前言

随着 Prometheus 监控的组件、数量、指标越来越多,Prometheus 对计算性能的要求会越来越高,存储占用也会越来越多。

在这种情况下,要优化 Prometheus 性能, 优化存储占用. 第一时间想到的可能是各种 Prometheus 的兼容存储方案, 如 Thanos 或 VM、Mimir 等。但是实际上虽然集中存储、长期存储、存储降采样及存储压缩可以一定程度解决相关问题,但是治标不治本。

  • 真正的本,还是在于指标量(series)过于庞大。
  • 治本之法,应该是减少指标量。有 2 种办法:

本次重点介绍第二种办法:如何根据实际的使用情况精简 Prometheus 的指标和存储占用?

思路

  1. 分析当前 Prometheus 中存储的所有的 metric name(指标项);
  2. 分析展示环节用到的所有 metric name,即 Grafana 的 Dashboards 用到的所有指标;
  3. 分析告警环节用到的所有 metric name,即 Prometheus Rule 配置中用到的所有指标;
  4. (可选)分析诊断环境用到的所有 metric name,即经常在 Prometheus UI 上 query 的指标;
  5. 通过 relabelmetric_relabel_configswrite_relabel_configskeep 2-4 中的指标, 以此大幅减少 Prometheus 需要存储的指标量.

要具体实现这个思路, 可以通过 Grafana Labs 出品的 mimirtool 来搞定.

我这里有个前后的对比效果, 可供参考这样做效果有多惊人:

  1. 精简前: 270336 活动 series
  2. 精简后: 61055 活动 series
  3. 精简效果: 将近 5 倍的精简率!

Grafana Mimirtool

Grafana Mimir 是一款以对象存储为存储方式的 Prometheus 长期存储解决方案, 从 Cortex 演化而来. 官方号称支持亿级别的 series 写入存储和查询.

Grafana Mimirtool 是 Mimir 发布的一个实用工具, 可单独使用.

Grafana Mimirtool 支持从以下方面提取指标:

  • Grafana 实例中的Grafana Dashboards(通过 Grafana API)
  • Mimir 实例中的 Prometheus alerting 和 recording rules
  • Grafana Dashboards JSON文件
  • Prometheus记alerting 和 recording rules 的 YAML文件

然后,Grafana Mimirtool可以将这些提取的指标与Prometheus或Cloud Prometheus实例中的活动 series 进行比较,并输出一个 used 指标和 unused 指标的列表。

Prometheus 精简指标实战

假设

假定:

  • 通过kube-prometheus-stack 安装 Prometheus
  • 已安装 Grafana 且作为展示端
  • 已配置相应的 告警规则
  • 除此之外, 无其他需要额外保留的指标

前提

  1. Grafana Mimirtool 从 releases 中找到 mimirtool 对应平台的版本下载即可使用;
  2. 创建 Grafana API token
  3. Prometheus已安装和配置.

第一步: 分析 Grafana Dashboards 用到的指标

通过 Grafana API

具体如下:

# 通过 Grafana API分析 Grafana 用到的指标
# 前提是现在 Grafana上创建 API Keys
mimirtool analyze grafana --address http://172.16.0.20:32651 --key=eyJrIjoiYjBWMGVoTHZTY3BnM3V5UzNVem9iWDBDSG5sdFRxRVoiLCJuIjoibWltaXJ0b29sIiwiaWQiOjF9

📝说明:

  • http://172.16.0.20:32651 是 Grafana 地址
  • --key=eyJr 是 Grafana API Token. 通过如下界面获得:

如何精简 Prometheus 的指标和存储占用-LMLPHP

获取到的是一个 metrics-in-grafana.json, 内容概述如下:

