KOCOON – KAKAO Automatic K8S Monitoring
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Open & Intelligent Infrastructure kr 2019 Day2 Track2 14:00 ~ 14:40 KOCOON – KAKAO Automatic K8S Monitoring
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KOCOON – KAKAO Automatic K8S Monitoring
- 1. 2019.07 KOCOON KAKAO Automatic k8s Monitoring @@cloud.telemetry / issac.lim(임성국)
- 3. Who am I?
- 4. Telemetry ? Telemetry is an automated communications process by which meas urements and other data are collected at remote or inaccessible points and transmitted to receiving equipment for monitoring. ref: https://en.wikipedia.org/wiki/Telemetry
- 5. cloud.telemetry ? • Remote & Inaccessible points • Baremetal, IaaS, CaaS … • We Develop & Provide automated communications process • MaaS (Monitoring as a Service) • KEMI Stats, KEMI Logs, KOCOON ref: https://en.wikipedia.org/wiki/Telemetry
- 6. KEMI Stats
- 7. KEMI Logs
- 9. Head: KEMI-* Longtail : ? ref: https://mgcabral.wordpress.com/2012/03/04/thelongtaileconomics/
- 10. Longtail • Users want Get own resource Deal with resources in their own way • We want Divide resources by users Provide self-monitoring service
- 11. DKOSv3 • k8s based container orchestrator @KAKAO • Kubernetes v1.11.5 • 카카오 T 택시 사례를 통해 살펴보는 카카오 클라우드의 Kubernetes as a Service (openinfradays days 2 Track 2 12:00 ~ 12:40)
- 12. 그렇다면 … 서비스 클러스터 별로 모니터링 리소스를 분리하자!
- 14. KOCOON • KakaO COntainer based service mONitoring 서비스 리소스 안에서 수집/조회/알람 등 모든 것을 해결
- 16. Metric based Self Monitoring
- 17. Log Routing
- 18. Rule based Log Event
- 21. KOCOON-Prometheus Install Prometheus-operator, Prometheus, Alertmanager PrometheusRule(for alarm & recreate metric) Cupido (Prometheus webhook manager for kakao) kube state metrics, node exporter Grafana & Dashboard kakao etcd & kakao ingress controller service monitor helm install kakao-stable/kocoon-prometheus --name $(kubectl config current-context) --namespace monitoring
- 23. KOCOON-Prometheus Set LB ## grafana ingress helm upgrade $(kubectl config current-context) kakao-stable/kocoon-prometheus --reuse-val ues --set grafana.ingress.enabled=true --set grafana.ingress.hosts[0]=example-grafana.dev. 9rum.cc ## prometheus ingress helm upgrade $(kubectl config current-context) kakao-stable/kocoon-prometheus --reuse-val ues --set prometheus.ingress.enabled=true --set prometheus.ingress.hosts[0]=example-pro metheus.dev.9rum.cc ## alertmanager ingress helm upgrade $(kubectl config current-context) kakao-stable/kocoon-prometheus --reuse-val ues --set alertmanager.ingress.enabled=true --set alertmanager.ingress.hosts[0]=example-al ertmanager.dev.9rum.cc
- 24. KOCOON-Prometheus Set Kakaotalk ## 받아야 할 watchcenter group id가 6663, 4443 등 다수인 경우 helm upgrade $(kubectl config current-context) kakao-stable/kocoon-prometheus --reuse-va lues --set cupido.watchcenterGroups="6663;4443;” ## 받아야 할 watchcenter group id가 1개인(6663) 경우 helm upgrade $(kubectl config current-context) kakao-stable/kocoon-prometheus --reuse-va lues --set cupido.watchcenterGroups="6663;" ## watchcenter group id를 설정하지 않아도 문제가 생길 시에 dkosv3 운영하는 cloud.deploy 셀에 함께 알람이 갑니다.
- 25. k8s Status View
- 27. k8s Ingress Controller View
- 28. k8s ETCD View
- 30. Any Problems?
