[trustfundbaby]

I hate subliminal attacks like these ... if you have a problem with someone, come out and say it, don't let me have to figure out what or who you're getting at.

And with regards to Mongo ... they've since added a feature that allows 'safe' writes to your database (confirms the data is written before returning a response) ... so what's the rant about?

[batasrki]

The 'safe' feature isn't on by default, yet. Also, the benchmarks 10gen publishes are based on default setup, so basically, Mongo writes to RAM, therefore it's fast.

I love Mongo and am using it in a few apps, but their marketing does blow, I admit.

Also, Eliot Horowitz came out and bashed on Riak's eventual consistency promise by basically misleading devs into thinking that writing to MongoDB will always result in 'full consistency'. Listen to the ChangeLog episode on Mongo to hear that.

[kchodorow]

10gen doesn't publish any benchmarks. See http://www.mongodb.org/display/DOCS/Benchmarks for the official position.

I transcribed the MongoDB vs. Riak part of the Changelog webcast (available at http://thechangelog.com/post/3742814720/episode-0-5-1-mongod...):

------------------------

Riak and all the dynamo-style databases are really distributed key/value stores and I think, you know, I've never used Riak in production, but I have no reason not to believe it's not a very good, highly scalable distributed key/value store.

The difference between something like Riak and Mongo is that Mongo tries to solve a more generic problem. A couple of key points: one is consistency. Mongo is fully consistent, and all dynamo implementations are eventually consistent and for a lot of developers and a lot of applications, eventual consistency just is not an option. So I think for the default data store for a web site, you need something that's fully consistent.

The other major difference is just data model and query-ability and being able to manipulate data. So for example with Mongo you can index on any fields you want, you can have compound indexes, you can sort, you know, all the same types of queries you do with a relational database work with Mongo. In addition, you can update individual fields, you can increment counters, you can do a lot of the same kinds of update operations you would do with a relational database. It maps much closer to a relational database than to a key/value store. Key/value stores are great if you've got billions of keys and you need to store them, they'll work very well, but if you need to replace a relational database with something that is pretty feature-comparable, they're not designed to do that.

-----------------------

It starts at minute 17.

edited: formatting.

[batasrki]

>Mongo is fully consistent

Can you please explain this for a case where there are multiple replica sets, the database is sharded and nodes are across data centers? What's sacrificed? Something must be.

[ethangunderson]

When we talk about consistency, we're talking about taking the database from one consistant state to another.

With replica sets, we're still only dealing with one master. We can get inconsistant reads from the replicas, but we're always writing to a single master, which allows that master to determine the integrity of a write.

With sharding, we're still only dealing with one canonical home for a specific key(defined by the shard key). (besides latency, I'm not sure how datacenters would affect this)

What we're giving up in this case is availability. If an entire replica set goes down, we can't read or write any data for the key ranges contained on those machines. This is where Riak shines.

With Riak, any node can accept writes, and nodes contain copys of several other nodes data. What that means is, as long as we have one node up, we can write to the database. Because of this, there is the possibility of nodes having different views of the data. This is handled in a number of ways(read repairs, vector clocks, etc). Check out the Amazon Dynamo paper for more info, great read.

I'm sure I'm missing some stuff, but I think that covers the gist of it.

EDIT: One thing that I want to make clear, I don't think that one architecture is better than the other. They each have their own pros and cons, and are really suited to solve different problems.

[batasrki]

None of this is guaranteed by default. By default, writes are flushed every 60 seconds. By default, there's no journaling. How can one claim full consistency if the the former two points are true?

Don't get me wrong, I love mongo. I'm building a web app backed by it. But the marketing talk is grating, which whT this post nails.

[mathias_10gen]

I think those two issues are orthogonal to consistency. In ACID, consistency and durability are two different letters and CAP doesn't even mention durability. Are you referring to another definition of consistency?

[kchodorow]

MongoDB is partition tolerant and consistent.

You can never have multi-master with MongoDB, which is required for "always writable." However, it can be readable. Our CEO did a series of posts on distributed consistency, see http://blog.mongodb.org/post/475279604/on-distributed-consis....

[benblack]

If a slave can continue serving reads whilst partitioned from a master that continues to accept writes then you cannot guarantee consistency. If a slave cannot serve reads when partitioned then you aren't available. If a master cannot accept writes when partitioned then you aren't available. See this excellent post from Coda Hale on why it is meaningless to claim a system is partition tolerant http://codahale.com/you-cant-sacrifice-partition-tolerance/.

One love.

- Lil' B

[kchodorow]

I interpreted "what is sacrificed?" as asking which letter of CAP MongoDB was giving up. Coda's article actually explains exactly the tradeoffs MongoDB makes for CP:

-------------------

Choosing Consistency Over Availability

If a system chooses to provide Consistency over Availability in the presence of partitions (again, read: failures), it will preserve the guarantees of its atomic reads and writes by refusing to respond to some requests. It may decide to shut down entirely (like the clients of a single-node data store), refuse writes (like Two-Phase Commit), or only respond to reads and writes for pieces of data whose "master" node is inside the partition component (like Membase).

This is perfectly reasonable. There are plenty of things (atomic counters, for one) which are made much easier (or even possible) by strongly consistent systems. They are a perfectly valid type of tool for satisfying a particular set of business requirements.

-------------------

[jorgeortiz85]

In a replica set configuration, all reads and writes are routed to the master by default. In this scenario, consistency is guaranteed. (You can optionally mark reads as "slaveOk", but then you admit inconsistency.)

This does sacrifice availability (in the CAP sense), but I haven't heard anyone claim otherwise.

[tuna]

Which C is lying ? CEO or CAP ? I let to the heart of pure and to the late night sysadmin to decide.

[aguynamedben]

Lil' B, stop trying to outsmart us all, MongoDB works, supports JSON, and autoshards.

[mihasya]

The implication is that the people for whom eventual consistency is not an option will never reach a data set size or availability requirement that'll require them to use replication and experience the lag (and eventual consistency) involved.

[alexpopescu]

That's not completely true. Take a look at Google's Megastore paper: http://www.cidrdb.org/cidr2011/Papers/CIDR11_Paper32.pdf

James Hamilton has a good summary of the ideas in the paper: http://perspectives.mvdirona.com/2011/01/09/GoogleMegastoreT...

[mihasya]

think you're viewing my statement out of the necessary context..

[batasrki]

Among major features touted are auto-sharding and replica sets. I don't know if the implication is that it's only for web apps/websites that won't need those

[mathias_10gen]

In the sharded case, at any given moment each object will still live on exactly one replica set, which will have at most one master. You can do operations (such as findAndModify http://bit.ly/ilomQo) that require a "current" version of an object because all writes are always sent to the master for that object. You can also choose to accept a weaker form of consistency for some reads by directing them to slaves for performance. This decision can be made per-operation from most languages.

As for trade-offs: Relative to a relational db, there is no way to guarantee a consistent view of multiple objects because they could live on different servers which disagree about when "now" is. Relative to an eventually consistent system, you are unable to do writes if you can't contact the master or a majority of nodes are down.