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Apache Solr for Indexing Data

You're reading from   Apache Solr for Indexing Data Enhance your Solr indexing experience with advanced techniques and the built-in functionalities available in Apache Solr

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Product type Paperback
Published in Dec 2015
Publisher
ISBN-13 9781783553235
Length 160 pages
Edition 1st Edition
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Author (1):
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Anshul Johri Anshul Johri
Author Profile Icon Anshul Johri
Anshul Johri
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Table of Contents (13) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Understanding Analyzers, Tokenizers, and Filters 3. Indexing Data 4. Indexing Data – The Basic Technique and Using Index Handlers 5. Indexing Data with the Help of Structured Datasources – Using DIH 6. Indexing Data Using Apache Tika 7. Apache Nutch 8. Commits, Real-Time Index Optimizations, and Atomic Updates 9. Advanced Topics – Multilanguage, Deduplication, and Others 10. Distributed Indexing 11. Case Study of Using Solr in E-Commerce Index

Understanding soft commit, optimize, and hard commit


Solr provides us a Near-Real-Time (NRT) search, which makes documents available for searching just after they have been indexed in Solr. Additions or updates to documents are seen nearly in real-time after we index them in Solr. This near-real-time search can be done by using a soft commit (available in Solr 4.0+), which avoids the high cost of calling fsync, and it will flush the index data into a stable storage so that it can be retrieved in the event of a JVM crash.

An optimize, on the other hand, will cause all index segments to be merged into a single segment first and then reindex them. It's just like the defragmentation that we do on an HDD, which reindexes and frees up space. Normally, index segments are merged over time as specified in the merge policy, but this happens immediately when forced using the optimize command.

Let's see how we can use soft commit and optimize in Solr. We'll use our musicCatalog example and create a new...

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