Monitoring Animate

← Back to topic

🎞 ANIMATE · Mode 3 of 5 · +100 XP— Application Monitoring

📋Metrics

⚙️Alerting

🔄Quantile

✅Thresholds

🎯 Next: Test →

Step 1 / 4

Speed

What's happening

Rate Metrics

rate(http_requests_total[5m]): per-second request rate over 5-minute window

💡 Analogy

Like a quality control gauge showing what percentage of products fail inspection in the last hour.

rate() calculates per-second average over the time window. Error rate = 5xx counts / total counts = fraction. Alert when error fraction > 5%.

Console Output

> rate(5xx[5m]) = 3.2/s
> rate(total[5m]) = 850/s
> error_rate = 3.2/850 = 0.38%
> 0.38% < 5% → not alerting ✅

●prometheus-rules.yml

Rate MetricsL6–11

1# Prometheus alerting rules for a web application

2groups:

3 - name: application

4 rules:

5 # Alert if error rate > 5% over 5 minutes

6 - alert: HighErrorRate

7 expr: |

8 sum(rate(http_requests_total{status=~"5.."}[5m]))

9 / sum(rate(http_requests_total[5m])) > 0.05

10 for: 2m # must be true for 2 min (avoids flapping)

11 labels:

12 severity: critical

13 annotations:

14 summary: "High HTTP error rate {{ $value | humanizePercentage }}"

15 runbook_url: "https://wiki.company.com/runbooks/high-error-rate"

17 # Alert if P95 latency > 2 seconds

18 - alert: SlowResponses

19 expr: |

20 histogram_quantile(0.95,

21 sum(rate(http_request_duration_seconds_bucket[5m])) by (le)

22 ) > 2

23 for: 5m

24 labels:

25 severity: warning

26 annotations:

27 summary: "P95 latency is {{ $value | humanizeDuration }}"

29 # Alert if pod memory usage > 90% of limit

30 - alert: HighMemoryUsage

31 expr: |

32 container_memory_working_set_bytes /

33 container_spec_memory_limit_bytes > 0.9

34 for: 10m

35 labels:

36 severity: warning

36 lines

Steps · 4

What's happening

Rate Metrics

rate(http_requests_total[5m]): per-second request rate over 5-minute window

💡 Analogy

Like a quality control gauge showing what percentage of products fail inspection in the last hour.

rate() calculates per-second average over the time window. Error rate = 5xx counts / total counts = fraction. Alert when error fraction > 5%.

Console Output

> rate(5xx[5m]) = 3.2/s
> rate(total[5m]) = 850/s
> error_rate = 3.2/850 = 0.38%
> 0.38% < 5% → not alerting ✅

Was this page helpful?

Discussion

← Back to topic

🎞 ANIMATE · Mode 3 of 5 · +100 XP— Application Monitoring

📋Metrics

⚙️Alerting

🔄Quantile

✅Thresholds

🎯 Next: Test →

Step 1 / 4

Speed

What's happening

Rate Metrics

rate(http_requests_total[5m]): per-second request rate over 5-minute window

💡 Analogy

Like a quality control gauge showing what percentage of products fail inspection in the last hour.

rate() calculates per-second average over the time window. Error rate = 5xx counts / total counts = fraction. Alert when error fraction > 5%.

Console Output

> rate(5xx[5m]) = 3.2/s
> rate(total[5m]) = 850/s
> error_rate = 3.2/850 = 0.38%
> 0.38% < 5% → not alerting ✅

●prometheus-rules.yml

Rate MetricsL6–11

1# Prometheus alerting rules for a web application

2groups:

3 - name: application

4 rules:

5 # Alert if error rate > 5% over 5 minutes

6 - alert: HighErrorRate

7 expr: |

8 sum(rate(http_requests_total{status=~"5.."}[5m]))

9 / sum(rate(http_requests_total[5m])) > 0.05

10 for: 2m # must be true for 2 min (avoids flapping)

11 labels:

12 severity: critical

13 annotations:

14 summary: "High HTTP error rate {{ $value | humanizePercentage }}"

15 runbook_url: "https://wiki.company.com/runbooks/high-error-rate"

17 # Alert if P95 latency > 2 seconds

18 - alert: SlowResponses

19 expr: |

20 histogram_quantile(0.95,

21 sum(rate(http_request_duration_seconds_bucket[5m])) by (le)

22 ) > 2

23 for: 5m

24 labels:

25 severity: warning

26 annotations:

27 summary: "P95 latency is {{ $value | humanizeDuration }}"

29 # Alert if pod memory usage > 90% of limit

30 - alert: HighMemoryUsage

31 expr: |

32 container_memory_working_set_bytes /

33 container_spec_memory_limit_bytes > 0.9

34 for: 10m

35 labels:

36 severity: warning

36 lines

Steps · 4

What's happening

Rate Metrics

rate(http_requests_total[5m]): per-second request rate over 5-minute window

💡 Analogy

Like a quality control gauge showing what percentage of products fail inspection in the last hour.

rate() calculates per-second average over the time window. Error rate = 5xx counts / total counts = fraction. Alert when error fraction > 5%.

Console Output

> rate(5xx[5m]) = 3.2/s
> rate(total[5m]) = 850/s
> error_rate = 3.2/850 = 0.38%
> 0.38% < 5% → not alerting ✅