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Signed-off-by: schou <pschou@users.noreply.github.com> Co-authored-by: George Krajcsovits <krajorama@users.noreply.github.com> Co-authored-by: Bryan Boreham <bjboreham@gmail.com>
377 lines
14 KiB
Markdown
377 lines
14 KiB
Markdown
---
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title: Querying basics
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nav_title: Basics
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sort_rank: 1
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---
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# Querying Prometheus
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Prometheus provides a functional query language called PromQL (Prometheus Query
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Language) that lets the user select and aggregate time series data in real
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time. The result of an expression can either be shown as a graph, viewed as
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tabular data in Prometheus's expression browser, or consumed by external
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systems via the [HTTP API](api.md).
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## Examples
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This document is a Prometheus basic language reference. For learning, it may be easier to
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start with a couple of [examples](examples.md).
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## Expression language data types
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In Prometheus's expression language, an expression or sub-expression can
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evaluate to one of four types:
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* **Instant vector** - a set of time series containing a single sample for each time series, all sharing the same timestamp
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* **Range vector** - a set of time series containing a range of data points over time for each time series
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* **Scalar** - a simple numeric floating point value
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* **String** - a simple string value; currently unused
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Depending on the use-case (e.g. when graphing vs. displaying the output of an
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expression), only some of these types are legal as the result of a
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user-specified expression. For example, an expression that returns an instant
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vector is the only type which can be graphed.
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_Notes about the experimental native histograms:_
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* Ingesting native histograms has to be enabled via a [feature
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flag](../../feature_flags.md#native-histograms).
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* Once native histograms have been ingested into the TSDB (and even after
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disabling the feature flag again), both instant vectors and range vectors may
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now contain samples that aren't simple floating point numbers (float samples)
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but complete histograms (histogram samples). A vector may contain a mix of
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float samples and histogram samples.
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## Literals
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### String literals
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String literals are designated by single quotes, double quotes or backticks.
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PromQL follows the same [escaping rules as
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Go](https://golang.org/ref/spec#String_literals). For string literals in single or double quotes, a
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backslash begins an escape sequence, which may be followed by `a`, `b`, `f`,
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`n`, `r`, `t`, `v` or `\`. Specific characters can be provided using octal
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(`\nnn`) or hexadecimal (`\xnn`, `\unnnn` and `\Unnnnnnnn`) notations.
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Conversely, escape characters are not parsed in string literals designated by backticks. It is important to note that, unlike Go, Prometheus does not discard newlines inside backticks.
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Example:
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"this is a string"
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'these are unescaped: \n \\ \t'
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`these are not unescaped: \n ' " \t`
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### Float literals
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Scalar float values can be written as literal integer or floating-point numbers in the format (whitespace only included for better readability):
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[-+]?(
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[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?
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| 0[xX][0-9a-fA-F]+
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| [nN][aA][nN]
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| [iI][nN][fF]
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)
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Examples:
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23
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-2.43
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3.4e-9
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0x8f
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-Inf
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NaN
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## Time series selectors
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Time series selectors are responsible for selecting the times series and raw or inferred sample timestamps and values.
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Time series *selectors* are not to be confused with higher level concept of instant and range *queries* that can execute the time series *selectors*. A higher level instant query would evaluate the given selector at one point in time, however the range query would evaluate the selector at multiple different times in between a minimum and maximum timestamp at regular steps.
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### Instant vector selectors
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Instant vector selectors allow the selection of a set of time series and a
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single sample value for each at a given timestamp (point in time). In the simplest
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form, only a metric name is specified, which results in an instant vector
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containing elements for all time series that have this metric name.
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This example selects all time series that have the `http_requests_total` metric
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name:
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http_requests_total
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It is possible to filter these time series further by appending a comma-separated list of label
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matchers in curly braces (`{}`).
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This example selects only those time series with the `http_requests_total`
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metric name that also have the `job` label set to `prometheus` and their
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`group` label set to `canary`:
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http_requests_total{job="prometheus",group="canary"}
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It is also possible to negatively match a label value, or to match label values
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against regular expressions. The following label matching operators exist:
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* `=`: Select labels that are exactly equal to the provided string.
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* `!=`: Select labels that are not equal to the provided string.
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* `=~`: Select labels that regex-match the provided string.
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* `!~`: Select labels that do not regex-match the provided string.
