常用指标
通过配置prometheus rules进行聚合运算，得出常用指标
[root@ice ~]# kubectl -n kube-system get cm prometheus-k8s-rulefiles-0 -o yaml
apiVersion: v1
data:
kube-system-prometheus-alerts.yaml: |
{}
kube-system-prometheus-records.yaml: |
groups:
- name: k8s-ag-data
rules:
- expr: kubelet_running_pod_count*0 + 1
record: kube_node_labels
- expr: sum by(node) (kube_node_status_capacity{resource="tencent_com_vcuda_core"})
record: kube_node_status_capacity_gpu
- expr: sum by(node) (kube_node_status_capacity{resource="tencent_com_vcuda_memory"})
record: kube_node_status_capacity_gpu_memory
- expr: sum by(node) (kube_node_status_allocatable{resource="tencent_com_vcuda_core"})
record: kube_node_status_allocatable_gpu
- expr: sum by(node) (kube_node_status_allocatable{resource="tencent_com_vcuda_memory"})
record: kube_node_status_allocatable_gpu_memory
- expr: kube_pod_info* on(node) group_left(node_role) kube_node_labels
record: __pod_info1
- expr: label_replace(label_replace(__pod_info1{workload_kind="ReplicaSet"} * on
(workload_name,namespace) group_left(owner_name, owner_kind) label_replace(kube_replicaset_owner,"workload_name","$1","replicaset","(.*)"),"workload_name","$1","owner_name","(.*)"),"workload_kind","$1","owner_kind","(.*)") or
on(pod_name,namesapce) __pod_info1{workload_kind != "ReplicaSet"}
record: __pod_info2
- expr: sum(kube_node_status_allocatable_cpu_cores * on(node) group_left kube_node_labels
{node_role="Node"})
record: k8s_cluster_cpu_core_total
- expr: sum(kube_node_status_allocatable_memory_bytes * on(node) group_left kube_node_labels
{node_role="Node"})
record: k8s_cluster_memory_total
- expr: sum(kube_node_status_allocatable_gpu * on(node) group_left kube_node_labels
{node_role="Node"})
record: k8s_cluster_gpu_total
- expr: sum(kube_node_status_allocatable_gpu_memory * on(node) group_left kube_node_labels
{node_role="Node"})
record: k8s_cluster_gpu_memory_total
- expr: rate(container_cpu_usage_seconds_total[2m]) * on(namespace, pod_name) group_left(workload_kind,
workload_name, node, node_role) __pod_info2
record: k8s_container_cpu_core_used
- expr: k8s_container_cpu_core_used * 100 / on (pod_name,namespace,container_name) group_left kube_pod_container_resource_requests{resource="cpu"}
record: k8s_container_rate_cpu_core_used_request
- expr: k8s_container_cpu_core_used * 100 / on (pod_name,namespace,container_name) group_left kube_pod_container_resource_limits{resource="cpu"}
record: k8s_container_rate_cpu_core_used_limit
- expr: k8s_container_cpu_core_used * 100 / on(node) group_left kube_node_status_capacity_cpu_cores
record: k8s_container_rate_cpu_core_used_node
- expr: container_memory_usage_bytes * on(namespace, pod_name) group_left(workload_kind,workload_name,node,
node_role) __pod_info2
record: k8s_container_mem_usage_bytes
- expr: (container_memory_usage_bytes - container_memory_cache) * on(namespace,
pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_mem_no_cache_bytes
- expr: k8s_container_mem_usage_bytes * 100 / on (pod_name,namespace,container_name) group_left
kube_pod_container_resource_requests{resource="memory"}
record: k8s_container_rate_mem_usage_request
- expr: k8s_container_mem_no_cache_bytes * 100 / on (pod_name,namespace,container_name) group_left
kube_pod_container_resource_requests{resource="memory"}
record: k8s_container_rate_mem_no_cache_request
- expr: k8s_container_mem_usage_bytes * 100 / on (pod_name,namespace,container_name) group_left
kube_pod_container_resource_limits{resource="memory"}
record: k8s_container_rate_mem_usage_limit
- expr: k8s_container_mem_no_cache_bytes * 100 / on (pod_name,namespace,container_name) group_left
kube_pod_container_resource_limits{resource="memory"}
record: k8s_container_rate_mem_no_cache_limit
- expr: k8s_container_mem_usage_bytes * 100 / on(node) group_left kube_node_status_capacity_memory_bytes
record: k8s_container_rate_mem_usage_node
- expr: k8s_container_mem_no_cache_bytes * 100 / on(node) group_left kube_node_status_capacity_memory_bytes
record: k8s_container_rate_mem_no_cache_node
- expr: container_gpu_utilization{gpu="total"} * on(namespace, pod_name) group_left(workload_kind,workload_name,node,
node_role) __pod_info2
record: k8s_container_gpu_used
- expr: k8s_container_gpu_used / on (pod_name,namespace,container_name) group_left
container_request_gpu_utilization
record: k8s_container_rate_gpu_used_request
- expr: k8s_container_gpu_used * 100 / on(node) group_left kube_node_status_capacity_gpu
record: k8s_container_rate_gpu_used_node
- expr: container_gpu_memory_total{gpu_memory="total"} / 256 * on(namespace, pod_name)
group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_gpu_memory_used
- expr: k8s_container_gpu_memory_used * 100 / on (pod_name,namespace,container_name)
group_left() (container_request_gpu_memory / 256)
record: k8s_container_rate_gpu_memory_used_request
- expr: k8s_container_gpu_memory_used * 100 / on(node) group_left() kube_node_status_capacity_gpu_memory
record: k8s_container_rate_gpu_memory_used_node
- expr: sum(rate(container_network_receive_bytes_total[2m])) without(interface) *
on(namespace, pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_network_receive_bytes_bw
- expr: sum(rate(container_network_transmit_bytes_total[2m])) without(interface) *
