promcraft 0.3.0

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promcraft

A Python library for building Prometheus QL queries programmatically. Instead of constructing raw query strings, use composable Python objects that serialize to valid PromQL syntax.

Installation

pip install promcraft
# or with Poetry
poetry add promcraft

Requires Python 3.10+.

Quick Start

from promcraft import InstantVector, RangeVector, Label, Duration

# Simple metric selector
query = InstantVector("up", [])
str(query)  # "up{}"

# With label filters
query = InstantVector("http_requests_total", [
    Label.eq("job", "api-server"),
    Label.eq("env", "production"),
])
str(query)  # 'http_requests_total{ job = "api-server", env = "production" }'

# Range vector with rate()
from promcraft import rate
query = rate(RangeVector("http_requests_total", [
    Label.eq("job", "api-server"),
], Duration(m=5)))
str(query)  # 'rate(http_requests_total{ job = "api-server" }[5m])'

# Binary operation
left = InstantVector("http_requests_total", [Label.eq("status", "500")])
right = InstantVector("http_requests_total", [])
ratio = left / right
str(ratio)  # 'http_requests_total{ status = "500" } / http_requests_total{}'

Raw values instead of wrapper types

Anywhere a String is expected (label values, count_values, label-manipulation function arguments), you can pass a plain str instead — it's converted with String.from_value() under the hood. Anywhere a Scalar is expected (Float-typed function/aggregation parameters, offset, at), you can pass a plain float/int instead, converted with Float.from_value(). Passing an existing String/Scalar instance through is a no-op, so both styles compose freely:

Label.eq("job", "api-server")        # same as Label.eq("job", String("api-server"))
topk(5, v)                           # same as topk(Float(5), v)

API Reference

Scalars

Float

Wraps a Python float for use in queries.

from promcraft import Float

Float(3.14)   # "3.14"
Float(0.0)    # "0.0"
Float(-2.5)   # "-2.5"

Float.from_value(3)      # Float(3.0) -- "3.0"
Float.from_value(Float(3.14))  # passthrough, still "3.14"

Hex

Represents a hexadecimal integer literal.

from promcraft import Hex

Hex(255)  # "0xff"
Hex(0)    # "0x0"
Hex(16)   # "0x10"

Duration

Represents a Prometheus duration literal. All parameters are keyword-only.

from promcraft import Duration

Duration()                              # "0s"  (default when no units given)
Duration(s=5)                           # "5s"
Duration(m=1, s=30)                     # "1m30s"
Duration(h=1, m=30)                     # "1h30m"
Duration(ms=500)                        # "500ms"
Duration(y=1, w=2, d=3, h=4, m=5, s=6, ms=7)  # "1y2w3d4h5m6s7ms"
Duration(m=5, neg=True)                 # "-5m"

Parameter	Unit
y	years
w	weeks
d	days
h	hours
m	minutes
s	seconds
ms	milliseconds
neg	negate the duration

Every Scalar subclass (Float, Hex, Duration) also implements a from_value() classmethod used throughout the library to accept raw numbers wherever a scalar is expected. Passing an existing Scalar through from_value() returns it unchanged.

---

String

Represents a PromQL string literal with configurable quoting.

from promcraft import String

String("hello")          # '"hello"'   (double-quote default)
String("hello", '"')     # '"hello"'
String("hello", "'")     # "'hello'"
String("hello", "`")     # "`hello`"

String.from_value("hello")       # same as String("hello")
String.from_value(String("hello", "'"))  # passthrough, still "'hello'"

Special characters are escaped automatically for " and ' quotes. Backtick strings are not escaped.

---

Variable

Represents a Grafana dashboard template variable ($name, or ${name} when braces are needed). Grafana interpolates the variable's selected value before the query text reaches Prometheus, so a Variable is accepted anywhere a query fragment, scalar, or string is expected — metric names, label names/values, offset/at/range/resolution, binary-operator operands, on/ignoring/group_left/group_right/by/without label lists, and aggregation/function arguments.

from promcraft import Variable

Variable("job")               # "$job"
Variable("job", braces=True)  # "${job}"

from promcraft import InstantVector, Label, sum_, topk

# dynamic metric name and label value
InstantVector(Variable("metric_name"), [Label.eq("env", Variable("environment"))])
# '$metric_name{ env = $environment }'

# dynamic offset and range
InstantVector("up", [], offset=Variable("offset_var"))[Variable("range_var")]
# 'up{}[$range_var] offset $offset_var'

# dynamic grouping labels
sum_(InstantVector("requests", [])).by([Variable("groupby")])
# 'sum(requests{}) by ($groupby)'

# dynamic aggregation parameter
topk(Variable("k"), InstantVector("requests", []))
# 'topk($k, requests{})'

A Variable is never coerced into a literal — Float.from_value(), Hex.from_value(), Duration.from_value(), and String.from_value() all pass an existing Variable through unchanged, the same way they already do for Scalar/String instances.

