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Multi-partition request processing

Ensuring that an operation is executed atomically, while satisfying constraints, becomes more interesting when several partitions are involved. In Example 12-2, there are potentially three partitions: the one containing the request ID, the one containing the payee account, and the one containing the payer account. There is no rea?son why those three things should be in the same partition, since they are all independent from each other.

In the traditional approach to databases, executing this transaction would require an atomic commit across all three partitions, which essentially forces it into a total order with respect to all other transactions on any of those partitions. Since there is now cross-partition coordination, different partitions can no longer be processed independently, so throughput is likely to suffer.

However, it turns out that equivalent correctness can be achieved with partitioned logs, and without an atomic commit:

  • 1. The request to transfer money from account A to account B is given a unique request ID by the client, and appended to a log partition based on the request ID.
  • 2. A stream processor reads the log of requests. For each request message it emits two messages to output streams: a debit instruction to the payer account A (partitioned by A), and a credit instruction to the payee account B (partitioned by B). The original request ID is included in those emitted messages.
  • 3. Further processors consume the streams of credit and debit instructions, deduplicate by request ID, and apply the changes to the account balances.

Steps 1 and 2 are necessary because if the client directly sent the credit and debit instructions, it would require an atomic commit across those two partitions to ensure that either both or neither happen. To avoid the need for a distributed transaction, we first durably log the request as a single message, and then derive the credit and debit instructions from that first message. Single-object writes are atomic in almost all data systems (see “Single-object writes” on page 230), and so the request either appears in the log or it doesn’t, without any need for a multi-partition atomic commit.

If the stream processor in step 2 crashes, it resumes processing from its last checkpoint. In doing so, it does not skip any request messages, but it may process requests multiple times and produce duplicate credit and debit instructions. However, since it is deterministic, it will just produce the same instructions again, and the processors in step 3 can easily deduplicate them using the end-to-end request ID.

If you want to ensure that the payer account is not overdrawn by this transfer, you can additionally have a stream processor (partitioned by payer account number) that maintains account balances and validates transactions. Only valid transactions would then be placed in the request log in step 1.

By breaking down the multi-partition transaction into two differently partitioned stages and using the end-to-end request ID, we have achieved the same correctness property (every request is applied exactly once to both the payer and payee accounts), even in the presence of faults, and without using an atomic commit protocol. The idea of using multiple differently partitioned stages is similar to what we discussed in “Multi-partition data processing” on page 514 (see also “Concurrency control” on page 462).

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