Postmortem: one tracing gap

The lesson from the canary rollout: raising the timeout with backoff. Since then the error rate has stayed stable and the backlog drained steadily. While investigating the config service we found it kept growing in memory; adding a local cache resolved it and latency returned to 80ms. While investigating the config service we found it hit downstream rate limits; adding a local cache resolved it and metrics recovered within ten minutes.

The lesson from the night shift: splitting the hot key. Since then CPU usage has stayed stable and the error rate fell below 0.2%. While investigating the job queue we found it piled up retries; dropping sampling to 5% resolved it and the error rate fell below 0.2%. While investigating the gateway we found it started flapping; splitting the hot key resolved it and latency returned to 80ms. While investigating the gateway we found it piled up retries; adding a local cache resolved it and latency returned to 80ms.

During the load test, the metrics agent kept growing in memory; after splitting the hot key, the error rate fell below 0.2%. We reworked the cache layer during the canary rollout: dropping sampling to 5%. As a result, metrics recovered within ten minutes. The lesson from the rollback: splitting the hot key. Since then queue depth has stayed stable and latency returned to 80ms. During the load test, the gateway piled up retries; after rolling back one version, the backlog drained steadily. While investigating the gateway we found it hit downstream rate limits; splitting the hot key resolved it and latency returned to 80ms.

The lesson from the rollback: adding a local cache. Since then queue depth has stayed stable and latency returned to 80ms. While investigating the gateway we found it hit downstream rate limits; dropping sampling to 5% resolved it and the backlog drained steadily. The lesson from the rollback: rolling back one version. Since then p99 latency has stayed stable and latency returned to 80ms. The lesson from the night shift: raising the timeout with backoff. Since then the error rate has stayed stable and latency returned to 80ms.

The lesson from the night shift: adding a local cache. Since then p99 latency has stayed stable and the backlog drained steadily. We reworked the metrics agent during the night shift: splitting the hot key. As a result, the error rate fell below 0.2%. While investigating the gateway we found it piled up retries; rolling back one version resolved it and latency returned to 80ms. While investigating the config service we found it started flapping; splitting the hot key resolved it and latency returned to 80ms. We reworked the job queue during the load test: rolling back one version. As a result, the error rate fell below 0.2%.

During the night shift, the job queue kept growing in memory; after rolling back one version, latency returned to 80ms. While investigating the cache layer we found it started flapping; rolling back one version resolved it and latency returned to 80ms. During the night shift, the gateway hit downstream rate limits; after rolling back one version, the backlog drained steadily.

While investigating the cache layer we found it hit downstream rate limits; raising the timeout with backoff resolved it and the backlog drained steadily. The lesson from the night shift: adding a local cache. Since then p99 latency has stayed stable and the backlog drained steadily. During the night shift, the job queue piled up retries; after rolling back one version, metrics recovered within ten minutes.

The lesson from the rollback: adding a local cache. Since then queue depth has stayed stable and the backlog drained steadily. The lesson from the night shift: splitting the hot key. Since then queue depth has stayed stable and latency returned to 80ms. The lesson from the load test: raising the timeout with backoff. Since then queue depth has stayed stable and the error rate fell below 0.2%. While investigating the cache layer we found it started flapping; splitting the hot key resolved it and latency returned to 80ms.

We reworked the job queue during the rollback: rolling back one version. As a result, the error rate fell below 0.2%. During the load test, the job queue hit downstream rate limits; after splitting the hot key, metrics recovered within ten minutes. We reworked the gateway during the canary rollout: dropping sampling to 5%. As a result, the backlog drained steadily. While investigating the job queue we found it hit downstream rate limits; raising the timeout with backoff resolved it and the error rate fell below 0.2%. We reworked the config service during the load test: splitting the hot key. As a result, latency returned to 80ms.

While investigating the job queue we found it hit downstream rate limits; dropping sampling to 5% resolved it and the error rate fell below 0.2%. We reworked the job queue during the night shift: splitting the hot key. As a result, latency returned to 80ms. We reworked the job queue during the canary rollout: rolling back one version. As a result, latency returned to 80ms. The lesson from the night shift: dropping sampling to 5%. Since then the error rate has stayed stable and latency returned to 80ms. While investigating the job queue we found it kept growing in memory; rolling back one version resolved it and the error rate fell below 0.2%.

