Download PDF High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
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High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
Download PDF High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
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Book Description
Best practices for scaling and optimizing Apache Spark
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About the Author
Holden Karau is transgender Canadian, and an active open source contributor. When not in San Francisco working as a software development engineer at IBM's Spark Technology Center, Holden talks internationally on Apache Spark and holds office hours at coffee shops at home and abroad. She is a Spark committer with frequent contributions, specializing in PySpark and Machine Learning. Prior to IBM she worked on a variety of distributed, search, and classification problems at Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a Bachelor of Mathematics in Computer Science. Outside of software she enjoys playing with fire, welding, scooters, poutine, and dancing.Rachel Warren is a data scientist and software engineer at Alpine Data Labs, where she uses Spark to address real world data processing challenges. She has experience working as an analyst both in industry and academia. She graduated with a degree in Computer Science from Wesleyan University in Connecticut.
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Product details
Paperback: 358 pages
Publisher: O'Reilly Media; 1 edition (June 16, 2017)
Language: English
ISBN-10: 9781491943205
ISBN-13: 978-1491943205
ASIN: 1491943203
Product Dimensions:
7 x 0.7 x 9.2 inches
Shipping Weight: 1.8 pounds (View shipping rates and policies)
Average Customer Review:
4.2 out of 5 stars
14 customer reviews
Amazon Best Sellers Rank:
#121,345 in Books (See Top 100 in Books)
this is not a beginner's guide, so you need some working knowledge of Scala and spark beforehand.
The authors state in their preface that "this book is intended for those who have some working knowledge of Spark, and may be difficult to understand for those with little or no experience with Spark or distributed computing", that they "expect this text will be most useful to those who care about optimizing repeated queries in production, rather than to those who are doing primarily exploratory work", and that they "want to help our readers ask questions such as 'How is my data distributed?', 'Is it skewed?', 'What is the range of values in a column?', and 'How do we expect a given value to group?' and then apply the answers to those questions to the logic of their Spark queries."This book is the second of three related books that I've had the chance to work through over the past few months, in the following order: "Spark: The Definitive Guide" (2018), "High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark" (2017), and "Practical Hive: A Guide to Hadoop's Data Warehouse System" (2016). If you are new to Apache Spark, these three texts will help enable your going in the right direction, although keep in mind that the related tech stack is evolving and you will obviously need to supplement this material with web documentation and developer forums, as well as to get hands-on with the tooling. Reading these books in opposite order of publication date enabled exposure to more current material sooner rather than later, but this was largely just a coincidence.Keep in mind one of the initial assertions of the authors that "this book was created using the Spark 2.0.1 APIs, but much of the code will work in earlier versions of Spark as well. In places where this is not the case we have attempted to call that out". As Spark 2.4.0 was just released in November 2018, you will find that some of the material provided here is either outdated or seen to be commonplace with the newest aforementioned Spark text. The unfortunate dilemma is that a book specifically focusing on Spark performance simply isn't available outside of what the authors provide here, so you will need to account for differences across versions, especially in the several instances where the authors provide workarounds that they warn are likely not to provide long term viability.Unlike "Spark: The Definitive Guide", which provides Python, Scala, and Spark SQL code, readers should be aware that the bulk of code provided in this book is Scala, "simply in the interest of time and space", because "it is the belief of the authors that 'serious' performant Spark development is most easily achieved in Scala", and while "these reasons are very specific to using Spark with Scala, there are many more general arguments for (and against) Scala's applications in other contexts." As the authors further state their case, they provide tips for learning Scala alongside additional arguments for picking up the language: "to be a Spark expert you have to learn a little Scala anyway", "the Spark Scala API is easier to use than the Java API", and "Scala is more performant than Python."This densely written book of slightly over 300 pages in length is broken down