Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (12 chapters)

Fault tolerance


In a streaming application there are typically three types of guarantees available, as follows:

Figure 5.11: Typical guarantees offered by a streaming application

In a streaming application, which generally comprises of data receivers, transformers, and components, producing different output failures can happen.

Figure 5.12: Components of a streaming application

Worker failure impact on receivers

When a Spark worker fails, it can impact the receiver that might be in the midst of reading data from a source.

Suppose you are working with a source that can be either a reliable filesystem or a messaging system such as Kafka/Flume, and the worker running the receiver responsible for getting the data from the system and replicating it within the cluster dies. Spark has the ability to recover from failed receivers, but its ability depends on the type of data source and can range from at least once to exactly once semantics.

If the data is being received from fault-tolerant systems such...