on(namespace, pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_network_transmit_bytes_bw
- expr: sum(idelta(container_network_receive_bytes_total[2m])) without(interface) *
on(namespace, pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_network_receive_bytes
- expr: sum(idelta(container_network_transmit_bytes_total[2m])) without(interface) *
on(namespace, pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_network_transmit_bytes
- expr: sum(rate(container_network_receive_packets_total[2m])) without(interface) *
on(namespace, pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_network_receive_packets
- expr: sum(rate(container_network_transmit_packets_total[2m])) without(interface) *
on(namespace, pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_network_transmit_packets
- expr: sum(rate(container_fs_reads_bytes_total[2m])) without(device) * on(namespace,
pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_fs_read_bytes
- expr: sum(rate(container_fs_writes_bytes_total[2m])) without(device) * on(namespace,
pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_fs_write_bytes
- expr: sum(rate(container_fs_reads_total[2m])) without(device) * on(namespace,
pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_fs_read_times
- expr: sum(rate(container_fs_writes_total[2m])) without(device) * on(namespace,
pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_container_fs_write_times
- expr: sum(k8s_container_cpu_core_used) without (container_name,container_id)
record: k8s_pod_cpu_core_used
- expr: sum(k8s_container_cpu_core_used + on (container_name, pod_name, namespace)
group_left kube_pod_container_resource_requests{resource="cpu"} * 0) without(container_name
) * 100 / on (pod_name,namespace) group_left sum(kube_pod_container_resource_requests{resource="cpu"}) without(container_name)
record: k8s_pod_rate_cpu_core_used_request
- expr: sum(k8s_container_cpu_core_used + on (container_name, pod_name, namespace)
group_left kube_pod_container_resource_limits{resource="cpu"} * 0) without(container_name
) * 100 / on (pod_name,namespace) group_left sum(kube_pod_container_resource_limits{resource="cpu"}) without(container_name)
record: k8s_pod_rate_cpu_core_used_limit
- expr: k8s_pod_cpu_core_used *100 / on(node) group_left kube_node_status_capacity_cpu_cores
record: k8s_pod_rate_cpu_core_used_node
- expr: sum(k8s_container_mem_usage_bytes) without (container_name,container_id)
record: k8s_pod_mem_usage_bytes
- expr: sum(k8s_container_mem_no_cache_bytes) without (container_name,container_id)
record: k8s_pod_mem_no_cache_bytes
- expr: sum(k8s_container_mem_usage_bytes + on (container_name, pod_name, namespace)
group_left kube_pod_container_resource_requests{resource="memory"} * 0) without(container_name
) * 100 / on (pod_name,namespace) group_left sum(kube_pod_container_resource_requests{resource="memory"})
without(container_name)
record: k8s_pod_rate_mem_usage_request
- expr: sum(k8s_container_mem_no_cache_bytes + on (container_name, pod_name, namespace)
group_left kube_pod_container_resource_requests{resource="memory"} * 0) without(container_name
) * 100 / on (pod_name,namespace) group_left sum(kube_pod_container_resource_requests{resource="memory"})
without(container_name)
record: k8s_pod_rate_mem_no_cache_request
- expr: sum(k8s_container_mem_usage_bytes + on (container_name, pod_name, namespace)
group_left kube_pod_container_resource_limits{resource="memory"} * 0) without(container_name
) * 100 / on (pod_name,namespace) group_left sum(kube_pod_container_resource_limits{resource="memory"}) without(container_name)
record: k8s_pod_rate_mem_usage_limit
- expr: sum(k8s_container_mem_no_cache_bytes + on (container_name, pod_name, namespace)
group_left kube_pod_container_resource_limits{resource="memory"} * 0) without(container_name
) * 100 / on (pod_name,namespace) group_left sum(kube_pod_container_resource_limits{resource="memory"}) without(container_name)
record: k8s_pod_rate_mem_no_cache_limit
- expr: k8s_pod_mem_usage_bytes * 100 / on(node) group_left kube_node_status_capacity_memory_bytes
record: k8s_pod_rate_mem_usage_node
- expr: k8s_pod_mem_no_cache_bytes * 100 / on(node) group_left kube_node_status_capacity_memory_bytes
record: k8s_pod_rate_mem_no_cache_node
- expr: sum(k8s_container_gpu_used) without (container_name,container_id)
record: k8s_pod_gpu_used
- expr: sum(container_request_gpu_utilization * 100) without(container_name)
record: k8s_pod_gpu_request
- expr: sum(k8s_container_gpu_used + on (container_name, pod_name, namespace) group_left
container_request_gpu_utilization * 0) without(container_name) * 100 / on (pod_name,namespace)
group_left k8s_pod_gpu_request
record: k8s_pod_rate_gpu_used_request
- expr: k8s_pod_gpu_used * 100 / on(node) group_left kube_node_status_capacity_gpu
record: k8s_pod_rate_gpu_used_node
- expr: sum(k8s_container_gpu_memory_used) without (container_name,container_id)
record: k8s_pod_gpu_memory_used
- expr: sum(container_request_gpu_memory / 256) without(container_name)
record: k8s_pod_gpu_memory_request
- expr: sum(k8s_container_gpu_memory_used + on (container_name, pod_name, namespace)
group_left container_request_gpu_memory * 0) without(container_name) * 100 /
on (pod_name,namespace) group_left k8s_pod_gpu_memory_request
