CouchOne + Membase merge
techcrunch.com
techcrunch.com
I do sometimes feel like I'm the only one using CouchDB sometimes, though... Hopefully this gets more devs on board with it.
At first I was in love with it and now I just like it. It is a good tool for some things but it is not a universal replacement for everything database-like.
I like :
* futon (yes, I said it, I like couch because of a pretty GUI). I can quickly look at the database and debug what the problem is. Visibility into the database, the serialization format and the protocol are important to me.
* HTTP interface. Access it with curl or any other tools that speak HTTP.
* Documents are json. I like json.
* Can attach binary blobs to documents + have mime content types markers. Can serve this data to a web browser.
* Durable -- when my request finished, I know the data is on the disk.
* Map/Reduce -- I just like it. It makes sense to me.
Don't like:
* Not very good for high rate updates. Need to compact it periodically
* Not that fast. I know I can tweak it here and there, but by default it is on the slow end of the NoSQL DB speed range. I can live with this.
* MVCC. For some people this is a GoodThing for me it is a "Meh". Sometimes I would like to just update the document in place and overwrite the old one. Perhaps Redis or Mongo would be a better tool here. Now I just do it by repeatedly reading (on failure), updating _rev and then writing back.
* Map/Reduce -- Am I the only one who likes this? It is hard to get others onboard and start thinking of this instead of traditional queries.
Things I don't care about but others like:
* Offline replication. Others rave about it. I used it only a couple of times. Always afraid to end up conflicts silently buried in the DB history.
* Javascript -- don't really like it that much. I use the Python view server to write views. It is actually faster! :
I think people are tainted on Map/Reduce because they see how it is done with other NoSQL DBs like MongoDB where it doesn't store the indexes. The fact that CouchDB stores the views like it does, makes complex queries fast, and easy to use. Maybe there is a little initial overhead in writing the views, but after that it is efficient and leaves a lot of code out of the program, so you are getting the data you want in the way you want it.
Maybe I misunderstand what you mean by stored indexes, but MongoDB supports indexes (http://www.mongodb.org/display/DOCS/Indexes), it just doesn't require them so you can perform exploratory ad-hoc queries when you need to.
Where as with CouchDB, every time the view (M/R results) are accessed, it gets incrementally updated and stored. So even complex M/R queries on large datasets will be fast subsequently.
While in CouchDB, all queries are created with Map/Reduce, in MongoDB Map/Reduce is designed for aggregation. There is a separate system for standard querying which performs much better than Map/Reduce (Before people jump on me here my performance comparison is Mongo queries to MONGO Map/Reduce. Not Couch Map/Reduce or Map/Reduce in general; Not looking to get into a benchmark discussion here, just feature clarification).
The two differ greatly---while Couch requires precalculation of "Views", MongoDB focuses on dynamic querying. Given CouchDB's use of Map/Reduce for these static views, the way they do the iterative addition of new data without re-reducing the entire dataset makes sense.
However, MongoDB doesn't require you to use Map/Reduce to run queries and its MapReduce not designed for day to day querying. Rather, one should use MongoDB Map/Reduce for data aggregation tasks.
Also notable is that in 1.8, MongoDB has added some additional functionality for those who are using Map/Reduce which allows you to merge output and build data across jobs. I did a write up on the changes a few weeks ago: http://blog.evilmonkeylabs.com/2011/01/27/MongoDB-1_8-MapRed...
(In the interest of Full Disclosure: I work for 10gen, the company behind MongoDB; I'm also working on a book about MongoDB.)
As far as I know, this does not work on mondodb.
Personally, I simply do not see the point of map reduce for something like couch: the whole point is to be able to parallel jobs on multiple machines so that data do not need to move back and forth through the network. Debugging them is particularly painful, especially if you need to support non programmers (sql is used by many non-programmers people in my experience).
The new stuff for M/R in mongodb looks interesting, thanks for the update.
Yeah, thanks for introducing me to CouchDB.
Really, my only complaint is the inability to query and update in one command for quick document updates. SQL has the leg up here.
> UPDATE blah WHERE blah = blah;
Please, if someone from couchone is reading this, a similar feature would be killer.
We can't commit to when we'd be able to deliver it just yet, but it's definitely high up on our lists.
BTW - the official merger announcement just dropped in my inbox:
Additionally there are so many use cases in the fields of data warehousing and analytics. I am sure that CouchDB will replace many cube-based solutions in the future. There is great potential for companies focusing on CouchDB, like CouchOne and also Cloudant.
It is simple, really: at CouchOne we were 100% committed on the Open Source side of things and at Couchbase we will continue to do so at the same degree. In terms of organisation, Couchbase will be it's own independent Open Source project that has Apache CouchDB and memcached as dependencies, but adds a few things of its own that warrant being its own project. Our combined engineering team, led by Damien, will continue to contribute to Apache CouchDB in the same way as we've been to date, only more.
If not - take a look at them.