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Mongodb never allows write to secondary. It allows optional reads from secondary but not writes. So if your primary goes down, you can't write till a secondary becomes primary again. That is how, you sacrifice High Availability in CAP theorem.
what is cap theorem? rdbms system such as oracle achieves which of the following two: consistency, availability, partition tolerance. nosql datastore such as hbase tends to achieve which of the following two: consistency, availability, partition tolerance.
CAP theorem or Eric Brewers theorem states that we can only achieve at most two out of three guarantees for a database: Consistency, Availability and Partition Tolerance. Here Consistency means that all nodes in the network see the same data at the same time.
It means mongo may not be be consistent with master/slaves(provided i do not configure write to all nodes before return). It does not makes sense to me to say mongo is consistent if all read and writes go to primary. In that case every other DB also(like cassandra) will be consistent .
MongoDB is strongly consistent by default - if you do a write and then do a read, assuming the write was successful you will always be able to read the result of the write you just read. This is because MongoDB is a single-master system and all reads go to the primary by default. If you optionally enable reading from the secondaries then MongoDB becomes eventually consistent where it's possible to read out-of-date results.
MongoDB also gets high-availability through automatic failover in replica sets: http://www.mongodb.org/display/DOCS/Replica+Sets
I agree with Luccas post. You can't just say that MongoDB is CP/AP/CA, because it actually is a trade-off between C, A and P, depending on both database/driver configuration and type of disaster: here's a visual recap, and below a more detailed explanation.
Scenario | Main Focus | Description
---------------------------|------------|------------------------------------
No partition | CA | The system is available
| | and provides strong consistency
---------------------------|------------|------------------------------------
partition, | AP | Not synchronized writes
majority connected | | from the old primary are ignored
---------------------------|------------|------------------------------------
partition, | CP | only read access is provided
majority not connected | | to avoid separated and inconsistent systems
MongoDB is strongly consistent when you use a single connection or the correct Write/Read Concern Level (Which will cost you execution speed). As soon as you don't meet those conditions (especially when you are reading from a secondary-replica) MongoDB becomes Eventually Consistent.
MongoDB gets high availability through Replica-Sets. As soon as the primary goes down or gets unavailable else, then the secondaries will determine a new primary to become available again. There is an disadvantage to this: Every write that was performed by the old primary, but not synchronized to the secondaries will be rolled back and saved to a rollback-file, as soon as it reconnects to the set(the old primary is a secondary now). So in this case some consistency is sacrificed for the sake of availability.
Through the use of said Replica-Sets MongoDB also achieves the partition tolerance: As long as more than half of the servers of a Replica-Set is connected to each other, a new primary can be chosen. Why? To ensure two separated networks can not both choose a new primary. When not enough secondaries are connected to each other you can still read from them (but consistency is not ensured), but not write. The set is practically unavailable for the sake of consistency.
As a brilliant new article showed up and also some awesome experiments by Kyle in this field, you should be careful when labeling MongoDB, and other databases, as C or A.
Of course CAP helps to track down without much words what the database prevails about it, but people often forget that C in CAP means atomic consistency (linearizability), for example. And this caused me lots of pain to understand when trying to classify. So, besides MongoDB give strong consistency, that doesn't mean that is C. In this way, if one make this classifications, I recommend to also give more depth in how it actually works to not leave doubts.
Yes, it is CP when using safe=true. This simply means, the data made it to the masters disk.
If you want to make sure it also arrived on some replica, look into the 'w=N' parameter where N is the number of replicas the data has to be saved on.
see this and this for more information.
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