---

Label

Represents a label matcher used inside vector selectors. Use the factory methods for convenience. The value accepts either a raw str or a String instance.

from promcraft import Label, String

Label.eq("job", "prometheus")    # 'job = "prometheus"'
Label.neq("env", "prod")         # 'env != "prod"'
Label.re("name", "prom.*")       # 'name =~ "prom.*"'
Label.nre("name", String("test.*"))      # 'name !~ "test.*"'

Method	Operator	Meaning
Label.eq	=	Exact match
Label.neq	!=	Not equal
Label.re	=~	Regex match
Label.nre	!~	Regex does not match

---

InstantVector

Selects a set of time series at a single point in time.

InstantVector(metric, labels, *, offset=None, at=None)

from promcraft import InstantVector, Label, String, Duration, Float

# Basic
InstantVector("up", [])
# "up{}"

# With labels
InstantVector("http_requests_total", [
    Label.eq("job", "api"),
    Label.re("status", "5.."),
])
# 'http_requests_total{ job = "api", status =~ "5.." }'

# With offset
InstantVector("up", [], offset=Duration(m=5))
# "up{} offset 5m"

# With @ modifier (Unix timestamp)
InstantVector("up", [], at=1609746000.0)
# "up{} @ 1609746000.0"

# With @ modifier (range function references)
InstantVector("up", [], at="start()")
# "up{} @ start()"

InstantVector("up", [], at="end()")
# "up{} @ end()"

---

RangeVector

Selects a range of samples over a time window. Required by functions like rate(), increase(), avg_over_time().

RangeVector(metric, labels, range, resolution=None, *, offset=None, at=None)

from promcraft import RangeVector, Label, String, Duration

# Basic range
RangeVector("http_requests_total", [], Duration(m=5))
# "http_requests_total{}[5m]"

# With resolution (subquery step)
RangeVector("up", [], Duration(m=5), resolution=Duration(s=30))
# "up{}[5m :30s]"

# With offset
RangeVector("up", [], Duration(m=5), offset=Duration(m=1))
# "up{}[5m] offset 1m"

# Combined
RangeVector(
    "http_requests_total",
    [Label.eq("job", "api")],
    Duration(m=5),
    resolution=Duration(s=30),
    offset=Duration(m=1),
    at="end()",
)
# 'http_requests_total{ job = "api" }[5m :30s] offset 1m @ end()'

InstantVector also supports subscript syntax as a shortcut for building a RangeVector that reuses its metric, labels, offset and @ modifier:

InstantVector("up", [])[Duration(m=5)]                       # equivalent to RangeVector("up", [], Duration(m=5))
InstantVector("up", [])[Duration(m=5), Duration(s=30)]        # range + resolution

---

Binary operators

Combine two query expressions with an arithmetic, comparison, logical/set, or trigonometric operator. Each operator has a module-level helper function, and this is the primary way to build binary expressions:

from promcraft import (
    add, sub, mul, div, mod, pow,
    eq, neq, lt, lte, gt, gte,
    and_, or_, unless, atan2,
    Float, InstantVector,
)

v1 = InstantVector("requests", [])
v2 = InstantVector("errors", [])

add(Float(1.0), Float(2.0))  # "1.0 + 2.0"
div(v2, v1)                  # "errors{} / requests{}"
gt(v1, Float(0.0))           # "requests{} > 0.0"
and_(v1, v2)                 # "requests{} and errors{}"

Helper	Symbol	Category
add	+	Arithmetic
sub	-	Arithmetic
mul	*	Arithmetic
div	/	Arithmetic
mod	%	Arithmetic
pow	^	Arithmetic
eq	==	Comparison
neq	!=	Comparison
lt	<	Comparison
lte	<=	Comparison
gt	>	Comparison
gte	>=	Comparison
and_	and	Logical/Set
or_	or	Logical/Set
unless	unless	Logical/Set
atan2	atan2	Trigonometric

Python's own operators also work directly on any Query (+ - * / % **):

requests + errors  # same as add(requests, errors)

Nested expressions

Nested binary expressions are automatically parenthesized to preserve evaluation order:

inner = add(Float(1.0), Float(2.0))
outer = mul(inner, Float(3.0))
str(outer)  # "(1.0 + 2.0) * 3.0"

Label matching

Use .on() or .ignoring() to control which labels are used for matching when combining two vector selectors:

requests = InstantVector("http_requests_total", [])
errors   = InstantVector("http_errors_total", [])

# Match on specific labels
expr = div(errors, requests).on(["job", "env"])
# 'http_errors_total{} / on(job, env) http_requests_total{}'

# Ignore specific labels
expr = div(errors, requests).ignoring(["instance"])
# 'http_errors_total{} / ignoring(instance) http_requests_total{}'

Grouping

Use .group_left() or .group_right() for many-to-one or one-to-many matching:

# group_left: result has labels from the left side
expr = mul(requests, errors).on(["job"]).group_left(["env"])
# '... on(job) group_left(env) ...'

