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You're reading from  Scaling Big Data with Hadoop and Solr, Second Edition

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Published inApr 2015
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ISBN-139781783553396
Edition1st Edition
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Hrishikesh Vijay Karambelkar
Hrishikesh Vijay Karambelkar
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Hrishikesh Vijay Karambelkar

Hrishikesh Vijay Karambelkar is an innovator and an enterprise architect with 16 years of software design and development experience, specifically in the areas of big data, enterprise search, data analytics, text mining, and databases. He is passionate about architecting new software implementations for the next generation of software solutions for various industries, including oil and gas, chemicals, manufacturing, utilities, healthcare, and government infrastructure. In the past, he has authored three books for Packt Publishing: two editions of Scaling Big Data with Hadoop and Solr and one of Scaling Apache Solr. He has also worked with graph databases, and some of his work has been published at international conferences such as VLDB and ICDE.
Read more about Hrishikesh Vijay Karambelkar

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Understanding the limits


Although you can have a completely distributed system for your big data search, there is a limit in terms of how far you can go. As you keep on distributing the shards, you may end up facing what is called the "laggard problem" for indexes for your instance.

This problem states that the response to your search query, which is an aggregation of results from all the shards, is controlled by the following formula:

QueryResponse = avg(max(shardResponseTime))

This means that if you have many shards, it is more likely that you will have one of them responding slowly (due to some anomaly) to your queries, and this will impact on your query response time, and this will start increasing.

The distributed search in Apache Solr has many limitations. Each document uploaded as distributed big data must have a unique key, and this unique key must be stored in the Solr repository. To do so, the Solr schema.xml file should have "stored=true" against the key attribute. This unique key...

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Scaling Big Data with Hadoop and Solr, Second Edition
Published in: Apr 2015Publisher: ISBN-13: 9781783553396

Author (1)

author image
Hrishikesh Vijay Karambelkar

Hrishikesh Vijay Karambelkar is an innovator and an enterprise architect with 16 years of software design and development experience, specifically in the areas of big data, enterprise search, data analytics, text mining, and databases. He is passionate about architecting new software implementations for the next generation of software solutions for various industries, including oil and gas, chemicals, manufacturing, utilities, healthcare, and government infrastructure. In the past, he has authored three books for Packt Publishing: two editions of Scaling Big Data with Hadoop and Solr and one of Scaling Apache Solr. He has also worked with graph databases, and some of his work has been published at international conferences such as VLDB and ICDE.
Read more about Hrishikesh Vijay Karambelkar