This commit is doing what I would have expected that Go generics do
for me. However, the PromQL benchmarks show a significant runtime and
allocation increase with `genericAdd`, so this replaces it with
hand-coded non-generic versions of it.
Signed-off-by: beorn7 <beorn@grafana.com>
In the past, every sample value was a float, so it was fine to call a
variable holding such a float "value" or "sample". With native
histograms, a sample might have a histogram value. And a histogram
value is still a value. Calling a float value just "value" or "sample"
or "V" is therefore misleading. Over the last few commits, I already
renamed many variables, but this cleans up a few more places where the
changes are more invasive.
Note that we do not to attempt naming in the JSON APIs or in the
protobufs. That would be quite a disruption. However, internally, we
can call variables as we want, and we should go with the option of
avoiding misunderstandings.
Signed-off-by: beorn7 <beorn@grafana.com>
This utilizes the fact that most sampleRings will only contain samples
of one type. In this case, the generic interface is circumvented, and
a bespoke buffer for the one actually occurring sample type is
used. Should a sampleRing receive a sample of a different kind later,
it will transparently switch to the generic behavior.
Signed-off-by: beorn7 <beorn@grafana.com>
Previously, we had one “polymorphous” `sample` type in the `storage`
package. This commit breaks it up into `fSample`, `hSample`, and
`fhSample`, each still implementing the `tsdbutil.Sample` interface.
This reduces allocations in `sampleRing.Add` but inflicts the penalty
of the interface wrapper, which makes things worse in total.
This commit therefore just demonstrates the step taken. The next
commit will tackle the interface overhead problem.
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>
This is a method used by some downstream projects; it was created to
optimize the implementation in `labels_string.go` but we should have one
for both implementations so the same code works with either.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
query_samples_total is a counter that tracks the total number of samples loaded by all queries.
The goal with this metric is to be able to see the amount of 'work' done by Prometheus to service queries.
At the moment we have metrics with the number of queries, plus more detailed metrics showing how much time each step of a query takes.
While those metrics do help they don't show us the whole picture.
Queries that do load more samples are (in general) more expensive than queries that do load fewer samples.
This means that looking only at the number of queries doesn't tell us how much 'work' Prometheus received.
Adding a counter that tracks the total number of samples loaded allows us to see if there was a spike in the cost of queries, not just the number of them.
Signed-off-by: Łukasz Mierzwa <l.mierzwa@gmail.com>
This makes it more consistent with other command like import rules. We
don't have stricts rules and uniformity accross promtool unfortunately,
but I think it's better to only have the http config on relevant check
commands to avoid thinking Prometheus can e.g. check the config over the
wire.
Signed-off-by: Julien Pivotto <roidelapluie@o11y.eu>
There are a few tests that will run prometheus command.
This can test if there's already something listening on port :9090 since --web.listen-address defaults to 0.0.0.0:9090.
To fix that we can tell prometheus to use a random port on loopback interface.
Signed-off-by: Łukasz Mierzwa <l.mierzwa@gmail.com>
Eliminate the need for a second config file for golangci-lint config
file by moving the list of errcheck exclude functions into the yaml
config.
Signed-off-by: SuperQ <superq@gmail.com>
Deleted labels are remembered, even if they were not in `base` or were
removed from `add`, so `base+add-del` could go negative.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
