Master Apache Oozie: Ace Your Interview with These Questions and Answers

Apache Oozie is a popular workflow scheduler system used for managing and scheduling Apache Hadoop jobs. As a critical component of the Hadoop ecosystem, an in-depth understanding of Oozie is essential for anyone working with Big Data technologies. In this comprehensive article, we’ll explore the most commonly asked Apache Oozie interview questions and provide detailed answers to help you prepare for your next interview.

1. What is Apache Oozie?

Apache Oozie is a workflow scheduler engine designed to manage and schedule Apache Hadoop jobs. It supports various types of Hadoop jobs out of the box, such as MapReduce jobs, Streaming jobs, Pig, Hive, and Sqoop. Additionally, Oozie supports system-specific jobs like shell scripts and Java jobs.

2. What kind of application is Oozie?

Oozie is a Java Web Application that runs in a Java servlet container.

3. What is an Apache Oozie Workflow?

An Apache Oozie Workflow is a collection of actions, such as Hadoop MapReduce jobs, Pig jobs, etc. These actions are arranged in a control dependency DAG (Directed Acyclic Graph), which controls how and when an action can be executed. Oozie workflow definitions are written in hPDL (XML Process Definition Language).

4. What are the key components of an Apache Oozie Workflow?

An Apache Oozie Workflow consists of two main components:

1. Control Flow Nodes:

   • Start: The first node that a workflow job transitions to and the entry point for the workflow.
   • End: The last node that a workflow job transitions to, indicating successful completion.
   • Kill: Allows a workflow job to kill itself, finishing in an error state.
   • Decision: Like a switch-case statement, enabling the workflow to make a selection on the execution path to follow.
   • Fork and Join: Used in pairs, where the fork node splits a single execution path into multiple concurrent paths, and the join node waits until every concurrent path of the corresponding fork node arrives.

2. Action Nodes:

   • MapReduce Action: Starts a Hadoop MapReduce job from the workflow.
   • Pig Action: Starts a Pig job from the workflow.
   • FS (HDFS) Action: Manipulates HDFS files and directories (move, delete, mkdir, chmod, touchz, chgrp).
   • SSH Action: Executes remote commands via SSH.
   • Sub-workflow Action: Executes a nested workflow.
   • Java Action: Executes the public static void main(String[] args) method of a specified Java class.

5. What are the different states of an Apache Oozie Workflow job?

An Apache Oozie Workflow job can have the following states:

• PREP: The initial state when the workflow job is created but not running.
• RUNNING: The state when the workflow job is started and actively running.
• SUSPENDED: The state when the workflow job is temporarily suspended.
• SUCCEEDED: The state when the workflow job reaches the end node and completes successfully.
• KILLED: The state when the workflow job is killed by an administrator.
• FAILED: The state when the workflow job fails with an unexpected error.

6. Does Apache Oozie Workflow support cycles?

No, Apache Oozie Workflow does not support cycles. Oozie Workflow definitions must be a strict DAG (Directed Acyclic Graph). If Oozie detects a cycle in the workflow definition during deployment, it will fail the deployment.

7. What are the different control flow nodes supported by Apache Oozie workflows that start and end the workflow?

Apache Oozie workflow supports the following control flow nodes that start or end the workflow execution:

• Start Control Node: The first node that a workflow job transitions to and the entry point for the workflow. Every Oozie workflow definition must have one start node.
• End Control Node: The last node that a workflow job transitions to, indicating successful completion. Every Oozie workflow definition must have one end node.
• Kill Control Node: Allows a workflow job to kill itself, finishing in an error state.

8. What are the different control flow nodes supported by Apache Oozie workflows that control the workflow execution path?

Apache Oozie workflow supports the following control flow nodes that control the execution path of the workflow:

• Decision Control Node: Like a switch-case statement, enabling the workflow to make a selection on the execution path to follow.
• Fork and Join Control Nodes: Used in pairs, where the fork node splits a single execution path into multiple concurrent paths, and the join node waits until every concurrent path of the corresponding fork node arrives.

9. What are the different Action nodes supported by Apache Oozie workflow?

Apache Oozie supports the following action nodes, which trigger the execution of computation and processing tasks:

• MapReduce Action: Starts a Hadoop MapReduce job from the workflow.
• Pig Action: Starts a Pig job from the workflow.
• FS (HDFS) Action: Manipulates HDFS files and directories (move, delete, mkdir, chmod, touchz, chgrp).
• SSH Action: Executes remote commands via SSH.
• Sub-workflow Action: Executes a nested workflow.
• Java Action: Executes the public static void main(String[] args) method of a specified Java class.

10. Describe the life-cycle of an Apache Oozie workflow job.

The Apache Oozie workflow job transitions through the following states:

1. PREP: The initial state when the workflow job is created but not running.
2. RUNNING: The state when the workflow job is started and actively running.
3. SUSPENDED: The state when the workflow job is temporarily suspended. It remains in this state until it is resumed or killed.
4. SUCCEEDED: A RUNNING workflow job transitions to this state when it reaches the end node, indicating successful completion.
5. KILLED: A CREATED, RUNNING, or SUSPENDED workflow job transitions to this state when it is killed by an administrator.
6. FAILED: A RUNNING workflow job transitions to this state when it fails with an unexpected error.

By mastering these Apache Oozie interview questions and answers, you’ll be well-prepared to showcase your knowledge and expertise during your upcoming interviews. Remember, practice is key to solidifying your understanding and demonstrating confidence in your responses. Good luck!