Since we are now getting really deep into floating point calculation,
the tests had to take into account the precision loss. Since the rule
tests are based on direct line matching in the output, implementing
the "almost equal" semantics was pretty cumbersome, but here we are.
The 2nd isCounter argument to delta is ugly, make it optional as the first step
of deprecating it. This will makes delta only ever applied to gauges.
Add a deriv function to calculate the least squares
slope of a gauge. This is more useful for prediction than delta,
as it isn't as heavily influenced by outliers at the boundaries.
- Move CONTRIBUTORS.md to the more common AUTHORS.
- Added the required NOTICE file.
- Changed "Prometheus Team" to "The Prometheus Authors".
- Reverted the erroneous changes to the Apache License.
A common problem in Prometheus alerting is to detect when no timeseries
exist for a given metric name and label combination. Unfortunately,
Prometheus alert expressions need to be of vector type, and
"count(nonexistent_metric)" results in an empty vector, yielding no
output vector elements to base an alert on. The newly introduced
absent() function solves this issue:
ALERT FooAbsent IF absent(foo{job="myjob"}) [...]
absent() has the following behavior:
- if the vector passed to it has any elements, it returns an empty
vector.
- if the vector passed to it has no elements, it returns a 1-element
vector with the value 1.
In the second case, absent() tries to be smart about deriving labels of
the 1-element output vector from the input vector:
absent(nonexistent{job="myjob"}) => {job="myjob"}
absent(nonexistent{job="myjob",instance=~".*"}) => {job="myjob"}
absent(sum(nonexistent{job="myjob"})) => {}
That is, if the passed vector is a literal vector selector, it takes all
"=" label matchers as the basis for the output labels, but ignores all
non-equals or regex matchers. Also, if the passed vector results from a
non-selector expression, no labels can be derived.
Change-Id: I948505a1488d50265ab5692a3286bd7c8c70cd78
After many transformations, it doesn't make sense to keep the metric
names, since the result of the transformation is no longer that metric.
This drops the metric name after such transformations and makes the web
UI deal well with missing metric names.
This depends on the current branch on the following things:
- prometheus/client_golang needs to be at
e237cf15c6
in branch "julius/int-fingerprints" (to be merged with new storage)
- prometheus/promdash needs to be at
dd7691c9c2
Change-Id: Ib3c8cad8d647d9854e8c653c424b8c235ccc231d
To achieve O(log n * k) runtime, this uses a heap to track the current
bottom-k or top-k elements while iterating over the full set of
available elements.
It would be possible to reuse more code between topk and bottomk, but I
decided for some more duplication for the sake of clarity.
This fixes https://github.com/prometheus/prometheus/issues/399
Change-Id: I7487ddaadbe7acb22ca2cf2283ba6e7915f2b336
Mostly, that means adding compliant doc strings to exported items.
Also, remove 'go vet' warnings where possible. (Some are unfortunately
not to avoid, arguably bugs in 'go vet'.)
Change-Id: I2827b6dd317492864c1383c3de1ea9eac5a219bb
So far we've been using Go's native time.Time for anything related to sample
timestamps. Since the range of time.Time is much bigger than what we need, this
has created two problems:
- there could be time.Time values which were out of the range/precision of the
time type that we persist to disk, therefore causing incorrectly ordered keys.
One bug caused by this was:
https://github.com/prometheus/prometheus/issues/367
It would be good to use a timestamp type that's more closely aligned with
what the underlying storage supports.
- sizeof(time.Time) is 192, while Prometheus should be ok with a single 64-bit
Unix timestamp (possibly even a 32-bit one). Since we store samples in large
numbers, this seriously affects memory usage. Furthermore, copying/working
with the data will be faster if it's smaller.
*MEMORY USAGE RESULTS*
Initial memory usage comparisons for a running Prometheus with 1 timeseries and
100,000 samples show roughly a 13% decrease in total (VIRT) memory usage. In my
tests, this advantage for some reason decreased a bit the more samples the
timeseries had (to 5-7% for millions of samples). This I can't fully explain,
but perhaps garbage collection issues were involved.
*WHEN TO USE THE NEW TIMESTAMP TYPE*
The new clientmodel.Timestamp type should be used whenever time
calculations are either directly or indirectly related to sample
timestamps.
For example:
- the timestamp of a sample itself
- all kinds of watermarks
- anything that may become or is compared to a sample timestamp (like the timestamp
passed into Target.Scrape()).
When to still use time.Time:
- for measuring durations/times not related to sample timestamps, like duration
telemetry exporting, timers that indicate how frequently to execute some
action, etc.
*NOTE ON OPERATOR OPTIMIZATION TESTS*
We don't use operator optimization code anymore, but it still lives in
the code as dead code. It still has tests, but I couldn't get all of them to
pass with the new timestamp format. I commented out the failing cases for now,
but we should probably remove the dead code soon. I just didn't want to do that
in the same change as this.
Change-Id: I821787414b0debe85c9fffaeb57abd453727af0f
This also removes the now obsolete scalar count() function and corrects the
expressions test naming (broken in
2202cd71c9 (L6R59))
so that the expression tests will actually run.
