Greenberg - Building Applications on Mesos
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How can Apache Mesos make a difference in your organization? With this practical guide, youll learn how this cluster manager directs your datacenters resources, and provides real time APIs for interacting with (and developing for) the entire cluster. Youll learn how to use Mesos as a deployment system, like Ansible or Chef, and as an execution platform for building and hosting higher-level applications, like Hadoop.
Author David Greenberg shows you how Mesos manages your entire datacenter as a single logical entity, eliminating the need to assign fixed sets of machines to applications. Youll quickly discover why Mesos is the ultimate DevOps tool.
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by David Greenberg
Copyright 2016 David Greenberg. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
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- December 2015: First Edition
- 2015-12-04: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781491926529 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Building Applications on Mesos, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.
978-1-491-92652-9
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This book took a huge amount of work, and it wouldnt have been possible without the help and support of many people.
First, Id like to thank Brian Foster and the team at OReilly. They did so much to make this book a reality.
Id also like to thank Two Sigma, my employer, for giving me the time and support to write this book.
The quality of the book was improved immeasurably thanks to the feedback and reviews I received from Matt Adereth, Adam Bordelon, Niklas Nielsen, and David Palaitis.
Finally, Id like to thank my wife, Aysylu Greenberg, for her love and support throughout the writing process.
Lets take a trip back in time, to the year 1957. Computers that use transistors are starting to proliferate across universities and research laboratories. There is a problem, thoughonly one person can use a computer at a time. So, we have paper sign-up sheets so that we can reserve time slots on the machines. Since computers are so much more powerful than pencil and paper, they are in high demand. At the same time, since the computers are so expensive, if people dont use their whole reservations, then thousands of dollars of compute-time could be wasted! Luckily, the idea of operating systems already existed, more or less, at the time. A brilliant man named John McCarthy, who also invented LISP, had a great ideawhat if all the users could submit their jobs to the computer, and the computer would automatically share its CPU resources among the many different jobs?
What we now call applications or programs used to be called jobs.We still can see this terminology in our shells, where, once weve backgrounded a process, we use the jobs command to inspect all the programs weve launched in the shell.
Once a single machine could be shared between jobs, we didnt need humans to supervise the sign-up sheetsnow, everyone could use the machine, and share it more easily, since the machine could enforce quotas, priorities, and even egalitarian fairness (if so desired).
Fast-forward to 2010: with the falling costs of networked data transmission and storage, its now possible to store every bit of information you can collect.To process all this data, you probably need to use Storm (a distributed real-time data processing system) and Hadoop.So, you get a whole mess of machines: a few for the Hadoop JobTracker and Storm Nimbus (each with its own painstakingly crafted configuration), a few more for the HDFS NameNode and Secondary NameNode, 15 more that youll install Hadoop TaskTrackers and HDFS DataNodes on, and 10 more that you use to run Storm Supervisors.At this point, youve managed to purchase 30 machines. However, if you decide that instead youd like to use five of your Hadoop machines as Storm workers, youre in for painful task, because you now need to completely reprovision the Hadoop machines as Storm machinesa process that, as many practitioners can vouch, is not as easy as wed wish.
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