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elasticsearch: All content tagged as elasticsearch in NoSQL databases and polyglot persistence

Terrastore and ElasticSearch to Replace MySQL, Memcached and Sphinx

Currently we are using PHP, MySQL, Sphinx, and Memcached to serve up pages so quick. […]

[…] Our (MY) final decision was to use Terrastore. I’m not sure if it is the fastest, but it is fast. The main reason is how easy it is to scale with growth, how well it protects the data and keeps multiple copies always available, and the fast release cycle which means it is always improving.

As a replacement for Sphinx , we have considered many, but have landed on ElasticSearch, which just so happens to have a direct integration with Terrastore. A no-brainer for us to choose ElasticSearch for our search and ranking algorithms.

While each piece is important, sometimes it is also about the combo.

Original title and link: Terrastore and ElasticSearch to Replace MySQL, Memcached and Sphinx (NoSQL databases © myNoSQL)


Integrating ElasticSearch and CouchDB

This tutorial explains the process of setting up ElasticSearch to automatically index data in CouchDB and make it search-able. ElasticSearch 0.11 introduced a feature named The River, which allows it to connect to external systems and listen for documents updates. On receiving a notification, Elasticsearch indexes the data and makes it available for search.

In a nutshell, the solution uses what I’ve mentioned in previous posts: a combination of CouchDB _changes and an ElasticSearch automatic pull mechanism.

Original title and link: Integrating ElasticSearch and CouchDB (NoSQL databases © myNoSQL)


Searchable CouchDB with ElasticSearch

Shay Banon (@kimchy) about ElasticSeach integration with CouchDB:

The CouchDB River allows to automatically index couchdb and make it searchable using the excellent _changes stream couchdb provides. […] On top of that, in case of a failover, the couchdb river will automatically be started on another elasticsearch node, and continue indexing from the last indexed seq.

Full text indexing in the NoSQL space seems to see some interesting solutions.

Update: if you are interested to find out more about CouchDB _changes, you should check the video below:

Original title and link: Searchable CouchDB with ElasticSearch (NoSQL databases © myNoSQL)


Riak Search and Riak Full Text Indexing

Announced a while back and ☞ not quite here yet, Riak Search is Basho’s solution to the full text indexing problem.

While waiting for the release of Riak Search, I think that you can already start doing full text indexing using one of the existing indexing solutions (Lucene[1], Solr[2], ElasticSearch[3], etc.) and Riak post-commit hooks.

Simply put, all you’ll have to do is to create a Riak post-commit hook that feeds data into your indexing system.

The downside of this solution is that:

  1. you’ll still have to make sure that your indexing system is scalable, elastic, etc.
  2. you’ll not be able to use indexed data directly from Riak mapreduce functions, a feature that will be available through Riak Search.

Anyways, until Riak Search is out, why not having some fun!

Update: Embedded below a presentation on Riak Search providing some more details about this upcoming Basho product:

Update: Looks like the other presentation is not available anymore, so here is another on Riak search:


Integrating MongoDB with Solr

Sounds like quite a few NoSQL projects are externalizing the full text indexing to either Lucene or Solr (take for example CouchDB integration with Lucene or Neo4j integration with Lucene and Solr).

Now even if there are some basic ways (see [1] and [2]) to achieve this with MongoDB alone, people are still looking for more scalable solutions as shown by this thread ☞ covering Solr integration with MongoDB. The thread also mentions a couple of existing Ruby or Rails plugins for this integration.

One concern that I’ve expressed about the integration with Lucene alone is that you’ll have to deal with its scalability. Solr is one way to do that automatically. Lately I have heard of a new solution for scalable search: ☞ ElasticSearch which sounds quite interesting (nb: I haven’t yet gone through its docs or played with it, but the creator of the project has a long search/indexing history behind. You can find more details about Elastic Search here[3]).