Scaling Big Data with Hadoop and Solr

By combining Apache Hadoop and Solr you can build super-efficient, high-speed enterprise search engines, and this book takes you through every stage of the process with a practical tutorial. Written specifically for Java programmers.

Scaling Big Data with Hadoop and Solr

Starting
Hrishikesh Vijay Karambelkar

By combining Apache Hadoop and Solr you can build super-efficient, high-speed enterprise search engines, and this book takes you through every stage of the process with a practical tutorial. Written specifically for Java programmers.
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Book Details

ISBN 139781783281374
Paperback144 pages

About This Book

  • Understand the different approaches of making Solr work on Big Data as well as the benefits and drawbacks
  • Learn from interesting, real-life use cases for Big Data search along with sample code
  • Work with the Distributed Enterprise Search without prior knowledge of Hadoop and Solr

Who This Book Is For

Scaling Big Data with Hadoop and Solr provides guidance to developers who wish to build high-speed enterprise search platforms using Hadoop and Solr. This book is primarily aimed at Java programmers who wish to extend the Hadoop platform to make it run as an enterprise search without any prior knowledge of Apache Hadoop and Solr.

Table of Contents

Chapter 1: Processing Big Data Using Hadoop and MapReduce
Understanding Apache Hadoop and its ecosystem
Storing large data in HDFS
Creating MapReduce to analyze Hadoop data
Installing and running Hadoop
Managing a Hadoop cluster
Summary
Chapter 2: Understanding Solr
Installing Solr
Apache Solr architecture
Configuring Apache Solr search
Loading your data for search
Summary
Chapter 3: Making Big Data Work for Hadoop and Solr
The problem
Understanding data-processing workflows
Using Solr 1045 patch – map-side indexing
Using Solr 1301 patch – reduce-side indexing
Using SolrCloud for distributed search
Using Katta for Big Data search (Solr-1395 patch)
Summary
Chapter 4: Using Big Data to Build Your Large Indexing
Understanding the concept of NOSQL
The CAP theorem
Understanding the concepts of distributed search
Lily – running Solr and Hadoop together
Deep dive – shards and indexing data of Apache Solr
Configuring SolrCloud to work with large indexes
Summary
Chapter 5: Improving Performance of Search while Scaling with Big Data
Understanding the limits
Optimizing the search schema
Index optimization
Optimization the search runtime
Monitoring the Solr instance
Summary

What You Will Learn

  • Understand Apache Hadoop, its ecosystem, and Apache Solr
  • Learn different industry-based architectures while designing Big Data enterprise search and understand their applicability and benefits
  • Write map/reduce tasks for indexing your data
  • Fine-tune the performance of your Big Data search while scaling your data
  • Increase your awareness of new technologies available today in the market that provide you with Hadoop and Solr
  • Use Solr as a NOSQL database
  • Configure your Big Data instance to perform in the real world
  • Address the key features of a distributed Big Data system such as ensuring high availability and reliability of your instances
  • Integrate Hadoop and Solr together in your industry by means of use cases

In Detail

As data grows exponentially day-by-day, extracting information becomes a tedious activity in itself. Technologies like Hadoop are trying to address some of the concerns, while Solr provides high-speed faceted search. Bringing these two technologies together is helping organizations resolve the problem of information extraction from Big Data by providing excellent distributed faceted search capabilities.

Scaling Big Data with Hadoop and Solr is a step-by-step guide that helps you build high performance enterprise search engines while scaling data. Starting with the basics of Apache Hadoop and Solr, this book then dives into advanced topics of optimizing search with some interesting real-world use cases and sample Java code.

Scaling Big Data with Hadoop and Solr starts by teaching you the basics of Big Data technologies including Hadoop and its ecosystem and Apache Solr. It explains the different approaches of scaling Big Data with Hadoop and Solr, with discussion regarding the applicability, benefits, and drawbacks of each approach. It then walks readers through how sharding and indexing can be performed on Big Data followed by the performance optimization of Big Data search. Finally, it covers some real-world use cases for Big Data scaling.

With this book, you will learn everything you need to know to build a distributed enterprise search platform as well as how to optimize this search to a greater extent resulting in maximum utilization of available resources.

Authors

Table of Contents

Chapter 1: Processing Big Data Using Hadoop and MapReduce
Understanding Apache Hadoop and its ecosystem
Storing large data in HDFS
Creating MapReduce to analyze Hadoop data
Installing and running Hadoop
Managing a Hadoop cluster
Summary
Chapter 2: Understanding Solr
Installing Solr
Apache Solr architecture
Configuring Apache Solr search
Loading your data for search
Summary
Chapter 3: Making Big Data Work for Hadoop and Solr
The problem
Understanding data-processing workflows
Using Solr 1045 patch – map-side indexing
Using Solr 1301 patch – reduce-side indexing
Using SolrCloud for distributed search
Using Katta for Big Data search (Solr-1395 patch)
Summary
Chapter 4: Using Big Data to Build Your Large Indexing
Understanding the concept of NOSQL
The CAP theorem
Understanding the concepts of distributed search
Lily – running Solr and Hadoop together
Deep dive – shards and indexing data of Apache Solr
Configuring SolrCloud to work with large indexes
Summary
Chapter 5: Improving Performance of Search while Scaling with Big Data
Understanding the limits
Optimizing the search schema
Index optimization
Optimization the search runtime
Monitoring the Solr instance
Summary

Book Details

ISBN 139781783281374
Paperback144 pages
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