容器内存分析
程序员文章站
2022-05-29 22:34:09
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背景
在容器化环境中，平台需要提供准确的业务监控指标，已方便业务查看。那么如何准确计算容器或Pod的内存使用率，k8s/docker又是如何计算，本文通过实验与源码阅读相结合来分析容器的内存实际使用量。
预备知识
不管docker还是k8s(通过cadvisor)最终都通过cgroup的memory group来得到内存的原始文件，memory相关的主要文件如下:
cgroup.event_control #用于eventfd的接口
memory.usage_in_bytes #显示当前已用的内存
memory.limit_in_bytes #设置/显示当前限制的内存额度
memory.failcnt #显示内存使用量达到限制值的次数
memory.max_usage_in_bytes #历史内存最大使用量
memory.soft_limit_in_bytes #设置/显示当前限制的内存软额度
memory.stat #显示当前cgroup的内存使用情况
memory.use_hierarchy #设置/显示是否将子cgroup的内存使用情况统计到当前cgroup里面
memory.force_empty #触发系统立即尽可能的回收当前cgroup中可以回收的内存
memory.pressure_level #设置内存压力的通知事件，配合cgroup.event_control一起使用
memory.swappiness #设置和显示当前的swappiness
memory.move_charge_at_immigrate #设置当进程移动到其他cgroup中时，它所占用的内存是否也随着移动过去
memory.oom_control #设置/显示oom controls相关的配置
memory.numa_stat #显示numa相关的内存
更多信息可参考Pod memory usage in k8s
查看源码
docker stat
docker stat的源码在stats_helpers.go,如下：
func calculateMemUsageUnixNoCache(mem types.MemoryStats) float64 {
return float64(mem.Usage - mem.Stats["cache"])
}
内存使用量为memory.usage=memory.usage_in_bytes-cache
kubectl top
在k8s中，kubectl top命令通过metric-server/heapster获取cadvisor中working_set的值，来表示Pod实例使用内存大小(不包括pause),metrics-server 中pod内存获取如下：
func decodeMemory(target *resource.Quantity, memStats *stats.MemoryStats) error {
if memStats == nil || memStats.WorkingSetBytes == nil {
return fmt.Errorf("missing memory usage metric")
}
*target = *uint64Quantity(*memStats.WorkingSetBytes, 0)
target.Format = resource.BinarySI
return nil
}
cadvisor中working_set计算如下：
func setMemoryStats(s *cgroups.Stats, ret *info.ContainerStats) {
ret.Memory.Usage = s.MemoryStats.Usage.Usage
ret.Memory.MaxUsage = s.MemoryStats.Usage.MaxUsage
ret.Memory.Failcnt = s.MemoryStats.Usage.Failcnt
if s.MemoryStats.UseHierarchy {
ret.Memory.Cache = s.MemoryStats.Stats["total_cache"]
ret.Memory.RSS = s.MemoryStats.Stats["total_rss"]
ret.Memory.Swap = s.MemoryStats.Stats["total_swap"]
ret.Memory.MappedFile = s.MemoryStats.Stats["total_mapped_file"]
} else {
ret.Memory.Cache = s.MemoryStats.Stats["cache"]
ret.Memory.RSS = s.MemoryStats.Stats["rss"]
ret.Memory.Swap = s.MemoryStats.Stats["swap"]
ret.Memory.MappedFile = s.MemoryStats.Stats["mapped_file"]
}
if v, ok := s.MemoryStats.Stats["pgfault"]; ok {
ret.Memory.ContainerData.Pgfault = v
ret.Memory.HierarchicalData.Pgfault = v
}
if v, ok := s.MemoryStats.Stats["pgmajfault"]; ok {
ret.Memory.ContainerData.Pgmajfault = v
ret.Memory.HierarchicalData.Pgmajfault = v
}
workingSet := ret.Memory.Usage
if v, ok := s.MemoryStats.Stats["total_inactive_file"]; ok {
if workingSet < v {
workingSet = 0
} else {
workingSet -= v
}
}
ret.Memory.WorkingSet = workingSet
}
working_set=memory.usage_in_bytes-total_inactive_file (>=0)
在kubelet中节点内存不足时同样以working_set判断pod是否OOM的标准
实验
- 创建Pod
Pod的资源申请如下：
resources:
limits:
cpu: "1"
memory: 1Gi
requests:
cpu: "0"
memory: "0"
- 查看cgroup内存情况
找到容器某个进程，查看memory cgroup
# cat /proc/16062/cgroup
...
8:memory:/kubepods.slice/kubepods-burstable.slice/kubepods-burstable-pod21a55da5_f9f8_11e9_b051_fa163e7e981a.slice/docker-57ba1991ab4ba50a9b2eaf5bf90e2c20073198d767653becf77d55ee25e1a6f9.scope
进入容器memory cgroup对应的目录
docker-57ba1991ab4ba50a9b2eaf5bf90e2c20073198d767653becf77d55ee25e1a6f9.scope]# ls
cgroup.clone_children memory.kmem.failcnt memory.kmem.tcp.limit_in_bytes memory.max_usage_in_bytes memory.move_charge_at_immigrate memory.stat tasks
cgroup.event_control memory.kmem.limit_in_bytes memory.kmem.tcp.max_usage_in_bytes memory.memsw.failcnt memory.numa_stat memory.swappiness
cgroup.procs memory.kmem.max_usage_in_bytes memory.kmem.tcp.usage_in_bytes memory.memsw.limit_in_bytes memory.oom_control memory.usage_in_bytes
memory.failcnt memory.kmem.slabinfo memory.kmem.usage_in_bytes memory.memsw.max_usage_in_bytes memory.pressure_level memory.use_hierarchy
memory.force_empty memory.kmem.tcp.failcnt memory.limit_in_bytes memory.memsw.usage_in_bytes memory.soft_limit_in_bytes notify_on_release
查看主要memory文件
# cat memory.limit_in_bytes (容器memory limit值，即1Gi)
1073741824
[[email protected] docker-57ba1991ab4ba50a9b2eaf5bf90e2c20073198d767653becf77d55ee25e1a6f9.scope]# cat memory.kmem.limit_in_bytes (容器内核使用memory limit值)
9223372036854771712
[[email protected] docker-57ba1991ab4ba50a9b2eaf5bf90e2c20073198d767653becf77d55ee25e1a6f9.scope]#
[[email protected] docker-57ba1991ab4ba50a9b2eaf5bf90e2c20073198d767653becf77d55ee25e1a6f9.scope]# cat memory.soft_limit_in_bytes
9223372036854771712
[docker-57ba1991ab4ba50a9b2eaf5bf90e2c20073198d767653becf77d55ee25e1a6f9.scope]# cat notify_on_release
0
[docker-57ba1991ab4ba50a9b2eaf5bf90e2c20073198d767653becf77d55ee25e1a6f9.scope]# cat memory.oom_control
oom_kill_disable 0
under_oom 0
oom_kill 0
[docker-57ba1991ab4ba50a9b2eaf5bf90e2c20073198d767653becf77d55ee25e1a6f9.scope]# cat memory.usage_in_bytes
2265088
[docker-57ba1991ab4ba50a9b2eaf5bf90e2c20073198d767653becf77d55ee25e1a6f9.scope]# cat memory.kmem.usage_in_bytes
901120
[docker-57ba1991ab4ba50a9b2eaf5bf90e2c20073198d767653becf77d55ee25e1a6f9.scope]# cat memory.stat
cache 12288
rss 1351680
rss_huge 0
shmem 4096
mapped_file 4096
dirty 0
writeback 0
swap 0
pgpgin 4544
pgpgout 4211
pgfault 1948
pgmajfault 0
inactive_anon 4096
active_anon 1351680
inactive_file 8192
active_file 0
unevictable 0
hierarchical_memory_limit 1073741824
hierarchical_memsw_limit 1073741824
total_cache 12288
total_rss 1351680
total_rss_huge 0
total_shmem 4096
total_mapped_file 4096
total_dirty 0
total_writeback 0
total_swap 0
total_pgpgin 4544
total_pgpgout 4211
total_pgfault 1948
total_pgmajfault 0
total_inactive_anon 4096
total_active_anon 1351680
total_inactive_file 8192
total_active_file 0
total_unevictable 0
根据memory可得到如下关系：memory.usage_in_bytes = memory.kmem.usage_in_bytes + rss + cache
即2265088=901120+1351680+12288
那么容器的真实内存即：memory.usage=memory.usage_in_bytes-cache
即rss+kmem_usage
通过docker stat查看，与公式相符合
CONTAINER ID NAME CPU % MEM USAGE / LIMIT MEM % NET I/O BLOCK I/O PIDS
57ba1991ab4b k8s...default_21a55da5-f9f8-11e9-b051-fa163e7e981a_0 0.00% 2.148MiB / 1GiB 0.21% 12MB / 68.8MB 0B / 0B 2
结论
实际环境中，docker与k8s两种内存表示方式不同，一般docker stat总体值会小于kubectl top
- docker中内存表示为：
memory.usage = memory.usage_in_bytes - cache - k8s中：
memory.usage = working_set = memory.usage_in_bytes - total_inactive_file (>=0)
根据cgroup memory关系有：memory.usage_in_bytes = memory.kmem.usage_in_bytes + rss + cache
真实环境中两种表示相差不大，但更推荐使用working_set作为容器内存真实使用量(kubelt判断OOM的依据)，
则容器内存使用率可表示为：container_memory_working_set_bytes / memory.limit_in_bytes
参考
- https://www.kernel.org/doc/Documentation/cgroup-v1/memory.txt
- https://medium.com/@zhimin.wen/memory-limit-of-pod-and-oom-killer-891ee1f1cad8