ElasticDict provides a python dict-like object that transparently stores it's keys and values in ElasticSearch.

Warning! This is a Work in Progress. At the moment, this is just an experiment. You probably don't want to use this for anything even remotely serious.

If you need a real library for ElasticSearch, check out one of the community-supported python clients

To get started, download elasticsearch, extract it, and run it locally:

$./bin/elasticsearch -f

Then, fire up a python shell, and create an instance of ElasticDict, and treat it like a regular python dictionary:

>>> d = ElasticDict()
>>> d['foo'] = 'bar'
>>> d['foo']
bar
>>> d.keys()
['foo']
>>> d.values()
['bar']
>>> d.items()
[('foo', 'bar')]

Remove this object, and start all over:

>>> del d
>>> d = ElasticDict()
>>> d['foo']
bar

Hey! Our bar value is still there!

This assumes elasticsearch is running on localhost using the default ports. You could also query elasticsearch locally:

$ curl -XGET 'http://localhost:9200/elasticdict/data/foo?pretty=true'
{
  "_index" : "tst",
  "_type" : "foo",
  "_id" : "1",
  "_version" : 1,
  "exists" : true, "_source" : {"foo":"bar"}
}

There are a few tests for this, though they are woefully incomplete. To run them, use:

$ nosetests

• Python 2.6+
• requests
• nose

What Next? ---------

Look at the source; There's obviously not much here at the moment. Right now the ElasticDict class just persists data to Elasticsearch, but there's a lot more that could be done. See the TODO file for more info.

Other ideas? Reach out on twitter (@bkmontgomery) or email me.

This work is available under the terms of the MIT license. See the LICENSE file for details.