Snapshot 25144
Normalized text
Scripts and page chrome removed; this is what change detection compares.
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"body": "The maintenance has concluded and we see near zero 401 errors.",
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"postmortem_body": "_Task search outage, August 26, 2026_\n\nOn August 26 between 12:10 and 1:00 PM Pacific, task pages across ModernLoop were slow or failed to load entirely. This included the dashboard most users see right after signing in. We fixed it by 1:08 PM. No data was lost or exposed, and no scheduled interviews were affected.\n\n_What happened_\n\nAt 12:09 PM we shipped an update that included a fix for a rare bug where task search could return stale entries. The fix had a flaw: it removed a limit on how much of the search index one query could read. After the deploy, every task search read far more of the index than it needed, and each extra read triggered its own database check. Search traffic roughly quadrupled within a minute. Our search infrastructure saturated, requests started running past their time limits, and the pages waiting on them failed.\n\nThe frustrating part is that the change was small and looked safe. It passed review and tests. What it didn't pass was a large index under real traffic, because nothing in our pipeline exercised that.\n\n_Timeline \\(Pacific\\)_\n\n* 12:09 PM. Update deployed.\n* 12:10 PM. Task search slowed down.\n* 12:15 PM. Timeouts reached customers. Monitoring and support tickets flagged it within minutes.\n* 12:23 PM. We traced it to the deploy and started on a rollback and a corrected fix in parallel.\n* 1:00 PM. We rolled the search service back to the previous version. Timeouts stopped.\n* 1:08 PM. Error rates and response times confirmed normal.\n\n\n\n\n\n_What we're changing_\n\nWe restored the previous version to end the incident, and we rewrote the original fix so it keeps the data-consistency improvement but caps the work a single search can do. The gap that let this ship is the one described above: no load test covers search changes at realistic index sizes, so we're adding one. Rolling back also took longer than it should have because it required someone at a terminal with production access; each service now gets a one-click rollback. And we've added an alert on search-cluster load, so a regression like this pages us at 12:11, not 12:15.\n\nSorry for the disruption. The affected pages recovered on their own, so there's nothing you need to do.",
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"body": "Monitoring — Slow loading of task pages, the dashboard, and scheduling, and intermittent timeouts, have been resolved.\n\nStarting at 10:15 UTC (3:15 AM PT), a subset of customers experienced slow page loads and occasional timeouts when opening task pages, the Upcoming Events dashboard, Reporting, and the scheduling flow. The public API and MCP integrations could also return timeout errors during this period. No data was lost, and no scheduled interviews were affected.\n\nWe identified the cause: a database read path that, for some accounts, repeated the same large query many times in a single request and kept running after the request had already timed out. This placed sustained load on our read database replicas. We deployed a fix at 15:20 UTC (8:20 AM PT) that limits that path, and response times returned to normal by 16:00 UTC (9:00 AM PT).\n\nWe are monitoring the platform closely while the remaining database load returns to normal, and we are rolling out additional safeguards so that a single request can no longer create this kind of load. We will post a final update once monitoring is complete.\n\nWe apologize for the disruption.",
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"postmortem_body": "# Incident Postmortem: Slow Page Loads and Timeouts on August 20, 2026\n\n**Status:** Resolved \n**Incident window:** 13:00 UTC to 16:00 UTC \\(6:00 AM to 9:00 AM Pacific\\) \n**Severity:** Degraded performance. No data was lost. No scheduled interviews were changed or missed.\n\n## Summary\n\nOn August 20, 2026, some customers saw slow page loads and timeouts in ModernLoop for about three hours. The affected areas were task pages, the Upcoming Events dashboard, Reporting, the scheduling flow, the public API, and MCP integrations.\n\nThe cause was a defect in how our servers read lists of records for users with limited job permissions. One request could cause the database to run the same large query many times. These queries did not stop when the request timed out. This put sustained load on the database servers that serve read traffic. We deployed a fix at 15:20 UTC. Response times returned to normal by 16:00 UTC.\n\nWe apologize for the disruption.\n\n## Impact\n\n* Task pages, the Upcoming Events dashboard, and the scheduling flow loaded slowly. Some requests timed out.\n* The Reporting page loaded slowly. Some reports timed out.\n* Some public API requests and MCP tool calls returned timeout errors.\n* The writer database was not affected. All writes completed normally.\n* No data was lost. No scheduled interviews were changed, cancelled, or missed.\n* Calendar invitations and email notifications were not affected.\n\nNot all customers saw the impact. Customers with large volumes of application data were more likely to see slow pages.\n\n## Timeline \\(UTC\\)\n\n* **13:00** — Database read load increased sharply. Read server CPU rose from 30% to more than 90%. The first customer requests timed out.\n* **14:15** — Our team identified the request pattern that caused the load.\n* **14:20** — The source of the requests stopped. Load started to decrease.\n* **14:30** — Queries that the dashboard had started earlier completed. Load increased for a short time, then decreased.\n* **15:20** — We deployed the first fix. It limits how many times one request can read from the database.\n* **15:30** — The defective request pattern stopped completely.\n* **16:00** — Response times returned to normal. No further timeouts occurred.\n* **16:30** — Database read load returned to its normal range.\n\n## Cause\n\nOn August 15, 2026, we released a change that applies job-level permissions to list requests in our public API and MCP integrations. This change filters each page of results after the database returns it. If a page contains records that the user cannot see, the server reads the next page, and repeats this until the page is full.