What is the high availability and fault tolerance mechanism of Hadoop?
Hadoop achieves high availability and fault tolerance primarily through the following methods:
- Multiple Replication Storage: Hadoop utilizes HDFS (Hadoop Distributed File System) to store data, which is divided into multiple blocks and stored on different data nodes. Each data block has multiple copies, typically three by default. This ensures that even if a data node fails, there are still copies of the data available on other nodes in the system.
- Heartbeat detection: Various components in the Hadoop cluster monitor each other’s status through heartbeat detection. If a component does not respond to the heartbeat for a long time, it will be considered a faulty node, and the system will automatically remove it from the cluster.
- Metadata backup: In Hadoop, metadata is typically stored in the NameNode. To ensure the high availability of metadata, Hadoop regularly backs up the metadata to another node, and metadata hot backup can be achieved through the Secondary NameNode.
- Fault tolerance: In Hadoop, MapReduce tasks will automatically restart if a node failure occurs to ensure successful completion. Additionally, Hadoop also provides checkpointing for tasks and data to save the execution status and quickly recover from failures.
In general, Hadoop improves the system’s high availability and fault tolerance through methods such as storing multiple copies of data, heartbeat detection, metadata backup, and fault-tolerant mechanisms, ensuring the cluster can run continuously and stably.