Refinements in Spark
Explore how Spark manages challenges like limited memory and worker failures using strategies such as LRU eviction, persistence priorities, and checkpointing. Understand the trade-offs in storage options for persistent RDDs and how Spark balances recomputation with performance for scalable, low-latency data processing.
We'll cover the following...
We'll cover the following...
The problems that Spark faces include worker failures and limited memory issues. It can also have driver failures for which Spark does not provide any tolerance.
Managing limited memory
A Least Recently Used (LRU) eviction policy is used at the RDD level to manage limited memory. Whenever there is insufficient memory to cache a newly computed RDD partition, Spark removes an RDD ...