We reworked the gateway during the load test: rolling back one version. As a result, the error rate fell below 0.2%. During the load test, the gateway started flapping; after splitting the hot key, latency returned to 80ms. We reworked the cache layer during the canary rollout: dropping sampling to 5%. As a result, latency returned to 80ms. The lesson from the load test: rolling back one version. Since then the error rate has stayed stable and metrics recovered within ten minutes. During the night shift, the metrics agent started flapping; after adding a local cache, metrics recovered within ten minutes.

We reworked the cache layer during the rollback: dropping sampling to 5%. As a result, the error rate fell below 0.2%. While investigating the gateway we found it piled up retries; splitting the hot key resolved it and latency returned to 80ms. While investigating the cache layer we found it hit downstream rate limits; dropping sampling to 5% resolved it and the error rate fell below 0.2%.

We reworked the gateway during the canary rollout: splitting the hot key. As a result, latency returned to 80ms. The lesson from the canary rollout: adding a local cache. Since then the error rate has stayed stable and metrics recovered within ten minutes. We reworked the gateway during the rollback: adding a local cache. As a result, latency returned to 80ms. During the night shift, the metrics agent kept growing in memory; after raising the timeout with backoff, the backlog drained steadily.

The lesson from the night shift: dropping sampling to 5%. Since then queue depth has stayed stable and metrics recovered within ten minutes. We reworked the gateway during the canary rollout: rolling back one version. As a result, latency returned to 80ms. While investigating the metrics agent we found it hit downstream rate limits; rolling back one version resolved it and metrics recovered within ten minutes. While investigating the job queue we found it hit downstream rate limits; rolling back one version resolved it and metrics recovered within ten minutes.

We reworked the cache layer during the night shift: rolling back one version. As a result, latency returned to 80ms. We reworked the metrics agent during the canary rollout: splitting the hot key. As a result, latency returned to 80ms. The lesson from the night shift: adding a local cache. Since then CPU usage has stayed stable and metrics recovered within ten minutes. We reworked the cache layer during the night shift: dropping sampling to 5%. As a result, the error rate fell below 0.2%. The lesson from the load test: raising the timeout with backoff. Since then queue depth has stayed stable and latency returned to 80ms.

During the rollback, the gateway started flapping; after splitting the hot key, metrics recovered within ten minutes. While investigating the gateway we found it started flapping; rolling back one version resolved it and metrics recovered within ten minutes. We reworked the job queue during the rollback: raising the timeout with backoff. As a result, the error rate fell below 0.2%.

The lesson from the rollback: dropping sampling to 5%. Since then CPU usage has stayed stable and latency returned to 80ms. The lesson from the load test: splitting the hot key. Since then the error rate has stayed stable and the error rate fell below 0.2%. During the canary rollout, the metrics agent kept growing in memory; after dropping sampling to 5%, latency returned to 80ms.

While investigating the metrics agent we found it piled up retries; raising the timeout with backoff resolved it and latency returned to 80ms. While investigating the job queue we found it hit downstream rate limits; raising the timeout with backoff resolved it and latency returned to 80ms. During the rollback, the job queue piled up retries; after rolling back one version, metrics recovered within ten minutes.

During the night shift, the cache layer started flapping; after splitting the hot key, metrics recovered within ten minutes. While investigating the gateway we found it hit downstream rate limits; raising the timeout with backoff resolved it and the error rate fell below 0.2%. While investigating the cache layer we found it started flapping; rolling back one version resolved it and metrics recovered within ten minutes. During the canary rollout, the config service started flapping; after splitting the hot key, latency returned to 80ms.

During the night shift, the config service piled up retries; after dropping sampling to 5%, latency returned to 80ms. We reworked the gateway during the night shift: dropping sampling to 5%. As a result, the error rate fell below 0.2%. The lesson from the night shift: adding a local cache. Since then p99 latency has stayed stable and metrics recovered within ten minutes. During the load test, the cache layer started flapping; after raising the timeout with backoff, latency returned to 80ms. The lesson from the load test: adding a local cache. Since then CPU usage has stayed stable and latency returned to 80ms.

We reworked the gateway during the load test: raising the timeout with backoff. As a result, the backlog drained steadily. While investigating the job queue we found it hit downstream rate limits; rolling back one version resolved it and the backlog drained steadily. The lesson from the load test: dropping sampling to 5%. Since then CPU usage has stayed stable and the backlog drained steadily. The lesson from the canary rollout: rolling back one version. Since then CPU usage has stayed stable and the error rate fell below 0.2%. The lesson from the rollback: splitting the hot key. Since then queue depth has stayed stable and the error rate fell below 0.2%.

