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使用 Prometheus 配置 kubernetes 环境中 Container 的 CPU 使用率时，会经常遇到 CPU 使用超出 100%，下面就来解释一下：
- container_spec_cpu_period
当对容器进行 CPU 限制时，CFS 调度的时间窗口，又称容器 CPU 的时钟周期通常是 100，000 微秒 - container_spec_cpu_quota
是指容器的使用 CPU 时间周期总量，如果 quota 设置的是 700，000，就代表该容器可用的 CPU 时间是 7*100,000 微秒，通常对应 kubernetes 的 resource.cpu.limits 的值 - container_spec_cpu_share
是指 container 使用分配主机 CPU 相对值，比如 share 设置的是 500m，代表窗口启动时向主机节点申请 0.5 个 CPU，也就是 50，000 微秒，通常对应 kubernetes 的 resource.cpu.requests 的值 - container_cpu_usage_seconds_total
统计容器的 CPU 在一秒内消耗使用率，应注意的是该 container 所有的 CORE - container_cpu_system_seconds_total
统计容器内核态在一秒时间内消耗的 CPU - container_cpu_user_seconds_total
统计容器用户态在一秒时间内消耗的 CPU
参考官方地址 https://docs.signalfx.com/en/latest/integrations/agent/monitors/cadvisor.htmlhttps://github.com/google/cadvisor/blob/master/docs/storage/prometheus.md
具体公式
- 默认如果直接使用 container_cpu_usage_seconds_total 的话，如下
sum(irate(container_cpu_usage_seconds_total{container="$Container",instance="$Node",pod="$Pod"}[5m])*100)by(pod)
默认统计的数据是该容器所有的 CORE 的平均使用率
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- 如果要精确计算每个容器的 CPU 使用率，使用 % 呈现的形式，如下
sum(irate(container_cpu_usage_seconds_total{container="$Container",instance="$Node",pod="$Pod"}[5m])*100)by(pod)/sum(container_spec_cpu_quota{container="$Container",instance="$Node",pod="$Pod"}/container_spec_cpu_period{container="$Container",instance="$Node",pod="$Pod"})by(pod)
其中 container_spec_cpu_quota/container_spec_cpu_period，就代表该容器有多少个 CORE
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docker stats
docker stats 输出的指标列是如何计算的，如下：
首先 docker stats 是通过 Docker API /containers/(id)/stats 接口来获得 live data stream，再通过 docker stats 进行整合。
在 Linux 中使用 docker stats 输出的内存使用率（MEM USAGE），实则该列的计算是不包含 Cache 的内存。
cache usage 在 ≤ docker 19.03 版本的 API 接口输出对应的字段是 memory_stats.total_inactive_file，而 > docker 19.03 的版本对应的字段是 memory_stats.cache。
docker stats 输出的 PIDS 一列代表的是该容器创建的进程或线程的数量，threads 是 Linux kernel 中的一个术语，又称 lightweight process & kernel task。
- 如何通过 Docker API 查看容器资源使用率，如下
$ curl -s --unix-socket /var/run/docker.sock "http://localhost/v1.40/containers/10f2db238edc/stats" | jq -r{ "read": "2022-01-05T06:14:47.705943252Z", "preread": "0001-01-01T00:00:00Z", "pids_stats": { "current": 240 }, "blkio_stats": { "io_service_bytes_recursive": [ { "major": 253, "minor": 0, "op": "Read", "value": 0 }, { "major": 253, "minor": 0, "op": "Write", "value": 917504 }, { "major": 253, "minor": 0, "op": "Sync", "value": 0 }, { "major": 253, "minor": 0, "op": "Async", "value": 917504 }, { "major": 253, "minor": 0, "op": "Discard", "value": 0 }, { "major": 253, "minor": 0, "op": "Total", "value": 917504 } ], "io_serviced_recursive": [ { "major": 253, "minor": 0, "op": "Read", "value": 0 }, { "major": 253, "minor": 0, "op": "Write", "value": 32 }, { "major": 253, "minor": 0, "op": "Sync", "value": 0 }, { "major": 253, "minor": 0, "op": "Async", "value": 32 }, { "major": 253, "minor": 0, "op": "Discard", "value": 0 }, { "major": 253, "minor": 0, "op": "Total", "value": 