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.
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.
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.
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/.
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.
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
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.
ethangunderson|15 years ago
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|15 years ago
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.
kchodorow|15 years ago
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|15 years ago
One love.
- Lil' B
mihasya|15 years ago
alexpopescu|15 years ago
James Hamilton has a good summary of the ideas in the paper: http://perspectives.mvdirona.com/2011/01/09/GoogleMegastoreT...
batasrki|15 years ago
mathias_10gen|15 years ago
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.