The 'ToFloat' method on integer histograms currently allocates new memory
each time it is called.
This commit adds an optional *FloatHistogram parameter that can be used
to reuse span and bucket slices. It is up to the caller to make sure the
input float histogram is not used anymore after the call.
Signed-off-by: Filip Petkovski <filip.petkovsky@gmail.com>
So far, `ValidateHistogram` would not detect if the count did not
include the count in the zero bucket. This commit fixes the problem
and updates all the tests that have been undetected offenders so far.
Note that this problem would only ever create false negatives, so we
never falsely rejected to store a histogram because of it.
On the other hand, `ValidateFloatHistogram` has been to strict with
the count being at least as large as the sum of the counts in all the
buckets. Float precision issues could create false positives here, see
products of PromQL evaluations, it's actually quite hard to put an
upper limit no the floating point imprecision. Users could produce the
weirdest expressions, maxing out float precision problems. Therefore,
this commit simply removes that particular check from
`ValidateFloatHistogram`.
Signed-off-by: beorn7 <beorn@grafana.com>
Native histograms without a zero threshold aren't federated properly.
This adds a test to prove the specific failure mode, which is that
histograms with a zero threshold of zero are federated as classic
histograms.
The underlying reason is that the protobuf parser identifies a native
histogram by detecting a zero bucket or by detecting integer buckets.
Therefore, a float histogram with a zero threshold of zero and an
unpopulated zero bucket falls through the cracks (no integer buckets,
no zero bucket).
This commit also addse a test case for the latter.
Signed-off-by: beorn7 <beorn@grafana.com>
So far, if a target exposes a histogram with both classic and native
buckets, a native-histogram enabled Prometheus would ignore the
classic buckets. With the new scrape config option
`scrape_classic_histograms` set, both buckets will be ingested,
creating all the series of a classic histogram in parallel to the
native histogram series. For example, a histogram `foo` would create a
native histogram series `foo` and classic series called `foo_sum`,
`foo_count`, and `foo_bucket`.
This feature can be used in a migration strategy from classic to
native histograms, where it is desired to have a transition period
during which both native and classic histograms are present.
Note that two bugs in classic histogram parsing were found and fixed
as a byproduct of testing the new feature:
1. Series created from classic _gauge_ histograms didn't get the
_sum/_count/_bucket prefix set.
2. Values of classic _float_ histograms weren't parsed properly.
Signed-off-by: beorn7 <beorn@grafana.com>
Wiser coders than myself have come to the conclusion that a `switch`
statement is almost always superior to a statement that includes any
`else if`.
The exceptions that I have found in our codebase are just these two:
* The `if else` is followed by an additional statement before the next
condition (separated by a `;`).
* The whole thing is within a `for` loop and `break` statements are
used. In this case, using `switch` would require tagging the `for`
loop, which probably tips the balance.
Why are `switch` statements more readable?
For one, fewer curly braces. But more importantly, the conditions all
have the same alignment, so the whole thing follows the natural flow
of going down a list of conditions. With `else if`, in contrast, all
conditions but the first are "hidden" behind `} else if `, harder to
spot and (for no good reason) presented differently from the first
condition.
I'm sure the aforemention wise coders can list even more reasons.
In any case, I like it so much that I have found myself recommending
it in code reviews. I would like to make it a habit in our code base,
without making it a hard requirement that we would test on the CI. But
for that, there has to be a role model, so this commit eliminates all
`if else` occurrences, unless it is autogenerated code or fits one of
the exceptions above.
Signed-off-by: beorn7 <beorn@grafana.com>
In other words: Instead of having a “polymorphous” `Point` that can
either contain a float value or a histogram value, use an `FPoint` for
floats and an `HPoint` for histograms.
This seemingly small change has a _lot_ of repercussions throughout
the codebase.
The idea here is to avoid the increase in size of `Point` arrays that
happened after native histograms had been added.
The higher-level data structures (`Sample`, `Series`, etc.) are still
“polymorphous”. The same idea could be applied to them, but at each
step the trade-offs needed to be evaluated.
The idea with this change is to do the minimum necessary to get back
to pre-histogram performance for functions that do not touch
histograms. Here are comparisons for the `changes` function. The test
data doesn't include histograms yet. Ideally, there would be no change
in the benchmark result at all.
