Cascading-Hive is an integration between Apache Hive and Cascading. It currently has the following major features:
- running Hive queries within a Cascade
- reading from Hive tables within a Cascading Flow (including transactional tables)
- writing/creating Hive tables from a Cascading Flow
- writing/creating partitioned Hive tables from a Cascading Flow
- deconstructing a Hive view into Taps
- reading/writing taps from HCatalog
demo sub-directory contains applications that demonstrate those
The code will pick up the configuration for a remote Hive MetaStore automatically, as long as it is present in your hive or hadoop configuration files.
Cascading-Hive works Hive 1.x, 2.x is not yet supported. When using the
cascading-hive in your project you have to specify the version of Hive you are
using as a runtime dependency yourself. This is done to avoid classpath issues
with the various Hadoop and Hive distributions in existence. See the
project for an example.
To install cascading-hive into your local maven repository do this:
> gradle install
Maven, Ivy, Gradle
Cascading-Hive is also available on http://conjars.org.
Please note that the support for Hive views is currently limited since views
pretend to be a resource or a
Tap in Cascading terms, but are actually
computation. It is currently not possible to read from a Hive View within a
Cascading Flow. To work around this, you can create a table in Hive instead, and
read from that within your Cascading Flow. This restriction might change in the
Also note that it is not yet possible to write to transactional tables and that
HiveTap will prevent any attempt to do so.
HCatTap to sink data to a Blob Storage service may lead to issues that
can be hard to deal with due to Blob Storage not being a real file system. If
you are planning to do so, use it at your own risk.
Users encountered various issues when using
HCatTap to sync tables in S3 but
there is a known workaround (tested on EMR 4.7.0):
mapred.output.direct.EmrFileSystem = false mapred.output.direct.NativeS3FileSystem = false
These settings are required in order be able to commit dynamic partitions. This also
implies that direct commits in EMR will be disabled and the job may take longer during
the commit phase of tasks and jobs since the underlying
FileSystem will have to copy
the files to their final locations and delete the temporary copies. Depending on your use
case this waiting time and relying on eventually consistent data may or may not be an
Note that even though direct commits won't be available, EMR consistent views can still be used.
Finally note that Hive relies on the
hadoop command being present in your
PATH when it executes the queries on the Hadoop cluster.