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## What changes were proposed in this pull request? This PR eliminates redundant cast from an `ArrayType` with `containsNull = false` or a `MapType` with `containsNull = false`. For example, in `ArrayType` case, current implementation leaves a cast `cast(value#63 as array<double>).toDoubleArray`. However, we can eliminate `cast(value#63 as array<double>)` if we know `value#63` does not include `null`. This PR apply this elimination for `ArrayType` and `MapType` in `SimplifyCasts` at a plan optimization phase. In summary, we got 1.2-1.3x performance improvements over the code before applying this PR. Here are performance results of benchmark programs: ``` test("Read array in Dataset") { import sparkSession.implicits._ val iters = 5 val n = 1024 * 1024 val rows = 15 val benchmark = new Benchmark("Read primnitive array", n) val rand = new Random(511) val intDS = sparkSession.sparkContext.parallelize(0 until rows, 1) .map(i => Array.tabulate(n)(i => i)).toDS() intDS.count() // force to create ds val lastElement = n - 1 val randElement = rand.nextInt(lastElement) benchmark.addCase(s"Read int array in Dataset", numIters = iters)(iter => { val idx0 = randElement val idx1 = lastElement intDS.map(a => a(0) + a(idx0) + a(idx1)).collect }) val doubleDS = sparkSession.sparkContext.parallelize(0 until rows, 1) .map(i => Array.tabulate(n)(i => i.toDouble)).toDS() doubleDS.count() // force to create ds benchmark.addCase(s"Read double array in Dataset", numIters = iters)(iter => { val idx0 = randElement val idx1 = lastElement doubleDS.map(a => a(0) + a(idx0) + a(idx1)).collect }) benchmark.run() } Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.4 Intel(R) Core(TM) i5-5257U CPU 2.70GHz without this PR Read primnitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Read int array in Dataset 525 / 690 2.0 500.9 1.0X Read double array in Dataset 947 / 1209 1.1 902.7 0.6X with this PR Read primnitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Read int array in Dataset 400 / 492 2.6 381.5 1.0X Read double array in Dataset 788 / 870 1.3 751.4 0.5X ``` An example program that originally caused this performance issue. ``` val ds = Seq(Array(1.0, 2.0, 3.0), Array(4.0, 5.0, 6.0)).toDS() val ds2 = ds.map(p => { var s = 0.0 for (i <- 0 to 2) { s += p(i) } s }) ds2.show ds2.explain(true) ``` Plans before this PR ``` == Parsed Logical Plan == 'SerializeFromObject [input[0, double, true] AS value#68] +- 'MapElements <function1>, obj#67: double +- 'DeserializeToObject unresolveddeserializer(upcast(getcolumnbyordinal(0, ArrayType(DoubleType,false)), ArrayType(DoubleType,false), - root class: "scala.Array").toDoubleArray), obj#66: [D +- LocalRelation [value#63] == Analyzed Logical Plan == value: double SerializeFromObject [input[0, double, true] AS value#68] +- MapElements <function1>, obj#67: double +- DeserializeToObject cast(value#63 as array<double>).toDoubleArray, obj#66: [D +- LocalRelation [value#63] == Optimized Logical Plan == SerializeFromObject [input[0, double, true] AS value#68] +- MapElements <function1>, obj#67: double +- DeserializeToObject cast(value#63 as array<double>).toDoubleArray, obj#66: [D +- LocalRelation [value#63] == Physical Plan == *SerializeFromObject [input[0, double, true] AS value#68] +- *MapElements <function1>, obj#67: double +- *DeserializeToObject cast(value#63 as array<double>).toDoubleArray, obj#66: [D +- LocalTableScan [value#63] ``` Plans after this PR ``` == Parsed Logical Plan == 'SerializeFromObject [input[0, double, true] AS value#6] +- 'MapElements <function1>, obj#5: double +- 'DeserializeToObject unresolveddeserializer(upcast(getcolumnbyordinal(0, ArrayType(DoubleType,false)), ArrayType(DoubleType,false), - root class: "scala.Array").toDoubleArray), obj#4: [D +- LocalRelation [value#1] == Analyzed Logical Plan == value: double SerializeFromObject [input[0, double, true] AS value#6] +- MapElements <function1>, obj#5: double +- DeserializeToObject cast(value#1 as array<double>).toDoubleArray, obj#4: [D +- LocalRelation [value#1] == Optimized Logical Plan == SerializeFromObject [input[0, double, true] AS value#6] +- MapElements <function1>, obj#5: double +- DeserializeToObject value#1.toDoubleArray, obj#4: [D +- LocalRelation [value#1] == Physical Plan == *SerializeFromObject [input[0, double, true] AS value#6] +- *MapElements <function1>, obj#5: double +- *DeserializeToObject value#1.toDoubleArray, obj#4: [D +- LocalTableScan [value#1] ``` ## How was this patch tested? Tested by new test cases in `SimplifyCastsSuite` Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #13704 from kiszk/SPARK-15985. |
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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 and project wiki. 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 developing Spark using an IDE, see Eclipse and IntelliJ.
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.