First runtime v2.39 compared to directly prior to this commit:
```
name old time/op new time/op delta
RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 542µs ± 1% +38.58% (p=0.000 n=9+8)
RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 617µs ± 2% +36.48% (p=0.000 n=10+10)
RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.36ms ± 2% +21.58% (p=0.000 n=8+10)
RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 8.94ms ± 1% +14.21% (p=0.000 n=10+10)
RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.30ms ± 1% +10.67% (p=0.000 n=9+10)
RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.10ms ± 1% +11.82% (p=0.000 n=10+10)
RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 11.8ms ± 1% +12.50% (p=0.000 n=8+10)
RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 87.4ms ± 1% +12.63% (p=0.000 n=9+9)
RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 32.8ms ± 1% +8.01% (p=0.000 n=10+10)
RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.6ms ± 2% +9.64% (p=0.000 n=10+10)
RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 117ms ± 1% +11.69% (p=0.000 n=10+10)
RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 876ms ± 1% +11.83% (p=0.000 n=9+10)
```
And then runtime v2.39 compared to after this commit:
```
name old time/op new time/op delta
RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 547µs ± 1% +39.84% (p=0.000 n=9+8)
RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 616µs ± 2% +36.15% (p=0.000 n=10+10)
RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.26ms ± 1% +12.20% (p=0.000 n=8+10)
RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 7.95ms ± 1% +1.59% (p=0.000 n=10+8)
RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.38ms ± 2% +13.49% (p=0.000 n=9+10)
RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.02ms ± 1% +9.80% (p=0.000 n=10+9)
RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 10.8ms ± 1% +3.08% (p=0.000 n=8+10)
RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 78.1ms ± 1% +0.58% (p=0.035 n=9+10)
RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 33.5ms ± 4% +10.18% (p=0.000 n=10+10)
RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.0ms ± 1% +7.98% (p=0.000 n=10+10)
RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 107ms ± 1% +1.92% (p=0.000 n=10+10)
RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 775ms ± 1% -1.02% (p=0.019 n=9+9)
```
In summary, the runtime doesn't really improve with this change for
queries with just a few steps. For queries with many steps, this
commit essentially reinstates the old performance. This is good
because the many-step queries are the one that matter most (longest
absolute runtime).
In terms of allocations, though, this commit doesn't make a dent at
all (numbers not shown). The reason is that most of the allocations
happen in the sampleRingIterator (in the storage package), which has
to be addressed in a separate commit.
Signed-off-by: beorn7 <beorn@grafana.com>
In most cases, there is no sample at `maxt`, so `PeekBack` has to be
used. So far, `PeekBack` did not return a float histogram, and we
disregarded even any returned normal histogram. This fixes both, and
also tweaks the unit test to discover the problem (by using an earlier
timestamp than "now" for the samples in the TSDB).
Signed-off-by: beorn7 <beorn@grafana.com>
Use `FromStrings` instead of assuming the data structure.
And don't sort individual labels, since `labels.Labels` are always sorted.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
"Labels is a sorted set of labels. Order has to be guaranteed upon
instantiation." says the comment, so fix all the tests that break this
rule.
For `BenchmarkLabelValuesWithMatchers()` and
`BenchmarkHeadLabelValuesWithMatchers()` the amount of work done changes
significantly if you put the labels in order, because all series refs
get neatly partitioned by the `tens` label, so I renamed the labels
to maintain the previous behaviour.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
Later on in the test we override fields of handler, so we need a handler
per sub-test, rather than a global one.
Signed-off-by: Mateusz Gozdek <mgozdekof@gmail.com>
This creates a new `model` directory and moves all data-model related
packages over there:
exemplar labels relabel rulefmt textparse timestamp value
All the others are more or less utilities and have been moved to `util`:
gate logging modetimevfs pool runtime
Signed-off-by: beorn7 <beorn@grafana.com>
* Testify: move to require
Moving testify to require to fail tests early in case of errors.
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* More moves
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* Make lookbackDelta a option of QueryEngine
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* julius' suggestion
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* remove trivial getter
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* Assume lookback delta is always > 0
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* add debug log
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* don't expose loopback delta
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* Specify that lookack delta is also used in federation
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* Fix federation test
While we have added some logic to the promql engine to keep it backwards
compatible and have a 5 minute loopback by default, the web/ package is
likely to really be internal to Prometheus and we should not add the
same kind of heuritstics here.
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
* loopback delta: Fix debug log
Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
The most common format (used by go, gcc and clang) for compiler error positions seems to be
`filename:line:char:` or `line:char:` if the filename is unknown.
This PR adapts the PromQL parser to use this convention.
Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com>
- Unmarshall external_labels config as labels.Labels, add tests.
- Convert some more uses of model.LabelSet to labels.Labels.
- Remove old relabel pkg (fixes#3647).
- Validate external label names.
Signed-off-by: Tom Wilkie <tom.wilkie@gmail.com>
This PR adds the `/status/config` endpoint which exposes the currently
loaded Prometheus config. This is the same config that is displayed on
`/config` in the UI in YAML format. The response payload looks like
such:
```
{
"status": "success",
"data": {
"yaml": <CONFIG>
}
}
```
This is needed for federating non-instance level metrics, so they don't
end up with the instance label of the prometheus target.
Also sort external labels, so label output order is consistent.
This will avoid duplicate MetricFamilies, thereby shrinking the size
of the federation payload and also creating legal text format.
Also, add unit tests for federation. They were also needed for the
previous state of the code, but were missing.
Oleksandr Redko