record: k8s_pod_rate_gpu_memory_used_request
- expr: k8s_pod_gpu_memory_used * 100 / on(node) group_left() kube_node_status_capacity_gpu_memory
record: k8s_pod_rate_gpu_memory_used_node
- expr: sum(k8s_container_network_receive_bytes_bw) without (container_name,container_id)
record: k8s_pod_network_receive_bytes_bw
- expr: sum(k8s_container_network_transmit_bytes_bw) without (container_name,container_id)
record: k8s_pod_network_transmit_bytes_bw
- expr: sum(k8s_container_network_receive_bytes) without (container_name,container_id)
record: k8s_pod_network_receive_bytes
- expr: sum(k8s_container_network_transmit_bytes) without (container_name,container_id)
record: k8s_pod_network_transmit_bytes
- expr: sum(k8s_container_network_receive_packets) without (container_name,container_id)
record: k8s_pod_network_receive_packets
- expr: sum(k8s_container_network_transmit_packets) without (container_name,container_id)
record: k8s_pod_network_transmit_packets
- expr: sum(k8s_container_fs_read_bytes) without (container_name,container_id)
record: k8s_pod_fs_read_bytes
- expr: sum(k8s_container_fs_write_bytes) without (container_name,container_id)
record: k8s_pod_fs_write_bytes
- expr: sum(k8s_container_fs_read_times) without (container_name,container_id)
record: k8s_pod_fs_read_times
- expr: sum(k8s_container_fs_write_times) without (container_name,container_id)
record: k8s_pod_fs_write_times
- expr: sum(kube_pod_status_ready{condition="true"}) by (namespace,pod_name) * on(namespace,
pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_pod_status_ready
- expr: sum(idelta(kube_pod_container_status_restarts_total [2m])) by (namespace,pod_name)
* on(namespace, pod_name) group_left(workload_kind,workload_name,node, node_role) __pod_info2
record: k8s_pod_restart_total
- expr: max(kube_node_status_condition{condition="Ready", status="true"} * on (node)
group_left(node_role) kube_node_labels) without(condition, status)
record: k8s_node_status_ready
- expr: sum(k8s_pod_restart_total) without (pod_name,workload_kind,workload_name,namespace)
record: k8s_node_pod_restart_total
- expr: sum(k8s_pod_cpu_core_used) without(namespace,pod_name,workload_kind,workload_name)
*100 / on(node) group_left kube_node_status_capacity_cpu_cores
record: k8s_node_cpu_usage
- expr: sum(k8s_pod_mem_usage_bytes) without(namespace,pod_name,workload_kind,workload_name)
*100 / on(node) group_left kube_node_status_capacity_memory_bytes
record: k8s_node_mem_usage
- expr: sum(k8s_pod_gpu_used) without(namespace,pod_name,workload_kind,workload_name)
*100 / on(node) group_left kube_node_status_capacity_gpu
record: k8s_node_gpu_usage
- expr: sum(k8s_pod_gpu_memory_used) without(namespace,pod_name,workload_kind,workload_name)
*100 / on(node) group_left() kube_node_status_capacity_gpu_memory
record: k8s_node_gpu_memory_usage
- expr: (sum by (node) (irate(node_disk_written_bytes_total[2m]))) *on(node) group_left(node_role)
kube_node_labels
record: k8s_node_fs_write_bytes
- expr: (sum by (node) (irate(node_disk_read_bytes_total[2m])))*on(node) group_left(node_role)
kube_node_labels
record: k8s_node_fs_read_bytes
- expr: (sum by (node) (irate(node_disk_writes_completed_total[2m])))*on(node) group_left(node_role)
kube_node_labels
record: k8s_node_fs_write_times
- expr: (sum by (node) (irate(node_disk_reads_completed_total[2m])))*on(node) group_left(node_role)
kube_node_labels
record: k8s_node_fs_read_times
- expr: count(k8s_pod_status_ready) without (pod_name,workload_kind,workload_name,namespace)
record: k8s_node_pod_num
- expr: (100 - sum (node_filesystem_avail_bytes{fstype=~"ext3|ext4|xfs"}) by (node)
/ sum (node_filesystem_size_bytes{fstype=~"ext3|ext4|xfs"}) by (node) *100)
*on(node) group_left(node_role) kube_node_labels
record: k8s_node_disk_space_rate
- expr: (sum by (node) (irate(node_network_receive_bytes_total{device!~"lo|veth(.*)|virb(.*)|docker(.*)|tunl(.*)|v-h(.*)|flannel(.*)"}[5m])))*on(node)
group_left(node_role) kube_node_labels
record: k8s_node_network_receive_bytes_bw
- expr: (sum by (node) (irate(node_network_transmit_bytes_total{device!~"lo|veth(.*)|virb(.*)|docker(.*)|tunl(.*)|v-h(.*)|flannel(.*)"}[5m])))*on(node)
group_left(node_role) kube_node_labels
record: k8s_node_network_transmit_bytes_bw
- expr: |-
max(label_replace(
label_replace(
label_replace(
kube_deployment_status_replicas_unavailable,
"workload_kind","Deployment","","")
,"workload_name","$1","deployment","(.*)"),
"__name__", "k8s_workload_abnormal", "__name__","(.*)") ) by (namespace, workload_name, workload_kind,__name__)
or on (namespace,workload_name,workload_kind, __name__)
max(label_replace(
label_replace(
label_replace(
kube_daemonset_status_number_unavailable,
"workload_kind","DaemonSet","","")
,"workload_name","$1","daemonset","(.*)"),
"__name__", "k8s_workload_abnormal", "__name__","(.*)") ) by (namespace, workload_name, workload_kind,__name__)
or on (namespace,workload_name,workload_kind, __name__)
max(label_replace(
label_replace(
label_replace(
(kube_statefulset_replicas - kube_statefulset_status_replicas_ready),
"workload_kind","StatefulSet","","")
,"workload_name","$1","statefulset","(.*)"),
"__name__", "k8s_workload_abnormal", "__name__","(.*)") ) by (namespace, workload_name, workload_kind,__name__)
