What is Hadoop data replication and why is it important?

What is Hadoop data replication and why is it important?

Hadoop data replication is the process of storing multiple copies of data across different nodes in a Hadoop cluster. It's important for fault tolerance, ensuring data availability, and improving read performance.

How does Hadoop decide how many replicas to create for each data block?

Hadoop typically uses a default replication factor of three, meaning each data block is stored on three different nodes. This can be configured based on the requirements for data reliability and storage capacity.

What happens if a node fails in a Hadoop cluster?

If a node fails, Hadoop automatically re-replicates the data blocks that were stored on that node to other healthy nodes, ensuring that the data remains available and the replication factor is maintained.

Can I change the replication factor for specific files?

Yes, you can change the replication factor for specific files in Hadoop using the command line or through the Hadoop API, allowing for flexibility based on data importance.

What are the trade-offs of increasing the replication factor?

Increasing the replication factor improves data reliability and availability but also consumes more storage space and can impact write performance due to the overhead of creating additional copies.