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Regex matches are fully anchored. A match of `env=~"foo"` is treated as `env=~"^foo$"`.
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For example, this selects all `http_requests_total` time series for `staging`,
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`testing`, and `development` environments and HTTP methods other than `GET`.
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http_requests_total{environment=~"staging|testing|development",method!="GET"}
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Label matchers that match empty label values also select all time series that
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do not have the specific label set at all. It is possible to have multiple matchers for the same label name.
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For example, given the dataset:
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http_requests_total
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http_requests_total{replica="rep-a"}
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http_requests_total{replica="rep-b"}
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http_requests_total{environment="development"}
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The query `http_requests_total{environment=""}` would match and return:
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http_requests_total
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http_requests_total{replica="rep-a"}
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http_requests_total{replica="rep-b"}
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and would exclude:
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http_requests_total{environment="development"}
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Multiple matchers can be used for the same label name; they all must pass for a result to be returned.
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The query:
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http_requests_total{replica!="rep-a",replica=~"rep.*"}
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Would then match:
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http_requests_total{replica="rep-b"}
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Vector selectors must either specify a name or at least one label matcher
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that does not match the empty string. The following expression is illegal:
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{job=~".*"} # Bad!
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In contrast, these expressions are valid as they both have a selector that does not
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match empty label values.
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{job=~".+"} # Good!
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{job=~".*",method="get"} # Good!
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Label matchers can also be applied to metric names by matching against the internal
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`__name__` label. For example, the expression `http_requests_total` is equivalent to
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`{__name__="http_requests_total"}`. Matchers other than `=` (`!=`, `=~`, `!~`) may also be used.
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The following expression selects all metrics that have a name starting with `job:`:
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{__name__=~"job:.*"}
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The metric name must not be one of the keywords `bool`, `on`, `ignoring`, `group_left` and `group_right`. The following expression is illegal:
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on{} # Bad!
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A workaround for this restriction is to use the `__name__` label:
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{__name__="on"} # Good!
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All regular expressions in Prometheus use [RE2
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syntax](https://github.com/google/re2/wiki/Syntax).
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### Range Vector Selectors
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Range vector literals work like instant vector literals, except that they
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select a range of samples back from the current instant. Syntactically, a [time
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duration](#time-durations) is appended in square brackets (`[]`) at the end of
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a vector selector to specify how far back in time values should be fetched for
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each resulting range vector element. The range is a closed interval,
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i.e. samples with timestamps coinciding with either boundary of the range are
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still included in the selection.
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In this example, we select all the values we have recorded within the last 5
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minutes for all time series that have the metric name `http_requests_total` and
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a `job` label set to `prometheus`:
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http_requests_total{job="prometheus"}[5m]
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### Time Durations
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Time durations are specified as a number, followed immediately by one of the
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following units:
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* `ms` - milliseconds
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* `s` - seconds
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* `m` - minutes
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* `h` - hours
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* `d` - days - assuming a day always has 24h
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* `w` - weeks - assuming a week always has 7d
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* `y` - years - assuming a year always has 365d<sup>1</sup>
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<sup>1</sup> For days in a year, the leap day is ignored, and conversely, for a minute, a leap second is ignored.
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Time durations can be combined by concatenation. Units must be ordered from the
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longest to the shortest. A given unit must only appear once in a time duration.
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Here are some examples of valid time durations:
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5h
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1h30m
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5m
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10s
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### Offset modifier
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The `offset` modifier allows changing the time offset for individual
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instant and range vectors in a query.
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For example, the following expression returns the value of
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`http_requests_total` 5 minutes in the past relative to the current
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query evaluation time:
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http_requests_total offset 5m
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Note that the `offset` modifier always needs to follow the selector
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immediately, i.e. the following would be correct:
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sum(http_requests_total{method="GET"} offset 5m) // GOOD.
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While the following would be *incorrect*:
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sum(http_requests_total{method="GET"}) offset 5m // INVALID.
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The same works for range vectors. This returns the 5-minute [rate](./functions.md#rate)
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that `http_requests_total` had a week ago:
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rate(http_requests_total[5m] offset 1w)
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When querying for samples in the past, a negative offset will enable temporal comparisons forward in time:
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rate(http_requests_total[5m] offset -1w)
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Note that this allows a query to look ahead of its evaluation time.
