I. Spark ecosystem includes multiple components
- Spark Core: The foundation for distributed data processing.
- Spark SQL: Enables structured data processing using SQL-like queries. It allows you to query data stored in various formats like Hive tables, Parquet files, and relational databases.
- MLlib: Provides machine learning algorithms for tasks like classification, regression, and clustering.
- GraphX: A library for graph processing, enabling analysis of large-scale graphs.
--> Think of Spark as a toolbox for big data. Each component provides specialized tools for different tasks, allowing you to analyze and manipulate data efficiently and effectively.
II. Basic architecture of Apache Spark
- Master Node: This node houses the "Driver Program" which contains the Spark Context. The Spark Context is responsible for initializing the Spark application and connecting to the cluster.
- Cluster Manager: The Cluster Manager is responsible for allocating resources and managing the worker nodes. It can be a standalone manager or utilize systems like YARN or Mesos.
- Worker Nodes: These nodes are the workhorses of the Spark cluster. They execute the tasks assigned by the Driver Program.
- Tasks: These are individual units of work that are distributed across the worker nodes.
- Cache: Worker nodes maintain a cache for storing frequently accessed data, speeding up processing.
Here is how it works:
- The Driver Program, running on the Master Node, submits a Spark application to the Cluster Manager.
- The Cluster Manager distributes the application's tasks across the worker nodes.
- Worker nodes execute the tasks in parallel, leveraging their resources and the data cached on their local storage.
- The Driver Program gathers and aggregates the results from the worker nodes.
Top comments (0)