10. Scaling Solr Capabilities with Big Data | Scaling Apache Solr

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Scaling Apache Solr

By : Vijay Karambelkar

4.7 (3)

Scaling Apache Solr

4.7 (3)

By: Vijay Karambelkar

Overview of this book

This book is a step-by-step guide for readers who would like to learn how to build complete enterprise search solutions, with ample real-world examples and case studies. If you are a developer, designer, or architect who would like to build enterprise search solutions for your customers or organization, but have no prior knowledge of Apache Solr/Lucene technologies, this is the book for you.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Understanding Apache Solr

1. Understanding Apache Solr

Challenges in enterprise search

Apache Solr – an overview

Features of Apache Solr

Apache Solr architecture

Practical use cases for Apache Solr

Summary

2. Getting Started with Apache Solr

2. Getting Started with Apache Solr

Setting up Apache Solr

Understanding the Solr structure

Configuring the Apache Solr for enterprise

Understanding SolrJ

Summary

3. Analyzing Data with Apache Solr

3. Analyzing Data with Apache Solr

Understanding enterprise data

Loading data using native handlers

Working with rich documents

Importing structured data from the database

Advanced topics with Solr

Summary

4. Designing Enterprise Search

4. Designing Enterprise Search

Designing aspects for enterprise search

Enterprise search data-processing patterns

Data integrating pattern for search

Case study – designing an enterprise knowledge repository search for software IT services

Summary

5. Integrating Apache Solr

5. Integrating Apache Solr

Empowering the Java Enterprise application with Solr search

Integration with client technologies

Case study – Apache Solr and Drupal

Summary

6. Distributed Search Using Apache Solr

6. Distributed Search Using Apache Solr

Need for distributed search

Understanding SolrCloud

Building enterprise distributed search using SolrCloud

Common problems and resolutions

Case study – distributed enterprise search server for the software industry

Summary

7. Scaling Solr through Sharding, Fault Tolerance, and Integration

7. Scaling Solr through Sharding, Fault Tolerance, and Integration

Enabling search result clustering with Carrot2

Sharding and fault tolerance

Searching Solr documents in near real time

Solr with MongoDB

Scaling Solr through Storm

Summary

8. Scaling Solr through High Performance

8. Scaling Solr through High Performance

Monitoring performance of Apache Solr

Tuning Solr JVM and container

Optimizing Solr schema and indexing

Speeding Solr through Solr caching

Improving runtime search for Solr

Optimizing SolrCloud

Summary

9. Solr and Cloud Computing

9. Solr and Cloud Computing

Enterprise search on Cloud

Solr on Cloud strategies

Running Solr on Cloud (IaaS and PaaS)

Running Solr on Cloud (SaaS) and enterprise search as a service

Summary

10. Scaling Solr Capabilities with Big Data

10. Scaling Solr Capabilities with Big Data

Apache Solr and HDFS

Big Data search on Katta

Using the Solr 1045 patch – map-side indexing

Using the Solr 1301 patch – reduce-side indexing

Apache Solr and Cassandra

Advanced analytics with Solr

Summary

A. Sample Configuration for Apache Solr

A. Sample Configuration for Apache Solr

schema.xml

solrconfig.xml

spellings.txt

synonyms.txt

protwords.txt

stopwords.txt

Index

Index

Using the Solr 1045 patch – map-side indexing

The Apache Solr 1045 patch provides Solr users a way to build Solr indexes using the MapReduce framework of Apache Hadoop. Once created, this index can be pushed to Solr storage. The following diagram depicts the mapper and reducer in Hadoop:

Each Apache Hadoop mapper transforms input records into a set of (key-value) pairs, which then gets transformed into SolrInputDocument. The Mapper task ends up creating an index from SolrInputDocument.

The focus of reducer is to perform de-duplication of different indexes and merge them if needed. Once the indexes are created, you can load them on your Solr instance and use them to search. You can read more about this patch on https://issues.apache.org/jira/browse/SOLR-1045.

The patch follows the standard process of patching up your label through SVN. To apply a patch to your Solr instance, you first need to build your Solr instance using source. The instance should be supported by the Solr 1045 patch. Now...

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