The blueprint for Enterprise Hadoop includes Apache™ Hadoop’s original data storage and data processing layers and also adds components for services that enterprises must have in a modern data architecture: data integration and governance, security and operations. Apache Oozie provides some of the operational services for a Hadoop cluster, specifically around job scheduling within the cluster.
Apache Oozie is a Java Web application used to schedule Apache Hadoop jobs. Oozie combines multiple jobs sequentially into one logical unit of work. It is integrated with the Hadoop stack, with YARN as its architectural center, and supports Hadoop jobs for Apache MapReduce, Apache Pig, Apache Hive, and Apache Sqoop. Oozie can also schedule jobs specific to a system, like Java programs or shell scripts.
Hortonworks Focus for Oozie
The Apache Oozie community is working to add support for more sophisticated workflow capabilities that will be able to invoke new sorts of actions. These plans include adding the ability to run Hive jobs through HiveServer2, thereby taking full advantage of HiveServer2’s security and multi-tenancy features. A future version of Oozie may also support dynamic forks.
Recent Progress in Oozie
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What Oozie Does
Apache Oozie is a tool for Hadoop operations that allows cluster administrators to build complex data transformations out of multiple component tasks. This provides greater control over jobs and also makes it easier to repeat those jobs at predetermined intervals. At its core, Oozie helps administrators derive more value from Hadoop.
There are two basic types of Oozie jobs:
- Oozie Workflow jobs are Directed Acyclical Graphs (DAGs), specifying a sequence of actions to execute. The Workflow job has to wait
- Oozie Coordinator jobs are recurrent Oozie Workflow jobs that are triggered by time and data availability.
Oozie Bundle provides a way to package multiple coordinator and workflow jobs and to manage the lifecycle of those jobs.
How Oozie Works
An Oozie Workflow is a collection of actions arranged in a Directed Acyclic Graph (DAG) . Control nodes define job chronology, setting rules for beginning and ending a workflow. In this way, Oozie controls the workflow execution path with decision, fork and join nodes. Action nodes trigger the execution of tasks.
Oozie triggers workflow actions, but Hadoop MapReduce executes them. This allows Oozie to leverage other capabilities within the Hadoop stack to balance loads and handle failures.
Oozie detects completion of tasks through callback and polling. When Oozie starts a task, it provides a unique callback HTTP URL to the task, thereby notifying that URL when it’s complete. If the task fails to invoke the callback URL, Oozie can poll the task for completion.
Often it is necessary to run Oozie workflows on regular time intervals, but in coordination with unpredictable levels of data availability or events. In these circumstances, Oozie Coordinator allows you to model workflow execution triggers in the form of the data, time or event predicates. The workflow job is started after those predicates are satisfied.
Oozie Coordinator can also manage multiple workflows that are dependent on the outcome of subsequent workflows. The outputs of subsequent workflows become the input to the next workflow. This chain is called a “data application pipeline”.