or on (namespace,workload_name,workload_kind, __name__)
max(label_replace(
label_replace(
label_replace(
(kube_job_status_failed),
"workload_kind","Job","","")
,"workload_name","$1","job_name","(.*)"),
"__name__", "k8s_workload_abnormal", "__name__","(.*)") ) by (namespace, workload_name, workload_kind,__name__)
or on (namespace,workload_name,workload_kind, __name__)
max(label_replace(
label_replace(
label_replace(
(kube_cronjob_info * 0),
"workload_kind","CronJob","","")
,"workload_name","","cronjob","(.*)"),
"__name__", "k8s_workload_abnormal", "__name__","(.*)") ) by (namespace, workload_name, workload_kind,__name__)
record: k8s_workload_abnormal
- expr: sum(k8s_pod_restart_total) by(namespace,workload_kind,workload_name)
record: k8s_workload_pod_restart_total
- expr: sum(k8s_pod_cpu_core_used) by(workload_name, workload_kind, namespace)
record: k8s_workload_cpu_core_used
- expr: k8s_workload_cpu_core_used * 100 / scalar(k8s_cluster_cpu_core_total)
record: k8s_workload_rate_cpu_core_used_cluster
- expr: sum(k8s_pod_mem_usage_bytes) by(workload_name, workload_kind, namespace)
record: k8s_workload_mem_usage_bytes
- expr: sum(k8s_pod_mem_no_cache_bytes) by(workload_name, workload_kind, namespace)
record: k8s_workload_mem_no_cache_bytes
- expr: k8s_workload_mem_usage_bytes * 100 / scalar(k8s_cluster_memory_total)
record: k8s_workload_rate_mem_usage_bytes_cluster
- expr: k8s_workload_mem_no_cache_bytes * 100 / scalar(k8s_cluster_memory_total)
record: k8s_workload_rate_mem_no_cache_cluster
- expr: sum(k8s_pod_network_receive_bytes_bw) by(workload_name, workload_kind, namespace)
record: k8s_workload_network_receive_bytes_bw
- expr: sum(k8s_pod_network_transmit_bytes_bw) by(workload_name, workload_kind,
namespace)
record: k8s_workload_network_transmit_bytes_bw
- expr: sum(k8s_pod_network_receive_bytes) by(workload_name, workload_kind, namespace)
record: k8s_workload_network_receive_bytes
- expr: sum(k8s_pod_network_transmit_bytes) by(workload_name, workload_kind, namespace)
record: k8s_workload_network_transmit_bytes
- expr: sum(k8s_pod_network_receive_packets) by(workload_name, workload_kind, namespace)
record: k8s_workload_network_receive_packets
- expr: sum(k8s_pod_network_transmit_packets) by(workload_name, workload_kind, namespace)
record: k8s_workload_network_transmit_packets
- expr: sum(k8s_pod_fs_read_bytes) by (workload_name, workload_kind, namespace)
record: k8s_workload_fs_read_bytes
- expr: sum(k8s_pod_fs_write_bytes) by (workload_name, workload_kind, namespace)
record: k8s_workload_fs_write_bytes
- expr: sum(k8s_pod_fs_read_times) by (workload_name, workload_kind, namespace)
record: k8s_workload_fs_read_times
- expr: sum(k8s_pod_fs_write_times) by (workload_name, workload_kind, namespace)
record: k8s_workload_fs_write_times
- expr: sum(k8s_pod_gpu_used) by(workload_name, workload_kind, namespace)
record: k8s_workload_gpu_used
- expr: k8s_workload_gpu_used * 100 / scalar(k8s_cluster_gpu_total)
record: k8s_workload_rate_gpu_used_cluster
- expr: sum(k8s_pod_gpu_memory_used) by(workload_name, workload_kind, namespace)
record: k8s_workload_gpu_memory_used
- expr: k8s_workload_gpu_memory_used * 100 / scalar(k8s_cluster_gpu_memory_total)
record: k8s_workload_rate_gpu_memory_used_cluster
- expr: sum(k8s_pod_cpu_core_used) by (namespace)
record: k8s_namespace_cpu_core_used
- expr: k8s_namespace_cpu_core_used * 100 / scalar(k8s_cluster_cpu_core_total)
record: k8s_namespace_rate_cpu_core_used_cluster
- expr: sum(k8s_pod_mem_usage_bytes) by (namespace)
record: k8s_namespace_mem_usage_bytes
- expr: sum(k8s_pod_mem_no_cache_bytes) by (namespace)
record: k8s_namespace_mem_no_cache_bytes
- expr: k8s_namespace_mem_usage_bytes * 100 / scalar(k8s_cluster_memory_total)
record: k8s_namespace_rate_mem_usage_bytes_cluster
- expr: k8s_namespace_mem_no_cache_bytes * 100 / scalar(k8s_cluster_memory_total)
record: k8s_namespace_rate_mem_no_cache_cluster
- expr: sum(k8s_pod_network_receive_bytes_bw) by(namespace)
record: k8s_namespace_network_receive_bytes_bw
- expr: sum(k8s_pod_network_transmit_bytes_bw) by(namespace)
record: k8s_namespace_network_transmit_bytes_bw
- expr: sum(k8s_pod_network_receive_bytes) by(namespace)
record: k8s_namespace_network_receive_bytes
- expr: sum(k8s_pod_network_transmit_bytes) by(namespace)
record: k8s_namespace_network_transmit_bytes
- expr: sum(k8s_pod_network_receive_packets) by(namespace)
record: k8s_namespace_network_receive_packets
- expr: sum(k8s_pod_network_transmit_packets) by(namespace)
record: k8s_namespace_network_transmit_packets
- expr: sum(k8s_workload_fs_read_bytes) by (namespace)
record: k8s_namespace_fs_read_bytes
- expr: sum(k8s_workload_fs_write_bytes) by (namespace)
record: k8s_namespace_fs_write_bytes
- expr: sum(k8s_workload_fs_read_times) by (namespace)
record: k8s_namespace_fs_read_times
- expr: sum(k8s_workload_fs_write_times) by (namespace)
record: k8s_namespace_fs_write_times
- expr: sum(k8s_pod_gpu_used) by (namespace)
record: k8s_namespace_gpu_used
- expr: k8s_namespace_gpu_used * 100 / scalar(k8s_cluster_gpu_total)
record: k8s_namespace_rate_gpu_used_cluster
- expr: sum(k8s_pod_gpu_memory_used) by (namespace)
record: k8s_namespace_gpu_memory_used
- expr: k8s_namespace_gpu_memory_used * 100 / scalar(k8s_cluster_gpu_memory_total)
