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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
3 (4)
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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
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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
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Index
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Index

Optimizing search schema

When Solr is used in the context of a specific requirement (for example, log search for an enterprise application) it holds a specific schema that can be defined in schema.xml and copied over to nodes. The schema is based on the schema attributes indexes and thus plays a vital role in the performance of your Solr instance.

Specifying default search field

In the schema.xml file of the Solr configuration, the system allows you to specify the <defaultSearchField> parameter. This is the parameter that controls when you search without an explicit field name in your query, and which field to pick up for searching. This is an optional parameter; if this is not specified, for all the queries that are not providing the field name, the search will run them on all the available fields in the schema. This will not only consume more CPU time but on the whole, slow down the search performance.

Configuring search schema fields

In custom schema, having a larger number of fields...

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Scaling Big Data with Hadoop and Solr, Second Edition
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