cb6c48c874
Existing implementation of arithmetic operators and BinaryComparison operators have redundant type checking codes, e.g.: Expression.n2 is used by Add/Subtract/Multiply. (1) n2 always checks left.dataType == right.dataType. However, this checking should be done once when we resolve expression types; (2) n2 requires dataType is a NumericType. This can be done once. This PR optimizes arithmetic and predicate operators by removing such redundant type-checking codes. Some preliminary benchmarking on 10G TPC-H data over 5 r3.2xlarge EC2 machines shows that this PR can reduce the query time by 5.5% to 11%. The benchmark queries follow the template below, where OP is plus/minus/times/divide/remainder/bitwise and/bitwise or/bitwise xor. SELECT l_returnflag, l_linestatus, SUM(l_quantity OP cnt1), SUM(l_quantity OP cnt2), ...., SUM(l_quantity OP cnt700) FROM ( SELECT l_returnflag, l_linestatus, l_quantity, 1 AS cnt1, 2 AS cnt2, ..., 700 AS cnt700 FROM lineitem WHERE l_shipdate <= '1998-09-01' ) GROUP BY l_returnflag, l_linestatus; Author: kai <kaizeng@eecs.berkeley.edu> Closes #4472 from kai-zeng/arithmetic-optimize and squashes the following commits: fef0cf1 [kai] Merge branch 'master' of github.com:apache/spark into arithmetic-optimize 4b3a1bb [kai] chmod a-x 5a41e49 [kai] chmod a-x Expression.scala cb37c94 [kai] rebase onto spark master 7f6e968 [kai] chmod 100755 -> 100644 6cddb46 [kai] format 7490dbc [kai] fix unresolved-expression exception for EqualTo 9c40bc0 [kai] fix bitwisenot 3cbd363 [kai] clean up test code ca47801 [kai] override evalInternal for bitwise ops 8fa84a1 [kai] add bitwise or and xor 6892fc4 [kai] revert override evalInternal f8eba24 [kai] override evalInternal 31ccdd4 [kai] rewrite all bitwise op and remove evalInternal 86297e2 [kai] generalized cb92ae1 [kai] bitwise-and: override eval 97a7d6c [kai] bitwise-and: override evalInternal using and func 0906c39 [kai] add bitwise test 62abbbc [kai] clean up predicate and arithmetic b34d58d [kai] add caching and benmark option 12c5b32 [kai] override eval 1cd7571 [kai] fix sqrt and maxof 03fd0c3 [kai] fix predicate 16fd84c [kai] optimize + - * / % -(unary) abs < > <= >= fd95823 [kai] remove unnecessary type checking 24d062f [kai] test suite |
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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, and Python, 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 structured data processing, 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:
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".
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-cluster" or "yarn-client" 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 all automated 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. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.