\n\nFor a user with access to a small number of jobs, and a filter that matched many records, one request caused the server to read almost every matching record in the organization. One request produced up to one thousand database queries.\n\nTwo other defects made the problem worse:\n\n1. When a request timed out, the server did not stop the database queries that the request had started. The queries continued to run until a database time limit stopped them. This limit was about 26 minutes.\n2. Two lookups in the scheduling flow did not have a database index. Under normal load they were fast. Under the increased load, each one took several minutes.\n\n## What we did\n\nDuring the incident:\n\n* We identified the request pattern and the code that caused it.\n* We deployed a change that limits the number of database reads for one list request.\n* We added a time limit to all read-replica database connections.\n\nAfter the incident, we completed or started these changes:\n\n* Each database query now stops when the request that started it times out or is cancelled.\n* Queries from our public API and MCP integrations now have a maximum run time of 10 seconds.\n* List requests now apply job permissions inside the database query. The server no longer reads records that the user cannot see.\n* List requests now return the newest records first by default.\n* We added database indexes for the slow lookups in the scheduling flow and for application lists.\n* We added a control that lets our team pause the public API or MCP integrations for one customer account if a request pattern causes load again. This control does not affect the ModernLoop application.\n\n## What we will do next\n\n* Complete the rollout of the database indexes during low-traffic hours.\n* Add monitoring that alerts our team when one request produces an unusual number of database queries.\n* Review other list requests in the application for the same pattern.\n\n## Questions\n\nIf you have questions about this incident, contact [[email protected]](mailto:[email protected]).",
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"postmortem_body": "# Elevated error rates on some read operations — August 3, 2026\n\n**Status:** Resolved \n**Duration:** 16:43 – 17:38 UTC \\(approximately 55 minutes\\)\n\n## Summary\n\nOn August 3, 2026, between 16:43 and 17:38 UTC, some customers experienced intermittent errors and slow responses on a subset of read operations, including loading team member directories, viewing scheduling suggestions, and loading metrics dashboards. Scheduling data and all previously saved information were unaffected, and no data was lost. Retrying the affected pages generally succeeded once the incident resolved.\n\n## What happened\n\nA routine database maintenance operation performed during a scheduled deployment placed unusually high load on our database infrastructure. This temporarily delayed our read-replica databases, causing a portion of read queries to time out or be cancelled while the replicas caught up. The initial recovery at 17:15 UTC was followed by a second period of elevated errors while database read traffic rebalanced; error rates fully returned to normal at approximately 17:38 UTC.\n\n## Impact\n\n* A subset of read requests failed or responded slowly during the incident window. Affected requests returned errors and could be retried.\n* Some customers were temporarily unable to create new schedules during the incident window; those attempts succeeded once the incident resolved. Previously created schedules, tasks, and candidate data were unaffected.\n* No data was lost or corrupted.\n\n## What we're doing to prevent this\n\n* Scheduling large database maintenance operations outside of peak usage hours.\n* Adding automatic retries for the class of transient read errors seen during this incident, so brief replication delays no longer surface as user-facing errors.\n* Adding earlier alerting on database replication delay so we can respond before customers are impacted.\n* Optimizing the specific queries that were most affected so they are more resilient to brief periods of database pressure.\n\nWe apologize for the disruption. If you continue to see errors, please contact [[email protected]](mailto:[email protected]).",
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"body": "# Elevated error rates on some read operations — August 3, 2026\n\n**Status:** Resolved\n**Duration:** 16:43 – 17:15 UTC (approximately 32 minutes)\n\n## Summary\n\nOn August 3, 2026, between 16:43 and 17:15 UTC, some customers experienced intermittent errors and slow responses on a subset of read operations, including loading team member directories, viewing scheduling suggestions, and loading metrics dashboards. Scheduling data and all previously saved information were unaffected, and no data was lost. Retrying the affected pages generally succeeded once the incident resolved.\n\n## What happened\n\nA routine scheduled deployment placed unusually high load on our database infrastructure. This temporarily delayed our read-replica databases, causing a portion of read queries to time out or be cancelled while the replicas caught up. Error rates returned to normal at approximately 17:15 UTC once the maintenance operation completed and replication fully recovered.\n\n## Impact\n\n- A subset of read requests failed or responded slowly during the incident window. Affected requests returned errors and could be retried.\n- Write operations (creating and updating schedules, tasks, and candidate data) were not materially affected.\n- No data was lost or corrupted.\n\n## What we're doing to prevent this\n\n- Adding automatic retries for the class of transient read errors seen during this incident, so brief replication delays no longer surface as user-facing errors.\n- Adding earlier alerting on database replication delay so we can respond before customers are impacted.\n- Optimizing the specific queries that were most affected so they are more resilient to brief periods of database pressure.\n\nWe apologize for the disruption. If you continue to see errors, please contact [email protected].",
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