Compare Couchbase and MongoDB 
- Users report that Couchbase excels in its integrated cache feature, which enhances performance significantly, while MongoDB's query optimization is noted for its efficiency, making it a strong contender for complex queries.
- Reviewers mention that Couchbase's auto-sharding capability is robust, allowing for seamless scaling, whereas MongoDB also offers auto-sharding but users feel Couchbase's implementation is more user-friendly.
- G2 users highlight that MongoDB's role-based authorization and authentication features are more advanced, providing a higher level of security, which is crucial for enterprise-level applications.
- Users on G2 report that Couchbase's built-in search functionality is a standout feature, making it easier to retrieve data quickly, while MongoDB's search capabilities are considered adequate but not as powerful.
- Reviewers say that Couchbase's ease of setup is slightly lower than MongoDB's, with some users finding MongoDB's initial configuration more straightforward, which can be a deciding factor for smaller teams.
- Users mention that while both products offer good support, MongoDB's quality of support is rated higher, with users appreciating the responsiveness and helpfulness of the support team compared to Couchbase.
The Python API is pretty good for data access and view deployment. It took me a little while to figure out how to do what I wanted because there weren't a lot of examples out there on how to do what I wanted, but once I figured it out it was pretty good. ...
The N1Ql queries is something useless, in my opinion. They are very slow! I've used couchbase in a very big project with many concurrent requests and I noticed that at some point when you make lots of requests with n1ql, the couchbase server restarts, even...
- Ease of use - Easy to become productive - Easy indexing - Easy to install - Query language - Easy to scale - I was able to implement a polyglot solution combining MongoDB and Neo4j, a graphdb. The architecture has not fundamentally changed since...
When there is an error, it should provide more information on what isn't working. Perhaps highlight the error field?
The Python API is pretty good for data access and view deployment. It took me a little while to figure out how to do what I wanted because there weren't a lot of examples out there on how to do what I wanted, but once I figured it out it was pretty good. ...
- Ease of use - Easy to become productive - Easy indexing - Easy to install - Query language - Easy to scale - I was able to implement a polyglot solution combining MongoDB and Neo4j, a graphdb. The architecture has not fundamentally changed since...
The N1Ql queries is something useless, in my opinion. They are very slow! I've used couchbase in a very big project with many concurrent requests and I noticed that at some point when you make lots of requests with n1ql, the couchbase server restarts, even...
When there is an error, it should provide more information on what isn't working. Perhaps highlight the error field?
