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How does HDFS provide high availability and reliability?
Hadoop Distributed File System (HDFS) provides high availability and reliability through a combination of techniques, including data replication, fault tolerance, and failover mechanisms.
Data Replication: HDFS automatically replicates data across multiple DataNodes in the cluster. This provides redundancy and ensures that data can still be accessed even if one or more DataNodes fail. By default, HDFS replicates each block three times across different DataNodes, although this can be configured based on specific needs.
Fault Tolerance: HDFS is designed to be fault-tolerant, meaning it can continue to operate even in the presence of hardware or software failures. HDFS provides fault tolerance through the following mechanisms:
Heartbeats: Each DataNode sends regular heartbeats to the NameNode to indicate that it is still operational. If the NameNode does not receive a heartbeat from a DataNode for a specified period of time, it assumes the DataNode has failed and marks its data as unavailable.
Block Replication: If a DataNode fails or becomes unavailable, HDFS automatically replicates the affected blocks to other available DataNodes to maintain the configured replication factor.
Checksums: HDFS uses checksums to detect data corruption or bit rot. If a block is found to be corrupted, HDFS automatically creates a new replica from one of the other available replicas.
Failover Mechanisms: HDFS also provides failover mechanisms to ensure high availability in the event of a NameNode failure. This includes:
Secondary NameNode: HDFS includes a Secondary NameNode that periodically pulls a copy of the NameNode’s metadata and merges it with its own copy. If the NameNode fails, the Secondary NameNode can be promoted to act as the primary NameNode.
High Availability NameNode (HA): In addition to the Secondary NameNode, HDFS also provides a High Availability NameNode (HA) option. In the HA configuration, two NameNodes run simultaneously in the cluster, with one active and the other in standby mode. If the active NameNode fails, the standby NameNode automatically takes over.
Overall, the combination of data replication, fault tolerance, and failover mechanisms in HDFS helps ensure that data is highly available, reliable, and can be accessed even in the presence of hardware or software failures.
How does HDFS handle data node failures?
Hadoop Distributed File System (HDFS) is designed to handle data node failures gracefully, without losing any data or causing disruptions to running applications. When a data node fails, HDFS uses the following mechanisms to ensure that data is still available and that the system remains operational:
Data Replication: HDFS replicates data across multiple data nodes in the cluster by default, so if one data node fails, there are still other copies of the data available. HDFS automatically creates additional replicas of any blocks that were stored on the failed data node and places them on other healthy data nodes in the cluster. This process is transparent to running applications, which can continue to access the data as usual.
Heartbeats: Each data node sends a heartbeat message to the name node at regular intervals to indicate that it is still operational. If the name node does not receive a heartbeat message from a data node within a specified time interval, it assumes that the data node has failed and removes it from the list of available nodes. This prevents HDFS from attempting to write new data to the failed node and ensures that applications do not try to read from it.
Rebalancing: When a data node fails, the distribution of data across the remaining nodes may become unbalanced. HDFS has a mechanism to rebalance data across the available data nodes in the cluster, which helps to prevent any single node from becoming too heavily loaded.
Checksums: HDFS uses checksums to detect data corruption or bit rot. If a block is found to be corrupted, HDFS automatically creates a new replica from one of the other available replicas.
Node Decommissioning: If a data node needs to be taken offline for maintenance or other reasons, HDFS has a mechanism to decommission it gracefully. When a data node is decommissioned, HDFS ensures that its data is replicated to other nodes in the cluster before it is taken offline.
Overall, HDFS provides several mechanisms for handling data node failures, ensuring that data is still available and that the system remains operational even in the presence of node failures. By replicating data across multiple nodes and monitoring node health through heartbeats and other mechanisms, HDFS can detect and recover from failures without losing any data or causing disruptions to running applications.
What is the role of secondary Namenode in HDFS?
The Secondary NameNode in Hadoop Distributed File System (HDFS) is a helper node that performs periodic checkpoints of the file system metadata stored in the NameNode. Its main role is to assist in reducing the time it takes to restart the NameNode after a failure by periodically merging the edits log file with the current state of the file system namespace.
The NameNode is a single point of failure in HDFS. If the NameNode fails, the entire HDFS cluster will be unavailable until the NameNode is restarted. The Secondary NameNode provides a way to reduce the time it takes to restart the NameNode after a failure by periodically copying the NameNode’s metadata to its local disk and merging it with the edits log. This results in a new checkpoint of the file system metadata that can be used to recover the file system in case of a failure.
The Secondary NameNode is not a true backup for the NameNode, as it does not store a complete copy of the metadata or the data blocks themselves. Instead, it helps to reduce the recovery time by creating a more up-to-date snapshot of the file system metadata than would otherwise be available from the NameNode’s edit log alone.
It’s worth noting that the role of the Secondary NameNode has been largely replaced by the NameNode High Availability (HA) feature in newer versions of Hadoop, which provides a more robust and scalable solution for ensuring high availability of the NameNode.
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