Storm Real-Time Processing Cookbook (Anglais) Broché – 27 août 2013
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Description du produit
Présentation de l'éditeur
Java developers can expand into real-time data processing with this fantastic guide to Storm. Using a cookbook approach with lots of practical recipes, it's the user-friendly way to learn how to process unlimited data streams.
- Learn the key concepts of processing data in real time with Storm
- Concepts ranging from Log stream processing to mastering data management with Storm
- Written in a Cookbook style, with plenty of practical recipes with well-explained code examples and relevant screenshots and diagrams
Storm is a free and open source distributed real-time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. Storm is simple, can be used with any programming language, and is a lot of fun to use!
Storm Real Time Processing Cookbook will have basic to advanced recipes on Storm for real-time computation.
The book begins with setting up the development environment and then teaches log stream processing. This will be followed by real-time payments workflow, distributed RPC, integrating it with other software such as Hadoop and Apache Camel, and more.
What you will learn from this book
- Create a log spout
- Consume messages from a JMS queue
- Implement unidirectional synchronization based on a data stream
- Execute disaster recovery on a separate AWS region
A Cookbook with plenty of practical recipes for different uses of Storm.
Who this book is written for
If you are a Java developer with basic knowledge of real-time processing and would like to learn Storm to process unbounded streams of data in real time, then this book is for you.
Biographie de l'auteur
Quinton Anderson is a software engineer with a background and focus on real-time computational systems. His career has been split between building real-time communication systems for defense systems and building enterprise applications within financial services and banking. Quinton does not align himself with any particular technology or programming language, but rather prefers to focus on sound engineering and polyglot development. He is passionate about open source, and is an active member of the Storm community; he has also enjoyed delivering various Storm-based solutions.
Quinton's next area of focus is machine learning; specifically, Deep Belief networks, as they pertain to robotics. Please follow his blog entries on Computational Theory, general IT concepts, and Deep Belief networks for more information.
You can find more information on Quinton via his LinkedIn profile (http://au.linkedin.com/pub/quinton-anderson/37/422/11b/) or more importantly, view and contribute to the source code available at his GitHub (https://github.com/quintona) and Bitbucket (https://bitbucket.org/qanderson) accounts.
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Commentaires client les plus utiles sur Amazon.com
This short and light book presents thematic examples of tasks you might be doing with Storm. An important proportion is actually more about details of other technologies like R or Cassandra. This provides a nice overview of modern development tools illustrated in context, at the cost of not leaving much space to Storm itself. This broad content is also necessarily quite shallow, you might be able to obtain an equivalent knowledge by reading introductionary tutorials on each of those technologies, although it's still pleasant to see them all illustrated in one place.
Cookbook does not mean production good practice, several examples appear very solid to me while others seem only acceptable in the context of a learning experience or very specific use cases (like usage a groupBy+repartitioning operation on a field with unique value, non anchored tuples in a "vanilla" topology, usage of a "vanilla" spout in a Trident topology, or usage of "transactional" kafka Trident spout, while "opaque" is now recommended since more reliable... all that might be ok in some cases but are not the most generally recommended approach).
I'm globally happy I read this since my perspective on IT landscape as well as Storm has now matured a bit, although I was expecting more content about topology design, scalability/partitioning and reliability.
This first edited book on Storm is welcome since there does not exist much documentation yet, and Storm code is a mix of Clojure and undocumented java whose structure is not always obvious to grasp (to me at least...).
Commented table of content of the book:
* chapter 1: setting up a development and test environment with git, maven, vagrant, puppet, jmock, Eclipse + a basic "vanilla" Storm example.
* chapter 2: very light content on Storm, many details about Logstash, Redis, JSON format, Drools, regular expressions, data indexing with Elastic Search, Cassandra, D3.js, REST web service with Jersey
* chapter 3: discussion about basic text indexing with Lucene + integration with Storm
* chapter 4: this is the first chapter really focused on Storm. I loved the "row key strategy" to fine tune the query key used to get data from state in a grouped stream (+ the author's code in github for that: [...] I would love if this kind of fined-grained control on state key would become standard in all Storm's iBackingMap.
* chapter 5: polyglot development: short explanation on how to use non-JVM languages, then lot's of comments on C (QT), Ruby and Clojure
* chapter 6: short (but interesting) introduction to the lambda architecture of Nathan Marz, + nice simple example of how to combine real time data (Storm) with batch processed data (Hadoop) within a single DRPC-based topology. Also, a rather long example of Hadoop query written in clojure/Cascalog
* chapter 7: overview of machine learning concepts, example of transactional kafka topology, lot's R code to fit RandomForrest classification rules and saving them into XML (which are then parsed in Java and "manually" executed in Storm, eeeek). There's also an example of how to interact with an R program in real-time through the author contributed storm-r package. Finally, a single perceptron model illustrating basic online learning is presented.
* chapter 8: continuous delivery: setup Jenkins + vagrant + topology acceptance for continous delivery
* chapter 9: deployment on AWS with Pallet
About the author
The author ([...] has contributed to several storm related git repositories, including storm-cassandra, storm-r and trident-ml and appears to be a skilled and experienced developer. He probably does not have more than a year of experience with Storm (I am guessing 8 months based on this fork [...] Storm is still a very young (released 2 years ago) and fast evolving framework, so we're all newbies at it ;D
This is my option as a freelance developer with 10 years of experience, including just 2 months with storm, who am I to claim I know anything? :D