abs()
absent()
ceil()
changes()
clamp_max()
clamp_min()
count_scalar()
day_of_month()
day_of_week()
days_in_month()
delta()
deriv()
drop_common_labels()
exp()
floor()
histogram_quantile()
holt_winters()
hour()
idelta()
increase()
irate()
label_replace()
ln()
log2()
log10()
minute()
month()
predict_linear()
rate()
resets()
round()
scalar()
sort()
sort_desc()
sqrt()
time()
vector()
year()
<aggregation>_over_time()
Some functions have default arguments, e.g. year(v=vector(time())
instant-vector)
. This means that there is one argument v
which is an instant
vector, which if not provided it will default to the value of the expression
vector(time())
.
abs()
abs(v instant-vector)
returns the input vector with all sample values converted to
their absolute value.
absent()
absent(v instant-vector)
returns an empty vector if the vector passed to it
has any elements and a 1-element vector with the value 1 if the vector passed to
it has no elements.
This is useful for alerting on when no time series exist for a given metric name and label combination.
absent(nonexistent{job="myjob"})
# => {job="myjob"}
absent(nonexistent{job="myjob",instance=~".*"})
# => {job="myjob"}
absent(sum(nonexistent{job="myjob"}))
# => {}
In the second example, absent()
tries to be smart about deriving labels of the
1-element output vector from the input vector.
ceil()
ceil(v instant-vector)
rounds the sample values of all elements in v
up to
the nearest integer.
changes()
For each input time series, changes(v range-vector)
returns the number of
times its value has changed within the provided time range as an instant
vector.
clamp_max()
clamp_max(v instant-vector, max scalar)
clamps the sample values of all
elements in v
to have an upper limit of max
.
clamp_min()
clamp_min(v instant-vector, min scalar)
clamps the sample values of all
elements in v
to have a lower limit of min
.
count_scalar()
count_scalar(v instant-vector)
returns the number of elements in a time series
vector as a scalar. This is in contrast to the count()
aggregation operator, which
always returns a vector (an empty one if the input vector is empty) and allows
grouping by labels via a by
clause.
day_of_month()
day_of_month(v=vector(time()) instant-vector)
returns the day of the month
for each of the given times in UTC. Returned values are from 1 to 31.
day_of_week()
day_of_week(v=vector(time()) instant-vector)
returns the day of the week for
each of the given times in UTC. Returned values are from 0 to 6, where 0 means
Sunday etc.
days_in_month()
days_in_month(v=vector(time()) instant-vector)
returns number of days in the
month for each of the given times in UTC. Returned values are from 28 to 31.
delta()
delta(v range-vector)
calculates the difference between the
first and last value of each time series element in a range vector v
,
returning an instant vector with the given deltas and equivalent labels.
The delta is interpolated to cover the full time range.
The following example expression returns the difference in CPU temperature between now and 2 hours ago:
delta(cpu_temp_celsius{host="zeus"}[2h])
delta
should only be used with gauges.
deriv()
deriv(v range-vector)
calculates the per-second derivative of the time series in a range
vector v
, using simple linear regression.
deriv
should only be used with gauges.
drop_common_labels()
drop_common_labels(instant-vector)
drops all labels that have the same name
and value across all series in the input vector.
exp()
exp(v instant-vector)
calculates the exponential function for all elements in v
.
Special cases are:
Exp(+Inf) = +Inf
Exp(NaN) = NaN
floor()
floor(v instant-vector)
rounds the sample values of all elements in v
down
to the nearest integer.
histogram_quantile()
histogram_quantile(φ float, b instant-vector)
calculates the
φ-quantile (0 ≤ φ ≤ 1) from the buckets b
of a
histogram. (See histograms
and summaries for a detailed explanation
of φ-quantiles and the usage of the histogram metric type in general.)
The samples in b
are the counts of observations in each bucket. Each
sample must have a label le
where the label value denotes the
inclusive upper bound of the bucket. (Samples without such a label are
silently ignored.) The histogram metric
type automatically provides
time series with the _bucket
suffix and the appropriate labels.
