Snippets Groups Projects

[SPARK-9451] [SQL] Support entries larger than default page size in...

Josh Rosen authored 9 years ago

[SPARK-9451] [SQL] Support entries larger than default page size in BytesToBytesMap & integrate with ShuffleMemoryManager

This patch adds support for entries larger than the default page size in BytesToBytesMap.  These large rows are handled by allocating special overflow pages to hold individual entries.

In addition, this patch integrates BytesToBytesMap with the ShuffleMemoryManager:

- Move BytesToBytesMap from `unsafe` to `core` so that it can import `ShuffleMemoryManager`.
- Before allocating new data pages, ask the ShuffleMemoryManager to reserve the memory:
  - `putNewKey()` now returns a boolean to indicate whether the insert succeeded or failed due to a lack of memory.  The caller can use this value to respond to the memory pressure (e.g. by spilling).
- `UnsafeFixedWidthAggregationMap. getAggregationBuffer()` now returns `null` to signal failure due to a lack of memory.
- Updated all uses of these classes to handle these error conditions.
- Added new tests for allocating large records and for allocations which fail due to memory pressure.
- Extended the `afterAll()` test teardown methods to detect ShuffleMemoryManager leaks.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7762 from JoshRosen/large-rows and squashes the following commits:

ae7bc56 [Josh Rosen] Fix compilation
82fc657 [Josh Rosen] Merge remote-tracking branch 'origin/master' into large-rows
34ab943 [Josh Rosen] Remove semi
31a525a [Josh Rosen] Integrate BytesToBytesMap with ShuffleMemoryManager.
626b33c [Josh Rosen] Move code to sql/core and spark/core packages so that ShuffleMemoryManager can be integrated
ec4484c [Josh Rosen] Move BytesToBytesMap from unsafe package to core.
642ed69 [Josh Rosen] Rename size to numElements
bea1152 [Josh Rosen] Add basic test.
2cd3570 [Josh Rosen] Remove accidental duplicated code
07ff9ef [Josh Rosen] Basic support for large rows in BytesToBytesMap.

8cb415a4

8cb415a4 9 years ago

Name	Last commit	Last update
R
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
unsafe
yarn
.gitattributes
.gitignore
.rat-excludes
CONTRIBUTING.md
LICENSE
NOTICE
README.md
make-distribution.sh
pom.xml
pylintrc
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 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.) 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 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. 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.