Spark Hadoop FS

In the context of Apache Spark and Hadoop, “Hadoop FS” typically refers to Hadoop’s file system interaction API. This API allows Spark to interact with Hadoop Distributed File System (HDFS) and other Hadoop-compatible distributed file systems. Here’s how Spark and Hadoop FS are related:

1. Apache Spark:

• Apache Spark is an open-source, distributed data processing framework that provides high-level APIs for various data processing tasks, including batch processing, real-time stream processing, machine learning, and graph processing.

2. Hadoop Distributed File System (HDFS):

• HDFS is the primary storage system used in the Hadoop ecosystem. It is a distributed file system designed for storing and managing large datasets across clusters of commodity hardware.

3. Hadoop FS in Spark:

• Spark can leverage the Hadoop FS API to read and write data from and to HDFS. This allows Spark to access data stored in HDFS and perform distributed data processing tasks on it.

How Spark Uses Hadoop FS:

Here’s how Spark interacts with Hadoop FS:

1. Reading Data: When you create a Spark application and specify a data source, such as a file in HDFS, Spark uses the Hadoop FS API to read data from that source. It understands HDFS paths and can read data in various formats, including text, Parquet, Avro, and more.

2. Writing Data: Similarly, when you want to save the results of a Spark job, you can use Spark’s APIs to write data back to HDFS. Spark will use the Hadoop FS API to perform the write operation.

3. Hadoop Configuration: Spark applications running in a Hadoop cluster automatically inherit the Hadoop configuration settings, including the HDFS configurations. This ensures that Spark can seamlessly access data in HDFS without additional configuration.

4. HDFS Integration: Spark integrates with HDFS for fault tolerance and data locality. When you run Spark jobs, they are distributed across the cluster, and data is processed where it resides in HDFS, minimizing data movement over the network.

5. Hive Integration: Spark can also integrate with Hive, a data warehousing framework in the Hadoop ecosystem. This integration allows you to execute Spark SQL queries against Hive tables, which can be backed by data in HDFS.