如何精简 Prometheus 的指标和存储占用

时间:2022-11-18 11:11:53

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

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

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