Monitoring at HelloFresh

Transcript

1. Monitoring at Hellofresh By Rafael Jesus ~ Prometheus Meetup - Berlin - 2019 ~
2. HelloFresh
3. Agenda Why we care about monitoring What we monitor How we monitor What comes next
4. Why monitoring Debugging certain bugs might take months, even years without proper monitoring. Has API success rate slightly dropped over time? What if a portion of customers are getting logged out? What if CDN caches 4-5xx response codes for 1m due to a miscon g? What if users from Japan/Australia complains about website home page being slow?
5. Why monitoring In the event of engineers need to rely on data to drive decisions, otherwise they will always have to depend exclusively from the opinion and feelings of the most senior engineer around. You don't have software ownership if you don't monitor it.
6. Why monitoring
7. What we monitor Infrastructure Reverse Proxies & Edge Services Storage Systems MongoDB, RDS, Elastic Search, Redis Observability Services Jaeger, Graylog, Prometheus Kubertenes Clusters k8s nodes, core-dns, pods
8. Infrastructure Engineering Core-DNS
9. Infrastructure Engineering Core-DNS
10. What we monitor Product Engineering APIs and AMQP consumers
11. How we monitor
12. How we monitor Our Monitoring Stack Prometheus Prometheus Operator Thanos Grafana Legacy Graphite & StatsD
13. How we monitor Prometheus Operator Serves to make running Prometheus on top of Kubernetes as easy as possible. Introduces additional resources in Kubernetes: Prometheus, ServiceMonitor, Alertmanager and PrometheusRule. Example apiVersion: monitoring.coreos.com/v1 kind: Prometheus
14. How we monitor Prometheus Operator
15. How we monitor Prometheus Operator Helm Chart Initial installation and con guration of: Prometheus servers Alertmanager Grafana Host node_exporter kube-state-metrics Default set of Alerts and Dashboards
16. How we monitor Thanos Global View Long term storage Why? We had 1 year of retention on Graphite Going global with prometheus federation might be hard for us
17. How we monitor Without thanos
18. How we monitor Without thanos Federation allows you to have a global Prometheus that pulls aggregated metrics from your datacenter Prometheus servers
19. How we monitor
20. How we monitor Thanos Global View
21. How we monitor Thanos Storing
22. How we monitor Thanos Querying Sidecar
23. How we monitor Thanos Querying Store
24. How we monitor Thanos Compactor
25. How we monitor How we started Prometheus operator helm chart Full k8s cluster monitoring Golden Signals w/ Ingress, cAdvisor metrics Thanos for global view & LTS compactor don't like empty blocks , ok we've removed all from S3 it couldn't compact istio metrics , ok we go w/out compactor then
26. How we monitor The need of Compactor Without downsampling, long term queries can bring huge amount of data affecting overall store availability. Product engineering services needs historical data for data driven decisions.
27. How we monitor Compactor + Ruler Long term recording rules for calculating SLOs.
28. How we monitor Compactor Gotchas There's no way to remove metrics with high cardinality or with too many time series from S3 bucket If object store has metrics with high cardinality or too many series and compactor fails, purging data might be the only solution
29. How we monitor msg="critical error detected; halting" err="overlaps found while gathering blocks.
30. How we monitor Good luck purging teams data Just need to announce to Engineering Teams (~300 ppl) and run. Running away is inevitable.
31. How we monitor Adopting SRE practices to operate Prometheus Monitoring Prometheus De ning Reliability Target Being On-Call Managing Overload
32. How we monitor Monitoring Prometheus prometheus_tsdb_head_series
33. How we monitor Monitoring Prometheus prometheus_tsdb_head_samples_appended_total
34. How we monitor Monitoring Prometheus avg(avg_over_time(up{job=~"$job"}[5m])) by (pod)
35. How we monitor Adopting SRE practices to operate Prometheus De ning querier SLO rule - record: thanos_query:availability_slo:rate2w expr: | sum(thanos_query:successful_queries:rate2w) / sum(thanos_query:total_queries:rate2w)
36. How we monitor Adopting SRE practices to operate Prometheus De ning prometheus SLO rule - record: kube_prometheus:uptime:max_avg5m expr: | max ( avg_over_time ( up{job="kube-prometheus"}[5m] ) )
37. How we monitor Adopting SRE practices to operate Prometheus De ning prometheus SLO rule - record: kube_prometheus:uptime:max_avg5m expr: | avg_over_time ( kube_prometheus:uptime:max_avg5m[2w] )
38. How we monitor Adopting SRE practices to operate Prometheus Composing SLOs - record: prometheus:slo:rate2w expr: | ( kube_prometheus:uptime:avg2w + prox_prometheus:uptime:avg2w + thanos_query:availability_slo:rate2w + ) / 3 * 100 )
