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Storm Blueprints: Patterns for Distributed Real-time Computation

4.1 (8)

Storm Blueprints: Patterns for Distributed Real-time Computation

4.1 (8)

Overview of this book

Storm Blueprints: Patterns for Distributed Real-time Computation

Storm Blueprints: Patterns for Distributed Real-time Computation

Credits

Credits

About the Authors

About the Authors

About the Reviewers

About the Reviewers

www.PacktPub.com

www.PacktPub.com

Preface

Preface

Free Chapter

Distributed Word Count

Distributed Word Count

Introducing elements of a Storm topology – streams, spouts, and bolts

Introducing the word count topology data flow

Implementing the word count topology

Introducing parallelism in Storm

Understanding stream groupings

Guaranteed processing

Summary

Configuring Storm Clusters

Configuring Storm Clusters

Introducing the anatomy of a Storm cluster

Introducing the Storm technology stack

Installing Storm on Linux

Submitting topologies to a Storm cluster

Automating the cluster configuration

A rapid introduction to Puppet

Summary

Trident Topologies and Sensor Data

Trident Topologies and Sensor Data

Examining our use case

Introducing Trident topologies

Introducing Trident spouts

Introducing Trident operations – filters and functions

Introducing Trident aggregators – Combiners and Reducers

Introducing the Trident state

Executing the topology

Summary

Real-time Trend Analysis

Real-time Trend Analysis

Use case

Architecture

Installing the required software

Introducing the sample application

Introducing the log analysis topology

The final topology

Running the log analysis topology

Summary

Real-time Graph Analysis

Real-time Graph Analysis

Use case

Architecture

A brief introduction to graph databases

Software installation

Setting up Titan to use the Cassandra storage backend

Graph data model

Connecting to the Twitter stream

Twitter graph topology

Implementing GraphState

Implementing GraphFactory

Implementing GraphTupleProcessor

Putting it all together – the TwitterGraphTopology class

Querying the graph with Gremlin

Summary

Artificial Intelligence

Artificial Intelligence

Designing for our use case

Establishing the architecture

Implementing the architecture

Summary

Integrating Druid for Financial Analytics

Integrating Druid for Financial Analytics

Use case

Integrating a non-transactional system

The topology

Implementing the architecture

Executing the implementation

Examining the analytics

Summary

Natural Language Processing

Natural Language Processing

Motivating a Lambda architecture

Examining our use case

Realizing a Lambda architecture

Designing the topology for our use case

Implementing the design

Examining the analytics

Batch processing / historical analysis

Hadoop

Summary

Deploying Storm on Hadoop for Advertising Analysis

Deploying Storm on Hadoop for Advertising Analysis

Examining the use case

Establishing the architecture

Configuring the infrastructure

Deploying the analytics

Performing the analytics

Deploying the topology

Executing the topology

Summary

Storm in the Cloud

Storm in the Cloud

Introducing Amazon Elastic Compute Cloud (EC2)

Introducing Apache Whirr

Configuring a Storm cluster with Whirr

Introducing Whirr Storm

Introducing Vagrant

Creating Storm-provisioning scripts

Summary

Index

Index

Batch processing / historical analysis

Now, let's turn our attention to the batch processing mechanism. For this, we will use Hadoop. Although a complete description of Hadoop is well beyond the scope of this section, we will give a brief overview of Hadoop alongside the Druid-specific setup.

Hadoop provides two major components: a distributed file system and a distributed processing framework. The distributed file system is aptly named the Hadoop Distributed Filesystem (HDFS). The distributed processing framework is known as MapReduce. Since we chose to leverage Cassandra as our storage mechanism in the hypothetical system architecture, we will not need HDFS. We will, however, use the MapReduce portion of Hadoop to distribute the processing across all of the historical data.

In our simple example, we will run a local Hadoop job that will read the local file written in our PersistenceFunction. Druid comes with a Hadoop job that we will use in this example.

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83

Tech Concepts

36

Programming languages

73

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