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Kubernetes 计算CPU 使用率
2022-08-10 22:59:00 【InfoQ】
- 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.html https://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="$Pod"}[5m])*100)by(pod)
- 默认统计的数据是该容器所有的 CORE 的平均使用率
- 如果要精确计算每个容器的 CPU 使用率,使用 % 呈现的形式,如下
sum(irate(container_cpu_usage_seconds_total{container="$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
- 参考官方 git issue
- https://github.com/google/cadvisor/issues/2026#issuecomment-415819667
docker stats
- 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
- The
precpu_stats
is the CPU 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.total_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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