- What is PromQL - PromQL operators - PromQL functions - Hands on: Building queries in PromQL - Hands on: Visualizing PromQL in Grafana - Prometheus alerts in PromQL - Hands on: Creating an alert in Prometheus with PromQL

1. Prometheus:
PromQL Deep Dive
Jeff Hoffer, Developer Experience
github.com/eudaimos

2. Agenda
1. What is PromQL
2. PromQL Operators
3. PromQL Functions
4. Hands On: Building Queries in PromQL
5. Hands On: Visualizing PromQL in Grafana
6. Training on real app
7. What’s next?

3. What is PromQL
• Powerful Query Language of Prometheus
• Provides built in operators and functions
• Vector-based calculations like Excel
• Expressions over time-series vectors

4. Expressions (and sub-expressions)
• Instant Vector - set of time series containing single sample for each time series, all sharing
same timestamp
• e.g. http_request_count => results in:
• http_request_count{status=“200”} 20
• http_request_count{status=“404”} 3
• http_request_count{status=“500”} 5

5. Expressions (and sub-expressions)
• Instant Vector - set of time series containing single sample for each time series, all sharing
same timestamp
• e.g. http_request_count => results in:
• http_request_count{status=“200”} 20
• http_request_count{status=“404”} 3
• http_request_count{status=“500”} 5
• Range Vector - set of time series containing a range of data points over time for each series
• e.g. http_request_count[5m] => results in:
• http_request_count{status=“200”}

6. Expressions (and sub-expressions)
• Instant Vector - set of time series containing single sample for each time series, all sharing
same timestamp
• e.g. http_request_count => results in:
• http_request_count{status=“200”} 20
• http_request_count{status=“404”} 3
• http_request_count{status=“500”} 5
• Range Vector - set of time series containing a range of data points over time for each series
• e.g. http_request_count[5m] => results in:
• http_request_count{status=“200”}
• Scalar - as a literal and as result of an expression
• String - only currently as a literal in an expression

7. Time Series Selectors
• Instant Vector Selectors
• num_nodes
• num_nodes{role=“backend”}

8. Time Series Selectors
• Instant Vector Selectors
• num_nodes
• num_nodes{role=“backend”}
• Range Vector Selectors (s, m, h, d, w, y)
• num_nodes{role=“backend”}[5m]

9. Time Series Selectors
• Instant Vector Selectors
• num_nodes
• num_nodes{role=“backend”}
• Range Vector Selectors (s, m, h, d, w, y)
• num_nodes{role=“backend”}[5m]
• Offset Modifier
• num_nodes{role=“backend”}[5m] offset 1w

10. Operators: Binary
• Arithmetic: +, -, *, /, %, ^
– scalar/scalar
– vector/scalar
– vector/vector

11. Operators: Binary
• Arithmetic: +, -, *, /, %, ^
– scalar/scalar
– vector/scalar
– vector/vector
• Comparison: ==, !=, >, <, >=, <=
– filters results unless bool operator provided (converts 0 or 1)
– scalar/scalar requires bool operator
– vector/scalar & vector/vector drops elements unless bool operator provided

12. Operators: Binary
• Arithmetic: +, -, *, /, %, ^
– scalar/scalar
– vector/scalar
– vector/vector
• Comparison: ==, !=, >, <, >=, <=
– filters results unless bool operator provided (converts 0 or 1)
– scalar/scalar requires bool operator
– vector/scalar & vector/vector drops elements unless bool operator provided
• Logical/Set Binary: only defined between Instant Vectors
– and = intersection between vector1 and vector2
– or = union of vector1 and vector2
– unless = elements of vector1 for which no matches in vector2

13. Operators: Vector Matching
• Label Matching
– ignoring keyword
– on keyword

14. Operators: Vector Matching
• Label Matching
– ignoring keyword
– on keyword
• One-to-one - finds unique pair of entries with all labels
matching

15. Operators: Vector Matching
• Label Matching
– ignoring keyword
– on keyword
• One-to-one - finds unique pair of entries with all labels
matching
• Many-to-one / One-to-many - where each element on a “one”
side can multiple elements on the “many” side
– group_left v group_right determines cardinality
– only used for comparison and arithmetic operations

16. Operators: Aggregation
• Aggregate elements of a single Instant Vector resulting in a new vector of fewer elements w/ aggregated values
– sum, avg
– min, max
– stddev, stdvar
– count, count_values*
– bottomk*, topk*
– quantile*
*takes a parameter before the vector

17. Operators: Aggregation
• Aggregate elements of a single Instant Vector resulting in a new vector of fewer elements w/ aggregated values
– sum, avg
– min, max
– stddev, stdvar
– count, count_values*
– bottomk*, topk*
– quantile*
*takes a parameter before the vector
• without clause removes listed labels from resulting vector

18. Operators: Aggregation
• Aggregate elements of a single Instant Vector resulting in a new vector of fewer elements w/ aggregated values
– sum, avg
– min, max
– stddev, stdvar
– count, count_values*
– bottomk*, topk*
– quantile*
*takes a parameter before the vector
• without clause removes listed labels from resulting vector
• by clause drops labels not listed from the resulting vector

19. Operators: Aggregation
• Aggregate elements of a single Instant Vector resulting in a new vector of fewer elements w/ aggregated values
– sum, avg
– min, max
– stddev, stdvar
– count, count_values*
– bottomk*, topk*
– quantile*
*takes a parameter before the vector
• without clause removes listed labels from resulting vector
• by clause drops labels not listed from the resulting vector
• keep_common (with by) will keep labels that exist in all elements but not listed in the by clause

