After posting about Scott Motte’s comparison of MongoDB and CouchDB, I thought there should be some more informative sources out there, so I’ve started to dig.
The first I came upon is an article about Raindrop requirements and the issues faced while attacking them with CouchDB and the pros and cons of possibly replacing CouchDB with MongoDB:
Another article comparing MongoDB and CouchDB is hosted by MongoDB docs. I find it well balanced and you should read it all as it covers a lot of different aspects: horizontal scalability, query expressions, atomicity, durability, mapreduce support, javascript, performance, etc.
I’d also mention this benchmark comparing the performance of MongoDB, CouchDB, Tokyo Cabinet/Tyrant (note: the author of the benchmark is categorizing Tokyo Cabinet as a document database, while Tokyo is a key-value store) and uses MySQL results as a reference.
In case you have other resources that you think would be worth including do not hesitate to send them over.
The first I came upon is an article about Raindrop requirements and the issues faced while attacking them with CouchDB and the pros and cons of possibly replacing CouchDB with MongoDB:
[Pros]Anyway while some of the points above are generic, you should definitely try to consider them through the Raindrop requirements perspective about which you can read more here.[Cons]
- Uses update-in-place, so the file system impact/need for compaction is less if we store our schemas in one document are likely to work better.
- Queries are done at runtime. Some indexes are still helpful to set up ahead of time though.
- Has a binary format for passing data around. One of the issues we have seen is the JSON encode/decode times as data passes around through couch and to our API layer. This may be improving though.
- Uses language-specific drivers. While the simplicity of REST with CouchDB sounds nice, due to our data model, the megaview and now needing a server API layer means that querying the raw couch with REST calls is actually not that useful. The harder issue is trying to figure out the right queries to do and how to do the “joins” effectively in our API app code.
- easy master-master replication. However, for me personally, this is not so important. […] So while we need backups, we probably are fine with master-slave. To support the sometimes-offline case, I think it is more likely that using HTML5 local storage is the path there. But again, that is just my opinion.
- ad-hoc query cost may still be too high. It is nice to be able to pass back a JavaScript function to do the query work. However, it is not clear how expensive that really is. On the other hand, at least it is a formalized query language — right now we are on the path to inventing our own with the server API with a “query language” made up of other API calls.
Another article comparing MongoDB and CouchDB is hosted by MongoDB docs. I find it well balanced and you should read it all as it covers a lot of different aspects: horizontal scalability, query expressions, atomicity, durability, mapreduce support, javascript, performance, etc.
I’d also mention this benchmark comparing the performance of MongoDB, CouchDB, Tokyo Cabinet/Tyrant (note: the author of the benchmark is categorizing Tokyo Cabinet as a document database, while Tokyo is a key-value store) and uses MySQL results as a reference.
In case you have other resources that you think would be worth including do not hesitate to send them over.
