suresh How Apache Spark Handles Fault Tolerance and Recovery in Cluster Computing
Apache Spark utilizes a mechanism called resilient distributed datasets (RDDs) to achieve fault tolerance and recovery in cluster computing. RDDs are immutable distributed collections of objects that are fault-tolerant and can be operated on in parallel.
When a node in the cluster fails, Spark can reconstruct the lost RDD partitions based on the lineage of transformations applied to the original data. This process, known as lineage-based fault tolerance, enables Spark to recover from failures without data loss.
Additionally, Spark's shuffle operations are also designed to be fault-tolerant. Data shuffled between nodes during operations like joins or aggregations is replicated to ensure that intermediate results can be reconstructed in case of failures.
In summary, Apache Spark ensures fault tolerance and recovery in cluster computing through the use of RDDs and lineage-based fault tolerance mechanisms, along with the replication of shuffle data to handle failures efficiently.
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