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Air Max 2015 Black On Feet

I don know much about MongoDB, but I been using a lot of CouchDB for my current project. You first query the relational view with all the ids at once and you get back a list of relational keys. Then you query the view with those relational Nike Air Max 2015 Premium

Air Max 2015 Black On Feet

Air Max 2015 Black On Feet

Air Max 2015 Black On Feet

My one and only reason to use Mongo over Couch is geo indexes. As far as I can tell this doesn exist natively. I also not sure how Geocouch comes in worth this.

jimbokun 289 days ago link

Offline capable peer to peer replication was the main reason I chose CouchDB we needed something that would realistically run on clients, even mobile devices. NoSQL we mainly chose because we needed schema less data (the whole system relies on ad hoc design updates). It basically an information system IDE with rapid application development.

What I still don understand about MongoDB is where it actually shines compared to Couch. The performance advantage would have to be quite big to offset the loss in flexibility as a general purpose DB. I also not trying to start a war but I like to get a picture about why Mongo seems to be used more often than Couch.

It looks like the CouchDB vs. MongoDB in the document store world is the equivalent of the Postgresql vs. MySQL debate in the relation world.

rdtsc 289 days ago link

rdtsc 289 days ago link

Cloudant will soon offer geo spatial queries. On the other, CouchDB is older (and we techies love the new hotness), and the CouchDB split confused a lot of people. Having your original founder found a different and incompatible project with almost the same name but very different goals would cause almost any project to stutter.

> What I still don understand about MongoDB is where it actually shines compared to CouchMarketing. They shipped with unacknowledged writes for a long time and it made them look really good in write benchmarks. Couch was actually trying to keep your data safe. I have 3 I think.

start a CouchDB vs MongoDB war here but again, I just say I surprised at the types of documents OP was storing in MongoDB.

split confused a lot of peopleYes, that really didn inspire confidence in the longevity of the project

´╗┐been using a lot of CouchDB for my cur

matthewcford 289 days ago link

Air Max 2015 Black On Feet

keys at once and you get a collection of all related documents all pretty quickly, since you never need to scan over anything (couchDB basically enforces this by having almost no features that will require a scan). If you have multiple levels of relations (analogous to multiple joins in RDBMs), just extract they keys from above document collection and repeat the first two steps, updating the final collection.

Air Max 2015 Black On Feet

virmundi 289 days ago link

Querying, and querying immediately after insertion. If you want queries after insertion (which require views), this can be slow in couch. But that not a use case everyone has.

jimbokun 289 days ago link

Air Max 2015 Black On Feet

Also CouchDB has better safety. Once you start having large number of clients needing realtime updates, having to periodically poll for data updates can get very expensive.

twic 289 Air Max 2015 Black On Feet days ago link

Air Max 2015 Black On Feet

I immediately wondered why Diaspora didn try CouchDB, since replication seems to be one of the key features they were after.

gadamc 289 days ago link

Spot on about CouchDB. I haven used MongoDB for anything of decent scale but I must say I was shocked to read in the OP that they store huge documents like from the Movie example in MongoDB. In CouchDB you can use Views to sort of recursively find all of the other documents that your current doucment has a document ID for. This takes advantage of CouchDB excellent indexing. I not trying to Nike Air Max Flyknit 2015 Sale

Air Max 2015 Black On Feet

bemurphy 289 days ago link

Interesting question, I can point out a few interesting differences I know of. It does not offer sharding. Mongo on the other hand does support sharding via mongos, and you can build replica sets within each shard. It does not as far as I know support master master. An additional like query facility is to use a lucene plugin and define searchable data. Cloudant has this baked into their offering, and their employees maintain the plugin on github (https: has the ability to do things like append a value to a document array. In Couch, you likely read the entire document, append to the array in your app, and put the document back on Couch. It does have an update functionality that can sometimes isolate things more than this, but I haven seen it used as much. Mongo has a much broader API, while CouchDB takes a more simple http verb like approach (get, put and delete see the heaviest use). The CouchDB view stuff is a bit more of a mindset shift (see the View Collation wiki entry for an example of this at http: CouchDB was also taken hits for being slower than Mongo, but I suspect it was the map reduce stuff that really steered some folks the other way.

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