4b2c793a27
This PR solves three SerializationDebugger issues. * SPARK-7180 - SerializationDebugger fails with ArrayOutOfBoundsException * SPARK-8090 - SerializationDebugger does not handle classes with writeReplace correctly * SPARK-8091 - SerializationDebugger does not handle classes with writeObject method The solutions for each are explained as follows * SPARK-7180 - The wrong slot desc was used for getting the value of the fields in the object being tested. * SPARK-8090 - Test the type of the replaced object. * SPARK-8091 - Use a dummy ObjectOutputStream to collect all the objects written by the writeObject() method, and then test those objects as usual. I also added more tests in the testsuite to increase code coverage. For example, added tests for cases where there are not serializability issues. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #6625 from tdas/SPARK-7180 and squashes the following commits: c7cb046 [Tathagata Das] Addressed comments on docs ae212c8 [Tathagata Das] Improved docs 304c97b [Tathagata Das] Fixed build error 26b5179 [Tathagata Das] more tests.....92% line coverage 7e2fdcf [Tathagata Das] Added more tests d1967fb [Tathagata Das] Added comments. da75d34 [Tathagata Das] Removed unnecessary lines. 50a608d [Tathagata Das] Fixed bugs and added support for writeObject |
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data/mllib | ||
dev | ||
docker | ||
docs | ||
ec2 | ||
examples | ||
external | ||
extras | ||
graphx | ||
launcher | ||
mllib | ||
network | ||
project | ||
python | ||
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sbin | ||
sbt | ||
sql | ||
streaming | ||
tools | ||
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CHANGES.txt | ||
CONTRIBUTING.md | ||
LICENSE | ||
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pom.xml | ||
README.md | ||
scalastyle-config.xml | ||
tox.ini |
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.