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Thanks for using Cascading.

Cascading 4.5

Cascading 4.5 includes a few major changes and additions from prior major releases:

  • Support for Apache Hadoop 3. and Apache Tez 0.10.x
  • Removed Apache Hadoop 2.x and Apache Tez 0.8.x support

Cascading 4 includes a few major changes and additions from prior major releases:

  • Local mode support improved for production use
  • Moved the website to
  • Changed the Maven group name to net.wensel (from cascading)
  • Moved to GitHub Packages (WIPs) and Maven Central (releases)
  • Added native JSON support via the cascading-nested-json sub-project
  • Removed cascading-xml sub-project
  • Removed Apache Hadoop 1.x support
  • Imported deprecated Apache Parquet classes to retain support and provide enhancements

General Information:

For current WIP releases, go to:

For project documentation and community support, visit:

The project includes nine Cascading jar files:

  • cascading-core-x.y.z.jar - all Cascading Core class files
  • cascading-expression-x.y.z.jar - all Cascading Janino expression operations class files
  • cascading-nested-json-x.y.z.jar - all Cascading JSON operations
  • cascading-nested-x.y.z.jar - all Cascading base classes for nested data-type operations
  • cascading-local-x.y.z.jar - all Cascading Local in-memory mode class files
  • cascading-local-kafka-x.y.z.jar - all Cascading Local support for Apache Kafka
  • cascading-local-neo4j-x.y.z.jar - all Cascading Local support for Neo4j
  • cascading-local-s3-x.y.z.jar - all Cascading Local support for AWS S3
  • cascading-local-splunk-x.y.z.jar - all Cascading Local support for Splunk
  • cascading-local-hadoop3-io-x.y.z.jar - all Cascading Local in-memory mode class files used with Hadoop
  • cascading-hadoop3-common-x.y.z.jar - all Cascading Hadoop 3.x common class files
  • cascading-hadoop3-io-x.y.z.jar - all Cascading Hadoop 3.x HDFS and IO related class files
  • cascading-hadoop3-mr1-x.y.z.jar - all Cascading Hadoop 3.x MapReduce mode class files
  • cascading-hadoop3-tez-x.y.z.jar - all Cascading Hadoop 3.x Tez mode class files
  • cascading-hadoop3-tez-stats-x.y.z.jar - all Cascading Tez YARN timeline server class files
  • cascading-hadoop3-parquet-x.y.z.jar - all Cascading Parquet class files
  • cascading-hadoop3-parquet-thrift-x.y.z.jar - all Cascading Parquet Thrift class files

These class jars, along with, tests, source and javadoc jars, are all available via the Maven repository.

Local mode is where the Cascading application will run locally in memory without cluster distribution. This implementation has minimal to no robustness in low memory situations, by design.

Hadoop 3.x MR1 mode is for running on Hadoop 3.x releases.

Hadoop 3.x Tez mode is where the Cascading application should run on an Apache Tez DAG cluster.

As of Cascading 4.x, all above jar files are built against Java 1.8. Prior versions of Cascading are built against Java 1.7 and 1.6.

Local Mode

Local mode has been much improved for production use in applications that do not need to run distributed across a cluster. Specifically in applications that trivially parallelize and run within AWS Lambda or Batch applications.

See for a collection of local mode integrations.

Note this project will merge into Cascading in then next minor release.

Extensions, the SDK, and DSLs

There are a number of projects based on and extensions to Cascading available.

Visit for links. As these projects are updated to depend on 4.x, we will update the main site.

Note many projects built and released against Cascading 3.x will work without modification with Cascading 4.x.


Cascading stable releases are always of the form x.y.z, where z is the current maintenance release.

x.y.z releases are maintenance releases. No public incompatible API changes will be made, but in an effort to fix bugs, remediation may entail throwing new Exceptions.

x.y releases are minor releases. New features are added. No public incompatible API changes will be made on the core processing APIs (Pipes, Functions, etc), but in an effort to resolve inconsistencies, minor semantic changes may be necessary.

It is important to note that we do reserve to make breaking changes to the new query planner API through the 4.x releases. This allows us to respond to bugs and performance issues without issuing new major releases.

All releases will be maintained here:

WIP (work in progress) releases are fully tested builds of code not yet deemed fully stable. On every build by our continuous integration servers, the WIP build number is increased. Successful builds are then tagged and published.

The WIP releases are always of the form x.y.z-wip-n, where x.y.z will be the next stable release version the WIP releases are leading up to. n is the current successfully tested build.

The source, working branches, and tags for all WIP releases can be found here:

When a WIP is deemed stable and ready for production use, it will be published as a x.y.z release, and made available from Maven Central.

Writing and Running Tests

Comprehensive tests should be written against the cascading.PlatformTestCase.

When running tests built against the PlatformTestCase, the local cluster can be disabled (if enabled by the test) by setting:


From Gradle, to run a single test case:

> ./gradlew :cascading-hadoop3-mr1:platformTest --tests=*.FieldedPipesPlatformTest -i

or a single test method:

> ./gradlew :cascading-hadoop3-mr1:platformTest --tests=*.FieldedPipesPlatformTest.testNoGroup -i

Debugging the Planner

When running tests, set the following


If you are on Mac OS X and have installed GraphViz, dot files can be converted to pdf on the fly. To enable, set:

Optionally, for stand alone applications, statistics and tracing can be enabled selectively with the following properties:

  • cascading.planner.stats.path - outputs detailed statistics on time spent by the planner
  • cascading.planner.plan.path - basic planner information
  • cascading.planner.plan.transforms.path - detailed information for each rule

Contributing and Reporting Issues

See at

Using with Maven/Ivy

It is strongly recommended developers pull Cascading from Maven Central.

Alternatively, see GitHub Packages for latest WIP releases:

When creating tests, make sure to add any of the relevant above dependencies to your test scope or equivalent configuration along with the cascading-platform dependency.

Note the cascading-platform compile dependency has no classes, you must pull the tests dependency with the tests classifier.

Source and Javadoc artifacts (using the appropriate classifier) are also available through Maven.

Note that cascading-hadoop3-mr1, and cascading-hadoop3-tez have a provided dependency on the Hadoop jars so that it won't get sucked into any application packaging as a dependency, typically.

Building and IDE Integration

For most cases, building Cascading is unnecessary as it has been pre-built, tested, and published to our Maven repository (above).

To build Cascading, run the following in the shell:

> git clone
> cd cascading
> ./gradlew build

Using with Apache Hadoop

First confirm you are using a supported version of Apache Hadoop by checking the Compatibility page.

To use Cascading with Hadoop, we suggest stuffing cascading-core and cascading-hadoop3-mr1, jar files and all third-party libs into the lib folder of your job jar and executing your job via $HADOOP_HOME/bin/hadoop jar your.jar <your args>.

For example, your job jar would look like this (via: jar -t your.jar)

/<all your class and resource files>
/lib/<cascading third-party jar files>

Hadoop will unpack the jar locally and remotely (in the cluster) and add any libraries in lib to the classpath. This is a feature specific to Hadoop.

History and Status

Cascading was started in 2007 by Chris K Wensel.

After a series of acquisitions, it was left unsupported and unmaintained by the copyright, domain name, and GitHub organization owners.

Chris has since continued his noodling with Cascading and has been pushing changes to the original repo.

Cascading remains under the Apache License v2.0.


Cascading is a feature rich API for defining and executing complex and fault tolerant data processing flows locally or on a cluster.







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