一、简单时间查询

1.查询metricKey="prometheus_http_requests_total"的所有数据
prometheus_http_requests_total
2.查询metrickey="prometheus_http_requests_total"且tagkey="job",tagvalue="prometheus"的所有数据
prometheus_http_requests_total{job="prometheus"}
3.查询最近2min,metrickey="prometheus_http_requests_total"且tagkey="job",tagvalue="prometheus"的所有数据
prometheus_http_requests_total{job="prometheus"}[2m]
4.查询最近1min的单个值，metrickey="prometheus_http_requests_total"且tagkey="job",tagvalue="prometheus"
prometheus_http_requests_total{job="prometheus"} offset 1m
5.查询 3min前到1min前的多个值，时间范围2m，metrickey="prometheus_http_requests_total"且tagkey="job",tagvalue="prometheus"
prometheus_http_requests_total{job="prometheus"}[2m] offset 1m

二、算数运算

1.除法
prometheus_http_requests_total{job="prometheus"} offset 1m /1024
2.乘法
prometheus_http_requests_total{job="prometheus"} offset 1m *1024
3.加法
prometheus_http_requests_total{job="prometheus"} offset 1m +1024
4.减法
prometheus_http_requests_total{job="prometheus"} offset 1m -1024
5.%取模
prometheus_http_requests_total{job="prometheus"} offset 1m %1024
6.^幂
prometheus_http_requests_total{job="prometheus"} offset 1m ^2
7.两个指标运算
prometheus_http_requests_total{job="prometheus"} offset 1m + prometheus_http_requests_total{job="prometheus"}

三、比较和过滤

1.复合运算&>
# 查询最近1min，metrickey="prometheus_http_requests_total"且tagkey="job",tagvalue="prometheus"
prometheus_http_requests_total{job="prometheus"} offset 1m -1024>1
2.复合运算& ==
prometheus_http_requests_total{job="prometheus"} offset 1m -1024==1
3.！=
prometheus_http_requests_total{job="prometheus"} offset 1m -1024!=1
4.<
prometheus_http_requests_total{job="prometheus"} offset 1m <1
5.<=
prometheus_http_requests_total{job="prometheus"} offset 1m <=1
6.>=
prometheus_http_requests_total{job="prometheus"} offset 1m >=1
7.比较+bool值->1返回true1，否则返回false0
prometheus_http_requests_total{job="prometheus"} offset 1m >= bool 1

四、逻辑运算

1. or =合并2个查询结果
prometheus_http_requests_total{job="prometheus"} or prometheus_http_requests_total{job="prometheus1"}
2.and =查询匹配2个查询结果的并集
container_memory_usage_bytes{id="/kubepods.slice/kubepods-burstable.slice/kubepods-burstable-pod22f01897_042a_11ea_91cd_005056967167.slice/docker-74b609f325ecf8262983d21f02446d58c75bfb4dca73c5f7317c3954c5fc4954.scope"} and container_memory_max_usage_bytes{pod="busybox-56546db479-hdf65"} 
3.unless 删除2个查询结果的并集，保留不匹配的结果
container_memory_max_usage_bytes{pod="busybox-56546db479-hdf65"} unless container_memory_usage_bytes{id="/kubepods.slice/kubepods-burstable.slice/kubepods-burstable-pod22f01897_042a_11ea_91cd_005056967167.slice/docker-74b609f325ecf8262983d21f02446d58c75bfb4dca73c5f7317c3954c5fc4954.scope"} 

五、复合运算优先级

^,*/%,+ -,== !=,<=,<,>=,>,and unless,or
# 匹配运算
1.匹配固定值整数、小数、负数
prometheus_http_requests_total{job="prometheus"} 10？-语法有问题待确认
2.ignore 获取prometheus_http_requests_total ,job= prometheus，rate=test 除以 忽略tagkey= rate的prometheus_http_requests_total指标值
prometheus_http_requests_total{job="prometheus",rate="test"} / ignoring(rate) prometheus_http_requests_total
3.group_left表示多对一？
prometheus_http_requests_total{job="prometheus",rate="test"} / ignoring(rate)  group_left prometheus_http_requests_total
4.group_right表示一对多？
prometheus_http_requests_total{job="prometheus",rate="test"} / ignoring(rate)  group_right prometheus_http_requests_total

