7c89a8f0c8
Given that a lot of users are trying to use hive 0.13 in spark, and the incompatibility between hive-0.12 and hive-0.13 on the API level I want to propose following approach, which has no or minimum impact on existing hive-0.12 support, but be able to jumpstart the development of hive-0.13 and future version support. Approach: Introduce “hive-version” property, and manipulate pom.xml files to support different hive version at compiling time through shim layer, e.g., hive-0.12.0 and hive-0.13.1. More specifically, 1. For each different hive version, there is a very light layer of shim code to handle API differences, sitting in sql/hive/hive-version, e.g., sql/hive/v0.12.0 or sql/hive/v0.13.1 2. Add a new profile hive-default active by default, which picks up all existing configuration and hive-0.12.0 shim (v0.12.0) if no hive.version is specified. 3. If user specifies different version (currently only 0.13.1 by -Dhive.version = 0.13.1), hive-versions profile will be activated, which pick up hive-version specific shim layer and configuration, mainly the hive jars and hive-version shim, e.g., v0.13.1. 4. With this approach, nothing is changed with current hive-0.12 support. No change by default: sbt/sbt -Phive For example: sbt/sbt -Phive -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 assembly To enable hive-0.13: sbt/sbt -Dhive.version=0.13.1 For example: sbt/sbt -Dhive.version=0.13.1 -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 assembly Note that in hive-0.13, hive-thriftserver is not enabled, which should be fixed by other Jira, and we don’t need -Phive with -Dhive.version in building (probably we should use -Phive -Dhive.version=xxx instead after thrift server is also supported in hive-0.13.1). Author: Zhan Zhang <zhazhan@gmail.com> Author: zhzhan <zhazhan@gmail.com> Author: Patrick Wendell <pwendell@gmail.com> Closes #2241 from zhzhan/spark-2706 and squashes the following commits: 3ece905 [Zhan Zhang] minor fix 410b668 [Zhan Zhang] solve review comments cbb4691 [Zhan Zhang] change run-test for new options 0d4d2ed [Zhan Zhang] rebase 497b0f4 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark 8fad1cf [Zhan Zhang] change the pom file and make hive-0.13.1 as the default ab028d1 [Zhan Zhang] rebase 4a2e36d [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark 4cb1b93 [zhzhan] Merge pull request #1 from pwendell/pr-2241 b0478c0 [Patrick Wendell] Changes to simplify the build of SPARK-2706 2b50502 [Zhan Zhang] rebase a72c0d4 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark cb22863 [Zhan Zhang] correct the typo 20f6cf7 [Zhan Zhang] solve compatability issue f7912a9 [Zhan Zhang] rebase and solve review feedback 301eb4a [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark 10c3565 [Zhan Zhang] address review comments 6bc9204 [Zhan Zhang] rebase and remove temparory repo d3aa3f2 [Zhan Zhang] Merge branch 'master' into spark-2706 cedcc6f [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark 3ced0d7 [Zhan Zhang] rebase d9b981d [Zhan Zhang] rebase and fix error due to rollback adf4924 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark 3dd50e8 [Zhan Zhang] solve conflicts and remove unnecessary implicts d10bf00 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark dc7bdb3 [Zhan Zhang] solve conflicts 7e0cc36 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark d7c3e1e [Zhan Zhang] Merge branch 'master' into spark-2706 68deb11 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark d48bd18 [Zhan Zhang] address review comments 3ee3b2b [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark 57ea52e [Zhan Zhang] Merge branch 'master' into spark-2706 2b0d513 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark 9412d24 [Zhan Zhang] address review comments f4af934 [Zhan Zhang] rebase 1ccd7cc [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark 128b60b [Zhan Zhang] ignore 0.12.0 test cases for the time being af9feb9 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark 5f5619f [Zhan Zhang] restructure the directory and different hive version support 05d3683 [Zhan Zhang] solve conflicts e4c1982 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark 94b4fdc [Zhan Zhang] Spark-2706: hive-0.13.1 support on spark 87ebf3b [Zhan Zhang] Merge branch 'master' into spark-2706 921e914 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark f896b2a [Zhan Zhang] Merge branch 'master' into spark-2706 789ea21 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark cb53a2c [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark f6a8a40 [Zhan Zhang] revert ba14f28 [Zhan Zhang] test dbedff3 [Zhan Zhang] Merge remote-tracking branch 'upstream/master' 70964fe [Zhan Zhang] revert fe0f379 [Zhan Zhang] Merge branch 'master' of https://github.com/zhzhan/spark 70ffd93 [Zhan Zhang] revert 42585ec [Zhan Zhang] test 7d5fce2 [Zhan Zhang] test |
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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. 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 with Maven".
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.