# group_right: result has labels from the right side
expr = mul(requests, errors).group_right([])

.on(), .ignoring(), .group_left(), and .group_right() are all chainable and each returns a new immutable instance, so they can be combined freely:

mul(requests, errors).ignoring(["env"]).group_right(["env"])

---

Aggregation operators

Apply a PromQL aggregation operator to a vector via a module-level helper function. Helpers for operators that require a parameter (topk, bottomk, quantile, limitk, limit_ratio, count_values) take it as their first argument.

No-parameter aggregations — sum_, avg, min_, max_, count, group, stddev, stdvar:

sum, min, and max are named sum_, min_, max_ in this library so they don't shadow the Python builtins.

from promcraft import sum_, avg, min_, max_, count, group, stddev, stdvar, InstantVector

v = InstantVector("http_requests_total", [])

sum_(v)                    # "sum(http_requests_total{})"
avg(v).by(["job"])         # "avg(http_requests_total{}) by (job)"
stddev(v).without(["env"])
# "stddev(http_requests_total{}) without (env)"

Use .by(labels) to keep only the listed labels in the grouped result, or .without(labels) to drop the listed labels and keep everything else. Both are chainable and return a new instance.

labels must be non-empty — .by([])/.without([]) raise ValueError. To aggregate away all labels, omit .by()/.without() entirely rather than passing an empty list.

Scalar-parameter aggregations — topk, bottomk, quantile, limitk, limit_ratio. The parameter accepts a raw float/int or a Float:

from promcraft import topk, bottomk, quantile, limitk, limit_ratio

topk(5, v)                            # "topk(5.0, http_requests_total{})"
bottomk(3, v)                         # "bottomk(3.0, http_requests_total{})"
quantile(0.95, v)                     # "quantile(0.95, http_requests_total{})"
limitk(10, v)                         # "limitk(10.0, http_requests_total{})"
limit_ratio(0.1, v)                   # "limit_ratio(0.1, http_requests_total{})"
topk(5, v).by(["job"])
# "topk(5.0, http_requests_total{}) by (job)"

String-parameter aggregation — count_values (label name, accepts a raw str or a String):

from promcraft import count_values

count_values("version", v)
# 'count_values("version", http_requests_total{})'

---

Functions

Every Prometheus query function has a typed module-level helper. Scalar and string arguments accept raw float/int/str values interchangeably with Float/String.

Rate / counter functions

from promcraft import RangeVector, Duration, rate, irate, increase, delta, deriv, resets

rv = RangeVector("http_requests_total", [], Duration(m=5))

rate(rv)      # "rate(http_requests_total{}[5m])"
irate(rv)     # "irate(http_requests_total{}[5m])"
increase(rv)  # "increase(http_requests_total{}[5m])"
delta(rv)     # "delta(http_requests_total{}[5m])"
deriv(rv)     # "deriv(http_requests_total{}[5m])"
resets(rv)    # "resets(http_requests_total{}[5m])"

*_over_time aggregations

from promcraft import (
    avg_over_time, min_over_time, max_over_time, sum_over_time,
    count_over_time, stddev_over_time, last_over_time,
    present_over_time, absent_over_time, quantile_over_time,
)

avg_over_time(rv)                # "avg_over_time(http_requests_total{}[5m])"
quantile_over_time(0.95, rv)     # "quantile_over_time(0.95, http_requests_total{}[5m])"

Math & rounding

from promcraft import InstantVector, abs, ceil, floor, sqrt, round, clamp, clamp_min, clamp_max

v = InstantVector("cpu_usage", [])

abs(v)                     # "abs(cpu_usage{})"
ceil(v)                    # "ceil(cpu_usage{})"
sqrt(v)                    # "sqrt(cpu_usage{})"
round(v)                   # "round(cpu_usage{})"
round(v, 0.5)               # "round(cpu_usage{}, 0.5)"
clamp(v, 0.0, 1.0)          # "clamp(cpu_usage{}, 0.0, 1.0)"
clamp_min(v, 0.0)           # "clamp_min(cpu_usage{}, 0.0)"
clamp_max(v, 1.0)           # "clamp_max(cpu_usage{}, 1.0)"

Exponential & logarithm

from promcraft import exp, ln, log2, log10

exp(v)    # "exp(cpu_usage{})"
ln(v)     # "ln(cpu_usage{})"
log2(v)   # "log2(cpu_usage{})"
log10(v)  # "log10(cpu_usage{})"

