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 This asynchronous log-shipping model means B-tree pages on S3 can be arbitrarily out-of-date compared to the real-time logs in SQS.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.
飞书/Lark: 拥有飞书开放平台应用创建权限
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The Harvard Business Review recently documented what it calls “workslop”: AI-generated work that looks polished but requires someone downstream to fix. When that work is a memo, it is annoying. When it is a cryptographic library, it is catastrophic. As AI accelerates the pace of software production, the verification gap does not shrink. It widens. Engineers stop understanding what their systems do. AI outsources not just the writing but the thinking.
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Despite the argument that the US could lose ground in terms of renewable energy innovation, the Trump administration points to the economic strain created by regulation.