{
    "metricsUsed": [
        ":node_memory_MemAvailable_bytes:sum",
        "alertmanager_alerts",
        "alertmanager_alerts_invalid_total",
        "alertmanager_alerts_received_total",
        "alertmanager_notification_latency_seconds_bucket",
        "alertmanager_notification_latency_seconds_count",
        "alertmanager_notification_latency_seconds_sum",
        "alertmanager_notifications_failed_total",
        "alertmanager_notifications_total",
        "cluster",
        "cluster:namespace:pod_cpu:active:kube_pod_container_resource_limits",
        "cluster:namespace:pod_cpu:active:kube_pod_container_resource_requests",
        "cluster:namespace:pod_memory:active:kube_pod_container_resource_limits",
        "cluster:namespace:pod_memory:active:kube_pod_container_resource_requests",
        "cluster:node_cpu:ratio_rate5m",
        "container_cpu_cfs_periods_total",
        "container_cpu_cfs_throttled_periods_total",
        "..."
    ],
    "dashboards": [
        {
            "slug": "",
            "uid": "alertmanager-overview",
            "title": "Alertmanager / Overview",
            "metrics": [
                "alertmanager_alerts",
                "alertmanager_alerts_invalid_total",
                "alertmanager_alerts_received_total",
                "alertmanager_notification_latency_seconds_bucket",
                "alertmanager_notification_latency_seconds_count",
                "alertmanager_notification_latency_seconds_sum",
                "alertmanager_notifications_failed_total",
                "alertmanager_notifications_total"
            ],
            "parse_errors": null
        },
        {
            "slug": "",
            "uid": "c2f4e12cdf69feb95caa41a5a1b423d9",
            "title": "etcd",
            "metrics": [
                "etcd_disk_backend_commit_duration_seconds_bucket",
                "etcd_disk_wal_fsync_duration_seconds_bucket",
                "etcd_mvcc_db_total_size_in_bytes",
                "etcd_network_client_grpc_received_bytes_total",
                "etcd_network_client_grpc_sent_bytes_total",
                "etcd_network_peer_received_bytes_total",
                "etcd_network_peer_sent_bytes_total",
                "etcd_server_has_leader",
                "etcd_server_leader_changes_seen_total",
                "etcd_server_proposals_applied_total",
                "etcd_server_proposals_committed_total",
                "etcd_server_proposals_failed_total",
                "etcd_server_proposals_pending",
                "grpc_server_handled_total",
                "grpc_server_started_total",
                "process_resident_memory_bytes"
            ],
            "parse_errors": null
        },
        {...}
    ]
}

(可选)通过 Grafana Dashboards json 文件

如果无法创建 Grafana API Token, 只要有 Grafana Dashboards json 文件, 也可以用来分析, 示例如下:

# 通过 Grafana Dashboard json 分析 Grafana 用到的指标
mimirtool analyze dashboard grafana_dashboards/blackboxexporter-probe.json
mimirtool analyze dashboard grafana_dashboards/es.json

得到的 json 结构和上一节类似, 就不赘述了.

第二步: 分析 Prometheus Alerting 和 Recording Rules 用到的指标

具体操作如下:

# (可选)通过 kubectl cp 将用到的 rule files 拷贝到本地
kubectl cp <prompod>:/etc/prometheus/rules/<releasename>-kube-prometheus-st-prometheus-rulefiles-0 -c prometheus ./kube-prometheus-stack/rulefiles/

# 通过 Prometheus rule files 分析 Prometheus Rule 用到的指标(涉及 recording rule 和 alert rules)
mimirtool analyze rule-file ./kube-prometheus-stack/rulefiles/*

结果如下 metrics-in-ruler.json:

{
  "metricsUsed": [
    "ALERTS",
    "aggregator_unavailable_apiservice",
    "aggregator_unavailable_apiservice_total",
    "apiserver_client_certificate_expiration_seconds_bucket",
    "apiserver_client_certificate_expiration_seconds_count",
    "apiserver_request_terminations_total",
    "apiserver_request_total",
    "blackbox_exporter_config_last_reload_successful",
    "..."
  ],
  "ruleGroups": [
    {
      "namspace": "default-monitor-kube-prometheus-st-kubernetes-apps-ae2b16e5-41d8-4069-9297-075c28c6969e",
      "name": "kubernetes-apps",
      "metrics": [
        "kube_daemonset_status_current_number_scheduled",
        "kube_daemonset_status_desired_number_scheduled",
        "kube_daemonset_status_number_available",
        "kube_daemonset_status_number_misscheduled",
        "kube_daemonset_status_updated_number_scheduled",
        "..."
      ]
      "parse_errors": null
    },
    {
      "namspace": "default-monitor-kube-prometheus-st-kubernetes-resources-ccb4a7bc-f2a0-4fe4-87f7-0b000468f18f",
      "name": "kubernetes-resources",
      "metrics": [
        "container_cpu_cfs_periods_total",
        "container_cpu_cfs_throttled_periods_total",
        "kube_node_status_allocatable",
        "kube_resourcequota",
        "namespace_cpu:kube_pod_container_resource_requests:sum",
        "namespace_memory:kube_pod_container_resource_requests:sum"
      ],
      "parse_errors": null
    }, 
    {...}
  ]
}            

