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Docs: remove keep_common, count_scalar, drop_common_labels
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@ -60,14 +60,6 @@ elements in `v` to have an upper limit of `max`.
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`clamp_min(v instant-vector, min scalar)` clamps the sample values of all
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elements in `v` to have a lower limit of `min`.
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## `count_scalar()`
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`count_scalar(v instant-vector)` returns the number of elements in a time series
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vector as a scalar. This is in contrast to the `count()`
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[aggregation operator](operators.md#aggregation-operators), which
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always returns a vector (an empty one if the input vector is empty) and allows
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grouping by labels via a `by` clause.
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## `day_of_month()`
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`day_of_month(v=vector(time()) instant-vector)` returns the day of the month
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@ -109,11 +101,6 @@ vector `v`, using [simple linear regression](http://en.wikipedia.org/wiki/Simple
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`deriv` should only be used with gauges.
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## `drop_common_labels()`
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`drop_common_labels(instant-vector)` drops all labels that have the same name
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and value across all series in the input vector.
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## `exp()`
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`exp(v instant-vector)` calculates the exponential function for all elements in `v`.
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@ -193,15 +193,13 @@ vector of fewer elements with aggregated values:
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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>)] [keep_common]
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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. The `keep_common` clause allows
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keeping those extra labels (labels that are identical between elements, but not
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in the `by` clause).
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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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