Book Image

Building Python Real time Applications with Storm

Book Image

Building Python Real time Applications with Storm

Overview of this book

Big data is a trending concept that everyone wants to learn about. With its ability to process all kinds of data in real time, Storm is an important addition to your big data “bag of tricks.” At the same time, Python is one of the fastest-growing programming languages today. It has become a top choice for both data science and everyday application development. Together, Storm and Python enable you to build and deploy real-time big data applications quickly and easily. You will begin with some basic command tutorials to set up storm and learn about its configurations in detail. You will then go through the requirement scenarios to create a Storm cluster. Next, you’ll be provided with an overview of Petrel, followed by an example of Twitter topology and persistence using Redis and MongoDB. Finally, you will build a production-quality Storm topology using development best practices.
Table of Contents (14 chapters)

Summary


As this chapter approaches its end, you must have got a brief idea about the Nimbus, supervisor, UI, and Zookeeper processes. This chapter also taught you how to tune parallelism in Storm by playing with the number of workers, executors, and tasks. You became familiar with the important problem of distributing computation, that is, failures and overcoming failures by different kinds of fault tolerance available in the system. And most importantly, you learned how to write a "reliable" spout to achieve guaranteed message processing and linking in bolts.

The next chapter will give you information about how to build a simple topology using a Python library called Petrel. Petrel addresses some limitations of Storm's built-in Python support, providing simpler and more streamlined development.