record: k8s_namespace_rate_gpu_memory_used_cluster
- expr: sum(k8s_pod_cpu_core_used{node_role="Node"})
record: k8s_cluster_cpu_core_used
- expr: sum(k8s_pod_mem_usage_bytes{node_role="Node"})
record: k8s_cluster_mem_usage_bytes
- expr: sum(k8s_pod_mem_no_cache_bytes{node_role="Node"})
record: k8s_cluster_mem_no_cache_bytes
- expr: k8s_cluster_cpu_core_used * 100 / scalar(k8s_cluster_cpu_core_total)
record: k8s_cluster_rate_cpu_core_used_cluster
- expr: sum(kube_pod_container_resource_requests{resource="cpu"} * on(node) group_left
kube_node_labels {node_role="Node"} ) * 100 / scalar(k8s_cluster_cpu_core_total)
record: k8s_cluster_rate_cpu_core_request_cluster
- expr: k8s_cluster_mem_usage_bytes * 100 / scalar(k8s_cluster_memory_total)
record: k8s_cluster_rate_mem_usage_bytes_cluster
- expr: k8s_cluster_mem_no_cache_bytes * 100 / scalar(k8s_cluster_memory_total)
record: k8s_cluster_rate_mem_no_cache_bytes_cluster
- expr: sum(kube_pod_container_resource_requests{resource="memory"} * on(node) group_left
kube_node_labels {node_role="Node"} ) * 100 / scalar(k8s_cluster_memory_total)
record: k8s_cluster_rate_mem_request_bytes_cluster
- expr: sum(k8s_pod_network_receive_bytes_bw{node_role="Node"})
record: k8s_cluster_network_receive_bytes_bw
- expr: sum(k8s_pod_network_transmit_bytes_bw{node_role="Node"})
record: k8s_cluster_network_transmit_bytes_bw
- expr: sum(k8s_pod_network_receive_bytes{node_role="Node"})
record: k8s_cluster_network_receive_bytes
- expr: sum(k8s_pod_network_transmit_bytes{node_role="Node"})
record: k8s_cluster_network_transmit_bytes
- expr: sum(k8s_pod_network_receive_packets{node_role="Node"})
record: k8s_cluster_network_receive_packets
- expr: sum(k8s_pod_network_transmit_packets{node_role="Node"})
record: k8s_cluster_network_transmit_packets
- expr: sum(k8s_pod_fs_read_bytes{node_role="Node"})
record: k8s_cluster_fs_read_bytes
- expr: sum(k8s_pod_fs_write_bytes{node_role="Node"})
record: k8s_cluster_fs_write_bytes
- expr: sum(k8s_pod_fs_read_times{node_role="Node"})
record: k8s_cluster_fs_read_times
- expr: sum(k8s_pod_fs_write_times{node_role="Node"})
record: k8s_cluster_fs_write_times
- expr: sum(k8s_pod_gpu_used{node_role="Node"})
record: k8s_cluster_gpu_used
- expr: k8s_cluster_gpu_used * 100 / scalar(k8s_cluster_gpu_total)
record: k8s_cluster_rate_gpu_used_cluster
- expr: sum(k8s_pod_gpu_request * on(node) group_left kube_node_labels {node_role="Node"})
* 100 / scalar(k8s_cluster_gpu_total)
record: k8s_cluster_rate_gpu_request_cluster
- expr: sum(k8s_pod_gpu_memory_used{node_role="Node"})
record: k8s_cluster_gpu_memory_used
- expr: k8s_cluster_gpu_memory_used * 100 / scalar(k8s_cluster_gpu_memory_total)
record: k8s_cluster_rate_gpu_memory_used_cluster
- expr: sum(k8s_pod_gpu_memory_request * on(node) group_left kube_node_labels {node_role="Node"}
) * 100 / scalar(k8s_cluster_gpu_memory_total)
record: k8s_cluster_rate_gpu_memory_request_cluster
- expr: k8s_namespace_cpu_core_used* on(namespace) group_left(project_name) kube_namespace_labels
record: project_namespace_cpu_core_used
- expr: k8s_namespace_mem_usage_bytes* on(namespace) group_left(project_name) kube_namespace_labels
record: project_namespace_mem_usage_bytes
- expr: k8s_namespace_mem_no_cache_bytes* on(namespace) group_left(project_name)
kube_namespace_labels
record: project_namespace_mem_no_cache_bytes
- expr: k8s_namespace_gpu_used* on(namespace) group_left(project_name) kube_namespace_labels
record: project_namespace_gpu_used
- expr: k8s_namespace_gpu_memory_used* on(namespace) group_left(project_name) kube_namespace_labels
record: project_namespace_gpu_memory_used
- expr: k8s_namespace_network_receive_bytes_bw* on(namespace) group_left(project_name)
kube_namespace_labels
record: project_namespace_network_receive_bytes_bw
- expr: k8s_namespace_network_transmit_bytes_bw* on(namespace) group_left(project_name)
kube_namespace_labels
record: project_namespace_network_transmit_bytes_bw
- expr: k8s_namespace_network_receive_bytes* on(namespace) group_left(project_name)
kube_namespace_labels
record: project_namespace_network_receive_bytes
- expr: k8s_namespace_network_transmit_bytes* on(namespace) group_left(project_name)
kube_namespace_labels
record: project_namespace_network_transmit_bytes
- expr: k8s_namespace_fs_read_bytes* on(namespace) group_left(project_name) kube_namespace_labels
record: project_namespace_fs_read_bytes
- expr: k8s_namespace_fs_write_bytes* on(namespace) group_left(project_name) kube_namespace_labels
record: project_namespace_fs_write_bytes
- expr: sum(project_namespace_cpu_core_used) by (project_name)
record: project_cluster_cpu_core_used
- expr: project_cluster_cpu_core_used * 100 / scalar(k8s_cluster_cpu_core_total)
record: project_cluster_rate_cpu_core_used_cluster
- expr: sum(project_namespace_mem_usage_bytes) by (project_name)
record: project_cluster_memory_usage_bytes
- expr: sum(project_namespace_mem_no_cache_bytes) by (project_name)
record: project_cluster_memory_no_cache_bytes
- expr: project_cluster_memory_usage_bytes * 100 / scalar(k8s_cluster_memory_total)
record: project_cluster_rate_memory_usage_bytes_cluster
- expr: project_cluster_memory_no_cache_bytes * 100 / scalar(k8s_cluster_memory_total)
record: project_cluster_rate_memory_no_cache_cluster
- expr: sum(project_namespace_gpu_used) by (project_name)
record: project_cluster_gpu_used
- expr: project_cluster_gpu_used * 100 / scalar(k8s_cluster_gpu_total)
record: project_cluster_rate_gpu_used_cluster
- expr: sum(project_namespace_gpu_memory_used) by (project_name)
record: project_cluster_gpu_memory_used
- expr: project_cluster_gpu_memory_used * 100 / scalar(k8s_cluster_gpu_memory_total)
record: project_cluster_rate_gpu_memory_used_cluster
- expr: sum(project_namespace_network_receive_bytes_bw) by (project_name)
record: project_cluster_network_receive_bytes_bw
- expr: sum(project_namespace_network_transmit_bytes_bw) by (project_name)
record: project_cluster_network_transmit_bytes_bw
- expr: sum(project_namespace_network_receive_bytes) by (project_name)
record: project_cluster_network_receive_bytes
- expr: sum(project_namespace_network_transmit_bytes) by (project_name)
record: project_cluster_network_transmit_bytes
- expr: sum(project_namespace_fs_read_bytes) by (project_name)
record: project_cluster_fs_read_bytes
- expr: sum(project_namespace_fs_write_bytes) by (project_name)
record: project_cluster_fs_write_bytes
- expr: up{instance=~"(.*)60001"} * on(node) group_left(node_role) kube_node_labels
record: k8s_component_apiserver_ready
- expr: up{instance=~"(.*)2379"} * on(node) group_left(node_role) kube_node_labels
record: k8s_component_etcd_ready
- expr: up{instance=~"(.*)10251"} * on(node) group_left(node_role) kube_node_labels
record: k8s_component_scheduler_ready
- expr: up{instance=~"(.*)10252"} * on(node) group_left(node_role) kube_node_labels
record: k8s_component_controller_manager_ready
- expr: sum(apiserver_request_latencies_summary_sum) by (node) / sum(apiserver_request_latencies_summary_count)
by (node)
record: k8s_component_apiserver_request_latency
- expr: sum(scheduler_e2e_scheduling_latency_microseconds_sum) by (node) / sum(scheduler_e2e_scheduling_latency_microseconds_count)
by (node)
record: k8s_component_scheduler_scheduling_latency
kind: ConfigMap
metadata:
creationTimestamp: "2020-05-28T03:42:51Z"
labels:
managed-by: prometheus-operator
prometheus-name: k8s
name: prometheus-k8s-rulefiles-0
namespace: kube-system
ownerReferences:
- apiVersion: monitoring.coreos.com/v1
blockOwnerDeletion: true
controller: true
kind: Prometheus
name: k8s
uid: 21c02639-a095-11ea-afac-52540002fcf3
resourceVersion: "971"
selfLink: /api/v1/namespaces/kube-system/configmaps/prometheus-k8s-rulefiles-0
uid: 4e7bd3c1-a095-11ea-afac-52540002fcf3
CPU使用量
HPA指标：CPU使用量 0.9核
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
labels:
app: test1
name: test1
namespace: default
spec:
maxReplicas: 5
metrics:
- pods:
metricName: k8s_pod_cpu_core_used
targetAverageValue: 900m
type: Pods
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1beta2
kind: Deployment
name: azzdeploy01
CPU利用率(占节点)
HPA指标：CPU利用率（占节点） 40%
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
labels:
app: test1
name: test1
namespace: default
spec:
maxReplicas: 5
metrics:
- pods:
metricName: k8s_pod_rate_cpu_core_used_node
targetAverageValue: "40"
type: Pods
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1beta2
kind: Deployment
name: azzdeploy01
CPU利用率（占Request）
HPA指标：CPU利用率（占Request） 50%
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
labels:
app: test1
name: test1
namespace: default
spec:
maxReplicas: 5
metrics:
- pods:
metricName: k8s_pod_rate_cpu_core_used_request
targetAverageValue: "50"
type: Pods
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1beta2
kind: Deployment
name: azzdeploy01
CPU利用率（占Limit)
HPA指标：CPU利用率（占Limit） 80%
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
labels:
app: test1
name: test1
namespace: default
spec:
maxReplicas: 5
metrics:
- pods:
metricName: k8s_pod_rate_cpu_core_used_limit
targetAverageValue: "80"
type: Pods
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1beta2
kind: Deployment
name: azzdeploy01
内存使用量
HPA指标：内存使用量 500MiB
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
labels:
app: test1
name: test1
namespace: default
spec:
maxReplicas: 5
metrics:
- pods:
metricName: k8s_pod_mem_usage_bytes
targetAverageValue: 500Mi
type: Pods
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1beta2
kind: Deployment
name: azzdeploy01
内存使用量（不包含 Cache）
HPA指标：内存使用量（不包含 Cache） 500MiB
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
labels:
qcloud-app: test1
name: test1
namespace: default
spec:
maxReplicas: 5
metrics:
- pods:
metricName: k8s_pod_mem_no_cache_bytes
targetAverageValue: 500Mi
type: Pods
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1beta2
kind: Deployment
name: azzdeploy01
内存利用率（占节点）
HPA指标：内存利用率（占节点） 30%
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
labels:
app: test1
name: test1
namespace: default
spec:
maxReplicas: 5
metrics:
- pods:
metricName: k8s_pod_rate_mem_usage_node
targetAverageValue: "30"
type: Pods
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1beta2
kind: Deployment
name: azzdeploy01
内存利用率（占节点，不包含 Cache）
HPA指标：内存利用率（占节点，不包含 Cache）30%
- pods:
metricName: k8s_pod_rate_mem_no_cache_node
targetAverageValue: "30"
type: Pods
内存利用率（占Request）
HPA指标：内存利用率（占Request）30%
- pods:
metricName: k8s_pod_rate_mem_usage_request
targetAverageValue: "30"
type: Pods
内存利用率（占 Request，不包含Cache）
HPA指标：内存利用率（占 Request，不包含Cache）30
