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249 lines
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249 lines
10 KiB
---
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title: Operators
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sort_rank: 2
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---
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# Operators
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## Binary operators
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Prometheus's query language supports basic logical and arithmetic operators.
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For operations between two instant vectors, the [matching behavior](#vector-matching)
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can be modified.
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### Arithmetic binary operators
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The following binary arithmetic operators exist in Prometheus:
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* `+` (addition)
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* `-` (subtraction)
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* `*` (multiplication)
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* `/` (division)
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* `%` (modulo)
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* `^` (power/exponentiation)
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Binary arithmetic operators are defined between scalar/scalar, vector/scalar,
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and vector/vector value pairs.
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**Between two scalars**, the behavior is obvious: they evaluate to another
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scalar that is the result of the operator applied to both scalar operands.
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**Between an instant vector and a scalar**, the operator is applied to the
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value of every data sample in the vector. E.g. if a time series instant vector
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is multiplied by 2, the result is another vector in which every sample value of
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the original vector is multiplied by 2.
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**Between two instant vectors**, a binary arithmetic operator is applied to
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each entry in the left-hand-side vector and its [matching element](#vector-matching)
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in the right hand vector. The result is propagated into the result vector and the metric
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name is dropped. Entries for which no matching entry in the right-hand vector can be
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found are not part of the result.
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### Comparison binary operators
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The following binary comparison operators exist in Prometheus:
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* `==` (equal)
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* `!=` (not-equal)
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* `>` (greater-than)
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* `<` (less-than)
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* `>=` (greater-or-equal)
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* `<=` (less-or-equal)
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Comparison operators are defined between scalar/scalar, vector/scalar,
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and vector/vector value pairs. By default they filter. Their behaviour can be
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modified by providing `bool` after the operator, which will return `0` or `1`
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for the value rather than filtering.
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**Between two scalars**, the `bool` modifier must be provided and these
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operators result in another scalar that is either `0` (`false`) or `1`
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(`true`), depending on the comparison result.
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**Between an instant vector and a scalar**, these operators are applied to the
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value of every data sample in the vector, and vector elements between which the
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comparison result is `false` get dropped from the result vector. If the `bool`
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modifier is provided, vector elements that would be dropped instead have the value
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`0` and vector elements that would be kept have the value `1`.
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**Between two instant vectors**, these operators behave as a filter by default,
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applied to matching entries. Vector elements for which the expression is not
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true or which do not find a match on the other side of the expression get
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dropped from the result, while the others are propagated into a result vector
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with their original (left-hand-side) metric names and label values.
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If the `bool` modifier is provided, vector elements that would have been
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dropped instead have the value `0` and vector elements that would be kept have
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the value `1` with the left-hand-side metric names and label values.
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### Logical/set binary operators
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These logical/set binary operators are only defined between instant vectors:
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* `and` (intersection)
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* `or` (union)
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* `unless` (complement)
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`vector1 and vector2` results in a vector consisting of the elements of
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`vector1` for which there are elements in `vector2` with exactly matching
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label sets. Other elements are dropped. The metric name and values are carried
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over from the left-hand-side vector.
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`vector1 or vector2` results in a vector that contains all original elements
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(label sets + values) of `vector1` and additionally all elements of `vector2`
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which do not have matching label sets in `vector1`.
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`vector1 unless vector2` results in a vector consisting of the elements of
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`vector1` for which there are no elements in `vector2` with exactly matching
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label sets. All matching elements in both vectors are dropped.
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## Vector matching
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Operations between vectors attempt to find a matching element in the right-hand-side
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vector for each entry in the left-hand side. There are two basic types of
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matching behavior:
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**One-to-one** finds a unique pair of entries from each side of the operation.
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In the default case, that is an operation following the format `vector1 <operator> vector2`.
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Two entries match if they have the exact same set of labels and corresponding values.
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The `ignoring` keyword allows ignoring certain labels when matching, while the
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`on` keyword allows reducing the set of considered labels to a provided list:
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<vector expr> <bin-op> ignoring(<label list>) <vector expr>
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<vector expr> <bin-op> on(<label list>) <vector expr>
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Example input:
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method_code:http_errors:rate5m{method="get", code="500"} 24
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method_code:http_errors:rate5m{method="get", code="404"} 30
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method_code:http_errors:rate5m{method="put", code="501"} 3
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method_code:http_errors:rate5m{method="post", code="500"} 6
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method_code:http_errors:rate5m{method="post", code="404"} 21
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method:http_requests:rate5m{method="get"} 600
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method:http_requests:rate5m{method="del"} 34
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method:http_requests:rate5m{method="post"} 120
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Example query:
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method_code:http_errors:rate5m{code="500"} / ignoring(code) method:http_requests:rate5m
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This returns a result vector containing the fraction of HTTP requests with status code
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of 500 for each method, as measured over the last 5 minutes. Without `ignoring(code)` there
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would have been no match as the metrics do not share the same set of labels.
