Using Solr and Hadoop as a NoSQL database

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The combination of Hadoop and Solr makes it easy to crunch lots of data and then quickly serve up the results via a fast, flexible search & query API. Because Solr supports query-style requests, it’s suitable as a NoSQL replacement for traditional databases in many situations, especially when the size of the data exceeds what is reasonable with a typical RDBMS.

Using Solr and Hadoop as a NoSQL database

I think the first time I’ve heard about Solr and Lucene mentioned as NoSQL-like storages was from Grant Ingersoll and from the Guardian.co.uk guys.

From a NoSQL perspective:

there’s no fixed schema
there’s key-value access — hopefully that’s very fast and scalable
even if not standardized, there’s an advanced querying language

But as the original article points out some characteristics are missing:

Updating the index works best as a batch operation. Individual records can be updated, but each commit (index update) generates a new Lucene segment, which will impact performance.

Current support for replication, fail-over, and other attributes that you’d want in a production-grade solution aren’t yet there in SolrCloud. If this matters to you, consider Katta instead.

Original title and link: Using Solr and Hadoop as a NoSQL database (NoSQL databases © myNoSQL)

via: http://architects.dzone.com/articles/solr-hadoop-big-data-love