Oozie: Workflow Scheduling and Coordination System for Apache Hadoop.

 Oozie is a workflow scheduling and coordination system for Apache Hadoop. It is designed to manage and run workflows that are composed of multiple Hadoop jobs or other tasks. Oozie allows you to define a workflow using XML, which specifies the sequence of actions and dependencies between them. Each action in the workflow can be a Hadoop job, a shell script, a MapReduce job, a Pig script, a Hive query, or any other executable.

Oozie provides a way to manage complex data processing workflows in Hadoop by allowing you to specify the dependencies between different tasks and control their execution. It supports various control structures such as sequential execution, parallel execution, conditional branching, and loops.

When you submit a workflow to Oozie, it parses the XML definition and creates a directed acyclic graph (DAG) representing the workflow. Oozie then schedules and runs the tasks in the workflow according to the defined dependencies and control structures. It also provides monitoring and logging capabilities to track the progress of the workflow and troubleshoot any issues.

Oozie is widely used in the Hadoop ecosystem to automate and manage data processing workflows. It simplifies the coordination and scheduling of jobs, making it easier to build and maintain complex data pipelines.

1. Architecture: Oozie consists of several core components:

   - Workflow Engine: It manages the scheduling and execution of workflows. It parses the XML definition, creates the DAG, and triggers the execution of tasks.

   - Coordinator: It is responsible for time-based scheduling of workflows. It can trigger workflows based on a cron-like schedule or based on data availability.

   - Executor: It runs the tasks defined in the workflow. It can execute Hadoop jobs, shell scripts, Pig scripts, Hive queries, and other types of tasks.

   - Workflow Database: It stores the metadata and state information of workflows, coordinators, and actions.

   - Web Console: It provides a user interface to monitor and manage workflows. It allows users to submit, schedule, and monitor workflows, as well as view logs and statistics.

2. Workflow Definition: Workflows in Oozie are defined using an XML language called the Oozie Application Language (OAL). The XML defines the workflow structure, actions, dependencies, and control flow. A workflow consists of a set of actions, and each action represents a task to be executed. Actions can have input and output data dependencies, and they can be executed in sequence, in parallel, or conditionally based on the result of a previous action.

3. Supported Actions: Oozie supports a variety of actions that can be included in workflows, including:

   - Hadoop MapReduce jobs

   - Pig scripts

   - Hive queries

   - Shell scripts

   - Java programs

   - Sub-workflows (allowing nesting of workflows)

   - Custom actions (allowing integration with other systems)

4. Coordination and Scheduling: Oozie's coordination features allow you to define complex workflows that depend on time or data availability. The Coordinator component provides the ability to schedule workflows based on a cron-like schedule or based on the availability of data in Hadoop. This enables you to automate the execution of workflows at specific times or when certain conditions are met.

5. Monitoring and Logging: Oozie provides monitoring and logging capabilities to track the progress of workflows and troubleshoot any issues. The Web Console allows users to view the status of workflows, monitor their execution, and access logs and statistics. Oozie also supports email notifications for workflow completion or failure.

6. Extensibility: Oozie is extensible, allowing you to add custom actions or integrate with other systems. Custom actions can be developed using Java and registered with Oozie, enabling you to incorporate your own processing logic into workflows.

Overall, Oozie is a powerful workflow scheduling and coordination system that simplifies the management of complex data processing pipelines in Hadoop. It provides a flexible and scalable solution for orchestrating tasks and automating the execution of workflows.