Book Image

Building Python Real-Time Applications with Storm

By : Kartik Bhatnagar, Barry Hart
Book Image

Building Python Real-Time Applications with Storm

By: Kartik Bhatnagar, Barry Hart

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)

Chapter 5. Persistence Using Redis and MongoDB

It is often necessary to store tuples in a persistent data store, such as a NoSQL database or a fast key-value cache, in order to perform additional analysis. In this chapter, we will revisit the Twitter trending analysis topology from Chapter 4, Example Topology – Twitter with the help of two popular persistence media: Redis and MongoDB.

Redis ( is an open source and BSD-licensed advanced key-value cache and store. MongoDB is a cross-platform, document-oriented database (

Here are the two problems that we will solve in this chapter:

  • Finding the top trending tweet topics using Redis

  • Computing hourly aggregates of city mentions using MongoDB