Elasticsearch vs MongoDB: What are the differences?
Developers describe Elasticsearch as "Open Source, Distributed, RESTful Search Engine". Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). On the other hand, MongoDB is detailed as "The database for giant ideas". MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
Elasticsearch can be classified as a tool in the "Search as a Service" category, while MongoDB is grouped under "Databases".
"Powerful api", "Great search engine" and "Open source" are the key factors why developers consider Elasticsearch; whereas "Document-oriented storage", "No sql" and "Ease of use" are the primary reasons why MongoDB is favored.
Elasticsearch and MongoDB are both open source tools. Elasticsearch with 42.4K GitHub stars and 14.2K forks on GitHub appears to be more popular than MongoDB with 16.3K GitHub stars and 4.1K GitHub forks.
According to the StackShare community, MongoDB has a broader approval, being mentioned in 2189 company stacks & 2224 developers stacks; compared to Elasticsearch, which is listed in 2003 company stacks and 979 developer stacks.
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