Book Image

Apache Mesos Essentials

By : Dharmesh Kakadia
Book Image

Apache Mesos Essentials

By: Dharmesh Kakadia

Overview of this book

<p>Apache Mesos is a cluster manager that provides efficient resource isolation and sharing across distributed applications, or frameworks. It allows developers to concurrently run the likes of Hadoop, Spark, Storm, and other applications on a dynamically shared pool of nodes. With Mesos, you have the power to manage a wide range of resources in a multi-tenant environment.</p> <p>Starting with the basics, this book will give you an insight into all the features that Mesos has to offer. You will first learn how to set up Mesos in various environments from data centers to the cloud. You will then learn how to implement self-managed Platform as a Service environment with Mesos using various service schedulers, such as Chronos, Aurora, and Marathon. You will then delve into the depths of Mesos fundamentals and learn how to build distributed applications using Mesos primitives.</p> <p>Finally, you will round things off by covering the operational aspects of Mesos including logging, monitoring, high availability, and recovery.</p>
Table of Contents (15 chapters)
Apache Mesos Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Spark Streaming


We already know that Spark can be used for processing a large amount of data. Spark Streaming is an extension of Spark API to enable the processing of stream data. It supports a large variety of input data sources, including Twitter, HDFS, Kafka, Flume, Akka Actor, TCP sockets, and ZeroMQ. Spark Streaming breaks up the input data stream in small batches, and this discretized stream is then processed by the Spark program. The processed batches can be routed for further processing or can be stored on HDFS, databases, and so on.

Spark Streaming has a basic abstraction of DStream or discretized streams (http://www.cs.berkeley.edu/~matei/papers/2012/hotcloud_spark_streaming.pdf). Internally, DStreams are represented as a sequence of RDDs, and the operations on DStreams are applied to the operations on the RDDs in the DStreams. It has all the benefits of the RDDs, such as persistence, check-pointing, and so on. The following figure shows how Spark enables stream processing.

Spark...