Topic

Alerting

1 post about Alerting. Every summary links to the original.

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Clickhouse
Scaling ClickStack alerting with built-in anomaly detection
Managed ClickStack now supports statistical anomaly detection on query-builder charts using z-scores, allowing teams to move alerting workloads from tools like Grafana. The detection calculates the expected value from a moving average of seven one-minute buckets and the standard deviation from a two-hour baseline, with a 5% minimum change threshold to prevent false positives from stable metrics. Users configure sensitivity, baseline duration, and direction of change, and preview how often the alert would have fired over the previous 24 hours, with the chart updating as settings are adjusted. The alerting framework scales to tens of thousands of alerts through an improved scheduler and sharding that distributes evaluation across multiple workers, with a backfill mechanism for skipped evaluations and improved search, filtering, and evaluation histories in the alerts UI. Future plans include support for seasonality, median-based estimates for noisy data, and alerting on PromQL queries.

The best engineering reads, once a week.

Topic

1 post about Alerting. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Clickhouse
Scaling ClickStack alerting with built-in anomaly detection
Managed ClickStack now supports statistical anomaly detection on query-builder charts using z-scores, allowing teams to move alerting workloads from tools like Grafana. The detection calculates the expected value from a moving average of seven one-minute buckets and the standard deviation from a two-hour baseline, with a 5% minimum change threshold to prevent false positives from stable metrics. Users configure sensitivity, baseline duration, and direction of change, and preview how often the alert would have fired over the previous 24 hours, with the chart updating as settings are adjusted. The alerting framework scales to tens of thousands of alerts through an improved scheduler and sharding that distributes evaluation across multiple workers, with a backfill mechanism for skipped evaluations and improved search, filtering, and evaluation histories in the alerts UI. Future plans include support for seasonality, median-based estimates for noisy data, and alerting on PromQL queries.

The best engineering reads, once a week.