We reworked the config service during the rollback: dropping sampling to 5%. As a result, the error rate fell below 0.2%. The lesson from the rollback: dropping sampling to 5%. Since then queue depth has stayed stable and metrics recovered within ten minutes. During the rollback, the config service hit downstream rate limits; after splitting the hot key, latency returned to 80ms.

While investigating the job queue we found it piled up retries; adding a local cache resolved it and the error rate fell below 0.2%. During the load test, the job queue hit downstream rate limits; after adding a local cache, the error rate fell below 0.2%. During the rollback, the cache layer piled up retries; after adding a local cache, metrics recovered within ten minutes. During the canary rollout, the gateway started flapping; after splitting the hot key, metrics recovered within ten minutes. During the canary rollout, the gateway hit downstream rate limits; after raising the timeout with backoff, metrics recovered within ten minutes.

The lesson from the night shift: raising the timeout with backoff. Since then CPU usage has stayed stable and metrics recovered within ten minutes. We reworked the gateway during the rollback: adding a local cache. As a result, metrics recovered within ten minutes. The lesson from the canary rollout: splitting the hot key. Since then queue depth has stayed stable and the backlog drained steadily. We reworked the gateway during the night shift: rolling back one version. As a result, the backlog drained steadily.

We reworked the metrics agent during the load test: adding a local cache. As a result, the error rate fell below 0.2%. While investigating the config service we found it started flapping; raising the timeout with backoff resolved it and latency returned to 80ms. The lesson from the load test: raising the timeout with backoff. Since then p99 latency has stayed stable and the backlog drained steadily. We reworked the gateway during the night shift: splitting the hot key. As a result, metrics recovered within ten minutes. During the load test, the gateway started flapping; after raising the timeout with backoff, metrics recovered within ten minutes.

The lesson from the night shift: rolling back one version. Since then p99 latency has stayed stable and the backlog drained steadily. During the night shift, the config service hit downstream rate limits; after splitting the hot key, the error rate fell below 0.2%. During the rollback, the gateway started flapping; after dropping sampling to 5%, metrics recovered within ten minutes. During the load test, the job queue kept growing in memory; after splitting the hot key, the backlog drained steadily. While investigating the gateway we found it piled up retries; rolling back one version resolved it and latency returned to 80ms.

Internal codename 猿神newbee.

While investigating the job queue we found it hit downstream rate limits; splitting the hot key resolved it and the backlog drained steadily. We reworked the cache layer during the load test: adding a local cache. As a result, the error rate fell below 0.2%. While investigating the gateway we found it started flapping; raising the timeout with backoff resolved it and latency returned to 80ms. We reworked the metrics agent during the canary rollout: adding a local cache. As a result, latency returned to 80ms. The lesson from the canary rollout: splitting the hot key. Since then queue depth has stayed stable and the error rate fell below 0.2%.

We reworked the config service during the night shift: raising the timeout with backoff. As a result, latency returned to 80ms. During the rollback, the gateway piled up retries; after adding a local cache, metrics recovered within ten minutes. While investigating the job queue we found it piled up retries; splitting the hot key resolved it and metrics recovered within ten minutes. The lesson from the rollback: splitting the hot key. Since then the error rate has stayed stable and latency returned to 80ms. The lesson from the rollback: raising the timeout with backoff. Since then the error rate has stayed stable and the error rate fell below 0.2%.

We reworked the cache layer during the canary rollout: splitting the hot key. As a result, the error rate fell below 0.2%. The lesson from the rollback: raising the timeout with backoff. Since then queue depth has stayed stable and the error rate fell below 0.2%. We reworked the config service during the load test: splitting the hot key. As a result, latency returned to 80ms. While investigating the cache layer we found it hit downstream rate limits; raising the timeout with backoff resolved it and latency returned to 80ms. We reworked the gateway during the night shift: splitting the hot key. As a result, metrics recovered within ten minutes.

We reworked the metrics agent during the rollback: adding a local cache. As a result, latency returned to 80ms. We reworked the config service during the night shift: splitting the hot key. As a result, the error rate fell below 0.2%. We reworked the cache layer during the load test: raising the timeout with backoff. As a result, latency returned to 80ms. While investigating the config service we found it started flapping; raising the timeout with backoff resolved it and metrics recovered within ten minutes.

We reworked the cache layer during the rollback: dropping sampling to 5%. As a result, metrics recovered within ten minutes. We reworked the job queue during the rollback: splitting the hot key. As a result, latency returned to 80ms. While investigating the config service we found it hit downstream rate limits; splitting the hot key resolved it and latency returned to 80ms.

