8f7141fbc0
JIRA issue: [SPARK-1368](https://issues.apache.org/jira/browse/SPARK-1368) This PR introduces two major updates: - Replaced FP style code with `while` loop and reusable `GenericMutableRow` object in critical path of `HiveTableScan`. - Using `ColumnProjectionUtils` to help optimizing RCFile and ORC column pruning. My quick micro benchmark suggests these two optimizations made the optimized version 2x and 2.5x faster when scanning CSV table and RCFile table respectively: ``` Original: [info] CSV: 27676 ms, RCFile: 26415 ms [info] CSV: 27703 ms, RCFile: 26029 ms [info] CSV: 27511 ms, RCFile: 25962 ms Optimized: [info] CSV: 13820 ms, RCFile: 10402 ms [info] CSV: 14158 ms, RCFile: 10691 ms [info] CSV: 13606 ms, RCFile: 10346 ms ``` The micro benchmark loads a 609MB CVS file (structurally similar to the `src` test table) into a normal Hive table with `LazySimpleSerDe` and a RCFile table, then scans these tables respectively. Preparation code: ```scala package org.apache.spark.examples.sql.hive import org.apache.spark.sql.hive.LocalHiveContext import org.apache.spark.{SparkConf, SparkContext} object HiveTableScanPrepare extends App { val sparkContext = new SparkContext( new SparkConf() .setMaster("local") .setAppName(getClass.getSimpleName.stripSuffix("$"))) val hiveContext = new LocalHiveContext(sparkContext) import hiveContext._ hql("drop table scan_csv") hql("drop table scan_rcfile") hql("""create table scan_csv (key int, value string) | row format serde 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' | with serdeproperties ('field.delim'=',') """.stripMargin) hql(s"""load data local inpath "${args(0)}" into table scan_csv""") hql("""create table scan_rcfile (key int, value string) | row format serde 'org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe' |stored as | inputformat 'org.apache.hadoop.hive.ql.io.RCFileInputFormat' | outputformat 'org.apache.hadoop.hive.ql.io.RCFileOutputFormat' """.stripMargin) hql( """ |from scan_csv |insert overwrite table scan_rcfile |select scan_csv.key, scan_csv.value """.stripMargin) } ``` Benchmark code: ```scala package org.apache.spark.examples.sql.hive import org.apache.spark.sql.hive.LocalHiveContext import org.apache.spark.{SparkConf, SparkContext} object HiveTableScanBenchmark extends App { val sparkContext = new SparkContext( new SparkConf() .setMaster("local") .setAppName(getClass.getSimpleName.stripSuffix("$"))) val hiveContext = new LocalHiveContext(sparkContext) import hiveContext._ val scanCsv = hql("select key from scan_csv") val scanRcfile = hql("select key from scan_rcfile") val csvDuration = benchmark(scanCsv.count()) val rcfileDuration = benchmark(scanRcfile.count()) println(s"CSV: $csvDuration ms, RCFile: $rcfileDuration ms") def benchmark(f: => Unit) = { val begin = System.currentTimeMillis() f val end = System.currentTimeMillis() end - begin } } ``` @marmbrus Please help review, thanks! Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #758 from liancheng/fastHiveTableScan and squashes the following commits: 4241a19 [Cheng Lian] Distinguishes sorted and possibly not sorted operations more accurately in HiveComparisonTest cf640d8 [Cheng Lian] More HiveTableScan optimisations: bf0e7dc [Cheng Lian] Added SortedOperation pattern to match *some* definitely sorted operations and avoid some sorting cost in HiveComparisonTest. 6d1c642 [Cheng Lian] Using ColumnProjectionUtils to optimise RCFile and ORC column pruning eb62fd3 [Cheng Lian] [SPARK-1368] Optimized HiveTableScan |
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Apache Spark
Lightning-Fast Cluster Computing - http://spark.apache.org/
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project webpage at http://spark.apache.org/documentation.html. This README file only contains basic setup instructions.
Building Spark
Spark is built on Scala 2.10. To build Spark and its example programs, run:
./sbt/sbt assembly
(You do not need to do this if you downloaded a pre-built package.)
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:
./sbt/sbt test
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.
You can change the version by setting the SPARK_HADOOP_VERSION
environment
when building Spark.
For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions without YARN, use:
# Apache Hadoop 1.2.1
$ SPARK_HADOOP_VERSION=1.2.1 sbt/sbt assembly
# Cloudera CDH 4.2.0 with MapReduce v1
$ SPARK_HADOOP_VERSION=2.0.0-mr1-cdh4.2.0 sbt/sbt assembly
For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
with YARN, also set SPARK_YARN=true
:
# Apache Hadoop 2.0.5-alpha
$ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
# Cloudera CDH 4.2.0 with MapReduce v2
$ SPARK_HADOOP_VERSION=2.0.0-cdh4.2.0 SPARK_YARN=true sbt/sbt assembly
# Apache Hadoop 2.2.X and newer
$ SPARK_HADOOP_VERSION=2.2.0 SPARK_YARN=true sbt/sbt assembly
When developing a Spark application, specify the Hadoop version by adding the
"hadoop-client" artifact to your project's dependencies. For example, if you're
using Hadoop 1.2.1 and build your application using SBT, add this entry to
libraryDependencies
:
"org.apache.hadoop" % "hadoop-client" % "1.2.1"
If your project is built with Maven, add this to your POM file's <dependencies>
section:
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>1.2.1</version>
</dependency>
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.
Contributing to Spark
Contributions via GitHub pull requests are gladly accepted from their original author. Along with any pull requests, please state that the contribution is your original work and that you license the work to the project under the project's open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project's open source license and warrant that you have the legal authority to do so.