For one of my newer projects at Lookout, I've been experimenting with Elasticsearch as the primary data store. The advantages of Elasticsearch are many for my particular use-case, but one of the things I particularly like about it is the distributed nature of its design.
Like most modern data stores, Elasticsearch was built to be deployed in a clustered environment where data is replicated automatically between various nodes (Cassandra is also in this category). Also like most modern data stores, most developers won't run a cluster of Elasticsearch nodes for their local development. They'll only run one, and miss a lot of simple bugs that can crop up from a distributed system. Such as (not a complete list):
To make my life easier, I created the
instant-elasticsearch uses a couple great tools to make it easy to
spin Elasticsearch nodes up and down in AWS:
Combining these four building blocks with a simple Puppet
and a clever
instant-elasticsearch is a
vagrant up away from automatically
provisioning a functional and self-discovering Elasticsearch cluster.
Once the cluster is up and running, you will have to do the manual work of copying some hostnames into your application's configuration files but only because I've not yet had a chance to automate that part (shame on me).
The workflow I use for
instant-elasticsearch is one where I come
into the office in the morning, spin up my entire cluster fresh which takes
about 3-4 minutes. After it's provisioned, I start my work. Throughout the day I
might shutdown some instances here and there to test some fault tolerance, but
for all intents and purposes the cluster remains mostly "up" until the end of
the day. At the end of the day, I make sure all my code is committed, then run
vagrant destroy -f to nuke my cluster.
All said and done I might spend a couple of bucks to save myself countless hours of hunting down the subtle application bugs that might occur in production without sufficient testing locally.
Not too bad for 80 lines of Puppet, and 50 lines of Ruby!