39. How we monitor Adopting SRE practices to operate Prometheus Alerting over SLOs violatation rules: - alert: PrometheusQueriesSLOViolation annotations: summary: 'Prometheus queries availability SLO is affected' description: 'Prometheus has query availability SLO violatio runbook_url: https://github.com/hellofresh/runbooks/blob/mas expr: prometheus:slo:rate2w < 99.9 labels: severity: page slack: platform-alerts
40. How we monitor Adopting SRE practices to operate Prometheus Alerting when SLI is in danger rules: - alert: TooManyThanosQueriesErrors annotations: summary: Percentage of gRPC errors are too high on Thanos description: 'Thanos Query has too many gRPC queries errors runbook_url: https://github.com/hellofresh/runbooks/blob/mas expr: | sum(thanos_query:grpc_queries_errors:rate5m) / sum(thanos_query:grpc_queries:rate5m) > 0.05
41. How we monitor Adopting SRE practices to operate Prometheus Being On-Call
42. How we monitor Adopting SRE practices to operate Prometheus Being On-Call
43. How we monitor Managing Overload Detecting a Problem Check if the total time series has increased prometheus_tsdb_head_series Check if Prometheus is ingesting metrics prometheus_tsdb_head_samples_appended_to tal
44. How we monitor Managing Overload Find Expensive Metrics, Rules and Targets Find rules that take take too long to evaluate prometheus_rule_group_iterations_missed_tot al Find metrics that have the most time series topk(10, count by (__name__, job ({__name__=~".+"})) Find expensive recording rules prometheus_rule_group_last_duration_second s
45. How we monitor Managing Overload Reducing Load Drop labels, metrics which introduces high cardinality or you don't use Increase scrape_interval and evaluation_interval sample_limit causes a scrape to fail if more than the given number of time series is returned
46. Managing Overload nginx_ingress_controller_request_duration_second s_bucket Up to 50k time series Product Eng. services relies on it for SLOs Queries towards it were Prom and Thanos metric_relabel_configs: - separator: ; regex: (controller_namespace|controller_pod|endpoint|exporte replacement: $1 action: labeldrop
47. How we monitor Sharding Prometheus Reverse Proxies & Edge services ┌────────────┬─────────┐ ┌────────────┬─────────┐ │ Prox Prom │ Sidecar │ ... │ Prox Prom │ Sidecar │ └────────────┴─────-───┘ └────────────┴────-────┘ NGINX Ingress, OpenResty, ELB, CDN Istio soon
48. How we monitor Results From 2h 22m of downtime/unavailability weekly
49. How we monitor Results To 18.1s of downtime/unavailability weekly
50. How we monitor Instrumenting Applications Prometheus SDKs OpenCensus for more observability Automating away metrics for php services Parsing nginx logs with OpenResty RED metrics out of the box Common dashboard for everyone
51. How we monitor Empowering Engineering Teams De ning alerts and recording rules on app source code Provisioned Grafana dashboards Exposing common dashboards for everyone The 4 golden signals FTW
52. How we monitor Empowering Engineering Teams On applications source code provisionDashboards: enabled: true dashboardLabel: grafana_dashboard prometheusRules: - name: svc-recording-rules rules: ... - name: svc-alerting-rules rules: ...
53. How we monitor Empowering Engineering Teams {{ if .Values.prometheusRules }} apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule # operator CRD metadata: labels: app: {{ template "svc.name" $ }} chart: {{ template "svc.chart" $ }} tribe: {{ $.Values.tribe }} prometheus: kube-prometheus name: {{ template "svc.name" $ }} spec: groups: {{ toYaml .Values.prometheusRules | indent 4 }} {{ end }}
54. How we monitor Empowering Engineering Teams {{- if .Values.provisionDashboards }} {{- if .Values.provisionDashboards.enabled }} {{- $root := . }} {{- range $path, $bytes := .Files.Glob "dashboards/*" }} --- kind: ConfigMap metadata: ... $.Values.provisionDashboards.dashboardLabel }}: "1" data: {{ base $path }}: |- {{ $root.Files.Get $path | indent 4}} {{ end }} {{- end }} {{- end }}
55. Empowering Engineering Teams NGINX Ingress
56. Empowering Engineering Teams NGINX Ingress
57. Infrastructure Engineering AWS RDS Metrics
58. How we alert At the Edge Sympthom Based Alerts Write Runbooks
59. How we alert At the Edge As close to the user as possible. If one backend goes away, you still have metrics to alert.
60. How we alert Sympthom Not only 5xx, high rates of 4xx should also page When SLO is in danger On user journeys (soon)
61. How we alert Sharing responsibility with runbooks
62. What comes next? Run chaos experiments on Prometheus Experiment 1: Query Availability Attack: Thanos Sidecar Pod Deletion Scope: Multiple pods Expected Results: Rate of good thanos querier gRPC queries are not affected TooManyQueriesErrors alert should not re
63. What comes next? More vertical sharding Storage Systems Observability Services Prometheus per k8s namespace Istio for out of the box metrics and tracing
64. What comes next? Query Federation Data engineering services don't run on k8s
65. Thank you