Apache Spark Certification 2025 – 400 Free Practice Questions to Pass the Exam

Cached RDDs are indeed partitioned. When an RDD is created in Apache Spark, it is inherently partitioned across the various nodes in the cluster. This partitioning allows Spark to distribute the computational workload and manage large datasets effectively.

When an RDD is cached, it means that Spark keeps the RDD in memory for faster access during subsequent actions, rather than recalculating it every time from its original source. This caching still respects the partitioning scheme established when the RDD was created. By keeping data partitioned in memory, Spark optimizes performance, enabling parallel processing across available resources, which is a key advantage in distributed computing environments.

The cache operation enhances access speed without altering the original partitioning structure of the RDD. This is essential for managing resources efficiently and ensuring that computations can proceed without unnecessary delays due to data serialization or deserialization across network boundaries.