Databases
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    <p>Storing data and searching are two different things. If you look at architectures like ebay, they have seperate services &amp; servers for search operation. 50m rows is nothing, you can store it with any of the datastores, none of them is perfect so the difference is use cases. Eg: cassandra has the fastest insert performance with any data size, can scale to petabytes with hundreds of machines easyly (no need to shard), has <a href="http://github.com/tjake/Lucandra" rel="noreferrer">lucandra</a> (cassndra-lucene integration, scales well with massive data but a toy when compared to elasticsearch), high durability,... MongoDB has more query options (uses btree as a dbms), has autosharding recently, can index all fields, but poor durability,... Postgresql is the most advanced opensource dbms out there, has builtin master/slave replication recently, can scale by sharding, acid &amp; sql compliant... couchdb has not any advantage compared to others in a use case I think, it's damn slow, If I need acid I probably use postgresql. Builtin fullText search functionality with these datastores has some problems and not scalable. </p> <p>The most advenced (massive data, high performance, simple, distributed, fault tolerant, rest api) open source search engine is <a href="http://www.elasticsearch.com/" rel="noreferrer">elasticsearch</a>, you can think of it as distributed lucene. Solr is lagecy compared to elascticsearch. use of raw lucene/sphinx is not scalable. </p> <p>If I were you, I probably choose one of the datastores and use elasticsearh for indexing and synhronize them on my data access layer (need to modify indexes on db insert/update/delete). </p> <p>Regards</p>
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      1. VTUpMod
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