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Mastering Apache Storm

By : Jain

1 (1)

Mastering Apache Storm

1 (1)

By: Jain

Overview of this book

Apache Storm is a real-time Big Data processing framework that processes large amounts of data reliably, guaranteeing that every message will be processed. Storm allows you to scale your data as it grows, making it an excellent platform to solve your big data problems. This extensive guide will help you understand right from the basics to the advanced topics of Storm. The book begins with a detailed introduction to real-time processing and where Storm fits in to solve these problems. You’ll get an understanding of deploying Storm on clusters by writing a basic Storm Hello World example. Next we’ll introduce you to Trident and you’ll get a clear understanding of how you can develop and deploy a trident topology. We cover topics such as monitoring, Storm Parallelism, scheduler and log processing, in a very easy to understand manner. You will also learn how to integrate Storm with other well-known Big Data technologies such as HBase, Redis, Kafka, and Hadoop to realize the full potential of Storm. With real-world examples and clear explanations, this book will ensure you will have a thorough mastery of Apache Storm. You will be able to use this knowledge to develop efficient, distributed real-time applications to cater to your business needs.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Real-Time Processing and Storm Introduction

Real-Time Processing and Storm Introduction

Apache Storm

Features of Storm

Storm components

The Storm data model

Programming languages

Summary

Storm Deployment, Topology Development, and Topology Options

Storm Deployment, Topology Development, and Topology Options

Storm prerequisites

Setting up the Storm cluster

Developing the hello world example

The different options of the Storm topology

Walkthrough of the Storm UI

Dynamic log level settings

Summary

Storm Parallelism and Data Partitioning

Storm Parallelism and Data Partitioning

Parallelism of a topology

Rebalance the parallelism of a topology

Different types of stream grouping in the Storm cluster

Guaranteed message processing

Tick tuple

Summary

Trident Introduction

Trident Introduction

Trident introduction

Understanding Trident's data model

Writing Trident functions, filters, and projections

Trident repartitioning operations

Trident aggregator

Utilizing the groupBy operation

When to use Trident

Summary

Trident Topology and Uses

Trident Topology and Uses

Trident groupBy operation

Non-transactional topology

Trident hello world topology

Trident state

Distributed RPC

When to use Trident

Summary

Storm Scheduler

Storm Scheduler

Introduction to Storm scheduler

Default scheduler

Isolation scheduler

Resource-aware scheduler

Custom scheduler

Summary

Monitoring of Storm Cluster

Monitoring of Storm Cluster

Cluster statistics using the Nimbus thrift client

Monitoring the Storm cluster using JMX

Monitoring the Storm cluster using Ganglia

Summary

Integration of Storm and Kafka

Integration of Storm and Kafka

Introduction to Kafka

Kafka architecture

Installation of Kafka brokers

Share ZooKeeper between Storm and Kafka

Kafka producers and publishing data into Kafka

Kafka Storm integration

Deploy the Kafka topology on Storm cluster

Summary

Storm and Hadoop Integration

Storm and Hadoop Integration

Introduction to Hadoop

Installation of Hadoop

Write Storm topology to persist data into HDFS

Integration of Storm with Hadoop

Setting up Storm-YARN

Storm-Starter topologies on Storm-YARN

Summary

Storm Integration with Redis, Elasticsearch, and HBase

Storm Integration with Redis, Elasticsearch, and HBase

Integrating Storm with HBase

Integrating Storm with Redis

Integrating Storm with Elasticsearch

Integrating Storm with Esper

Summary

Apache Log Processing with Storm

Apache Log Processing with Storm

Apache log processing elements

Producing Apache log in Kafka using Logstash

Splitting the Apache log line

Identifying country, operating system type, and browser type from the log file

Calculate the search keyword

Persisting the process data

Kafka spout and define topology

Deploy topology

MySQL queries

Summary

Twitter Tweet Collection and Machine Learning

Twitter Tweet Collection and Machine Learning

Exploring machine learning

Twitter sentiment analysis

Kafka spout, sentiments bolt, and HDFS bolt

Summary

Customer Reviews

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Different types of stream grouping in the Storm cluster

When defining a topology, we create a graph of computation with the number of bolt-processing streams. At a more granular level, each bolt executes multiple tasks in the topology. Thus, each task of a particular bolt will only get a subset of the tuples from the subscribed streams.

Stream grouping in Storm provides complete control over how this partitioning of tuples happens among the many tasks of a bolt subscribed to a stream. Grouping for a bolt can be defined on the instance of org.apache.storm.topology.InputDeclarer returned when defining bolts using the org.apache e.storm.topology.TopologyBuilder.setBolt method.

Storm supports the following types of stream groupings.

Shuffle grouping

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