Snippets Groups Projects

[SPARK-6627] Some clean-up in shuffle code.

Patrick Wendell authored 10 years ago

Before diving into review #4450 I did a look through the existing shuffle
code to learn how it works. Unfortunately, there are some very
confusing things in this code. This patch makes a few small changes
to simplify things. It is not easily to concisely describe the changes
because of how convoluted the issues were, but they are fairly small
logically:

1. There is a trait named `ShuffleBlockManager` that only deals with
one logical function which is retrieving shuffle block data given shuffle
block coordinates. This trait has two implementors FileShuffleBlockManager
and IndexShuffleBlockManager. Confusingly the vast majority of those
implementations have nothing to do with this particular functionality.
So I've renamed the trait to ShuffleBlockResolver and documented it.
2. The aforementioned trait had two almost identical methods, for no good
reason. I removed one method (getBytes) and modified callers to use the
other one. I think the behavior is preserved in all cases.
3. The sort shuffle code uses an identifier "0" in the reduce slot of a
BlockID as a placeholder. I made it into a constant since it needs to
be consistent across multiple places.

I think for (3) there is actually a better solution that would avoid the
need to do this type of workaround/hack in the first place, but it's more
complex so I'm punting it for now.

Author: Patrick Wendell <patrick@databricks.com>

Closes #5286 from pwendell/cleanup and squashes the following commits:

c71fbc7 [Patrick Wendell] Open interface back up for testing
f36edd5 [Patrick Wendell] Code review feedback
d1c0494 [Patrick Wendell] Style fix
a406079 [Patrick Wendell] [HOTFIX] Some clean-up in shuffle code.

6562787b

6562787b 10 years ago

Name	Last commit	Last update
assembly
bagel
bin
build
conf
core
data/mllib
dev
docker
docs
ec2
examples
external
extras
graphx
launcher
mllib
network
project
python
repl
sbin
sbt
sql
streaming
tools
yarn
.gitattributes
.gitignore
.rat-excludes
CONTRIBUTING.md
LICENSE
NOTICE
README.md
make-distribution.sh
pom.xml
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.

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:

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.