- 31. Goals • Workers: 500 • Metric scrapes interval: 30s ~ 60s • Check resource status • service namespace (default) • kube-system • ingress
- 32. Step 1 • Basic Resources for Prometheus • CPU request 1, limit 4 • Memory : request 2GB, limit 6GB • it’s ok ~ 100 workers but… • Internal node IP issue (max 128 nodes…)
- 33. Step 2 Workers Scrape Interval CPU Memory # of metrics/sec 12 30s 0.1 780MB 4605 271 30s OOM • Basic Resources for Prometheus • CPU request 1, limit 4 • Memory : request 2GB, limit 6GB
- 34. Out Of Memory… • Prometheus killed due to OOM in large scale • https://groups.google.com/forum/#!topic/prometheus-users/DE LLNNSVCSw • https://github.com/prometheus/prometheus/issues/4553 • https://github.com/prometheus/prometheus/issues/1358
- 35. We considered … • # of targets • # of metrics • # of rules • frequency of scrapes & rule evaluations • …
- 36. Must item • # of targets • # of rules • frequency of scrapes & rule evaluations (every minutes)
- 37. # of metrics & topk • # of metrics per sec rate(prometheus_tsdb_head_samples_appended_total[5m]) • Top 10 metrics topk(10, count({job=~".+"}) by(__name__))
- 38. Step 3 Workers Scrape Interval CPU Memory # of metrics/sec topk 271 30s OOM 271 60s 1.15 OOM 27500 container_network_tcp_usage_total: 246676 • Upgrade Resources for Prometheus • CPU request 1, limit 4 • Memory : request 4GB, limit 8GB • Scrape interval: 30s -> 60s
- 39. Useless Metrics • cadvisor • container_(network_tcp_usage_total|network_udp_usage_total |tasks_state|cpu_load_average_10s|memory_failures_total) • container_(cpu_schedstat_run_seconds_total|cpu_schedstat_ru nqueue_seconds_total|cpu_schedstat_run_periods_total|cpu_s ystem_seconds_total|cpu_user_seconds_total) • container_(last_seen|memory_working_set_bytes|memory_cac he|memory_failcnt|memory_max_usage_bytes|memory_swap| start_time_seconds) • container_(network_receive_packets_total|network_transmit_p ackets_dropped_total|network_receive_errors_total|network_r eceive_packets_dropped_total|network_transmit_packets_total |network_transmit_errors_total)
- 40. Useless Metrics • cadvisor • container_(spec_([a-z_]+)|fs_([a-z_]+)) • kubelet_(runtime_operations_latency_microseconds|docker_op erations_latency_microseconds)
- 41. Useless Metrics • kube api • apiserver_(admission_controller_admission_latencies_seconds_ bucket|admission_step_admission_latencies_seconds_bucket|a dmission_controller_admission_latencies_seconds_sum|admissi on_controller_admission_latencies_seconds_count|admission_s tep_admission_latencies_seconds_summary) • apiserver_(request_latencies_bucket|response_sizes_bucket|re quest_latencies_summary)
- 42. Useless Metrics • kube state metrics • kube_pod_container_status_waiting_reason
- 43. Step 4 Workers Scrape Interval CPU Memory # of metrics/sec topk 271 60s 1.15 OOM 27500 container_network_tcp_usage_total: 246676 310 60s 0.4 2.9GB 9748 storage_operation_duration_seconds_bucket: 31834 container_memory_usage_bytes: 25737 container_memory_rss: 25737 • Upgrade Resources for Prometheus • CPU request 1, limit 4 • Memory : request 4GB, limit 8GB • Drop useless metrics: kube api, cadvisor, kube state metrics • 1629 pods
- 44. Step 5 Workers Scrape Interval CPU Memory # of metrics/sec topk 500 60s 1.1 12.88GB 8.35GB(RSS) 18324 storage_operation_duration_seconds_bucket: 50402 container_memory_rss: 45149 container_memory_usage_bytes: 45149 • Upgrade Resources for Prometheus • CPU request 1, limit 4 • Memory : request 9GB, limit 14GB • 4107 pods
- 45. KOCOON-Prometheus Default SLA • Metric 보관 주기 : 3 days • Metric 수집 주기 : Every 60s • Alarm 주기: • 특정 시간(5M)동안 이벤트가 반복되면 1시간에 한 번 알림 • Target Cluster • ~ 200 nodes, ~ 1500 pods • 5분 평균 append되는 metric 수 : ~ 10,000/sec • 현재 설정 그대로 사용 가능 • cpu 1~4 Core, memory 4~6GB • 더 많은 node / pod를 돌리고 싶다면?
- 46. KOCOON-Prometheus SLA for 500 • Target Cluster • ~ 500 nodes(worker+ingress), ~ 4000 pods • 5분 평균 append되는 metric 수 : 18,000/sec • upgrade memory! ## Upgrade prometheus memory request & limit helm upgrade $(kubectl config current-context) kocoon-stg/kocoon-prometheus --reuse-values --set prometheus.prom
- 47. KOCOON-Prometheus Requirements • ~ 200 nodes / ~1500 pods • krane VM / PM: 8 Core, 8GB * 2 • ~ 500 nodes / ~4000 pods • krane VM / PM : 8 Core, 16GB * 2
- 48. Consideration for Service.. • Resource Request & Limit • cpu, memory request는 최소로 주고 limit 제한을 없앰 • 성능 시험을 진행하면서 앞에 공유했던 drill down (namespace to pod) view를 보고 필요한 cpu, memory를 확인 • 실제 Production 투입 때는 앞에 실험한 결과를 가지고 request, limit을 설정 • 500 node cluster는 가능하지만 • 100 node 단위로 서비스 / 서비스 그룹들을 넣고, • 5개의 master node, 2개의 Prometheus & Alertmanager, • LB를 통해서 각 cluster에 RoundRobin로 접근하게 하는게 좀 더 안정적 으로 운영
- 49. Sum Up • KOCOON-* is provided with helm charts • KOCOON-Prometheus -> today’s main topic • KOCOON-Cupido : included in KOCOON-Prometheus • KOCOON-Hermes -> will be present at ifKakao 2019 • KOCOON-DIKE -> developing… helm? : package manager for k8s https://helm.sh/
- 50. Thanks @@cloud.telemetry with andi, beemo, issac, jenny, joanne & cloud part
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