Stream Processing with Apache Spark

Chapter 6. Spark’s Resilience Model

In most cases, a streaming job is a long-running job. By definition, streams of data observed and processed over time lead to jobs that run continuously. As they process data, they might accumulate intermediary results that are difficult to reproduce after the data has left the processing system. Therefore, the cost of failure is considerable and, in some cases, complete recovery is intractable.

In distributed systems, especially those relying on commodity hardware, failure is a function of size: the larger the system, the higher the probability that some component fails at any time. Distributed stream processors need to factor this chance of failure in their operational model.

In this chapter, we look at the resilience that the Apache Spark platform provides us: how it’s able to recover partial failure and what kinds of guarantees we are given for the data passing through the system when a failure occurs. We begin by getting an overview of the different internal components of Spark and their relation to the core data structure. With this knowledge, you can proceed to understand the impact of failure at the different levels and the measures that Spark offers to recover from such failure.

Resilient Distributed Datasets in Spark

Spark builds its data representations on Resilient Distributed Datasets (RDDs). Introduced in 2011 by the paper “Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing”