Kubernetes / Linux Note / 运维笔记

Kubernetes 计算容器CPU使用率 

Einic Yeo · 1月3日 · 2021年 · ·

一、参数解释


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

  1. 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100,0版权声明:本文遵循 CC 4.0 BY-SA 版权协议,若要转载请务必附上原文出处链接及本声明,谢谢合作!00微秒
  2. 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
  3. 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
  4. 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
  5. container_cpu_system_seconds_total统计容器内核态在一秒时间内消耗的CPU
  6. 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/c版权声明:本文遵循 CC 4.0 BY-SA 版权协议,若要转载请务必附上原文出处链接及本声明,谢谢合作!advisor.html
      https://github.com/google/cadvisor/blob/master/docs/storage/prometheus.md

二、具体公式


  1. 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

2. 如果要精确计算每个容器的CPU使用率,使用%版权声明:本文遵循 CC 4.0 BY-SA 版权协议,若要转载请务必附上原文出处链接及本声明,谢谢合作!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

3. 参考官方git issue

https://github.com/google/cadvisor/issues/2026#issuecomment-415819667

三、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

2. 如何通过Docker API查看容器资源使用率,如下

<[email protected] ~># curl -s --unix-socket /var/run/docker.sock "http://localhost/v1.40/containers/10f2db238edc/stats" | jq -r
{
  "read": "2021-01-03T06:14:47.705943252Z",
  "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"

2. 如何计算

  官方地址

https://docs.docker.com/engine/api/v1.40/#operation/ContainerStats

The precpu_stats is the CPU 版权声明:本文遵循 CC 4.0 BY-SA 版权协议,若要转载请务必附上原文出处链接及本声明,谢谢合作!statistic of the previous read, and is used to calculate the CPU usage percentage. It is not an exact copy of the cpu_stats field.

If either precpu_stats.online_cpus or cpu_stats.online_cpus is nil then for compatibility with older daemons the length of the corresponding cpu_usage.percpu_usage array should be used.

To calculate the values shown by the stats command 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.tota版权声明:本文遵循 CC 4.0 BY-SA 版权协议,若要转载请务必附上原文出处链接及本声明,谢谢合作!l_usage
  • system_cpu_delta = cpu_stats.system_cpu_usage - precpu_stats.system_cpu_usage
  • number_cpus = lenght(cpu_stats.cpu_usage.percpu_usage) or cpu_stats.online_cpus
  • CPU usage % = (cpu_delta / system_cpu_delta) * number_cpus * 100.0
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