https://kubernetes.io/docs/tasks/debug-application-cluster/debug-running-pod/#ephemeral-container
https://towardsdatascience.com/the-easiest-way-to-debug-kubernetes-workloads-ff2ff5e3cc75
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https://towardsdatascience.com/the-easiest-way-to-debug-kubernetes-workloads-ff2ff5e3cc75
kubectl debug -it some-app –image=busybox –share-processes –copy-to=some-app-debug
➜ 2305 pidstat -t -p 2305 1
Linux 5.4.0-74-generic (labile-T30) 2021年06月24日 _x86_64_ (2 CPU)
18时37分39秒 UID TGID TID %usr %system %guest %wait %CPU CPU Command
18时37分40秒 64055 2305 - 1.00 2.00 14.00 3.00 17.00 1 qemu-system-x86
18时37分40秒 64055 - 2305 1.00 1.00 0.00 3.00 2.00 1 |__qemu-system-x86
18时37分40秒 64055 - 2307 0.00 0.00 0.00 0.00 0.00 0 |__qemu-system-x86
18时37分40秒 64055 - 2312 0.00 0.00 0.00 0.00 0.00 0 |__IO mon_iothread
18时37分40秒 64055 - 2313 0.00 0.00 9.00 5.00 9.00 1 |__CPU 0/KVM
18时37分40秒 64055 - 2314 0.00 2.00 5.00 5.00 7.00 0 |__CPU 1/KVM
18时37分40秒 64055 - 2316 0.00 0.00 0.00 1.00 0.00 1 |__SPICE Worker
18时37分40秒 64055 - 76701 0.00 0.00 0.00 0.00 0.00 0 |__worker
perf kvm stat live
18:42:23.185815
Analyze events for all VMs, all VCPUs:
VM-EXIT Samples Samples% Time% Min Time Max Time Avg time
MSR_WRITE 648 60.67% 5.04% 0.47us 181016.17us 715.22us ( +- 44.28% )
HLT 207 19.38% 92.36% 2.05us 3684299.87us 40991.17us ( +- 43.93% )
EXTERNAL_INTERRUPT 85 7.96% 1.43% 0.34us 43275.57us 1540.36us ( +- 50.18% )
PREEMPTION_TIMER 78 7.30% 0.25% 0.66us 12804.28us 294.09us ( +- 69.79% )
PENDING_INTERRUPT 49 4.59% 0.93% 0.60us 84909.22us 1735.07us ( +- 99.87% )
PAUSE_INSTRUCTION 1 0.09% 0.00% 0.82us 0.82us 0.82us ( +- 0.00% )
Total Samples:1068, Total events handled time:9187522.33us.
Java | OpenTelemetry Manual Instrumentation | OpenTelemetry OpenTelemetry Client Design Principles | OpenTelemetry Instrumentation Examples | OpenTelemetry OpenTelemetry to Jaeger Transformation | OpenTelemetry Java | OpenTelemetry
GitHub - open-telemetry/opentelemetry-java-docs Getting Started | OpenTelemetry Java | OpenTelemetry Manual Instrumentation | OpenTelemetry OpenTelemetry Client Design Principles | OpenTelemetry Instrumentation Examples | OpenTelemetry OpenTelemetry to Jaeger Transformation | OpenTelemetry Java | OpenTelemetry GitHub - open-telemetry/opentelemetry-java-docs Getting Started | OpenTelemetry Java | OpenTelemetry Manual Instrumentation | OpenTelemetry OpenTelemetry Client Design Principles | OpenTelemetry Instrumentation Examples | OpenTelemetry OpenTelemetry to Jaeger Transformation | OpenTelemetry Java | OpenTelemetry GitHub - open-telemetry/opentelemetry-java-docs Getting Started | OpenTelemetryhttps://docs.docker.com/storage/storagedriver/#images-and-layers
https://grafana.com/docs/grafana/latest/variables/variable-types/global-variables/
Currently only supported for Prometheus and Loki data sources. This variable represents the range for the current dashboard. It is calculated by to - from. It has a millisecond and a second representation called $__range_ms and $__range_s.
You can use the $__interval variable as a parameter to group by time (for InfluxDB, MySQL, Postgres, MSSQL), Date histogram interval (for Elasticsearch), or as a summarize function parameter (for Graphite).
https://iximiuz.com/en/posts/prometheus-metrics-labels-time-series/
*Side note 1: Despite being born in the age of distributed systems, every Prometheus server node is autonomous. I.e., there is no distributed metric storage in the default Prometheus setup, and every node acts as a self-sufficient monitoring server with local metric storage. It simplifies a lot of things, including the following explanation, because we don’t need to think of how to merge overlapping series from different Prometheus nodes *😉
https://iximiuz.com/en/posts/prometheus-functions-agg-over-time/
Almost all the functions in the aggregation family accept just a single parameter - a range vector. It means that the over time part, i.e., the duration of the aggregation period, comes from the range vector definition itself.
The only way to construct a range vector in PromQL is by appending a bracketed duration to a vector selector. E.g. http_requests_total[5m]. Therefore, an <agg>_over_time() function can be applied only to a vector selector, meaning the aggregation will always be done using raw scrapes.
