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Limitations of Quorum Consistency

If you have n replicas, and you choose w and r such that w + r > n, you can generally expect every read to return the most recent value written for a key. This is the case because the set of nodes to which you’ve written and the set of nodes from which you’ve read must overlap. That is, among the nodes you read there must be at least one node with the latest value (illustrated in Figure 5-11).

Often, r and w are chosen to be a majority (more than n/2) of nodes, because that ensures w + r > n while still tolerating up to n/2 node failures. But quorums are not necessarily majorities—it only matters that the sets of nodes used by the read and write operations overlap in at least one node. Other quorum assignments are possible, which allows some flexibility in the design of distributed algorithms [45].

You may also set w and r to smaller numbers, so that w + r < n (i.e., the quorum condition is not satisfied). In this case, reads and writes will still be sent to n nodes, but a smaller number of successful responses is required for the operation to succeed.

With a smaller w and r you are more likely to read stale values, because it’s more likely that your read didn’t include the node with the latest value. On the upside, this configuration allows lower latency and higher availability: if there is a network interruption and many replicas become unreachable, there’s a higher chance that you can continue processing reads and writes. Only after the number of reachable replicas falls below w or r does the database become unavailable for writing or reading, respectively.

However, even with w + r > n, there are likely to be edge cases where stale values are returned. These depend on the implementation, but possible scenarios include:

  • • If a sloppy quorum is used (see “Sloppy Quorums and Hinted Handoff’ on page 183), the w writes may end up on different nodes than the r reads, so there is no longer a guaranteed overlap between the r nodes and the w nodes [46].
  • • If two writes occur concurrently, it is not clear which one happened first. In this case, the only safe solution is to merge the concurrent writes (see “Handling Write Conflicts” on page 171). If a winner is picked based on a timestamp (last write wins), writes can be lost due to clock skew [35]. We will return to this topic in “Detecting Concurrent Writes” on page 184.
  • • If a write happens concurrently with a read, the write may be reflected on only some of the replicas. In this case, it’s undetermined whether the read returns the old or the new value.
  • • If a write succeeded on some replicas but failed on others (for example because the disks on some nodes are full), and overall succeeded on fewer than w replicas, it is not rolled back on the replicas where it succeeded. This means that if a write was reported as failed, subsequent reads may or may not return the value from that write [47].
  • • If a node carrying a new value fails, and its data is restored from a replica carrying an old value, the number of replicas storing the new value may fall below w, breaking the quorum condition.
  • • Even if everything is working correctly, there are edge cases in which you can get unlucky with the timing, as we shall see in “Linearizability and quorums” on page 334.

Thus, although quorums appear to guarantee that a read returns the latest written value, in practice it is not so simple. Dynamo-style databases are generally optimized for use cases that can tolerate eventual consistency. The parameters w and r allow you to adjust the probability of stale values being read, but it’s wise to not take them as absolute guarantees.

In particular, you usually do not get the guarantees discussed in “Problems with Replication Lag” on page 161 (reading your writes, monotonic reads, or consistent prefix reads), so the previously mentioned anomalies can occur in applications. Stronger guarantees generally require transactions or consensus. We will return to these topics in Chapter 7 and Chapter 9.

 
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