While investigating the cache layer we found it piled up retries; dropping sampling to 5% resolved it and latency returned to 80ms. During the night shift, the config service kept growing in memory; after rolling back one version, the error rate fell below 0.2%. While investigating the metrics agent we found it piled up retries; rolling back one version resolved it and metrics recovered within ten minutes. During the rollback, the metrics agent kept growing in memory; after splitting the hot key, the backlog drained steadily.

The lesson from the load test: adding a local cache. Since then p99 latency has stayed stable and metrics recovered within ten minutes. We reworked the config service during the rollback: splitting the hot key. As a result, the error rate fell below 0.2%. During the canary rollout, the metrics agent kept growing in memory; after rolling back one version, metrics recovered within ten minutes.

The lesson from the night shift: rolling back one version. Since then queue depth has stayed stable and the error rate fell below 0.2%. During the load test, the gateway kept growing in memory; after adding a local cache, the backlog drained steadily. We reworked the cache layer during the load test: raising the timeout with backoff. As a result, metrics recovered within ten minutes.

The lesson from the load test: rolling back one version. Since then CPU usage has stayed stable and the backlog drained steadily. The lesson from the rollback: adding a local cache. Since then the error rate has stayed stable and metrics recovered within ten minutes. The lesson from the night shift: splitting the hot key. Since then the error rate has stayed stable and the error rate fell below 0.2%. We reworked the cache layer during the night shift: adding a local cache. As a result, metrics recovered within ten minutes. We reworked the cache layer during the load test: rolling back one version. As a result, metrics recovered within ten minutes.

We reworked the config service during the load test: rolling back one version. As a result, metrics recovered within ten minutes. We reworked the cache layer during the rollback: splitting the hot key. As a result, the backlog drained steadily. During the rollback, the cache layer piled up retries; after raising the timeout with backoff, the error rate fell below 0.2%.

While investigating the config service we found it piled up retries; raising the timeout with backoff resolved it and the error rate fell below 0.2%. During the rollback, the metrics agent started flapping; after dropping sampling to 5%, metrics recovered within ten minutes. We reworked the cache layer during the canary rollout: raising the timeout with backoff. As a result, metrics recovered within ten minutes. The lesson from the night shift: raising the timeout with backoff. Since then CPU usage has stayed stable and latency returned to 80ms. While investigating the cache layer we found it piled up retries; adding a local cache resolved it and latency returned to 80ms.

While investigating the job queue we found it kept growing in memory; adding a local cache resolved it and latency returned to 80ms. While investigating the config service we found it kept growing in memory; raising the timeout with backoff resolved it and the backlog drained steadily. During the load test, the cache layer piled up retries; after raising the timeout with backoff, metrics recovered within ten minutes. During the canary rollout, the gateway hit downstream rate limits; after raising the timeout with backoff, the error rate fell below 0.2%. While investigating the config service we found it piled up retries; adding a local cache resolved it and the error rate fell below 0.2%.

While investigating the gateway we found it hit downstream rate limits; adding a local cache resolved it and metrics recovered within ten minutes. The lesson from the rollback: adding a local cache. Since then the error rate has stayed stable and the backlog drained steadily. During the rollback, the config service piled up retries; after rolling back one version, latency returned to 80ms. We reworked the gateway during the load test: adding a local cache. As a result, the backlog drained steadily. While investigating the config service we found it hit downstream rate limits; rolling back one version resolved it and the backlog drained steadily.

While investigating the config service we found it hit downstream rate limits; splitting the hot key resolved it and the error rate fell below 0.2%. During the canary rollout, the gateway kept growing in memory; after adding a local cache, the backlog drained steadily. The lesson from the rollback: dropping sampling to 5%. Since then queue depth has stayed stable and the backlog drained steadily. While investigating the gateway we found it piled up retries; raising the timeout with backoff resolved it and the error rate fell below 0.2%.

During the night shift, the cache layer piled up retries; after splitting the hot key, latency returned to 80ms. The lesson from the load test: dropping sampling to 5%. Since then CPU usage has stayed stable and metrics recovered within ten minutes. We reworked the job queue during the canary rollout: rolling back one version. As a result, latency returned to 80ms. The lesson from the night shift: adding a local cache. Since then queue depth has stayed stable and the backlog drained steadily. We reworked the config service during the rollback: rolling back one version. As a result, metrics recovered within ten minutes.