Use the rate()
function to specify the time window for the quantile
calculation.
Example: A histogram metric is called http_request_duration_seconds
. To
calculate the 90th percentile of request durations over the last 10m, use the
following expression:
histogram_quantile(0.9, rate(http_request_duration_seconds_bucket[10m]))
The quantile is calculated for each label combination in
http_request_duration_seconds
. To aggregate, use the sum()
aggregator
around the rate()
function. Since the le
label is required by
histogram_quantile()
, it has to be included in the by
clause. The following
expression aggregates the 90th percentile by job
:
histogram_quantile(0.9, sum(rate(http_request_duration_seconds_bucket[10m])) by (job, le))
To aggregate everything, specify only the le
label:
histogram_quantile(0.9, sum(rate(http_request_duration_seconds_bucket[10m])) by (le))
The histogram_quantile()
function interpolates quantile values by
assuming a linear distribution within a bucket. The highest bucket
must have an upper bound of +Inf
. (Otherwise, NaN
is returned.) If
a quantile is located in the highest bucket, the upper bound of the
second highest bucket is returned. A lower limit of the lowest bucket
is assumed to be 0 if the upper bound of that bucket is greater than
0. In that case, the usual linear interpolation is applied within that
bucket. Otherwise, the upper bound of the lowest bucket is returned
for quantiles located in the lowest bucket.
If b
contains fewer than two buckets, NaN
is returned. For φ < 0, -Inf
is
returned. For φ > 1, +Inf
is returned.
holt_winters()
holt_winters(v range-vector, sf scalar, tf scalar)
produces a smoothed value
for time series based on the range in v
. The lower the smoothing factor sf
,
the more importance is given to old data. The higher the trend factor tf
, the
more trends in the data is considered. Both sf
and tf
must be between 0 and
1.
holt_winters
should only be used with gauges.
hour()
hour(v=vector(time()) instant-vector)
returns the hour of the day
for each of the given times in UTC. Returned values are from 0 to 23.
idelta()
idelta(v range-vector)
idelta(v range-vector)
calculates the difference between the last two samples
in the range vector v
, returning an instant vector with the given deltas and
equivalent labels.
idelta
should only be used with gauges.
increase()
increase(v range-vector)
calculates the increase in the
time series in the range vector. Breaks in monotonicity (such as counter
resets due to target restarts) are automatically adjusted for.
The following example expression returns the number of HTTP requests as measured over the last 5 minutes, per time series in the range vector:
increase(http_requests_total{job="api-server"}[5m])
increase
should only be used with counters. It should be used primarily for
human readability. Use rate
in recording rules so that increases are tracked
consistently on a per-second basis.
irate()
irate(v range-vector)
calculates the per-second instant rate of increase of
the time series in the range vector. This is based on the last two data points.
Breaks in monotonicity (such as counter resets due to target restarts) are
automatically adjusted for.
The following example expression returns the per-second rate of HTTP requests looking up to 5 minutes back for the two most recent data points, per time series in the range vector:
irate(http_requests_total{job="api-server"}[5m])
irate
should only be used when graphing volatile, fast-moving counters.
Use rate
for alerts and slow-moving counters, as brief changes
in the rate can reset the FOR
clause and graphs consisting entirely of rare
spikes are hard to read.
Note that when combining irate()
with an
aggregation operator (e.g. sum()
)
or a function aggregating over time (any function ending in _over_time
),
always take a irate()
first, then aggregate. Otherwise irate()
cannot detect
counter resets when your target restarts.
label_replace()
For each timeseries in v
, label_replace(v instant-vector, dst_label string, replacement string,
src_label string, regex string)
matches the regular expression regex
against
the label src_label
. If it matches, then the timeseries is returned with the
label dst_label
replaced by the expansion of replacement
. $1
is replaced
with the first matching subgroup, $2
with the second etc. If the regular
expression doesn't match then the timeseries is returned unchanged.
