This commit adds `@ <timestamp>` modifier as per this design doc: https://docs.google.com/document/d/1uSbD3T2beM-iX4-Hp7V074bzBRiRNlqUdcWP6JTDQSs/edit.
An example query:
```
rate(process_cpu_seconds_total[1m])
and
topk(7, rate(process_cpu_seconds_total[1h] @ 1234))
```
which ranks based on last 1h rate and w.r.t. unix timestamp 1234 but actually plots the 1m rate.
Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
* Separate tests into:
** Aggregators
** Functions
** Operators
** Selectors
* Remove simple files and place tests into other files.
* Eliminate some of the _over_time tests since there are already plenty of
edge cases present in the functions.test file.
Relates to #740
Signed-off-by: Harold Dost <harolddost@gmail.com>
* use Welford/Knuth method to compute standard deviation and variance, avoids float precision issues
* use better method for calculating avg and avg_over_time
Signed-off-by: Dan Cech <dcech@grafana.com>
Make the timestamp of instant vectors be the timestamp of the sample
rather than the evaluation. We were not using this anywhere, so this is
safe.
Add a function to return the timestamp of samples in an instant vector.
Fixes#1557
Fixes https://github.com/prometheus/prometheus/issues/1401
This remove the last (and in fact bogus) use of BoundaryValues.
Thus, a whole lot of unused (and arguably sub-optimal / ugly) code can
be removed here, too.
The new implementation detects the start and end of a series by
looking at the average sample interval within the range. If the first
(last) sample in the range is more than 1.1*interval distant from the
beginning (end) of the range, it is considered the first (last) sample
of the series as a whole, and extrapolation is limited to half the
interval (rather than all the way to the beginning (end) of the
range). In addition, if the extrapolated starting point of a counter
(where it is zero) is within the range, it is used as the starting
point of the series.
Fixes#581
irate is a rate function that only looks at the most
recent two data points, and calucaltes a per-second value
from that. This produces much more granular graphs for
fast moving data, and works sanely across many scrape intervals.
It doesn't do so well for slowly moving data.
Currently the only way to convert a scalar to a vector is to
use absent(), which isn't very clean. This adds a vector()
function that's the inverse of scalar() and lets your optionally
set labels.
Example usage would be
vector(time() % 86400) < 3600
to filter to only the first hour of the day.
This calculates how much a counter increases over
a given period of time, which is the area under the curve
of it's rate.
increase(x[5m]) is equivilent to rate(x[5m]) * 300.