20. Operators: Aggregation
• Aggregate elements of a single Instant Vector resulting in a new vector of fewer elements w/ aggregated values
– sum, avg
– min, max
– stddev, stdvar
– count, count_values*
– bottomk*, topk*
– quantile*
*takes a parameter before the vector
• without clause removes listed labels from resulting vector
• by clause drops labels not listed from the resulting vector
• keep_common (with by) will keep labels that exist in all elements but not listed in the by clause
• topk/bottomk - only subset of original values are returned including original labels - by and without only bucket
the input

21. Functions: Utilities
• time() - number of seconds since Unix
Epoch when the expression is run
• vector(s scalar) - returns a vector
from a scalar
• scalar(v vector) - returns scalar
value of a single sampled vector or NaN

22. Functions: Time-based Instant Vector
• default v=vector(time())
• day_of_month(v)
• day_of_week(v)
• days_in_month(v)
• hour(v)
• minute(v)
• month(v)
• year(v)

23. Functions: Instant Vector
• abs(v)
• absent(v)
• ceil(v)
• clamp_max(v, scalar), clamp_min(v, scalar) - clamps the sample values to have
an upper/lower limit
• count_scalar(v)
• drop_common_labels(v)
• exp(v)
• floor(v), round(v)
• label_replace(v, dst_label string, replacement string, src_label
string, regex string)
• ln(v), log2(v), log10(v)
• sort(v), sort_desc(v)
• sqrt(v)

24. Functions: Range Vector
• changes()ˆ
• delta()˚*, idelta()˚* - diff between first and last in each time series element
• deriv()* - per sec derivative using simple linear regression
• holt_winters(v, sf scalar, tf scalar)* - smooth value for time series based on
range in v
• increase()ˆ - syntactic sugar for rate(v[T]) * (seconds in T)
• irate()ˆ, rate()ˆ - per second instant/avg rate of increase
• predict_linear(v, t scalar)* - predict value at time t using simple linear regression
• resets()ˆ - number of times a counter reset
• <aggregation>_over_time()˚ - aggregate each series of a range vector over time
returning instant vector with per series aggregation results
• ˚returns an instant vector, *should only be used with gauges, ˆshould only be used with
counters

25. Metrics Types
Basic Counters Sampling Counters
counter histogram
gauge summary

26. Metrics Types - Basic Counters
• counter - single numeric metric that only
goes up
• gauge - single numeric metric that
arbitrarily goes up or down

27. Metric Types - Sampling Counters
• histogram - samples observations and
counts them in configurable buckets
• summary - samples observations and
counts them

28. Metric Types - Sampling Counters
• histogram - samples observations and
counts them in configurable buckets
• summary - samples observations and
counts them

29. Metrics Types - Sampling Counters
Histogram!?

30. Metric Types - Sampling Counters
• both histogram and summary have:
– <name>_sum - time series summing the value of all observations
– <name>_count - time series counter for the number of observations taken

31. Metric Types - Sampling Counters
• both histogram and summary have:
– <name>_sum - time series summing the value of all observations
– <name>_count - time series counter for the number of observations taken
• histograms:
– buckets are configured on client when creating metrics
– time series for each bucket as <name>_bucket{…,le=“<bucket-upper-bound>”} counting the number
of observations less than or equal to the upper bound of the bucket
– ad-hoc quantile specification using the histogram_quantile(quantile, instant-vector) function

32. Metric Types - Sampling Counters
• both histogram and summary have:
– <name>_sum - time series summing the value of all observations
– <name>_count - time series counter for the number of observations taken
• histograms:
– buckets are configured on client when creating metrics
– time series for each bucket as <name>_bucket{…,le=“<bucket-upper-bound>”} counting the number
of observations less than or equal to the upper bound of the bucket
– ad-hoc quantile specification using the histogram_quantile(quantile, instant-vector) function
• summaries:
– quantiles are defined on the client when creating metrics
– time series for each quantile as <name>{…,quantile=“<quantile-upper-bound>”} keeping the
streaming quantile calculation from the client
– are generally not aggregatable

33. Refining Rate
rate(requests[5m])

34. Refining Rate
rate(requests[5m])
sum(rate(requests[5m])) by(service_name)

35. Refining Rate
rate(requests[5m])
sum(rate(requests[5m])) by(service_name)
sum(rate(requests{service_name=“catalogue”}[5m]))
by(instance)

36. Refining Rate
rate(requests[5m])
sum(rate(requests[5m])) by(service_name)
sum(rate(requests{service_name=“catalogue”}[5m]))
by(instance)
request_duration as a histogram

37. Refining Rate
rate(requests[5m])
sum(rate(requests[5m])) by(service_name)
sum(rate(requests{service_name=“catalogue”}[5m]))
by(instance)
request_duration as a histogram
- derive average request duration over a rolling 5
minute period

38. Refining Rate
rate(requests[5m])
sum(rate(requests[5m])) by(service_name)
sum(rate(requests{service_name=“catalogue”}[5m]))
by(instance)
request_duration as a histogram
- derive average request duration over a rolling 5
minute period
rate(request_duration_sum[5m]) /
rate(request_duration_count[5m])

39. RED Monitoring
• (Request) Rate - the number of requests
per second your services are serving
• (Request) Errors - the number of failed
requests per second
• (Request) Duration - distributions of the
amount of time each request takes

40. Training!

41. Join the Weave user group!
meetup.com/pro/Weave/
weave.works/help

42. Other topics
• Kubernetes 101
• Continuous delivery: hooking up my CI/CD
pipeline to Kubernetes
• Network policy for security
We have talks on all these topics in the Weave
user group!

43. Thanks! Questions?
We are hiring!
DX in San Francisco
Engineers in London & SF
weave.works/weave-company/hiring