- pods:
metricName: k8s_pod_rate_mem_no_cache_request
targetAverageValue: "30"
type: Pods
内存利用率（占 Limit)
HPA指标：内存利用率（占 Limit) 30%
- pods:
metricName: k8s_pod_rate_mem_usage_limit
targetAverageValue: "30"
type: Pods
内存利用率（占 Limit，不包含 Cache）
HPA指标：内存利用率（占 Limit，不包含 Cache）30%
- pods:
metricName: k8s_pod_rate_mem_no_cache_limit
targetAverageValue: "30"
type: Pods
硬盘写流量
HPA指标：硬盘写流量 1000KB/s
- pods:
metricName: k8s_pod_fs_write_bytes
targetAverageValue: 1000Ki
type: Pods
硬盘读流量
HPA指标：硬盘读流量 1000KB/s
- pods:
metricName: k8s_pod_fs_read_bytes
targetAverageValue: 1000Ki
type: Pods
硬盘读 IOPS
HPA指标：硬盘读 IOPS 1000次/s
- pods:
metricName: k8s_pod_fs_read_times
targetAverageValue: 1k
type: Pods
硬盘写 IOPS
HPA指标：硬盘写 IOPS 1000次/s
- pods:
metricName: k8s_pod_fs_write_times
targetAverageValue: 1k
type: Pods
网络入带宽
HPA指标：网络入带宽 100Mbps
- pods:
metricName: k8s_pod_network_receive_bytes_bw
targetAverageValue: 100Mi
type: Pods
网络出带宽
HPA指标：网络出带宽 100Mbps
- pods:
metricName: k8s_pod_network_transmit_bytes_bw
targetAverageValue: 100Mi
type: Pods
网络入流量
HPA指标：网络入流量 100KB/s
- pods:
metricName: k8s_pod_network_receive_bytes
targetAverageValue: 100Ki
type: Pods
网络出流量
HPA指标：网络出流量 100KB/s
- pods:
metricName: k8s_pod_network_transmit_bytes
targetAverageValue: 100Ki
type: Pods
网络入包量
HPA指标：网络入包量 100个
- pods:
metricName: k8s_pod_network_receive_packets
targetAverageValue: "100"
type: Pods
网络出包量
HPA指标：网络出包量 100个
- pods:
metricName: k8s_pod_network_transmit_packets
targetAverageValue: "100"
type: Pods
自定义指标的autoscaling
prometheus-adapter
创建一个nginx应用，暴露的prometheus metric路径为/status/format/prometheus
# vim /hpa-prom-demo.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: hpa-prom-demo
spec:
selector:
matchLabels:
app: nginx-server
template:
metadata:
labels:
app: nginx-server
annotations:
prometheus.io/scrape: "true"
prometheus.io/port: "80"
prometheus.io/path: "/status/format/prometheus"
spec:
containers:
- name: nginx-demo
image: vaibhavthakur/nginx-vts:v1.0
resources:
limits:
cpu: 50m
requests:
cpu: 50m
ports:
- containerPort: 80
name: http
---
apiVersion: v1
kind: Service
metadata:
name: hpa-prom-demo
annotations:
prometheus.io/scrape: "true"
prometheus.io/port: "80"
prometheus.io/path: "/status/format/prometheus"
spec:
ports:
- port: 80
targetPort: 80
name: http
selector:
app: nginx-server
type: NodePort
# kubectl apply -f hpa-prom-demo.yaml
# kubectl get svc hpa-prom-demo
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
hpa-prom-demo NodePort 172.20.255.206 <none> 80:50933/TCP 6h14m
获取prometheus指标
curl http://172.20.255.206/status/format/prometheus
# HELP nginx_vts_info Nginx info
# TYPE nginx_vts_info gauge
nginx_vts_info{hostname="nginx-deployment-76d685cfc7-qvnxh",version="1.13.12"} 1
# HELP nginx_vts_start_time_seconds Nginx start time
# TYPE nginx_vts_start_time_seconds gauge
nginx_vts_start_time_seconds 1590993544.152
# HELP nginx_vts_main_connections Nginx connections
# TYPE nginx_vts_main_connections gauge
nginx_vts_main_connections{status="accepted"} 3
nginx_vts_main_connections{status="active"} 2
部署k8s-prometheus-adapter
# vim /root/hpa-prome-adapter-values.yaml
rules:
default: false
custom:
- seriesQuery: 'nginx_vts_server_requests_total'
resources:
overrides:
kubernetes_namespace:
resource: namespace
kubernetes_pod_name:
resource: pod
name:
matches: "^(.*)_total"
as: "${1}_per_second"
metricsQuery: (sum(rate(<<.Series>>{<<.LabelMatchers>>}[5m])) by (<<.GroupBy>>))
prometheus:
url: http://10.125.233.66
port: 29175
# helm install --name prometheus-adapter stable/prometheus-adapter -f /root/hpa-prome-adapter-values.yaml
# helm ls
NAME REVISION UPDATED STATUS CHART APP VERSION NAMESPACE
prometheus-adapter 1 Mon Jun 1 16:18:32 2020 DEPLOYED prometheus-adapter-2.3.1 v0.6.0 default
查看自定义指标的资源
# kubectl get --raw="/apis/custom.metrics.k8s.io/v1beta1" | jq .
{
"kind": "APIResourceList",
"apiVersion": "v1",
"groupVersion": "custom.metrics.k8s.io/v1beta1",
"resources": [
{
"name": "pods/nginx_vts_server_requests_per_second",
"singularName": "",
"namespaced": true,
"kind": "MetricValueList",
"verbs": [
"get"
]
},
{
"name": "namespaces/nginx_vts_server_requests_per_second",
"singularName": "",
"namespaced": false,
"kind": "MetricValueList",
"verbs": [
"get"
]
}
]
}
通过自定义metrics api获取pod指标
# kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1/namespaces/default/pods/*/nginx_vts_server_requests_per_second" | jq .
{
"kind": "MetricValueList",
"apiVersion": "custom.metrics.k8s.io/v1beta1",
"metadata": {
"selfLink": "/apis/custom.metrics.k8s.io/v1beta1/namespaces/default/pods/%2A/nginx_vts_server_requests_per_second"
},
"items": [
{
"describedObject": {
"kind": "Pod",
"namespace": "default",
"name": "hpa-prom-demo-dfd69c997-gb6nx",
"apiVersion": "/v1"
},
"metricName": "nginx_vts_server_requests_per_second",
"timestamp": "2020-06-01T11:38:45Z",
"value": "200m",
"selector": null
}
]
}
压测
# while true; do wget -q -O- http://172.20.255.206; done
打开另一个窗口，观测hpa变化
# kubectl describe horizontalpodautoscalers.autoscaling nginx-custom-hpa
Name: nginx-custom-hpa
Namespace: default
Labels: <none>
Annotations: kubectl.kubernetes.io/last-applied-configuration:
{"apiVersion":"autoscaling/v2beta1","kind":"HorizontalPodAutoscaler","metadata":{"annotations":{},"name":"nginx-custom-hpa","namespace":"d...
