How to perform data replication and fault tolerance in Hadoop?

In Hadoop, data replication and fault tolerance are achieved through HDFS (Hadoop Distributed File System). HDFS splits data into blocks of a certain size (usually defaulting to 128MB) and replicates these data blocks across multiple nodes to ensure data availability in case of node failures.

The main steps of data replication and fault tolerance processing are as follows:

  1. Data replication: When data is written into HDFS, it will be divided into multiple blocks and each block will be copied to multiple nodes. The default replication factor is usually set to 3, meaning each block will be copied to 3 different nodes.
  2. Transfer and replication of data blocks: HDFS transfers data blocks to destination nodes and replicates the data blocks on the destination nodes. If a data block on a node is corrupt or unavailable, HDFS automatically copies the data block from replicas on other nodes to ensure data availability.
  3. Fault tolerance: in the event of a node failure, HDFS automatically replaces the data block on the failed node with a copy from another node to ensure fault tolerance.
  4. Deletion of data blocks: When a data block is no longer needed, HDFS will automatically remove its replicas to free up storage space.

By utilizing data replication and fault tolerance, Hadoop can achieve high reliability and availability in distributed data storage and processing.

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