HADOOP IN PRACTICE, 2nd Edition – An updated guide to handling some of the ‘trickier and dirtier aspects of Hadoop’ – #programming #bookreview


Hadoop in Practice, Second Edition

Alex Holmes

(Manning – paperback )


The Hadoop world has undergone some big changes lately, and this hefty, updated edition offers excellent coverage of a lot of what’s new. If you currently work with Hadoop and MapReduce or are planning to take them up soon, give serious consideration to adding this well-written book to your technical library. A key feature of the book is its “104 techniques.” These show how to accomplish practical and important tasks when working with Hadoop, MapReduce and their growing array of software “friends.”

The author, Alex Holmes, has been working with Hadoop for more than six years and is a software engineer, author, speaker, and blogger specializing in large-scale Hadoop projects.

His new second edition, he writes, “covers Hadoop 2, which at the time of writing is the current production-ready version of Hadoop. The first edition of the book covered Hadoop 0.22 (Hadoop 1 wasn’t yet out), and Hadoop 2 has turned the world upside-down and opened up the Hadoop platform to processing paradigms beyond MapReduce. YARN, the new scheduler and application manager in Hadoop 2, is complex and new to the community, which prompted me to dedicate a new chapter 2 to covering YARN basics and to discussing how MapReduce now functions as a YARN application.”

In the book, Holmes notes that “Parquet has also recently emerged as a new way to store data in HDFS—its columnar format can yield both space and time efficiencies in your data pipelines, and it’s quickly becoming the ubiquitous way to store data. Chapter 4 includes extensive coverage of Parquet, which includes how Parquet supports sophisticated object models such as Avro and how various Hadoop tools can use Parquet.”

Furthermore, “[h]ow data is being ingested into Hadoop has also evolved since the first edition,” Holmes points out, “and Kafka has emerged as the new data pipeline, which serves as the transport tier between your data producers and data consumers, where a consumer would be a system
such as Camus that can pull data from Kafka into HDFS. Chapter 5, which covers moving data into and out of Hadoop, now includes coverage of Kafka and Camus.”  [Reviewer’s note: Interesting software names here. Franz Kafka and Alfred Camus were writers deeply concerned about finding clarity and meaning in a world that seemed to offer none.]

Holmes adds that “[t]here are many new technologies that YARN now can support side by side in the same cluster, and some of the more exciting and promising technologies are covered in the new part 4, titled ‘Beyond MapReduce,’ where I cover some compelling new SQL technologies such as Impala and Spark SQL. The last chapter, also new for this edition, looks at how you can write your own YARN application, and it’s packed with information about important features to support your YARN application.”

Hadoop and MapReduce have gained reputations (well-earned) for being difficult to set up, use and master. In his new edition, Holmes describes his own early experiences: “The greatest challenge we faced when working with Hadoop, and specifically MapReduce, was relearning how to solve problems with it. MapReduce is its own flavor of parallel programming, and it’s quite different from the in-JVM programming that we were accustomed to. The first big hurdle was training our brains to think MapReduce, a topic which the book Hadoop in Action by Chuck Lam (Manning Publications, 2010) covers well.”

(These days, of course, there are both open source and commercial releases of Hadoop, as well as quickstart virtual machine versions that are good for learning Hadoop.)

Holmes continues: “After one is used to thinking in MapReduce, the next challenge is typically related to the logistics of working with Hadoop, such as how to move data in and out of HDFS and effective and efficient ways to work with data in Hadoop. These areas of Hadoop haven’t received much coverage, and that’s what attracted me to the potential of this book—the chance to go beyond the fundamental word-count Hadoop uses and covering some of the trickier and dirtier aspects of Hadoop.”

If you have difficulty explaining Hadoop to others (such as a manager or executive hesitant to let it be implemented), Holmes offers a succint summation in his updated book:

“Doug Cutting, the creator of Hadoop, likes to call Hadoop the kernel for big data, and I would tend to agree. With its distributed storage and compute capabilities, Hadoop is fundamentally an enabling technology for working with huge datasets. Hadoop provides a bridge between structured (RDBMS) and unstructured (log files, XML, text) data and allows these datasets to be easily joined together.”

One book cannot possibly cover everything you need to know about Hadoop, MapReduce, Parquet, Kafka, Camus, YARN and other technologies. And this book and its software examples assume that you have some experience with Java, XML and JSON. Yet Hadoop in Practice, Second Edition gives a very good and reasonably deep overview, spanning such major categories as background and fundamentals, data logistics, Big Data patterns, and moving beyond MapReduce.

Si Dunn




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