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## What changes were proposed in this pull request? This PR allows us to identify what JVM is used when someone ran a benchmark program. In some cases, a JVM version may affect performance result. Thus, it would be good to show processor information and JVM version information. ``` model name : Intel(R) Xeon(R) CPU E5-2697 v2 2.70GHz JVM information : OpenJDK 64-Bit Server VM, 1.7.0_65-mockbuild_2014_07_14_06_19-b00 Int and String Scan: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- SQL Parquet Vectorized 981 / 994 10.7 93.5 1.0X SQL Parquet MR 2518 / 2542 4.2 240.1 0.4X ``` ``` model name : Intel(R) Xeon(R) CPU E5-2697 v2 2.70GHz JVM information : IBM J9 VM, pxa6480sr2-20151023_01 (SR2) String Dictionary: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- SQL Parquet Vectorized 693 / 740 15.1 66.1 1.0X SQL Parquet MR 2501 / 2562 4.2 238.5 0.3X ``` ## How was this patch tested? Tested by using existing benchmark programs (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #11893 from kiszk/SPARK-14072. |
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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.) 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.