SQS as WAL and S3 as PagestoreThe 2008 S3 was painfully slow, and 100 ms reads weren't unusual. To hide that latency, the database separates "commit" from "apply". Clients write small, idempotent redo logs to Amazon Simple Queue Service (SQS) instead of touching S3 directly. An asynchronous checkpoint by a client applies those logs to B-tree pages on S3 later.
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Surviving SQS and building B-link Trees on S3As mentioned above, to bypass the severe latency of writing full data pages directly to S3, clients commit transactions by shipping small redo log records to SQS queues. Subsequently, clients act as checkpointers, asynchronously pulling these queued logs and applying the updates to their local copies before writing the newly materialized B-tree pages back to S3. This asynchronous log-shipping model means B-tree pages on S3 can be arbitrarily out-of-date compared to the real-time logs in SQS. Working on such stale state seems impossible, but the authors bound the staleness: writers (and probabilistically readers) run asynchronous checkpoints that pull batches of logs from SQS and apply them to S3, keeping the database consistent despite delays.