MongoDB vs RethinkDB: What are the differences?
MongoDB: 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; RethinkDB: JSON. Scales to multiple machines with very little effort. Open source. RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.
MongoDB and RethinkDB can be categorized as "Databases" tools.
"Document-oriented storage" is the top reason why over 788 developers like MongoDB, while over 46 developers mention "Powerful query language" as the leading cause for choosing RethinkDB.
MongoDB and RethinkDB are both open source tools. RethinkDB with 22.4K GitHub stars and 1.74K 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 & 2218 developers stacks; compared to RethinkDB, which is listed in 37 company stacks and 25 developer stacks.
What is MongoDB?
What is RethinkDB?
Want advice about which of these to choose?Ask the StackShare community!
What are the cons of using RethinkDB?
What tools integrate with MongoDB?
Used MongoDB as primary database. It holds trip data of NYC taxis for the year 2013. It is a huge dataset and it's primary feature is geo coordinates with pickup and drop off locations. Also used MongoDB's map reduce to process this large dataset for aggregation. This aggregated result was then used to show visualizations.
MongoDB fills our more traditional database needs. We knew we wanted Trello to be blisteringly fast. One of the coolest and most performance-obsessed teams we know is our next-door neighbor and sister company StackExchange. Talking to their dev lead David at lunch one day, I learned that even though they use SQL Server for data storage, they actually primarily store a lot of their data in a denormalized format for performance, and normalize only when they need to.
Nearly all of our backend storage is on MongoDB. This has also worked out pretty well. It's enabled us to scale up faster/easier than if we had rolled our own solution on top of PostgreSQL (which we were using previously). There have been a few roadbumps along the way, but the team at 10gen has been a big help with thing.
We are testing out MongoDB at the moment. Currently we are only using a small EC2 setup for a delayed job queue backed by
agenda. If it works out well we might look to see where it could become a primary document storage engine for us.
Used for proofs of concept and personal projects with a document data model, especially with need for strong geographic queries. Often not chosen in long term apps due to chance data model can end up relational as needs develop.
High-speed update-aware storage used in our region server infrastructure; provides a good middle layer for storage of rapidly modified information.
Main database, using it in multiple datacenters in an active-active configuration.