(Updated version. Originally Published on: Nov 3, 2013) Here are some things to keep in mind as you go about designing your ElasticSearch cluster. Many of these are from real life experiences and IMO are the basic common sense items you should consider. In addition to these settings noted here, there maybe other settings that are relevant to your use case.
While I had the privilege of using ElasticSearch to implement media analytics in a past role, it is only recently that I have started looking at it for log analysis.
I have been playing with ElasticSearch for a while now, both at work as well as personally. In recent discussions I came across a use case to perform faceted searches and figured this would be a good topic for a blog post. Lets explore by example how to implement faceted searches using both the older facet module as well as the newer aggregations module.
If you have used ElasticSearch (ES) you will be familiar with the two ways you can access the index – the RESTful HTTP API’s and the Java API which uses a binary protocol. What is missing is a pure RESTful HTTP Java Client API. Open source Jest library attempts to fill that gap. Updated July 2016 to use ElasticSearch 2.3.4 and Jest 2.0.0.
You must have surely heard the tag lines “Data is gold” or “Data is oil”! If not, then you heard it now. The notion is that with the right type and volume of data, you can pull out very valuable insights to help support your business/IT goals. This data might be coming from your own applications, log files, social media data, blogs, online news media, etc. Data is everywhere. And when you have that data, you want to search through it for intelligent information. That is where search engines come to the rescue. I will cover one such search engine – ElasticSearch.