CreationTimestamp: Mon, 01 Jun 2020 20:11:13 +0800
Reference: Deployment/hpa-prom-demo
Metrics: ( current / target )
"nginx_vts_server_requests_per_second" on pods: 29796m / 10
Min replicas: 2
Max replicas: 5
Deployment pods: 5 current / 5 desired
Conditions:
Type Status Reason Message
---- ------ ------ -------
AbleToScale True ScaleDownStabilized recent recommendations were higher than current one, applying the highest recent recommendation
ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from pods metric nginx_vts_server_requests_per_second
ScalingLimited True TooManyReplicas the desired replica count is more than the maximum replica count
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulRescale 11m horizontal-pod-autoscaler New size: 2; reason: Current number of replicas below Spec.MinReplicas
Normal SuccessfulRescale 63s horizontal-pod-autoscaler New size: 4; reason: pods metric nginx_vts_server_requests_per_second above target
Normal SuccessfulRescale 48s horizontal-pod-autoscaler New size: 5; reason: pods metric nginx_vts_server_requests_per_second above target
kube-metrics-adapter
克隆kube-metrics-adapter项目
# git clone https://github.com/zalando-incubator/kube-metrics-adapter.git
# cd docs/
修改deployment启动参数，指定prometheus地址
# vim deployment.yaml
args:
- --v=9
- --prometheus-server=http://<prometheus-server地址>:9090
- --skipper-ingress-metrics
- --aws-external-metrics
apply yaml部署
# kubectl apply -f ./
查看kube-metrics-adapter deployment
# kubectl -n kube-system get deployments. kube-metrics-adapter
NAME READY UP-TO-DATE AVAILABLE AGE
kube-metrics-adapter 1/1 1 1 177m
kube-metrics-adapter的prometheus collector采用的是External metric api
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
name: myapp-hpa
namespace: default
annotations:
metric-config.external.prometheus-query.prometheus/k8s_pod_cpu_core_used-hpa-prom-demo: |
avg(k8s_pod_cpu_core_used{cluster_id="cls-7ffb5c05", namespace="default",workload_kind="Deployment",workload_name="hpa-prom-demo"})
metric-config.external.prometheus-query.prometheus/k8s_pod_mem_usage_bytes-hpa-prom-demo: |
avg(k8s_pod_mem_usage_bytes{cluster_id="cls-7ffb5c05", namespace="default",workload_kind="Deployment",workload_name="hpa-prom-demo"})
metric-config.external.prometheus-query.prometheus/k8s_pod_fs_write_bytes-hpa-prom-demo: |
avg(k8s_pod_fs_write_bytes{cluster_id="cls-7ffb5c05", namespace="default",workload_kind="Deployment",workload_name="hpa-prom-demo"})
metric-config.external.prometheus-query.prometheus/k8s_pod_network_receive_bytes-hpa-prom-demo: |
avg(k8s_pod_network_receive_bytes{cluster_id="cls-7ffb5c05", namespace="default",workload_kind="Deployment",workload_name="hpa-prom-demo"})
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: hpa-prom-demo
minReplicas: 1
maxReplicas: 10
metrics:
- type: External
external:
metric:
name: prometheus-query
selector:
matchLabels:
query-name: k8s_pod_cpu_core_used-hpa-prom-demo
target:
type: AverageValue
averageValue: 900m
- type: External
external:
metric:
name: prometheus-query
selector:
matchLabels:
query-name: k8s_pod_mem_usage_bytes-hpa-prom-demo
target:
type: AverageValue
averageValue: 500Mi
- type: External
external:
metric:
name: prometheus-query
selector:
matchLabels:
query-name: k8s_pod_fs_write_bytes-hpa-prom-demo
target:
type: AverageValue
averageValue: 1000Ki
- type: External
external:
metric:
name: prometheus-query
selector:
matchLabels:
query-name: k8s_pod_network_receive_bytes-hpa-prom-demo
target:
type: AverageValue
averageValue: 100Ki
annotation那边定义的metrtic configKey不能重复
查看external metric api资源列表
# kubectl get --raw="/apis/external.metrics.k8s.io/v1beta1" | jq .
{
"kind": "APIResourceList",
"apiVersion": "v1",
"groupVersion": "external.metrics.k8s.io/v1beta1",
"resources": [
{
"name": "prometheus-query",
"singularName": "",
"namespaced": true,
"kind": "ExternalMetricValueList",
"verbs": [
"get"
]
}
]
}
查看external metric的指标值列表
# kubectl get --raw "/apis/external.metrics.k8s.io/v1beta1/namespaces/*/prometheus-query" | jq .
{
"kind": "ExternalMetricValueList",
"apiVersion": "external.metrics.k8s.io/v1beta1",
"metadata": {
"selfLink": "/apis/external.metrics.k8s.io/v1beta1/namespaces/default/prometheus-query"
},
"items": [
{
"metricName": "prometheus-query",
"metricLabels": {
"query-name": "k8s_pod_cpu_core_used"
},
"timestamp": "2020-06-02T12:03:06Z",
"value": "0"
}
]
}
kube-metrics-adapter vs prometheus-adapter
kube-metrics-adapter的Prometheus collector是通用收集器，可以将Prometheus查询映射到可用于扩展的指标。 这种方法与k8s-prometheus-adapter中的方法不同，在k8s-prometheus-adapter中， 所有可用的Prometheus指标都被收集并转换为HPA可以扩展的指标，并且不可能进行自定义查询。 使用kube-metrics-adapter的Prometheus collector实现的方法，用户可以定义自定义查询，并且只有从这些查询返回的指标才可用，从而减少了所存储指标的总数。
但是使用kube-metrics-adapter的Prometheus collector实现的方法也有缺点，查询效果不佳会减慢/杀死Prometheus，因此允许在多租户群集中进行查询操作可能很危险。 也不可能使用RBAC之类的方法来限制可用指标，因为任何用户都可以基于自定义查询创建指标。
面向开发者的话，自定义查询可能会更有用，但是最好知道这两种方法之间的差异以及面向的用户，然后再做取舍。
参考链接
- Kubernetes：HPA 详解-基于 CPU、内存和自定义指标自动扩缩容
- Prometheus Metrics based autoscaling in Kubernetes`
- https://github.com/zalando-incubator/kube-metrics-adapter
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