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Coordination-avoiding data systems

We have now made two interesting observations:

  • 1. Dataflow systems can maintain integrity guarantees on derived data without atomic commit, linearizability, or synchronous cross-partition coordination.
  • 2. Although strict uniqueness constraints require timeliness and coordination, many applications are actually fine with loose constraints that may be temporarily violated and fixed up later, as long as integrity is preserved throughout.

Taken together, these observations mean that dataflow systems can provide the data management services for many applications without requiring coordination, while still giving strong integrity guarantees. Such coordination-avoiding data systems have a lot of appeal: they can achieve better performance and fault tolerance than systems that need to perform synchronous coordination [56].

For example, such a system could operate distributed across multiple datacenters in a multi-leader configuration, asynchronously replicating between regions. Any one datacenter can continue operating independently from the others, because no synchronous cross-region coordination is required. Such a system would have weak timeliness guarantees—it could not be linearizable without introducing coordination —but it can still have strong integrity guarantees.

In this context, serializable transactions are still useful as part of maintaining derived state, but they can be run at a small scope where they work well [8]. Heterogeneous distributed transactions such as XA transactions (see “Distributed Transactions in Practice” on page 360) are not required. Synchronous coordination can still be introduced in places where it is needed (for example, to enforce strict constraints before an operation from which recovery is not possible), but there is no need for everything to pay the cost of coordination if only a small part of an application needs it [43].

Another way of looking at coordination and constraints: they reduce the number of apologies you have to make due to inconsistencies, but potentially also reduce the performance and availability of your system, and thus potentially increase the number of apologies you have to make due to outages. You cannot reduce the number of apologies to zero, but you can aim to find the best trade-off for your needs—the sweet spot where there are neither too many inconsistencies nor too many availability problems.

 
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