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### @ modifier
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The `@` modifier allows changing the evaluation time for individual instant
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and range vectors in a query. The time supplied to the `@` modifier
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is a unix timestamp and described with a float literal.
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For example, the following expression returns the value of
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`http_requests_total` at `2021-01-04T07:40:00+00:00`:
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http_requests_total @ 1609746000
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Note that the `@` modifier always needs to follow the selector
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immediately, i.e. the following would be correct:
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sum(http_requests_total{method="GET"} @ 1609746000) // GOOD.
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While the following would be *incorrect*:
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sum(http_requests_total{method="GET"}) @ 1609746000 // INVALID.
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The same works for range vectors. This returns the 5-minute rate that
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`http_requests_total` had at `2021-01-04T07:40:00+00:00`:
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rate(http_requests_total[5m] @ 1609746000)
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The `@` modifier supports all representations of numeric literals described above.
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It works with the `offset` modifier where the offset is applied relative to the `@`
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modifier time. The results are the same irrespective of the order of the modifiers.
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For example, these two queries will produce the same result:
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# offset after @
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http_requests_total @ 1609746000 offset 5m
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# offset before @
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http_requests_total offset 5m @ 1609746000
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Additionally, `start()` and `end()` can also be used as values for the `@` modifier as special values.
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For a range query, they resolve to the start and end of the range query respectively and remain the same for all steps.
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For an instant query, `start()` and `end()` both resolve to the evaluation time.
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http_requests_total @ start()
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rate(http_requests_total[5m] @ end())
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Note that the `@` modifier allows a query to look ahead of its evaluation time.
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## Subquery
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Subquery allows you to run an instant query for a given range and resolution. The result of a subquery is a range vector.
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Syntax: `<instant_query> '[' <range> ':' [<resolution>] ']' [ @ <float_literal> ] [ offset <duration> ]`
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* `<resolution>` is optional. Default is the global evaluation interval.
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## Operators
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Prometheus supports many binary and aggregation operators. These are described
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in detail in the [expression language operators](operators.md) page.
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## Functions
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Prometheus supports several functions to operate on data. These are described
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in detail in the [expression language functions](functions.md) page.
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## Comments
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PromQL supports line comments that start with `#`. Example:
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# This is a comment
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## Gotchas
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### Staleness
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The timestamps at which to sample data, during a query, are selected
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independently of the actual present time series data. This is mainly to support
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cases like aggregation (`sum`, `avg`, and so on), where multiple aggregated
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time series do not precisely align in time. Because of their independence,
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Prometheus needs to assign a value at those timestamps for each relevant time
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series. It does so by taking the newest sample before this timestamp within the lookback period.
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The lookback period is 5 minutes by default.
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If a target scrape or rule evaluation no longer returns a sample for a time
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series that was previously present, this time series will be marked as stale.
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If a target is removed, the previously retrieved time series will be marked as
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stale soon after removal.
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If a query is evaluated at a sampling timestamp after a time series is marked
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as stale, then no value is returned for that time series. If new samples are
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subsequently ingested for that time series, they will be returned as expected.
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A time series will go stale when it is no longer exported, or the target no
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longer exists. Such time series will disappear from graphs
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at the times of their latest collected sample, and they will not be returned
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in queries after they are marked stale.
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Some exporters, which put their own timestamps on samples, get a different behaviour:
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series that stop being exported take the last value for (by default) 5 minutes before
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disappearing. The `track_timestamps_staleness` setting can change this.
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### Avoiding slow queries and overloads
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If a query needs to operate on a substantial amount of data, graphing it might
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time out or overload the server or browser. Thus, when constructing queries
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over unknown data, always start building the query in the tabular view of
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Prometheus's expression browser until the result set seems reasonable
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(hundreds, not thousands, of time series at most). Only when you have filtered
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or aggregated your data sufficiently, switch to graph mode. If the expression
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still takes too long to graph ad-hoc, pre-record it via a [recording
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rule](../configuration/recording_rules.md#recording-rules).
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This is especially relevant for Prometheus's query language, where a bare
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metric name selector like `api_http_requests_total` could expand to thousands
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of time series with different labels. Also, keep in mind that expressions that
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aggregate over many time series will generate load on the server even if the
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output is only a small number of time series. This is similar to how it would
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be slow to sum all values of a column in a relational database, even if the
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output value is only a single number.
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