prometheus学习系列七： Prometheus promQL查询语言

Prometheus promQL查询语言

Prometheus提供了一种名为PromQL (Prometheus查询语言)的函数式查询语言，允许用户实时选择和聚合时间序列数据。表达式的结果既可以显示为图形，也可以在Prometheus的表达式浏览器中作为表格数据查看，或者通过HTTP API由外部系统使用。

准备工作

在进行查询，这里提供下我的配置文件如下

[root@node00 prometheus]# cat prometheus.yml
# my global config
global:
  scrape_interval:     15s # Set the scrape interval to every  seconds. Default is every  minute.
  evaluation_interval: 15s # Evaluate rules every  seconds. The default is every  minute.
  # scrape_timeout is set to the global default (10s).

# Alertmanager configuration
alerting:
  alertmanagers:
  - static_configs:
    - targets:
      # - alertmanager:

# Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
rule_files:
  # - "first_rules.yml"
  # - "second_rules.yml"

# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
  - job_name: 'prometheus'

    # metrics_path defaults to '/metrics'
    # scheme defaults to 'http'.

    static_configs:
    - targets: ['localhost:9090']
  - job_name: "node"
    file_sd_configs:
    - refresh_interval: 1m
      files:
      - "/usr/local/prometheus/prometheus/conf/node*.yml"
remote_write:
  - url: "http://localhost:8086/api/v1/prom/write?db=prometheus"

remote_read:
  - url: "http://localhost:8086/api/v1/prom/read?db=prometheus"

[root@node00 prometheus]# cat conf/node-dis.yml
- targets:
  - "192.168.100.10:20001"
  labels:
    __datacenter__: dc0
    __hostname__: node00
    __businees_line__: "line_a"
    __region_id__: "cn-beijing"
    __availability_zone__: "a"
- targets:
  - "192.168.100.11:20001"
  labels:
    __datacenter__: dc1
    __hostname__: node01
    __businees_line__: "line_a"
    __region_id__: "cn-beijing"
    __availability_zone__: "a"
- targets:
  - "192.168.100.12:20001"
  labels:
    __datacenter__: dc0
    __hostname__: node02
    __businees_line__: "line_c"
    __region_id__: "cn-beijing"
    __availability_zone__: "b"

简单时序查询

直接查询特定metric_name

# 节点的forks的总次数
node_forks_total
#结果如下

Element	Value
node_forks_total{instance="192.168.100.10:20001",job="node"}	201518
node_forks_total{instance="192.168.100.11:20001",job="node"}	23951
node_forks_total{instance="192.168.100.12:20001",job="node"}	24127

带标签的查询

node_forks_total{instance="192.168.100.10:20001"}
# 结果如下

Element	Value
node_forks_total{instance="192.168.100.10:20001",job="node"}	201816

多标签查询

node_forks_total{instance="192.168.100.10:20001",job="node"}

# 结果如下

Element	Value
node_forks_total{instance="192.168.100.10:20001",job="node"}	201932

查询2分钟的时序数值

node_forks_total{instance="192.168.100.10:20001",job="node"}[2m]

Element	Value
node_forks_total{instance="192.168.100.10:20001",job="node"}	201932 @1569492864.036 201932 @1569492879.036 201932 @1569492894.035 201932 @1569492909.036 201985 @1569492924.036 201989 @1569492939.036 201993 @1569492954.036

正则匹配

node_forks_total{instance=~"192.168.*:20001",job="node"}

Element	Value
node_forks_total{instance="192.168.100.10:20001",job="node"}	202107
node_forks_total{instance="192.168.100.11:20001",job="node"}	24014
node_forks_total{instance="192.168.100.12:20001",job="node"}	24186

常用函数查询

官方提供的函数比较多， 具体可以参考地址如下： https://prometheus.io/docs/prometheus/latest/querying/functions/

这里主要就常用函数进行演示。

irate

irate用于计算速率。

# 通过标签查询，特定实例特定job，特定cpu 在idle状态下的cpu次数速率
irate(node_cpu_seconds_total{cpu="",instance="192.168.100.10:20001",job="node",mode="idle"}[1m])

Element	Value
{cpu="0",instance="192.168.100.10:20001",job="node",mode="idle"}	0.9833988932595507

count_over_time

计算特定的时序数据中的个数。

# 这个数值个数和采集频率有关， 我们的采集间隔是15s，在一分钟会有4个点位数据。
count_over_time(node_boot_time_seconds[1m])

Element	Value
{instance="192.168.100.10:20001",job="node"}	4
{instance="192.168.100.11:20001",job="node"}	4
{instance="192.168.100.12:20001",job="node"}	4

子查询

# 过去的10分钟内， 每分钟计算下过去5分钟的一个速率值。 一个采集10m/1m一共10个值。
rate(node_cpu_seconds_total{cpu="",instance="192.168.100.10:20001",job="node",mode="idle"}[5m])[10m:1m]

Element	Value
{cpu="0",instance="192.168.100.10:20001",job="node",mode="idle"}	0.9865228543057867 @1569494040 0.9862807017543735 @1569494100 0.9861087231885309 @1569494160 0.9864946894550303 @1569494220 0.9863192502430038 @1569494280 0.9859649122807017 @1569494340 0.9859298245613708 @1569494400 0.9869122807017177 @1569494460 0.9867368421052672 @1569494520 0.987438596491273 @1569494580

复杂查询

计算内存使用百分比

node_memory_MemFree_bytes / node_memory_MemTotal_bytes  *  

Element	Value
{instance="192.168.100.10:20001",job="node"}	9.927579722322251
{instance="192.168.100.11:20001",job="node"}	59.740727403673034
{instance="192.168.100.12:20001",job="node"}	63.2080982675149

获取所有实例的内存使用百分比前2个

topk(,node_memory_MemFree_bytes / node_memory_MemTotal_bytes  *  )

Element	Value
{instance="192.168.100.12:20001",job="node"}	63.20129636298163
{instance="192.168.100.11:20001",job="node"}	59.50586164125955

实用查询样例

获取cpu核心个数

# 计算所有的实例cpu核心数
count by (instance) ( count by (instance,cpu) (node_cpu_seconds_total{mode="system"}) ) 
# 计算单个实例的
count by (instance) ( count by (instance,cpu) (node_cpu_seconds_total{mode="system",instance="192.168.100.11:20001"})  

计算内存使用率

( - (node_memory_MemAvailable_bytes{instance=~"192.168.100.10:20001"} / (node_memory_MemTotal_bytes{instance=~"192.168.100.10:20001"})))* 100

Element	Value
{instance="192.168.100.10:20001",job="node"}	87.09358620413717

计算根分区使用率

 - ((node_filesystem_avail_bytes{instance="192.168.100.10:20001",mountpoint="/",fstype=~"ext4|xfs"} * ) / node_filesystem_size_bytes {instance=~"192.168.100.10:20001",mountpoint="/",fstype=~"ext4|xfs"})

Element	Value
{device="/dev/mapper/centos-root",fstype="xfs",instance="192.168.100.10:20001",job="node",mountpoint="/"}	4.175111443575972

预测磁盘空间

 # 整体分为 2个部分， 中间用and分割， 前面部分计算根分区使用率大于85的， 后面计算根据近6小时的数据预测接下来24小时的磁盘可用空间是否小于0 。
 (-  node_filesystem_avail_bytes{fstype=~"ext4|xfs",mountpoint="/"}
  / node_filesystem_size_bytes{fstype=~"ext4|xfs",mountpoint="/"}) *  >=      and (predict_linear(node_filesystem_avail_bytes[6h], * ) < )