Why does my query drop from 3ms to 4 seconds on Tuesday with no deploy?
Возможность
PostgreSQL's query planner re-evaluates execution plans whenever table statistics change, which happens asynchronously through autovacuum and ANALYZE, independent of any code deployment. A batch import, a routine vacuum after deletion, or a gradual shift in data distribution can flip an index scan to a sequential scan overnight, turning a 3ms query into a 4-second one with no error emitted and no deployment in the history. These regressions are invisible in CI because they are driven by statistics state, not code state, and no standard test harness captures plans across different statistics snapshots. Pganalyze and similar tools detect plan flips after they occur in production, but there is no upstream gate that tests query plan stability before a schema change or data migration ships. PostgreSQL proposed the pg_plan_advice contrib module in October 2025 to let operators hint or pin plan
Почему это важно
Silent query plan regressions are one of the most common causes of unexplained production latency spikes and they remain undetectable until a user reports slowness.
Как я оцениваю возможность
Оценка возможности отражает моё личное суждение, а не точное измерение: насколько проблема болезненна, как часто она встречается и насколько мало существует решений сегодня. Чем выше оценка, тем более достойной реализации я считаю эту задачу.
Насколько серьёзные проблемы это создаёт.
Как часто люди с этим сталкиваются.
Насколько мало хороших инструментов для этого существует сегодня.
Ещё задачи, достойные решения
Почему программное обеспечение, от которого мы зависим больше всего, так неудобно использовать?
TechПочему я по-прежнему не владею ни одними из данных, которые генерирую?
TechThis is a fundamental limitation of how most data deletion works today, and it comes down to a few core problems: **Deletion is an absence, not a presence** A receipt is a positive artifact -- proof that something *happened*. Proving a negative ("this data no longer exists anywhere") is cryptographically and architecturally much harder than proving a positive ("this file was signed at time T"). **No standard audit trail** Most systems are not built to emit a verifiable, tamper-proof log of what was deleted, when, and from which storage layer. Even if a company tells you "your data was deleted," that statement lives in their system, which they control. **Backups and replication** Data often lives in dozens of places: primary DBs, read replicas, backups, logs, caches, CDN edge nodes, analytics pipelines. A deletion receipt would need to account for all of them -- and companies rarely track this end-to-end. **No legal requirement to provide one** GDPR Article 17 gives you the right to erasure, and Article 19 requires notification to recipients -- but neither mandates a cryptographically verifiable receipt. Companies only have to confirm deletion, not *prove* it in a way you can independently verify. **Technical proof would require architectural changes** A trustworthy receipt would need something like: a Merkle-tree deletion proof, a signed timestamp from an independent witness, and a commitment scheme proving the original data existed before deletion. Almost no consumer product is built this way. The gap here is a policy and incentive problem as much as a technical one -- companies have little motivation to build verifiable deletion infrastructure when regulators don't require it.
TechПочему я не могу знать, соответствует ли то, что запущено, тому, что задекларировано в моём SBOM?
TechПочему каждая цепочка происхождения C2PA разрывается в тот момент, когда контент попадает в социальные сети?
TechПочему тесты, сгенерированные с помощью ИИ, пропускают ошибки, которые они должны были обнаружить?