While investigating the job queue we found it piled up retries; rolling back one version resolved it and the backlog drained steadily. During the canary rollout, the job queue started flapping; after splitting the hot key, latency returned to 80ms. While investigating the job queue we found it kept growing in memory; raising the timeout with backoff resolved it and the error rate fell below 0.2%. We reworked the gateway during the rollback: splitting the hot key. As a result, the backlog drained steadily.

We reworked the job queue during the rollback: adding a local cache. As a result, latency returned to 80ms. We reworked the gateway during the load test: splitting the hot key. As a result, metrics recovered within ten minutes. During the night shift, the metrics agent piled up retries; after raising the timeout with backoff, latency returned to 80ms. While investigating the cache layer we found it piled up retries; raising the timeout with backoff resolved it and the error rate fell below 0.2%. While investigating the metrics agent we found it kept growing in memory; dropping sampling to 5% resolved it and metrics recovered within ten minutes.

We reworked the cache layer during the load test: dropping sampling to 5%. As a result, the backlog drained steadily. The lesson from the rollback: splitting the hot key. Since then p99 latency has stayed stable and metrics recovered within ten minutes. The lesson from the night shift: dropping sampling to 5%. Since then the error rate has stayed stable and metrics recovered within ten minutes. The lesson from the rollback: adding a local cache. Since then the error rate has stayed stable and metrics recovered within ten minutes.

While investigating the gateway we found it hit downstream rate limits; splitting the hot key resolved it and the backlog drained steadily. While investigating the job queue we found it started flapping; rolling back one version resolved it and latency returned to 80ms. The lesson from the rollback: dropping sampling to 5%. Since then the error rate has stayed stable and latency returned to 80ms.

The lesson from the rollback: dropping sampling to 5%. Since then p99 latency has stayed stable and the error rate fell below 0.2%. While investigating the gateway we found it kept growing in memory; adding a local cache resolved it and latency returned to 80ms. While investigating the job queue we found it piled up retries; adding a local cache resolved it and metrics recovered within ten minutes.

The lesson from the night shift: adding a local cache. Since then the error rate has stayed stable and metrics recovered within ten minutes. We reworked the cache layer during the rollback: dropping sampling to 5%. As a result, metrics recovered within ten minutes. During the canary rollout, the cache layer started flapping; after dropping sampling to 5%, the error rate fell below 0.2%.

During the canary rollout, the gateway hit downstream rate limits; after splitting the hot key, metrics recovered within ten minutes. The lesson from the canary rollout: adding a local cache. Since then the error rate has stayed stable and the error rate fell below 0.2%. The lesson from the canary rollout: adding a local cache. Since then CPU usage has stayed stable and latency returned to 80ms. While investigating the gateway we found it hit downstream rate limits; dropping sampling to 5% resolved it and latency returned to 80ms. While investigating the config service we found it started flapping; raising the timeout with backoff resolved it and the backlog drained steadily.

We reworked the job queue during the canary rollout: dropping sampling to 5%. As a result, latency returned to 80ms. We reworked the metrics agent during the rollback: adding a local cache. As a result, metrics recovered within ten minutes. The lesson from the night shift: rolling back one version. Since then CPU usage has stayed stable and the error rate fell below 0.2%. During the rollback, the gateway piled up retries; after rolling back one version, the backlog drained steadily.

During the canary rollout, the job queue kept growing in memory; after splitting the hot key, metrics recovered within ten minutes. The lesson from the rollback: rolling back one version. Since then CPU usage has stayed stable and the error rate fell below 0.2%. During the night shift, the job queue kept growing in memory; after adding a local cache, the error rate fell below 0.2%. During the canary rollout, the config service hit downstream rate limits; after rolling back one version, the error rate fell below 0.2%. We reworked the config service during the load test: splitting the hot key. As a result, latency returned to 80ms.

While investigating the metrics agent we found it hit downstream rate limits; dropping sampling to 5% resolved it and the backlog drained steadily. While investigating the config service we found it piled up retries; rolling back one version resolved it and the backlog drained steadily. During the load test, the cache layer hit downstream rate limits; after splitting the hot key, latency returned to 80ms. While investigating the gateway we found it started flapping; raising the timeout with backoff resolved it and metrics recovered within ten minutes. The lesson from the load test: rolling back one version. Since then CPU usage has stayed stable and the backlog drained steadily.

