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This PR introduces a 64-bit hashcode expression. Such an expression is especially usefull for HyperLogLog++ and other probabilistic datastructures. I have implemented xxHash64 which is a 64-bit hashing algorithm created by Yann Colet and Mathias Westerdahl. This is a high speed (C implementation runs at memory bandwidth) and high quality hashcode. It exploits both Instruction Level Parralellism (for speed) and the multiplication and rotation techniques (for quality) like MurMurHash does. The initial results are promising. I have added a CG'ed test to the `HashBenchmark`, and this results in the following results (running from SBT): Running benchmark: Hash For simple Running case: interpreted version Running case: codegen version Running case: codegen version 64-bit Intel(R) Core(TM) i7-4750HQ CPU 2.00GHz Hash For simple: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- interpreted version 1011 / 1016 132.8 7.5 1.0X codegen version 1864 / 1869 72.0 13.9 0.5X codegen version 64-bit 1614 / 1644 83.2 12.0 0.6X Running benchmark: Hash For normal Running case: interpreted version Running case: codegen version Running case: codegen version 64-bit Intel(R) Core(TM) i7-4750HQ CPU 2.00GHz Hash For normal: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- interpreted version 2467 / 2475 0.9 1176.1 1.0X codegen version 2008 / 2115 1.0 957.5 1.2X codegen version 64-bit 728 / 758 2.9 347.0 3.4X Running benchmark: Hash For array Running case: interpreted version Running case: codegen version Running case: codegen version 64-bit Intel(R) Core(TM) i7-4750HQ CPU 2.00GHz Hash For array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- interpreted version 1544 / 1707 0.1 11779.6 1.0X codegen version 2728 / 2745 0.0 20815.5 0.6X codegen version 64-bit 2508 / 2549 0.1 19132.8 0.6X Running benchmark: Hash For map Running case: interpreted version Running case: codegen version Running case: codegen version 64-bit Intel(R) Core(TM) i7-4750HQ CPU 2.00GHz Hash For map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- interpreted version 1819 / 1826 0.0 444014.3 1.0X codegen version 183 / 194 0.0 44642.9 9.9X codegen version 64-bit 173 / 174 0.0 42120.9 10.5X This shows that algorithm is consistently faster than MurMurHash32 in all cases and up to 3x (!) in the normal case. I have also added this to HyperLogLog++ and it cuts the processing time of the following code in half: val df = sqlContext.range(1<<25).agg(approxCountDistinct("id")) df.explain() val t = System.nanoTime() df.show() val ns = System.nanoTime() - t // Before ns: Long = 5821524302 // After ns: Long = 2836418963 cc cloud-fan (you have been working on hashcodes) / rxin Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #11209 from hvanhovell/xxHash. |
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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.