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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

Product type Book
Published in Apr 2015
Publisher
ISBN-13 9781783553396
Pages 166 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Hrishikesh Vijay Karambelkar Hrishikesh Vijay Karambelkar
Profile icon Hrishikesh Vijay Karambelkar

Optimizing search runtime


The search runtime speed is also a primary concern, and so it should be performed. You can also perform optimization at various levels at runtime. When Solr fetches the results for the queries passed by the user, you can limit the fetching of the result to a certain number by specifying the rows attribute in your search. The following query will return 10 rows of results from 10 to 20.

q=Scaling Big Data&rows=10&start=10

This can also be specified in solrconfig.xml as queryResultWindowSize, thereby setting the size to a limited number of query results.

Let's look at various other optimizations possible in the search runtime.

Optimizing through search query

Whenever a query request is forwarded to a search instance, Solr can respond in various ways, such as XML or JSON. A typical Solr response not only contains information about the matched results, but also contains information about your facets, highlighted text, and many other things which are used by the client...

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