Cluster computing is one of the ways of implementing parallel processing for large-scale algorithms. In cluster computing, we have multiple nodes connected via a very high-speed network. Large-scale algorithms are submitted as jobs. Each job is divided into various tasks and each task is run on a separate node.

Apache Spark is one of the most popular ways of implementing cluster computing. In Apache Spark, the data is converted into distributed fault-tolerant datasets, which are called Resilient Distributed Datasets (RDDs). RDDs are the core Apache Spark abstraction. They are immutable collections of elements that can be operated in parallel. They are split into partitions and are distributed across the nodes, as shown here:

Through this parallel data structure, we can run algorithms in parallel.