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  • Book Overview & Buying Scaling Big Data with Hadoop and Solr, Second Edition
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Scaling Big Data with Hadoop and Solr, Second Edition

Scaling Big Data with Hadoop and Solr, Second Edition

By : Vijay Karambelkar
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Scaling Big Data with Hadoop and Solr, Second Edition

Scaling Big Data with Hadoop and Solr, Second Edition

3 (4)
By: Vijay Karambelkar
Buy this Book

Overview of this book

This book is aimed at developers, designers, and architects who would like to build big data enterprise search solutions for their customers or organizations. No prior knowledge of Apache Hadoop and Apache Solr/Lucene technologies is required.
Table of Contents (8 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Processing Big Data Using Hadoop and MapReduce
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1. Processing Big Data Using Hadoop and MapReduce
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Apache Hadoop's ecosystem
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Configuring Apache Hadoop
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Running Hadoop
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Setting up a Hadoop cluster
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Common problems and their solutions
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Summary
2
2. Understanding Apache Solr
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2. Understanding Apache Solr
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Setting up Apache Solr
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The Apache Solr architecture
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Configuring Solr
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Loading data in Apache Solr
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Querying for information in Solr
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Summary
3
3. Enabling Distributed Search using Apache Solr
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3. Enabling Distributed Search using Apache Solr
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Understanding a distributed search
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Working with SolrCloud
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Sharding algorithm and fault tolerance
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Apache Solr and Big Data – integration with MongoDB
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Summary
4
4. Big Data Search Using Hadoop and Its Ecosystem
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4. Big Data Search Using Hadoop and Its Ecosystem
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Understanding NoSQL
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Working with the Solr HDFS connector
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Big data search using Katta
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Using Solr 1045 Patch – map-side indexing
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Using Solr 1301 Patch – reduce-side indexing
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Distributed search using Apache Blur
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Apache Solr and Cassandra
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Scaling Solr through Storm
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Advanced analytics with Solr
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Summary
5
5. Scaling Search Performance
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5. Scaling Search Performance
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Understanding the limits
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Optimizing search schema
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Index optimization
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Optimizing search runtime
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Monitoring Solr instance
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Summary
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A. Use Cases for Big Data Search
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A. Use Cases for Big Data Search
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E-Commerce websites
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Log management for banking
7
Index
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Index

Understanding a distributed search

The decision to move to a distributed search from a standalone system should be driven by the needs of enterprises, because distributed search applications are not always efficient in terms of performance. In this section, we will focus on understanding distributed search patterns and how Apache Solr supports distributed search. There have been efforts made to enable Apache Solr work with Apache Hadoop platform in the past, and we will look at more details in coming chapters.

Distributed search patterns

There are two important functions of any enterprise search: creation of indexes and run-time searching on indexes. Any or either of these functions can run in distributed mode, depending upon the requirements from an enterprise. To utilize the distributed search, the indexing must be split into multiple shards and should be kept across multiple nodes of a distributed system.

Tip

Sharding is a process of breaking one index into multiple logical units called ...

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