1. 1. Préambule
  2. 2. PromQL
    1. 2.1. Exercice 01
    2. 2.2. Exercice 02
    3. 2.3. Exercice 03
    4. 2.4. Exercice 04
    5. 2.5. Exercice 05
    6. 2.6. Exercice 06
    7. 2.7. Exercice 07
    8. 2.8. Exercice 08
    9. 2.9. Exercice 09
    10. 2.10. Exercice 10
    11. 2.11. Exercice 11
    12. 2.12. Exercice 12
    13. 2.13. Exercice 13
    14. 2.14. Exercice 14
  3. 3. Installation et Paramétrage
    1. 3.1. Exercice 15
    2. 3.2. Exercice 16
    3. 3.3. Exercice 17
    4. 3.4. Exercice 18
    5. 3.5. Exercice 19
    6. 3.6. Exercice 20
    7. 3.7. Exercice 21
    8. 3.8. Exercice 22
    9. 3.9. Exercice 23
    10. 3.10. Exercice 24
    11. 3.11. Exercice 25
    12. 3.12. Exercice 26
    13. 3.13. Exercice 27
    14. 3.14. Exercice 28
    15. 3.15. Exercice 29
    16. 3.16. Exercice 30
    17. 3.17. Exercice 31
    18. 3.18. Exercice 32
    19. 3.19. Exercice 33
  4. 4. Références

Formation Prometheus

Références utiles

  • Principe de Heartbeat (Deadman's switch) https://jpweber.io/blog/taking-advantage-of-deadmans-switch-in-prometheus/ pour les petites infrastructures
  • Liste d'alertes communes : https://awesome-prometheus-alerts.grep.to/rules.html
  • Stockage long terme à surveiller : https://thanos.io/
  • Outils connexes : https://openapm.io/
  • Exemples de requêtes :
    • https://github.com/infinityworks/prometheus-example-queries
    • https://coralogix.com/blog/promql-tutorial-5-tricks-to-become-a-prometheus-god/
    • https://timber.io/blog/promql-for-humans/
    • https://archive.fosdem.org/2017/schedule/event/alerting_with_time_series/attachments/slides/1736/export/events/attachments/alerting_with_time_series/slides/1736/FOSDEM__Alerting_with_Time_Series.pdf
    • https://towardsdatascience.com/practical-monitoring-with-prometheus-grafana-part-iii-81f019ecee19
    • https://about.gitlab.com/blog/2019/07/23/anomaly-detection-using-prometheus/
    • https://rancher.com/docs/rancher/v2.x/en/monitoring-alerting/v2.0.x-v2.4.x/cluster-monitoring/expression/