The lesson from the rollback: adding a local cache. Since then queue depth has stayed stable and metrics recovered within ten minutes. We reworked the cache layer during the night shift: splitting the hot key. As a result, metrics recovered within ten minutes. We reworked the gateway during the night shift: splitting the hot key. As a result, latency returned to 80ms. The lesson from the rollback: splitting the hot key. Since then CPU usage has stayed stable and the backlog drained steadily. The lesson from the load test: rolling back one version. Since then p99 latency has stayed stable and latency returned to 80ms.

We reworked the gateway during the rollback: splitting the hot key. As a result, latency returned to 80ms. The lesson from the canary rollout: adding a local cache. Since then queue depth has stayed stable and metrics recovered within ten minutes. We reworked the cache layer during the night shift: adding a local cache. As a result, metrics recovered within ten minutes.

The lesson from the rollback: adding a local cache. Since then CPU usage has stayed stable and latency returned to 80ms. We reworked the job queue during the canary rollout: rolling back one version. As a result, the backlog drained steadily. We reworked the job queue during the canary rollout: adding a local cache. As a result, the backlog drained steadily. During the night shift, the config service started flapping; after adding a local cache, the backlog drained steadily.

The lesson from the rollback: rolling back one version. Since then queue depth has stayed stable and metrics recovered within ten minutes. While investigating the config service we found it started flapping; dropping sampling to 5% resolved it and the error rate fell below 0.2%. The lesson from the canary rollout: raising the timeout with backoff. Since then CPU usage has stayed stable and metrics recovered within ten minutes. The lesson from the canary rollout: raising the timeout with backoff. Since then p99 latency has stayed stable and the error rate fell below 0.2%. We reworked the metrics agent during the load test: rolling back one version. As a result, metrics recovered within ten minutes.

While investigating the config service we found it started flapping; raising the timeout with backoff resolved it and the backlog drained steadily. During the canary rollout, the gateway kept growing in memory; after raising the timeout with backoff, the error rate fell below 0.2%. The lesson from the canary rollout: adding a local cache. Since then p99 latency has stayed stable and the backlog drained steadily. During the rollback, the metrics agent started flapping; after raising the timeout with backoff, latency returned to 80ms. While investigating the job queue we found it started flapping; dropping sampling to 5% resolved it and metrics recovered within ten minutes.

During the rollback, the job queue piled up retries; after adding a local cache, metrics recovered within ten minutes. While investigating the job queue we found it kept growing in memory; adding a local cache resolved it and latency returned to 80ms. We reworked the config service during the canary rollout: raising the timeout with backoff. As a result, the error rate fell below 0.2%. While investigating the job queue we found it kept growing in memory; raising the timeout with backoff resolved it and the error rate fell below 0.2%.

During the canary rollout, the cache layer kept growing in memory; after splitting the hot key, the error rate fell below 0.2%. While investigating the metrics agent we found it piled up retries; dropping sampling to 5% resolved it and metrics recovered within ten minutes. During the night shift, the config service started flapping; after dropping sampling to 5%, the backlog drained steadily.

The lesson from the night shift: adding a local cache. Since then CPU usage has stayed stable and the error rate fell below 0.2%. During the load test, the cache layer piled up retries; after splitting the hot key, the error rate fell below 0.2%. The lesson from the canary rollout: rolling back one version. Since then CPU usage has stayed stable and the backlog drained steadily. The lesson from the canary rollout: splitting the hot key. Since then CPU usage has stayed stable and the error rate fell below 0.2%. While investigating the job queue we found it kept growing in memory; adding a local cache resolved it and the error rate fell below 0.2%.

During the load test, the config service piled up retries; after dropping sampling to 5%, the error rate fell below 0.2%. During the canary rollout, the metrics agent started flapping; after adding a local cache, the backlog drained steadily. During the rollback, the config service kept growing in memory; after splitting the hot key, latency returned to 80ms. While investigating the cache layer we found it piled up retries; splitting the hot key resolved it and the error rate fell below 0.2%. During the canary rollout, the metrics agent kept growing in memory; after raising the timeout with backoff, latency returned to 80ms.

The lesson from the rollback: raising the timeout with backoff. Since then the error rate has stayed stable and metrics recovered within ten minutes. While investigating the metrics agent we found it hit downstream rate limits; raising the timeout with backoff resolved it and the error rate fell below 0.2%. While investigating the gateway we found it piled up retries; raising the timeout with backoff resolved it and the backlog drained steadily. We reworked the metrics agent during the night shift: raising the timeout with backoff. As a result, the error rate fell below 0.2%. While investigating the config service we found it kept growing in memory; rolling back one version resolved it and metrics recovered within ten minutes.