Searching is a critical aspect to most business and web-centric applications. They provide accessibility of informational documents from a large array of data through a generalized inverted index. There are obviously many approaches that abound in the field of informational retrieval. And, as we face the information navigability spectrum of big data we naturally desire scalable search solutions. Elasticsearch and Solr are few open source and workable solutions with a plethora of features. They also both extend Lucene as a baseline for implementation. Deciding on which option to use between the two can be difficult especially as it often boils down to the varied domain contexts and with their varying approaches to design and implementation. It seems a simple comparison at times helps one decide, but the factors may just be too generic for a particular business need. Solr and Elasticsearch over the years have almost converged in the manner of the features they provide and as they evolve per each release. Although, Elasticsearch is relatively new compared to Solr, this does not hold it back in the way it is actively being used in industry across a multitude of domains. Even the Kibana dashboard in some ways has taken it further towards easing integration and for the domain of log analysis.
