CODEWITHSUNDEEP
distributed-computingdistributed
Nataraj
Nataraj

Reputation: 107

Real life scenarios of when would anyone choose availability over consistency (Who would be in interested in stale data?)

I was trying to wrap my brain around the CAP theorem. I understand that Network partitions can occur (eventually leading to the nodes in the cluster not able to sync up with the WRITE operations happening on the other nodes.)

In this case, either the Cluster could still be up and the load-balancer in front of the cluster could route the request to any of the nodes and after a WRITE operation on one of the nodes, the other nodes who can't sync with that data, still have STALE data and any subsequent READS to these nodes will serve STALE data. [So we are Loosing CONSISTENCY as we choose AVAILABILITY (i.e., we have choose the cluster to give STALE responses back.)]

Or we could SHUTDOWN the cluster whenever a network partition occurs! (There by loosing AVAILABILITY as we don't want to hamper consistency among the nodes.)

I have 2 things I would like to know the answer for it:

  1. In Reality, When would anyone choose to be AVAILABLE and still trade off CONSISTENCY? Who on this earth (practically) would be interested in STALE data? Please help me understand by listing more than one scenarios.

  2. In case, we would like to choose CONSISTENCY over AVAILABILITY, the cluster is down. Who on earth (real-time scenarios) practically would accept to design their system to be DOWN in order to preserve CONSISTENCY. Please list some scenarios.

Won't majority of us look for High availability no matter what? what are our options? please enlighten.

Upvotes: 0

Views: 1534

Answers (2)

YZats
YZats

Reputation: 61

  1. An example where you may choose Availability over Consistency is collaborative editing (e.g. Google Docs). It may be perfectly acceptable (and in fact desirable) to allow users to make local modifications to the documents and deal with conflict resolution once network is restored.

  2. A bank ATM is an example where you'd choose Consistency over Availability. Once ATM is disconnected from the network you would not want to allow withdrawals (thus, no Availability). Or, you could pick partial Availability, and allow deposits or read-only access to your bank statements.

Upvotes: 1

Artem Vovsia
Artem Vovsia

Reputation: 1570

  1. If I send you a message on FB and you send one to me, I'd rather prefer to see messages in an incorrect order(message sent at 1pm comes before message sent at 2pm) rather than not seeing them at all(example of AVAILABILITY of messages prefered over read-after-write CONSISTENCY of messages). Another example, If I gather web site metrics, I'd rather skip or drop some signal rather then force my users to wait for a page load while my consistent transaction is stuck.

Keep in mind that consistency doesn't mean STALE data, also data can be inconsistent in different ways(https://aphyr.com/posts/313-strong-consistency-models)

  1. Financial transactions are a classic example of data that requires consistency over availability. As a bank, I'd rather decline user request for money transfer, than accept it and lose customer's money due to DB being down.

I'd like to point out that CAP theorem is a high-level concept. There are a lot of ways you can treat terms consistency, availability or even partitioning, and different businesses have different requirements. Software engineering as a whole and distributed systems engineering, in particular, is about making trade-offs.

Upvotes: 1

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