
With the advent of large complex datasets, NOSQL databases have gained immense popularity for their efficiency to handle such datasets in comparison to relational databases. There are a number of NOSQL data stores for e.g. Mongo DB, Apache Couch DB etc. Operations in these data stores are executed quickly. In this paper we aim to get familiar with 2 most popular NoSQL databases: Elasticsearch and Apache CouchDB. This paper also aims to analyze the performance of Elasticsearch and CouchDB on image data sets. This analysis is based on the results carried out by instantiate, read, update and delete operations on both document-oriented stores and thus justifying how CouchDB is more efficient than Elasticsearch during insertion, updation and deletion operations but during selection operation Elasticsearch performs much better than CouchDB. The implementation has been done on LINUX platform.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 10 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
