

SIREn – Enhanced Structured Data Search for Solr and Elasticearch - rendel
http://siren.solutions/siren/overview/

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techtalsky
My takeaways from the site for people who would like a summary:

SIREn encodes [...] the index using a completely different model than Lucene,
[...] it uses its own disk format, low level compression algorithm and query
operator implementation. A comparison between regular method (Blockjoin) and
SIREn can be found in this blog post: [http://siren.solutions/24-times-less-
memory-11-times-faster-...](http://siren.solutions/24-times-less-
memory-11-times-faster-measuring-the-impact-of-siren-on-elasticsearch-and-
solr-systems/)

In SIREn, parent-child relationships of the nested elements are materialised
and indexed into the same document. This is at the core of SIREn very high
performance and scalability. This means that changes in the nested part of the
document will require reindexing of the full document.

SIREn is Free and Open Source under an identical licensing model than MongoDB:
[http://www.mongodb.org/about/licensing/](http://www.mongodb.org/about/licensing/)

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rakoo
It seems like SIREn is optimized for highly nested documents.

One of the best use cases for Elasticsearch is log analysis, where 1 log event
has little to no nesting (at most a few tags). How does SIREn evaluate in
these cases ?

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harishkm
The standard Lucene indexing model will be faster for simple flat documents

SIREn makes sense for any document with 1..* nested relations. The performance
boost is proportional to the number of nested objects.

Further, SIREn also is truly schemaless. Which means that the type for a
property can be different in different documents. Something that is likely to
happen in complex scenarios.

ps : I work for siren.solutions.

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PaulHoule
I know Giovanni and some other SIREn people and this is a product that has
amazing computer science and software engineering built in.

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dholowiski
Me too! (I'm a customer). These guys are brilliant, and the science behind
what they do is (usually) way over my head.

