Application metrics with Prometheus

on YouTube ↗50m

" summary

A practical walkthrough of instrumenting PHP services with Prometheus to drive decisions: a feedback pipeline is built with counters and histograms, metrics stored in APCu/Redis and exposed on a /metrics endpoint, and Prometheus uses a Prometheus pull model to scrape them. Team picks 150ms SLO for submit-response based on user perception, uses histogram buckets and percentiles to spot slow tails, and warns against over‑optimizing or hiding reliability by exceeding required SLOs.

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" chapters (24)

1. 0:00Intro
2. 0:51How do you do metrics?
3. 4:08Metrics are insights into the current state of your application.
4. 4:35Metrics tell you if your service is healthy.
5. 5:08Metrics tell you what
6. 7:04Service Level Indicators
7. 7:34Service Level Objectives
8. 8:13Help you drive architectural decisions, like optimisation
9. 10:19Service Level Agreements
10. 11:18Measuring
11. 15:56Request Latency
12. 17:33Picking good SLS
13. 18:28Understanding the nature of your system
14. 18:44User-Facing serving system? availability, throughput, latency
15. 19:16Storage System? availability, durability, latency
16. 19:42Big Data Systems? throughput, end-to-end latency
17. 23:08Don't pick a target based on current performance
18. 27:31Don't over achieve. The Chubby example.
19. 30:25Attach consequences to your objectives.
20. 30:48The night is dark and full of loopholes. take a friend from legal with you.
21. 31:12Safety Margins. like setting the alarm 5 minutes before the meeting.
22. 34:26Telemetry Statistics
23. 47:45You can't act on what you can't see.
24. 49:49Metrics without actionability are just numbers on a screen.

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