3a8b698e96
Thanks for Diana Carroll to report this issue (https://spark-project.atlassian.net/browse/SPARK-1100) the current saveAsTextFile/SequenceFile will overwrite the output directory silently if the directory already exists, this behaviour is not desirable because overwriting the data silently is not user-friendly if the partition number of two writing operation changed, then the output directory will contain the results generated by two runnings My fix includes: add some new APIs with a flag for users to define whether he/she wants to overwrite the directory: if the flag is set to true, then the output directory is deleted first and then written into the new data to prevent the output directory contains results from multiple rounds of running; if the flag is set to false, Spark will throw an exception if the output directory already exists changed JavaAPI part default behaviour is overwriting Two questions should we deprecate the old APIs without such a flag? I noticed that Spark Streaming also called these APIs, I thought we don't need to change the related part in streaming? @tdas Author: CodingCat <zhunansjtu@gmail.com> Closes #11 from CodingCat/SPARK-1100 and squashes the following commits: 6a4e3a3 [CodingCat] code clean ef2d43f [CodingCat] add new test cases and code clean ac63136 [CodingCat] checkOutputSpecs not applicable to FSOutputFormat ec490e8 [CodingCat] prevent Spark from overwriting directory silently and leaving dirty directory |
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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 requires Scala 2.10. The project is built using Simple Build Tool (SBT), which can be obtained here. If SBT is installed we will use the system version of sbt otherwise we will attempt to download it automatically. To build Spark and its example programs, run:
./sbt/sbt assembly
Once you've built Spark, the easiest way to start using it is the shell:
./bin/spark-shell
Or, for the Python API, the Python shell (./bin/pyspark
).
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 org.apache.spark.examples.SparkLR local[2]
will run the Logistic Regression example locally on 2 CPUs.
Each of the example programs prints usage help if no params are given.
All of the Spark samples take a <master>
parameter that is the cluster URL
to connect to. This can be a mesos:// or spark:// URL, or "local" to run
locally with one thread, or "local[N]" to run locally with N threads.
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.