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[SPARK-18546][CORE] Fix merging shuffle spills when using encryption.

Marcelo Vanzin authored 8 years ago

The problem exists because it's not possible to just concatenate encrypted
partition data from different spill files; currently each partition would
have its own initial vector to set up encryption, and the final merged file
should contain a single initial vector for each merged partiton, otherwise
iterating over each record becomes really hard.

To fix that, UnsafeShuffleWriter now decrypts the partitions when merging,
so that the merged file contains a single initial vector at the start of
the partition data.

Because it's not possible to do that using the fast transferTo path, when
encryption is enabled UnsafeShuffleWriter will revert back to using file
streams when merging. It may be possible to use a hybrid approach when
using encryption, using an intermediate direct buffer when reading from
files and encrypting the data, but that's better left for a separate patch.

As part of the change I made DiskBlockObjectWriter take a SerializerManager
instead of a "wrap stream" closure, since that makes it easier to test the
code without having to mock SerializerManager functionality.

Tested with newly added unit tests (UnsafeShuffleWriterSuite for the write
side and ExternalAppendOnlyMapSuite for integration), and by running some
apps that failed without the fix.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #15982 from vanzin/SPARK-18546.

93e9d880

93e9d880 8 years ago

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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, Python, and R, 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 DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

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:

build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)

You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".

For general development tips, including info on developing Spark using an IDE, see http://spark.apache.org/developer-tools.html.

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" 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 tests for a module, or individual 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.

Configuration

Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.

## Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.