Latest News & Updates

Announcing Cascading 3.0 on Apache Flink

Thanks to our partners, data Artisans, Cascading users now have an additional compute fabric to execute Cascading 3.0 applications on, Apache Flink.

From the project site..

“Apache Flink is a platform for scalable stream and batch processing. Flink’s execution engine features low-latency pipelined and scalable batched data transfers and high-performance, in-memory operators for sorting and joining that gracefully go out-of-core in case of scarce memory resources.

Apache Flink uses in-memory storage to achieve massive performance gains over MapReduce. It’s active memory management and custom serialization stack enables highly efficient operations on binary data and effectively prevents JVM OutOfMemoryErrors as well as frequent Garbage Collection pauses. Memory-safe execution means very little parameter tuning is necessary to reliably execute Cascading programs on Flink.”

According to data Artisans, with virtually no code changes, Cascading 3.0 applications will run in Apache Flink, furthering the portability promise of Cascading through their contribution.

We are very excited to see another alternative for high performance production deployments made available to our community.

Link to Source code:
Data Artisans blog:


Cascading Newsletter - September 2015

September 2015 Newsletter

Cascading 3.0 Maintenance Release

We have just published Cascading 3.0.2, a minor maintenance release.  Upgrading is recommended for all users.

This release resolves the following issues:

  • Updated Apache Tez to 0.6.2 to prevent deadlocks in complex DAGs. Note this release is incompatible with Tez 0.6.1.
  • Fixed issues concerning detailed stats retrieval robustness for both MapReduce and Tez platforms.
  • Updated build to exclude jgrapht-ext, further isolation of jgrapht apis to support reliable shading.
  • Fixed issue on Apache Tez where a split before and subsequent splicing back into a c.p.HashJoin could create an invalid plan.
  • Fixed issue where an unreachable YARN timeline server could cause the application to fail.
  • Fixed issue with NPE when retrieving Tez task status from timeline server.

For a detailed list of changes in this release, see:

It can be downloaded here:

The source can be found here:


Cascading 2.7 Maintenance Release

We have just published Cascading 2.7.1, a minor maintenance release.
This release resolves the following issues:
  • Fixed issue where c.p.GroupBy or c.p.CoGroup would fail if attempting to group or join incoming Fields.UNKNOWN tuple streams using relative positions in the grouping fields selectors.
  • Fixed issue where c.u.ShutdownUtil could log a NPE if a hook is removed during JVM shutdown.

It can be downloaded here:


Cascading Newsletter - July 2015

July 2015 Newsletter

Cascading 3.0 Maintenance Release

We have just published a new maintenance release 3.0.1 of Cascading.
This release resolves the following issue:
– Fixed issue in c.f.t.p.Hadoop2TezFlowStepJob where the LocalResources were not passed to the AppMaster correctly causing ClassNotFoundException during split calculation for custom InputFormats.
It can be downloaded from these locations:

Cascading Newsletter - June 2015

There has been a lot going on in the last month. Cascading 3.0 release is now available. This release helps future-proof your data infrastructure investmentsand by supporting newer compute fabrics as they become available. Also, a new EAP version for Driven is freely available for doing real time performance testing of your Cascading/Scalding apps. A Scalding blog reports dramatic improvements in performance of Cascading 3.0 on Apache Tez. Netflix / PigPen published a Getting Started Guide for Cascading users. New Concurrent blogs include a tutorial on how to run Cascading on AWS EMR and how to boost Hadoop performance through better Dev and Ops collaboration. Continue reading

Cascading-Hive 2.0 Release

We are happy to announce the release of Cascading-Hive 2.0. This release adds compatibility with Cascading 3.0. Furthermore it contains a major contribution from the Cascading community, namely It is now possible to read and write ACID ORC tables with Cascading-Hive. This feature relies on corc, an ORC integration for Cascading, also created by The demo directory contains a new application demonstrating this new feature.

The jars are deployed on conjars and the code is available on github.

Cascading-Hive allows you to read and write Hive tables from within Cascading Flows as well as running any HiveQL query as part of a Cascade.

– Andre

Cascading 3.0 release

We are happy to announce Cascading 3.0 is now publicly available for download.

The biggest change in this version, compared to previous releases, is Cascading has added native support for Apache Tez along side Apache Hadoop MapReduce and Cascading’s native local in memory mode. It is now trivial (a matter of changing a few lines of code) to move your application to run on Tez instead of MapReduce. We’ve seen others run performance tests with Scalding and Tez and are reporting significant performance improvements.

This milestone release of Cascading with Apache Tez support means we’ve completed the work to the query planner to make it faster for us and the community to integrate Cascading with other compute fabrics, as they become available. We hope to announce additional platform support in the near future.

Along with the ease of adding new platforms, the new query planner should also show some improvements over Cascading 2.x execution times on MapReduce. Additionally, we’ve given the developer direct control over how they optimize their MapReduce and Tez jobs perform so you can tune performance to your specific needs.

Please note this is a major release, thus all deprecated methods have been removed, along with some incompatible API changes to the Cascading public API, you will need to edit and recompile in order to upgrade to 3.0.

As we continue to advance the code base, a number of other enhancements and bug fixes are included in the release. For the complete list of changes in Cascading 3.0, please see the change log.


Cascading 2.7 Release

We are happy to announce that Cascading 2.7 is now publicly available for download. This is the last planned minor release of Cascading in the 2.x line before we make Cascading 3.0 final.

This release contains new features and bug fixes. In summary, two features of particular interest are PartitionTap support for small files, and Traps can now capture diagnostic information on the failure. Changes of note are:

  • Added support for o.a.h.m.l.CombineFileInputFormat in the Hadoop specific c.t.h.PartitionTap implementation.
  • Added c.t.Tap#prepareResourceForRead() and c.t.Tap#prepareResourceForWrite() methods to allow for client side tap resource initialization.
  • Updated trap handling to capture diagnostic information within a trap when configured via a c.t.TrapProps instance.
  • Updated c.t.u.TupleHasher to use MurmurHash3 32bit for hashCode calculation.
  • Added ability to provide a custom cache to be used in c.p.a.AggregateBy and c.p.a.Unique.
  • Updated c.f.h.MapReduceFlow to support both the org.apache.hadoop.mapred.* and org.apache.hadoop.mapreduce.* APIs.
  • Updated Cascading SDK

For more details on new features and resolved issues see the change log.