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Scaling Big Data with Hadoop and Solr, Second Edition

You're reading from   Scaling Big Data with Hadoop and Solr, Second Edition Understand, design, build, and optimize your big data search engine with Hadoop and Apache Solr

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Product type Paperback
Published in Apr 2015
Publisher
ISBN-13 9781783553396
Length 166 pages
Edition 1st Edition
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Hrishikesh Vijay Karambelkar Hrishikesh Vijay Karambelkar
Author Profile Icon Hrishikesh Vijay Karambelkar
Hrishikesh Vijay Karambelkar
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Using Solr 1045 Patch – map-side indexing


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 the input records into a set of (key, value) pairs, which then get transformed into SolrInputDocument. The Mapper task then 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 for searching. You can read more about this patch at https://issues.apache.org/jira/browse/SOLR-1045.

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

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