = end.longValue(); /* 140 */ } /* 141 */ /* 142 */ java.math.BigInteger startToEnd = java.math.BigInteger.valueOf(partitionEnd).subtract( /* 143 */ java.math.BigInteger.valueOf(range_number)); /* 144 */ range_numElementsTodo = startToEnd.divide(step).longValue(); /* 145 */ if (range_numElementsTodo < 0) { /* 146 */ range_numElementsTodo = 0; /* 147 */ } else if (startToEnd.remainder(step).compareTo(java.math.BigInteger.valueOf(0L)) != 0) { /* 148 */ range_numElementsTodo++; /* 149 */ } /* 150 */ } /* 151 */ /* 152 */ protected void processNext() throws java.io.IOException { /* 153 */ while (!agg_initAgg) { /* 154 */ agg_initAgg = true; /* 155 */ long agg_beforeAgg = System.nanoTime(); /* 156 */ agg_doAggregateWithoutKey(); /* 157 */ agg_aggTime.add((System.nanoTime() - agg_beforeAgg) / 1000000); /* 158 */ /* 159 */ // output the result /* 160 */ /* 161 */ agg_numOutputRows.add(1); /* 162 */ agg_rowWriter.zeroOutNullBytes(); /* 163 */ /* 164 */ if (agg_bufIsNull) { /* 165 */ agg_rowWriter.setNullAt(0); /* 166 */ } else { /* 167 */ agg_rowWriter.write(0, agg_bufValue); /* 168 */ } /* 169 */ append(agg_result); /* 170 */ } /* 171 */ } /* 172 */ } ``` A part of suppressing `shouldStop()` was originally developed by inouehrs ## How was this patch tested? Add new tests into `DataFrameRangeSuite` Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #17122 from kiszk/SPARK-19786. |
||
---|---|---|
.github | ||
assembly | ||
bin | ||
build | ||
common | ||
conf | ||
core | ||
data | ||
dev | ||
docs | ||
examples | ||
external | ||
graphx | ||
launcher | ||
licenses | ||
mllib | ||
mllib-local | ||
project | ||
python | ||
R | ||
repl | ||
resource-managers | ||
sbin | ||
sql | ||
streaming | ||
tools | ||
.gitattributes | ||
.gitignore | ||
.travis.yml | ||
appveyor.yml | ||
CONTRIBUTING.md | ||
LICENSE | ||
NOTICE | ||
pom.xml | ||
README.md | ||
scalastyle-config.xml |
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples
directory.
To run one of them, use ./bin/run-example <class> [params]
. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
## Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.