4 ms·

"The A for availability in CAP means that ALL database servers are fully available" Is this true? I always thought it meant that clients could continue to read

by jshen 8y ago

"The A for availability in CAP means that ALL database servers are fully available" Is this true? I always thought it meant that clients could continue to read and write to "the database" which could include the client switching to another node. There is nothing in CAP theorem about latency, so switching, even if it adds high latency, is fine by CAP theorem. This lack of accounting for latency is what makes CAP theorem less useful than a lot of people realize IMO.

zzzcpan 8y ago
From implementation point of view networks are asynchronous and therefore are always partitioned. We don't actually have a luxury of arbitrary CAP interpretations, we can't know whether other nodes are available or not at any given moment. So instead we have to make requests to other nodes and always choose either to wait for responses (or more complex communications to achieve consensus) and get C or not wait for anything and get A, although each node can be a bit behind on updates from other nodes. Thus the CAP choices are pretty much about latency: waiting for globally visible updates vs not waiting and getting low latency. Both can be mixed in various proportions to get consistency with good latency, but still reasonable tolerance of byzantine failures.
jules 8y ago
It is true: if you allow clients to switch then you can have all three C,A,P.
- zbentley 8y ago
  No, you can't. Say you have two nodes, and a client has just sent a COMMIT to node 1. Then Node 1 gets partitioned away from node 2 and the committing client vanishes. Giving clients the ability to switch to node 2 doesn't help you determine whether node 2 does or does not have the data that was committed (consistency), so you have to choose between CP and AP.
  - jules 8y ago
    You can trivially redirect all clients to node 1 whether or not there is a partition, and voila, you have a CAP system under that definition. If clients can be partitioned from servers then we're back in the original scenario in which switching is not allowed: just partition each client from all but one server (but a different server for each client). Maybe there is a way to modify the CAP theorem so that it says something non-trivial (e.g. a theorem about the limit of how many node failures a Paxos-like algorithm can handle, but even this is probably trivial, namely half), but the CAP theorem as stated by the originators is trivial, and the proof is a dressed up version of what I stated above. I don't think the authors would disagree with this at all, since they explicitly state: "The basic idea of the proof is to assume that all messages between G1 and G2 are lost. If a write occurs in G1 and later a read occurs in G2, then the read operation cannot return the results of the earlier write operation." Isn't it interesting that such a paper has 1600 citations and has reshaped the database industry?

by jshen 8y ago

zzzcpan 8y ago

From implementation point of view networks are asynchronous and therefore are always partitioned. We don't actually have a luxury of arbitrary CAP interpretations, we can't know whether other nodes are available or not at any given moment. So instead we have to make requests to other nodes and always choose either to wait for responses (or more complex communications to achieve consensus) and get C or not wait for anything and get A, although each node can be a bit behind on updates from other nodes. Thus the CAP choices are pretty much about latency: waiting for globally visible updates vs not waiting and getting low latency. Both can be mixed in various proportions to get consistency with good latency, but still reasonable tolerance of byzantine failures.

jules 8y ago

It is true: if you allow clients to switch then you can have all three C,A,P.

zbentley 8y ago
No, you can't. Say you have two nodes, and a client has just sent a COMMIT to node 1. Then Node 1 gets partitioned away from node 2 and the committing client vanishes. Giving clients the ability to switch to node 2 doesn't help you determine whether node 2 does or does not have the data that was committed (consistency), so you have to choose between CP and AP.
- jules 8y ago
  You can trivially redirect all clients to node 1 whether or not there is a partition, and voila, you have a CAP system under that definition. If clients can be partitioned from servers then we're back in the original scenario in which switching is not allowed: just partition each client from all but one server (but a different server for each client). Maybe there is a way to modify the CAP theorem so that it says something non-trivial (e.g. a theorem about the limit of how many node failures a Paxos-like algorithm can handle, but even this is probably trivial, namely half), but the CAP theorem as stated by the originators is trivial, and the proof is a dressed up version of what I stated above. I don't think the authors would disagree with this at all, since they explicitly state: "The basic idea of the proof is to assume that all messages between G1 and G2 are lost. If a write occurs in G1 and later a read occurs in G2, then the read operation cannot return the results of the earlier write operation." Isn't it interesting that such a paper has 1600 citations and has reshaped the database industry?