32 } ], "io_queue_recursive": [], "io_service_time_recursive": [], "io_wait_time_recursive": [], "io_merged_recursive": [], "io_time_recursive": [], "sectors_recursive": [] }, "num_procs": 0, "storage_stats": {}, "cpu_stats": { "cpu_usage": { "total_usage": 251563853433744, "percpu_usage": [ 22988555937059, 6049382848016, 22411490707722, 5362525449957, 25004835766513, 6165050456944, 27740046633494, 6245013152748, 29404953317631, 5960151933082, 29169053441816, 5894880727311, 25772990860310, 5398581194412, 22856145246881, 5140195759848 ], "usage_in_kernelmode": 30692640000000, "usage_in_usermode": 213996900000000 }, "system_cpu_usage": 22058735930000000, "online_cpus": 16, "throttling_data": { "periods": 10673334, "throttled_periods": 1437, "throttled_time": 109134709435 } }, "precpu_stats": { "cpu_usage": { "total_usage": 0, "usage_in_kernelmode": 0, "usage_in_usermode": 0 }, "throttling_data": { "periods": 0, "throttled_periods": 0, "throttled_time": 0 } }, "memory_stats": { "usage": 8589447168, "max_usage": 8589926400, "stats": { "active_anon": 0, "active_file": 260198400, "cache": 1561460736, "dirty": 3514368, "hierarchical_memory_limit": 8589934592, "hierarchical_memsw_limit": 8589934592, "inactive_anon": 6947250176, "inactive_file": 1300377600, "mapped_file": 0, "pgfault": 3519153, "pgmajfault": 0, "pgpgin": 184508478, "pgpgout": 184052901, "rss": 6947373056, "rss_huge": 6090129408, "total_active_anon": 0, "total_active_file": 260198400, "total_cache": 1561460736, "total_dirty": 3514368, "total_inactive_anon": 6947250176, "total_inactive_file": 1300377600, "total_mapped_file": 0, "total_pgfault": 3519153, "total_pgmajfault": 0, "total_pgpgin": 184508478, "total_pgpgout": 184052901, "total_rss": 6947373056, "total_rss_huge": 6090129408, "total_unevictable": 0, "total_writeback": 0, "unevictable": 0, "writeback": 0 }, "limit": 8589934592 }, "name": "/k8s_prod-xc-fund_prod-xc-fund-646dfc657b-g4px4_prod_523dcf9d-6137-4abf-b4ad-bd3999abcf25_0", "id": "10f2db238edc13f538716952764d6c9751e5519224bcce83b72ea7c876cc0475"
- 如何计算
官方地址
https://docs.docker.com/engine/api/v1.40/#operation/ContainerStats
Theprecpu_statsis the CPU statistic of the previous read, and is used to calculate the CPU usage percentage. It is not an exact copy of thecpu_statsfield.
If eitherprecpu_stats.online_cpusorcpu_stats.online_cpusis nil then for compatibility with older daemons the length of the correspondingcpu_usage.percpu_usagearray should be used.
To calculate the values shown by thestatscommand of the docker cli tool the following formulas can be used:
used_memory =memory_stats.usage - memory_stats.stats.cache
available_memory =memory_stats.limit
Memory usage % =(used_memory / available_memory) * 100.0
cpu_delta =cpu_stats.cpu_usage.total_usage - precpu_stats.cpu_usage.total_usage
system_cpu_delta =cpu_stats.system_cpu_usage - precpu_stats.system_cpu_usage
number_cpus =lenght(cpu_stats.cpu_usage.percpu_usage)orcpu_stats.online_cpus
CPU usage % =(cpu_delta / system_cpu_delta) * number_cpus * 100.0
