# File contains default list of alerts for VictoriaMetrics single server. # The alerts below are just recommendations and may require some updates # and threshold calibration according to every specific setup. groups: # Alerts group for VM single assumes that Grafana dashboard # https://grafana.com/grafana/dashboards/10229 is installed. # Pls update the `dashboard` annotation according to your setup. - name: vmsingle interval: 30s concurrency: 2 rules: - alert: DiskRunsOutOfSpaceIn3Days expr: | vm_free_disk_space_bytes / ignoring(path) ( ( rate(vm_rows_added_to_storage_total[1d]) - ignoring(type) rate(vm_deduplicated_samples_total{type="merge"}[1d]) ) * scalar( sum(vm_data_size_bytes{type!~"indexdb.*"}) / sum(vm_rows{type!~"indexdb.*"}) ) ) < 3 * 24 * 3600 > 0 for: 30m labels: severity: critical annotations: dashboard: "http://localhost:3000/d/wNf0q_kZk?viewPanel=73&var-instance={{ $labels.instance }}" summary: "Instance {{ $labels.instance }} will run out of disk space soon" description: "Taking into account current ingestion rate, free disk space will be enough only for {{ $value | humanizeDuration }} on instance {{ $labels.instance }}.\n Consider to limit the ingestion rate, decrease retention or scale the disk space if possible." - alert: DiskRunsOutOfSpace expr: | sum(vm_data_size_bytes) by(instance) / ( sum(vm_free_disk_space_bytes) by(instance) + sum(vm_data_size_bytes) by(instance) ) > 0.8 for: 30m labels: severity: critical annotations: dashboard: "http://localhost:3000/d/wNf0q_kZk?viewPanel=53&var-instance={{ $labels.instance }}" summary: "Instance {{ $labels.instance }} will run out of disk space soon" description: "Disk utilisation on instance {{ $labels.instance }} is more than 80%.\n Having less than 20% of free disk space could cripple merges processes and overall performance. Consider to limit the ingestion rate, decrease retention or scale the disk space if possible." - alert: RequestErrorsToAPI expr: increase(vm_http_request_errors_total[5m]) > 0 for: 15m labels: severity: warning annotations: dashboard: "http://localhost:3000/d/wNf0q_kZk?viewPanel=35&var-instance={{ $labels.instance }}" summary: "Too many errors served for path {{ $labels.path }} (instance {{ $labels.instance }})" description: "Requests to path {{ $labels.path }} are receiving errors. Please verify if clients are sending correct requests." - alert: ConcurrentFlushesHitTheLimit expr: avg_over_time(vm_concurrent_insert_current[1m]) >= vm_concurrent_insert_capacity for: 15m labels: severity: warning show_at: dashboard annotations: dashboard: "http://localhost:3000/d/wNf0q_kZk?viewPanel=59&var-instance={{ $labels.instance }}" summary: "VictoriaMetrics on instance {{ $labels.instance }} is constantly hitting concurrent flushes limit" description: "The limit of concurrent flushes on instance {{ $labels.instance }} is equal to number of CPUs.\n When VictoriaMetrics constantly hits the limit it means that storage is overloaded and requires more CPU." - alert: RowsRejectedOnIngestion expr: sum(rate(vm_rows_ignored_total[5m])) by (instance, reason) > 0 for: 15m labels: severity: warning annotations: dashboard: "http://localhost:3000/d/wNf0q_kZk?viewPanel=58&var-instance={{ $labels.instance }}" summary: "Some rows are rejected on \"{{ $labels.instance }}\" on ingestion attempt" description: "VM is rejecting to ingest rows on \"{{ $labels.instance }}\" due to the following reason: \"{{ $labels.reason }}\"" - alert: TooHighChurnRate expr: | ( sum(rate(vm_new_timeseries_created_total[5m])) by(instance) / sum(rate(vm_rows_inserted_total[5m])) by (instance) ) > 0.1 for: 15m labels: severity: warning annotations: dashboard: "http://localhost:3000/d/wNf0q_kZk?viewPanel=66&var-instance={{ $labels.instance }}" summary: "Churn rate is more than 10% on \"{{ $labels.instance }}\" for the last 15m" description: "VM constantly creates new time series on \"{{ $labels.instance }}\".\n This effect is known as Churn Rate.\n High Churn Rate tightly connected with database performance and may result in unexpected OOM's or slow queries." - alert: TooHighChurnRate24h expr: | sum(increase(vm_new_timeseries_created_total[24h])) by(instance) > (sum(vm_cache_entries{type="storage/hour_metric_ids"}) by(instance) * 3) for: 15m labels: severity: warning annotations: dashboard: "http://localhost:3000/d/wNf0q_kZk?viewPanel=66&var-instance={{ $labels.instance }}" summary: "Too high number of new series on \"{{ $labels.instance }}\" created over last 24h" description: "The number of created new time series over last 24h is 3x times higher than current number of active series on \"{{ $labels.instance }}\".\n This effect is known as Churn Rate.\n High Churn Rate tightly connected with database performance and may result in unexpected OOM's or slow queries." - alert: TooHighSlowInsertsRate expr: | ( sum(rate(vm_slow_row_inserts_total[5m])) by(instance) / sum(rate(vm_rows_inserted_total[5m])) by (instance) ) > 0.05 for: 15m labels: severity: warning annotations: dashboard: "http://localhost:3000/d/wNf0q_kZk?viewPanel=68&var-instance={{ $labels.instance }}" summary: "Percentage of slow inserts is more than 5% on \"{{ $labels.instance }}\" for the last 15m" description: "High rate of slow inserts on \"{{ $labels.instance }}\" may be a sign of resource exhaustion for the current load. It is likely more RAM is needed for optimal handling of the current number of active time series. See also https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3976#issuecomment-1476883183" - alert: LabelsLimitExceededOnIngestion expr: sum(increase(vm_metrics_with_dropped_labels_total[5m])) by (instance) > 0 for: 15m labels: severity: warning annotations: dashboard: "http://localhost:3000/d/wNf0q_kZk?viewPanel=74&var-instance={{ $labels.instance }}" summary: "Metrics ingested in ({{ $labels.instance }}) are exceeding labels limit" description: "VictoriaMetrics limits the number of labels per each metric with `-maxLabelsPerTimeseries` command-line flag.\n This prevents from ingesting metrics with too many labels. Please verify that `-maxLabelsPerTimeseries` is configured correctly or that clients which send these metrics aren't misbehaving."