Trigonometry

from promcraft import sin, cos, tan, asin, acos, atan, sinh, cosh, tanh, deg, rad, pi, sgn

sin(v)  # "sin(cpu_usage{})"
deg(v)  # "deg(cpu_usage{})"
rad(v)  # "rad(cpu_usage{})"
pi()    # "pi()"
sgn(v)  # "sgn(cpu_usage{})"

Date / time

All accept an optional instant vector; when omitted they default to vector(time()) in PromQL.

from promcraft import hour, minute, day_of_week, day_of_month, month, year, time

hour()      # "hour()"
hour(v)     # "hour(cpu_usage{})"
time()      # "time()"

Sorting

from promcraft import sort, sort_desc, sort_by_label, sort_by_label_desc

sort(v)                             # "sort(cpu_usage{})"
sort_desc(v)                        # "sort_desc(cpu_usage{})"
sort_by_label(v, "job")             # 'sort_by_label(cpu_usage{}, "job")'
sort_by_label_desc(v, "job", "env")
# 'sort_by_label_desc(cpu_usage{}, "job", "env")'

Label manipulation

from promcraft import label_join, label_replace

label_join(v, "addr", ":", "host", "port")
# 'label_join(cpu_usage{}, "addr", ":", "host", "port")'

label_replace(v, "job", "${1}", "job", "(.+)")
# 'label_replace(cpu_usage{}, "job", "${1}", "job", "(.+)")'

Type conversion

from promcraft import scalar, vector

scalar(v)    # "scalar(cpu_usage{})"
vector(1.0)  # "vector(1.0)"

Histogram functions

from promcraft import (
    histogram_quantile, histogram_fraction,
    histogram_count, histogram_sum, histogram_avg,
    histogram_stddev, histogram_stdvar,
)

histogram_quantile(0.95, v)         # "histogram_quantile(0.95, cpu_usage{})"
histogram_fraction(0.0, 1.0, v)     # "histogram_fraction(0.0, 1.0, cpu_usage{})"
histogram_count(v)                  # "histogram_count(cpu_usage{})"

Prediction & smoothing

from promcraft import predict_linear, double_exponential_smoothing

predict_linear(rv, 3600.0)
# "predict_linear(http_requests_total{}[5m], 3600.0)"

double_exponential_smoothing(rv, 0.1, 0.5)
# "double_exponential_smoothing(http_requests_total{}[5m], 0.1, 0.5)"

Absence detection

from promcraft import absent, absent_over_time

absent(v)             # "absent(cpu_usage{})"
absent_over_time(rv)  # "absent_over_time(http_requests_total{}[5m])"

Low-level: Function

All of the helpers above build a Function instance internally. It isn't part of the top-level promcraft public API — import it from its submodule if you need to call a custom or newly-added Prometheus function that doesn't have a helper yet:

from promcraft.functions import Function
from promcraft import InstantVector, Float

Function("my_custom_func", [InstantVector("up", []), Float(42.0)])
# "my_custom_func(up{}, 42.0)"

---

Composing Complex Queries

Every Query implements to_string() (which __str__ delegates to), so you can compose arbitrarily deep expressions and print them with str():

from promcraft import (
    InstantVector, RangeVector,
    Label, Duration, rate, div,
)

job_label = Label.eq("job", "api")

# rate of 5xx errors divided by rate of total requests
error_rate = rate(RangeVector("http_requests_total", [
    job_label,
    Label.re("status", "5.."),
], Duration(m=5)))

total_rate = rate(RangeVector("http_requests_total", [job_label], Duration(m=5)))

ratio = div(error_rate, total_rate).on(["job"])
str(ratio)
# '(rate(http_requests_total{ job = "api", status =~ "5.." }[5m]))
#   / on(job) (rate(http_requests_total{ job = "api" }[5m]))'

Pretty printing

Every to_string() accepts an optional indent argument to render multi-line, indented output instead of the single-line compact form. Pass an int (spaces per level) or a whitespace string (e.g. "\t"):

from promcraft import InstantVector, Duration, sum_

vec = InstantVector("metric", [])[Duration(m=1)]
expr = sum_(vec).by(["job", "env"])

print(expr.to_string(indent=4))
# sum(
#     metric{}[1m]
# ) by (job, env)

Omitting indent (or passing None) keeps the existing single-line output — str(query) is unaffected either way.

Development

# Install dependencies
poetry install

# Run tests (includes type checking and linting)
poetry run pytest

# Run only unit tests
poetry run pytest -o addopts=""

The test suite uses pytest with pytest-mypy for static type checking and pytest-ruff for linting. All checks run together by default.