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Product details
File Size: 5543 KB
Print Length: 124 pages
Publisher: Packt Publishing (June 25, 2014)
Publication Date: June 25, 2014
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00LB6DIS8
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Amazon Best Sellers Rank:
#642,311 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
I don't want this to come off as a hit job so I am going to mention what I liked about the book before I go into what I found disappointing about the book.The ASYNCIO section is the best part of the book and it does the best job of explaining coroutines and the event loop that I have found yet. The Celery section was good and it inspired me to go check out the project. The Parallel Python section was OK but didn't go into enough detail.The book really needs the attention of an editor that is skilled in editing English. The text, especially in the beginning of the book is pretty bad. There are a lot of misused words and garbled phrases. Also there were many instances of poor word choices and strange sentences.The treatment of multithreading and multiprocessing in this book is very superficial. When discussing the ProcessPoolExecutor and ThreadPoolExcecutor the author didn't even mention Future, as_completed, wait and Executor.map. These are key features of the concurrent.futures module. Also the examples of using the Executors was poor and should be considered an anti-pattern. If you were to try and create a multithreaded/multiprocess application of any complexity, just using the info in this book, you wouldn't be successful. There's nothing on how to us the various Locks or about how queue.Queue is thread safe and doesn't need to be guarded. If you followed the examples in the book you would assume that the Queue was not thread safe.Hopefully there will be a new release with these deficiencies corrected.
3 stars. A somewhat thin volume that fails to dive into sufficient detail; it's not a bad overview of the subject but treatment of the topics (multithreading and multiprocessing) is weak. This book felt more like a university term paper overview of the subject than a textbook and I didn't come away feeling like I had gained the depth of knowledge worthy of the effort reading it.Code examples didn't contribute sufficiently to the discussion or fail to clearly demonstrate the topic at hand. For instance, the Fibonacci series code felt contrived and added needless complexity to what could have been a clear demonstration of refactoring for threading. A few diagrams showing thread state (especially w/r/t/ join) would have helped make this discussion clear. Does it cover the subject? Yes. Does it teach it? Not really.My greatest disappointment is with the brief and vague discussion of the python Global Interpreter Lock (the core of the issue with Python's difficulty with multithreading). In my opinion it is the essense of the subject - I hoped to see a thorough explanation and numerous strategies for ways to work around Python's GIL limitations but this book never really delivered.Some comparisons to multithreading and parallel programming in other common languages would have been illustrative (for instance: C, C#, Java, Ruby) but making these suggestions should be the job of the editors.I am sorry to say I believe one would be better off with a few hours spent chasing links on Google. Sadly PACKT publishing is leaving a rather poor impression with me. PACKT editors need to push back on authors and insist on a more thorough exploration of topics. This is the third PACKT book in a row I've purchased that disappoints. I don't think I will volunteer for a forth experience.
The most important drawback of this book is a lack of details on python implementations of parallel computational paradigm. Recomended as a first step.
Good book but not useful.
Great book and very easy to understand!
Parallel Programming is an increasingly hot topic in today's IT circles. For those who ponder why I can tell in short it is because of the CPU clock speeds stagnation. We, software engineers, are dealing with ever increasing volumes of data and are asked to deliver even faster, more robust applications and websites. This is tough. Parallel Programming is the answer. I hope I whet your appetite for exploring the Parallel Programming so now I can switch the focus to the book.It is not terribly long. Not costly either. In fact if you care I managed to read it whole in 3 hours plus (stats are from my ebook reader app) and managed to run a few examples that worked on my laptop with Windows 7. I am planning on running more examples later on a POSIX machine. Thing with the examples is they are classic ones: the Fibonacci series which is boring to me and far from what anybody would be dealing with at work and web crawling which is better done using say Nutch. The same code examples go through the entire book, just different techniques applied. What I wish Jan had done is explaining at least what technique helps in what case in real life. My other pet peeves are that there was no mention on how to leverage the GPU, how to eliminate the For Loops - this is actually a must in my opinion, and there was no coverage on how to debug parralel processes. Let me stop at debugging a tad longer: since Python allows mutability it becomes critical to exterminate nasty mutation bugs!In terms of closing, I have an advice to the author: it is hard to write a technical book, but I wish it could be longer and covered more ground, another advice is to the publisher, this book qualifies for the "Instant" moniker type of the books from Packt. By the way I like your website redesign!Three stars out of five.
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