第三步: 分析没用到的指标

具体如下:

# 综合分析 Prometheus 采集到的 VS. (展示(Grafana Dashboards) + 记录及告警(Rule files))
mimirtool analyze prometheus --address=http://172.16.0.20:30090/ --grafana-metrics-file="metrics-in-grafana.json" --ruler-metrics-file="metrics-in-ruler.json"

📝说明:

  • --address=http://172.16.0.20:30090/ 为 prometheus 地址
  • --grafana-metrics-file="metrics-in-grafana.json" 为第一步得到的 json 文件
  • --ruler-metrics-file="kube-prometheus-stack-metrics-in-ruler.json" 为第二步得到的 json 文件

输出结果prometheus-metrics.json 如下:

{
  "total_active_series": 270336,
  "in_use_active_series": 61055,
  "additional_active_series": 209281,
  "in_use_metric_counts": [
    {
      "metric": "rest_client_request_duration_seconds_bucket",
      "count": 8855,
      "job_counts": [
        {
          "job": "kubelet",
          "count": 4840
        }, 
        {
          "job": "kube-controller-manager",
          "count": 1958
        },
        {...}
      ]
    },
    {
      "metric": "grpc_server_handled_total",
      "count": 4394,
      "job_counts": [
        {
          "job": "kube-etcd",
          "count": 4386
        },
        {
          "job": "default/kubernetes-ebao-ebaoops-pods",
          "count": 8
        }
      ]
    },
    {...}
  ],
  "additional_metric_counts": [    
    {
      "metric": "rest_client_rate_limiter_duration_seconds_bucket",
      "count": 81917,
      "job_counts": [
        {
          "job": "kubelet",
          "count": 53966
        },
        {
          "job": "kube-proxy",
          "count": 23595
        },
        {
          "job": "kube-scheduler",
          "count": 2398
        },
        {
          "job": "kube-controller-manager",
          "count": 1958
        }
      ]
    },  
    {
      "metric": "rest_client_rate_limiter_duration_seconds_count",
      "count": 7447,
      "job_counts": [
        {
          "job": "kubelet",
          "count": 4906
        },
        {
          "job": "kube-proxy",
          "count": 2145
        },
        {
          "job": "kube-scheduler",
          "count": 218
        },
        {
          "job": "kube-controller-manager",
          "count": 178
        }
      ]
    },
    {...}
  ]
}                                 

第四步: 仅 keep 用到的指标

write_relabel_configs 环节配置

如果你有使用 remote_write, 那么直接在 write_relabel_configs 环节配置 keep relabel 规则, 简单粗暴.

可以先用 jp 命令得到所有需要 keep 的metric name:

jq '.metricsUsed' metrics-in-grafana.json \
| tr -d '", ' \
| sed '1d;$d' \
| grep -v 'grafanacloud*' \
| paste -s -d '|' -

输出结果类似如下:

instance:node_cpu_utilisation:rate1m|instance:node_load1_per_cpu:ratio|instance:node_memory_utilisation:ratio|instance:node_network_receive_bytes_excluding_lo:rate1m|instance:node_network_receive_drop_excluding_lo:rate1m|instance:node_network_transmit_bytes_excluding_lo:rate1m|instance:node_network_transmit_drop_excluding_lo:rate1m|instance:node_vmstat_pgmajfault:rate1m|instance_device:node_disk_io_time_seconds:rate1m|instance_device:node_disk_io_time_weighted_seconds:rate1m|node_cpu_seconds_total|node_disk_io_time_seconds_total|node_disk_read_bytes_total|node_disk_written_bytes_total|node_filesystem_avail_bytes|node_filesystem_size_bytes|node_load1|node_load15|node_load5|node_memory_Buffers_bytes|node_memory_Cached_bytes|node_memory_MemAvailable_bytes|node_memory_MemFree_bytes|node_memory_MemTotal_bytes|node_network_receive_bytes_total|node_network_transmit_bytes_total|node_uname_info|up