This example will return a vector with each time series having a foo
label with the value a
added to it:
label_replace(up{job="api-server",service="a:c"}, "foo", "$1", "service", "(.*):.*")
ln()
ln(v instant-vector)
calculates the natural logarithm for all elements in v
.
Special cases are:
ln(+Inf) = +Inf
ln(0) = -Inf
ln(x < 0) = NaN
ln(NaN) = NaN
log2()
log2(v instant-vector)
calculates the binary logarithm for all elements in v
.
The special cases are equivalent to those in ln
.
log10()
log10(v instant-vector)
calculates the decimal logarithm for all elements in v
.
The special cases are equivalent to those in ln
.
minute()
minute(v=vector(time()) instant-vector)
returns the minute of the hour for each
of the given times in UTC. Returned values are from 0 to 59.
month()
month(v=vector(time()) instant-vector)
returns the month of the year for each
of the given times in UTC. Returned values are from 1 to 12, where 1 means
January etc.
predict_linear()
predict_linear(v range-vector, t scalar)
predicts the value of time series
t
seconds from now, based on the range vector v
, using simple linear
regression.
predict_linear
should only be used with gauges.
rate()
rate(v range-vector)
calculates the per-second average rate of increase of the
time series in the range vector. Breaks in monotonicity (such as counter
resets due to target restarts) are automatically adjusted for.
The following example expression returns the per-second rate of HTTP requests as measured over the last 5 minutes, per time series in the range vector:
rate(http_requests_total{job="api-server"}[5m])
rate
should only be used with counters. It is best suited for alerting,
and for graphing of slow-moving counters.
Note that when combining rate()
with an aggregation operator (e.g. sum()
)
or a function aggregating over time (any function ending in _over_time
),
always take a rate()
first, then aggregate. Otherwise rate()
cannot detect
counter resets when your target restarts.
resets()
For each input time series, resets(v range-vector)
returns the number of
counter resets within the provided time range as an instant vector. Any
decrease in the value between two consecutive samples is interpreted as a
counter reset.
resets
should only be used with counters.
round()
round(v instant-vector, to_nearest=1 scalar)
rounds the sample values of all
elements in v
to the nearest integer. Ties are resolved by rounding up. The
optional to_nearest
argument allows specifying the nearest multiple to which
the sample values should be rounded. This multiple may also be a fraction.
scalar()
Given a single-element input vector, scalar(v instant-vector)
returns the
sample value of that single element as a scalar. If the input vector does not
have exactly one element, scalar
will return NaN
.
sort()
sort(v instant-vector)
returns vector elements sorted by their sample values,
in ascending order.
sort_desc()
Same as sort
, but sorts in descending order.
sqrt()
sqrt(v instant-vector)
calculates the square root of all elements in v
.
time()
time()
returns the number of seconds since January 1, 1970 UTC. Note that
this does not actually return the current time, but the time at which the
expression is to be evaluated.
vector()
vector(s scalar)
returns the scalar s
as a vector with no labels.
year()
year(v=vector(time()) instant-vector)
returns the year
for each of the given times in UTC.
<aggregation>_over_time()
The following functions allow aggregating each series of a given range vector over time and return an instant vector with per-series aggregation results:
avg_over_time(range-vector)
: the average value of all points in the specified interval.min_over_time(range-vector)
: the minimum value of all points in the specified interval.max_over_time(range-vector)
: the maximum value of all points in the specified interval.sum_over_time(range-vector)
: the sum of all values in the specified interval.count_over_time(range-vector)
: the count of all values in the specified interval.quantile_over_time(scalar, range-vector)
: the φ-quantile (0 ≤ φ ≤ 1) of the values in the specified interval.stddev_over_time(range-vector)
: the population standard deviation of the values in the specified interval.stdvar_over_time(range-vector)
: the population standard variance of the values in the specified interval.Note that all values in the specified interval have the same weight in the aggregation even if the values are not equally spaced throughout the interval.