into 10 chapters and an appendix: (1) "Introduction to High Performance Spark", (2) "How Spark Works", (3) "DataFrames, Datasets, and Spark SQL", (4) "Joins (SQL and Core)", (5) "Effective Transformations", (6) "Working with Key/Value Data", (7) "Going Beyond Scala", (8) "Testing and Validation", (9) "Spark MLlib and ML", (10) "Spark Components and Packages", and an appendix on "Tuning, Debugging, and Other Things Developers Like to Pretend Don't Exist". While the chapters aren't provided in the context of broader sections, chapters 1 and 2 are essentially an introduction, and chapters 3, 4, 5, 6, and 8 provide the bulk of the content (chapter 8 should likely join these other 4 chapters, as testing and validation are a likely follow-up to much of what is discussed. As far as the remaining 3 chapters are concerned, while chapter 7 would likely provide value as a last chapter, chapters 9 and 10 seem a bit misplaced, with chapter 10 seemingly better suited for an appendix alongside the one appendix provided.The diagrams in chapters 3 through 6 are especially well done, and supplement the discussions very well. While the diagrams in chapters 1 and 2 are beneficial, these can be largely found in the documentation (perhaps with the exception of the diagrams provided in the section entitled "The Anatomy of a Spark Job"). For example, the diagram in chapter 3 on Spark SQL windowing (which personally helped supplement the cursory explanation in "Spark: The Definitive Guide"), the diagrams in chapter 4 on joins, the diagrams in chapter 5 on narrow versus wide dependencies between partitions and caching versus checkpointing, and the diagrams in chapter 6 on GroupByKey (although I found one of several errors here) and SortByKey.The appendix is beneficial to the point that it could likely have been expanded and included in the body of the text, possibly following the introductory chapters, because the discussion here is all about what one can do outside one's application code (what the bulk of this book is essentially about). Topics covered here are broken down into sections on "Spark Tuning and Cluster Sizing", "Basic Spark Core Settings: How Many Resources to Allocate to the Spark Application?", "Serialization Options", and "Some Additional Debugging Techniques". Highly recommended text for anyone looking to broaden their understanding of the hows and whys behind optimizing Spark.
much improved compared with 1st edition, more elaboration on joining datasets. good to explain in one language.
This book clarifies lots of my questions on Spark. I especially appreciate the walk through joins.
If you're a real hacker, you'll love this book. If you're not, and you would like to be, you'll find it frustrating, but if you stick with it, you will grow as a professional. If you're not, and you know you never will be, I suggest you start working on a nursing, phlebotomy or massage therapy certifation before people in the first two groups figure out how to automate your job.
Not worth it. Online Spark documentation is probably more helpful. Important concepts like Executors, Jobs, Stages, Cluster etc are not very well explained. You'll start as beginner and stay beginner. May be helpful if you are very new to Spark, but then I would recommend official documentation should be enough to get familiar.
Overall, I thought this was a very good book. It strikes a good balance between detailed instruction and depth and being a guidebook, not an instruction manual. It's the usual high quality that I've come to expect from O'Reilly, and I feel much more confident about my understanding of Spark, both as a user and of the inner workings.Much of the book is written with a focus on performance. There's some discussion of statistical concepts, but the book is clearly aimed at helping the reader use Spark in a resource-efficient manner (which makes a lot of sense, given that Spark comes into play when you're tackling large data sets).Virtually all of the code examples are written in Scala. When I began reading, my Scala abilities were fairly limited, but the authors do a good job of parsing and commenting on the code such that I now feel much stronger in Scala, as well. They do have a chapter that discusses using Python and Java (including JVM), but most of the book is presented through Scala.My one complaint about this book is that it's a bit heavy on the code. It's possible that it's necessary, but I ended up skimming most of the coding examples, and it made for some tedious reading at times. Then again, there were several examples that I scrutinized closely, and having thorough examples did help me learn quite a bit of Scala.
This book is heavily Scala centric and for beginners the only takeaway should be that you should be fairly comfortable with Scala If you hope to have a "Spark" Centered carrear. If you are in the big data / Warehouse space with Spark in the center of action, I highly recommend this book. It focuses heavily on all areas of Performance. You can keep this book handy as a reference guide as well.Good job.
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