Book Image

Scaling Big Data with Hadoop and Solr

By : Hrishikesh Vijay Karambelkar
Book Image

Scaling Big Data with Hadoop and Solr

By: Hrishikesh Vijay Karambelkar

Overview of this book

<p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p>
Table of Contents (15 chapters)
Scaling Big Data with Hadoop and Solr
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Index optimization


The indexes used in Apache Solr are inverted indexes. In the case of inverted indexing technique, all your text will be parsed and words will be extracted out of it. These words are then stored as index items, with the location of their appearance. For example, consider the following statements:

  1. Mike enjoys playing on a beach

  2. Playing on ground is a good exercise

  3. Mike loves to exercise daily

The index with location information for all these sentences will look like the following (The numbers in brackets denote (sentence no, word no):

Mike (1,1), (3,1)

enjoys (1,2)

playing (1,3), (2,1)

on (1,4), (2,2)

a (1,5), (2,5)

beach (1,6)

ground (2,3)

is (2,4)

good (2,6)

loves (3,2)

to (3,3)

exercise (2,7), (3,4)

daily (3,5)

When you perform delete on your inverted index, it does not delete it, it only marks the document as deleted. It will get cleaned only when the segment, the index is part of are merged. When you create index, you should avoid modifying the index.

Limiting the indexing buffer size...