然后直接在 write_relabel_configs 环节配置 keep relabel 规则:

remote_write:
- url: <remote_write endpoint>
  basic_auth:
    username: <按需>
    password: <按需>
  write_relabel_configs:
  - source_labels: [__name__]
    regex: instance:node_cpu_utilisation:rate1m|instance:node_load1_per_cpu:ratio|instance:node_memory_utilisation:ratio|instance:node_network_receive_bytes_excluding_lo:rate1m|instance:node_network_receive_drop_excluding_lo:rate1m|instance:node_network_transmit_bytes_excluding_lo:rate1m|instance:node_network_transmit_drop_excluding_lo:rate1m|instance:node_vmstat_pgmajfault:rate1m|instance_device:node_disk_io_time_seconds:rate1m|instance_device:node_disk_io_time_weighted_seconds:rate1m|node_cpu_seconds_total|node_disk_io_time_seconds_total|node_disk_read_bytes_total|node_disk_written_bytes_total|node_filesystem_avail_bytes|node_filesystem_size_bytes|node_load1|node_load15|node_load5|node_memory_Buffers_bytes|node_memory_Cached_bytes|node_memory_MemAvailable_bytes|node_memory_MemFree_bytes|node_memory_MemTotal_bytes|node_network_receive_bytes_total|node_network_transmit_bytes_total|node_uname_info|up
    action: keep

metric_relabel_configs 环节配置

如果没有使用 remote_write, 那么只能在 metric_relabel_configs 环节配置了.

以 etcd job 为例: (以 prometheus 配置为例, Prometheus Operator 请自行按需调整)

- job_name: serviceMonitor/default/monitor-kube-prometheus-st-kube-etcd/0
  honor_labels: false
  kubernetes_sd_configs:
  - role: endpoints
    namespaces:
      names:
      - kube-system
  scheme: https
  tls_config:
    insecure_skip_verify: true
    ca_file: /etc/prometheus/secrets/etcd-certs/ca.crt
    cert_file: /etc/prometheus/secrets/etcd-certs/healthcheck-client.crt
    key_file: /etc/prometheus/secrets/etcd-certs/healthcheck-client.key
  relabel_configs:
  - source_labels:
    - job
    target_label: __tmp_prometheus_job_name
  - ...
  metric_relabel_configs: 
  - source_labels: [__name__]
    regex: etcd_disk_backend_commit_duration_seconds_bucket|etcd_disk_wal_fsync_duration_seconds_bucket|etcd_mvcc_db_total_size_in_bytes|etcd_network_client_grpc_received_bytes_total|etcd_network_client_grpc_sent_bytes_total|etcd_network_peer_received_bytes_total|etcd_network_peer_sent_bytes_total|etcd_server_has_leader|etcd_server_leader_changes_seen_total|etcd_server_proposals_applied_total|etcd_server_proposals_committed_total|etcd_server_proposals_failed_total|etcd_server_proposals_pending|grpc_server_handled_total|grpc_server_started_total|process_resident_memory_bytes|etcd_http_failed_total|etcd_http_received_total|etcd_http_successful_duration_seconds_bucket|etcd_network_peer_round_trip_time_seconds_bucket|grpc_server_handling_seconds_bucket|up
    action: keep    

不用 keep 而使用 drop

同样滴, 不用 keep 而改为使用 drop 也是可以的. 这里不再赘述.

🎉🎉🎉

总结

本文中,介绍了精简 Prometheus 指标的需求, 然后说明如何使用 mimirtool analyze 命令来确定Grafana Dashboards 以及 Prometheus Rules 中用到的指标。然后用 analyze prometheus 分析了展示和告警中usedunused 的活动 series,最后配置了 Prometheus 以仅 keep 用到的指标。

结合这次实战, 精简率可以达到 5 倍左右, 效果还是非常明显的. 推荐试一试. 👍️👍️👍️

📚️ 参考文档

11-18 10:12