六、聚合运算与分组

根据job做分组求不同聚合方式下的值Min、max、avg、sum、count
1.sum(prometheus_http_requests_total) by (job)
2.Min(prometheus_http_requests_total) by (job)
3.avg(prometheus_http_requests_total) by (job)
4.max(prometheus_http_requests_total) by (job)
5.count(prometheus_http_requests_total) by (job1,job2)

七、标准差和方差

6.方差
stddev(container_memory_usage_bytes{node="ingressnode01",pod="node-exporter-kf2cr"}/1024/1024 ) 
7.标准差
stdvar(container_memory_usage_bytes{node="ingressnode01",pod="node-exporter-kf2cr"}/1024/1024 ) 
9.统计value相同的指标个数，统计value相同的元素个数，形成标签为"newlabel"=value的新的向量 
count_values ("newlabel",kube_deployment_created) 
10.获取最小的2个值
bottomk(2, prometheus_http_requests_total{job="prometheus",rate="test"}/2)
11.获取最大的2个值
topk(2, prometheus_http_requests_total{job="prometheus",rate="test"}/2)
12.获取历史的最大值
获取prometheus_http_requests_total 该metrickey下，tagkey=quantile=0.25
获取quantile=0.25的历史metricvalue的最大值
quantile(0.25,prometheus_http_requests_total{rate="test"}

八、函数

1.abs(XX)绝对值
abs(prometheus_http_requests_total{job="prometheus",rate="test"})
2.absent(XX) value存在返回空，value不存在返回1
absent(prometheus_http_requests_total{job="prometheus",rate="test"})
3.ceil(XX) 向上取整，当前值为整数则不变
ceil(prometheus_http_requests_total{job="prometheus",rate="test"})
4.floor(XX) 向下取整，当前值为整数则不变
floor(prometheus_http_requests_total{job="prometheus",rate="test"})
5.sqrt(XX) 返回value的平方根
sqrt(prometheus_http_requests_total{job="prometheus",rate="test"})
6.exp(XX) 返回value中所有元素自然常数e为底的指数函数?
exp(prometheus_http_requests_total{job="prometheus",rate="test"})
7.resets(v range-vector) 对于每个 time series , 它都返回一个 counter resets的次数
resets(prometheus_http_requests_total{job="prometheus",rate="test"}[5m])?
8.changes(v)返回value中所有元素更改的次数
changes(prometheus_http_requests_total{job="prometheus",rate="test"})
9.round(v, t) 按四舍五入取接近当前值的t的整数倍的值，t可以是小数和分数
10.返回限制范围内的最大值clamp_max(X,max),其上限为max
clamp_max(mysql.innodb.rollback.on.timeout_chris{appId="npcs_pubs_cpsgw"},1)
11.返回限制范围内的最小值clamp_min(X,min)，其上限为min
clamp_min(mysql.innodb.rollback.on.timeout_chris{appId="npcs_pubs_cpsgw"},1)
12.time()获取时间
13.delta(v range-vector) 计算第一个值和最后一个值的差值
delta(mysql.innodb.rollback.on.timeout_chris{appId="npcs_pubs_cpsgw"}-mysql.innodb.rollback.on.timeout_chris{appId="npcs_pubs_cpsgw"})
14.idelta(v range-vector) 计算最新的2个样本值之间的差别，和13类似
idelta(mysql.innodb.rollback.on.timeout_chris{appId="npcs_pubs_cpsgw"}-mysql.innodb.rollback.on.timeout_chris{appId="npcs_pubs_cpsgw"})
15.获取计算指定范围内的增长
increase(mysql.innodb.rollback.on.timeout_chris{appId="npcs_pubs_cpsgw"}[5m])
16.计算每秒的平均增长值, 基于的是最新的2个数据点
irate(mysql.innodb.rollback.on.timeout_chris{appId="npcs_pubs_cpsgw"}[5m])
17.计算每秒的增长值
rate(mysql.innodb.rollback.on.timeout_chris{appId="npcs_pubs_cpsgw"}[5m])
18.sort升序排序
sort(mysql.innodb.rollback.on.timeout_chris{appId="npcs_pubs_cpsgw"}[5m])
19.sort_desc降序排序
sort_desc(mysql.innodb.rollback.on.timeout_chris{appId="npcs_pubs_cpsgw"}[5m])

九、多指标查询

#多个指标查询语法
1.查询cpu or memory的数据
{__name__=~"cpu|memory"}

# 正则匹配 查询以pro开头
prometheus_http_requests_total{job=~"pro.*"}