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The entries with methods `put` and `del` have no match and will not show up in the result:
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{method="get"} 0.04 // 24 / 600
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{method="post"} 0.05 // 6 / 120
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**Many-to-one** and **one-to-many** matchings refer to the case where each vector element on
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the "one"-side can match with multiple elements on the "many"-side. This has to
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be explicitly requested using the `group_left` or `group_right` modifier, where
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left/right determines which vector has the higher cardinality.
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<vector expr> <bin-op> ignoring(<label list>) group_left(<label list>) <vector expr>
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<vector expr> <bin-op> ignoring(<label list>) group_right(<label list>) <vector expr>
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<vector expr> <bin-op> on(<label list>) group_left(<label list>) <vector expr>
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<vector expr> <bin-op> on(<label list>) group_right(<label list>) <vector expr>
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The label list provided with the group modifier contains additional labels from
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the "one"-side to be included in the result metrics. For `on` a label can only
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appear in one of the lists. Every time series of the result vector must be
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uniquely identifiable.
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_Grouping modifiers can only be used for
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[comparison](#comparison-binary-operators) and
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[arithmetic](#arithmetic-binary-operators). Operations as `and`, `unless` and
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`or` operations match with all possible entries in the right vector by
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default._
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Example query:
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method_code:http_errors:rate5m / ignoring(code) group_left method:http_requests:rate5m
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In this case the left vector contains more than one entry per `method` label
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value. Thus, we indicate this using `group_left`. The elements from the right
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side are now matched with multiple elements with the same `method` label on the
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left:
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{method="get", code="500"} 0.04 // 24 / 600
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{method="get", code="404"} 0.05 // 30 / 600
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{method="post", code="500"} 0.05 // 6 / 120
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{method="post", code="404"} 0.175 // 21 / 120
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_Many-to-one and one-to-many matching are advanced use cases that should be carefully considered.
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Often a proper use of `ignoring(<labels>)` provides the desired outcome._
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## Aggregation operators
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Prometheus supports the following built-in aggregation operators that can be
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used to aggregate the elements of a single instant vector, resulting in a new
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vector of fewer elements with aggregated values:
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* `sum` (calculate sum over dimensions)
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* `min` (select minimum over dimensions)
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* `max` (select maximum over dimensions)
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* `avg` (calculate the average over dimensions)
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* `stddev` (calculate population standard deviation over dimensions)
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* `stdvar` (calculate population standard variance over dimensions)
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* `count` (count number of elements in the vector)
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* `count_values` (count number of elements with the same value)
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* `bottomk` (smallest k elements by sample value)
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* `topk` (largest k elements by sample value)
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* `quantile` (calculate φ-quantile (0 ≤ φ ≤ 1) over dimensions)
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These operators can either be used to aggregate over **all** label dimensions
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or preserve distinct dimensions by including a `without` or `by` clause.
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<aggr-op>([parameter,] <vector expression>) [without|by (<label list>)]
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`parameter` is only required for `count_values`, `quantile`, `topk` and
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`bottomk`. `without` removes the listed labels from the result vector, while
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all other labels are preserved the output. `by` does the opposite and drops
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labels that are not listed in the `by` clause, even if their label values are
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identical between all elements of the vector.
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`count_values` outputs one time series per unique sample value. Each series has
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an additional label. The name of that label is given by the aggregation
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parameter, and the label value is the unique sample value. The value of each
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time series is the number of times that sample value was present.
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`topk` and `bottomk` are different from other aggregators in that a subset of
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the input samples, including the original labels, are returned in the result
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vector. `by` and `without` are only used to bucket the input vector.
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Example:
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If the metric `http_requests_total` had time series that fan out by
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`application`, `instance`, and `group` labels, we could calculate the total
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number of seen HTTP requests per application and group over all instances via:
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sum(http_requests_total) without (instance)
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If we are just interested in the total of HTTP requests we have seen in **all**
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applications, we could simply write:
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sum(http_requests_total)
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To count the number of binaries running each build version we could write:
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count_values("version", build_version)
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To get the 5 largest HTTP requests counts across all instances we could write:
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topk(5, http_requests_total)
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## Binary operator precedence
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The following list shows the precedence of binary operators in Prometheus, from
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highest to lowest.
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1. `^`
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2. `*`, `/`, `%`
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3. `+`, `-`
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4. `==`, `!=`, `<=`, `<`, `>=`, `>`
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5. `and`, `unless`
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6. `or`
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Operators on the same precedence level are left-associative. For example,
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`2 * 3 % 2` is equivalent to `(2 * 3) % 2`. However `^` is right associative,
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so `2 ^ 3 ^ 2` is equivalent to `2 ^ (3 ^ 2)`.
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