Introduction Querying Metrics

Querying

Prometheus provides a functional expression language called PromQL
- Provides built in operators and functions
- Vector-based calculations like Excel
- Expressions over time-series vectors
PromQL is read-only
Example:
- 100 - (avg by (instance) (irate(node_cpu_seconds_total{job='node_exporter',mode="idle"}[5m])) * 100)

Instance vector
- a set of time series containing a single sample for each time series, all sharing the same timestamp
- Example:
  - node_cpu_secounds_total
Range vector
- a set of time series containing a range of data points over time for each time series
- Example:
  - node_cpu_seconds_total[5m]
Scalar
- a simple numeric floating point value
- Example:
  - -3.14
String
- a simple string value; currently unused

Arithmetic binary operators
- Example:
  - Operators description
    - subtraction
    * multiplication
    / division
    % modulo
    ^ power/exponentiation)
Comparison binary operators
- Example:
  - Operators description
    == equal
    != not-equal
    > greater-then
    < less-than
    >= greater-or-equal
    <= less-or-equal
Logical/set binary operators
- Example:
  - Operators description
    and intersection
    or union
    unless complement

Aggregation operators

Example:

Operators	description
sum	calculate sum over dimensions
min	select minimum over dimensions
max	select maximum over dimensions
avg	calculate the average over dimensions
stddev	calculate population standard deviation over dimensions
stdvar	calculate population standard variance over dimensions
count	count number of elements in the vector
count_values	count number of elements with the same value
bottomk	smallest k elements by sample value
topk	largest k elements by sample value
quantile	calculate over dimensions