Reader small image

You're reading from  Real-time Analytics with Storm and Cassandra

Product typeBook
Published inMar 2015
Reading LevelIntermediate
Publisher
ISBN-139781784395490
Edition1st Edition
Languages
Right arrow
Author (1)
Shilpi Saxena
Shilpi Saxena
author image
Shilpi Saxena

Shilpi Saxena is an IT professional and also a technology evangelist. She is an engineer who has had exposure to various domains (machine to machine space, healthcare, telecom, hiring, and manufacturing). She has experience in all the aspects of conception and execution of enterprise solutions. She has been architecting, managing, and delivering solutions in the Big Data space for the last 3 years; she also handles a high-performance and geographically-distributed team of elite engineers. Shilpi has more than 12 years (3 years in the Big Data space) of experience in the development and execution of various facets of enterprise solutions both in the products and services dimensions of the software industry. An engineer by degree and profession, she has worn varied hats, such as developer, technical leader, product owner, tech manager, and so on, and she has seen all the flavors that the industry has to offer. She has architected and worked through some of the pioneers' production implementations in Big Data on Storm and Impala with autoscaling in AWS. Shilpi has also authored Real-time Analytics with Storm and Cassandra (https://www.packtpub.com/big-data-and-business-intelligence/learning-real-time-analytics-storm-and-cassandra) with Packt Publishing.
Read more about Shilpi Saxena

Right arrow

Delving into the internals of Storm


Now that we know which physical components are present in a Storm cluster, let's understand what happens inside various Storm components when a topology is submitted. When we say topology submission, it means that we have submitted a distributed job to Storm Nimbus for execution over the cluster of supervisors. In this section, we will explain the various steps that are executed in various Storm components when a Storm topology is executed:

  • Topology is submitted on the Nimbus node.

  • Nimbus uploads the code jars on all the supervisors and instructs the supervisors to launch workers as per the NumWorker configuration or the TOPOLOGY_WORKERS configuration defined in Storm.

  • During the same duration all the Storm nodes (Nimbus and Supervisors) constantly co-ordinate with the Zookeeper clusters to maintain a log of workers and their activities.

As per the following figure, we have depicted the topology and distribution of the topology components, which are the same across clusters:

In our case, let's assume that our cluster constitutes of one Nimbus node, three Zookeepers in a Zookeeper cluster, and one supervisor node.

By default, we have four slots allocated to each supervisor, so four workers would be launched per Storm supervisor node unless the configuration is tweaked.

Let's assume that the depicted topology is allocated four workers, and it has two bolts each with a parallelism of two and one spout with a parallelism of four. So in total, we have eight tasks to be distributed across four workers.

So this is how the topology would be executed: two workers on each supervisor and two executors within each worker, as shown in the following figure:

Previous PageNext Page
You have been reading a chapter from
Real-time Analytics with Storm and Cassandra
Published in: Mar 2015Publisher: ISBN-13: 9781784395490
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
undefined
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime

Author (1)

author image
Shilpi Saxena

Shilpi Saxena is an IT professional and also a technology evangelist. She is an engineer who has had exposure to various domains (machine to machine space, healthcare, telecom, hiring, and manufacturing). She has experience in all the aspects of conception and execution of enterprise solutions. She has been architecting, managing, and delivering solutions in the Big Data space for the last 3 years; she also handles a high-performance and geographically-distributed team of elite engineers. Shilpi has more than 12 years (3 years in the Big Data space) of experience in the development and execution of various facets of enterprise solutions both in the products and services dimensions of the software industry. An engineer by degree and profession, she has worn varied hats, such as developer, technical leader, product owner, tech manager, and so on, and she has seen all the flavors that the industry has to offer. She has architected and worked through some of the pioneers' production implementations in Big Data on Storm and Impala with autoscaling in AWS. Shilpi has also authored Real-time Analytics with Storm and Cassandra (https://www.packtpub.com/big-data-and-business-intelligence/learning-real-time-analytics-storm-and-cassandra) with Packt Publishing.
Read more about Shilpi Saxena