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>
It took a `Labels` where the memory could be re-used, but in practice
this hardly ever benefitted. Especially after converting `relabel.Process`
to `relabel.ProcessBuilder`.
Comparing the parameter to `nil` was a bug; `EmptyLabels` is not `nil`
so the slice was reallocated multiple times by `append`.
Lastly `Builder.Labels()` now estimates that the final size will depend
on labels added and deleted.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
This commit adds a new 'keep_firing_for' field to Prometheus alerting
rules. The 'resolve_delay' field specifies the minimum amount of time
that an alert should remain firing, even if the expression does not
return any results.
This feature was discussed at a previous dev summit, and it was
determined that a feature like this would be useful in order to allow
the expression time to stabilize and prevent confusing resolved messages
from being propagated through Alertmanager.
This approach is simpler than having two PromQL queries, as was
sometimes discussed, and it should be easy to implement.
This commit does not include tests for the 'resolve_delay' field. This
is intentional, as the purpose of this commit is to gather comments on
the proposed design of the 'resolve_delay' field before implementing
tests. Once the design of the 'resolve_delay' field has been finalized,
a follow-up commit will be submitted with tests."
See https://github.com/prometheus/prometheus/issues/11570
Signed-off-by: Julien Pivotto <roidelapluie@o11y.eu>
This will allow correlation of executed rule queries with their associated rule names and type
Signed-off-by: Danny Kopping <danny.kopping@grafana.com>
Patterned after `Chunk.Iterator()`: pass the old iterator in so it
can be re-used to avoid allocating a new object.
(This commit does not do any re-use; it is just changing all the method
signatures so re-use is possible in later commits.)
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
errors.Unwrap() actually dangerously returns nil if the error does not have an
Unwrap() method, which is the case in at least one of these places where I
noticed that no error was being logged at all when it should have.
Signed-off-by: Julius Volz <julius.volz@gmail.com>
And a number of `EmptyLabels()` instead of `nil`.
Replacing code which assumes the internal structure of `Labels`.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* model/relabel: Add benchmark
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* model/relabel: re-use Builder across relabels
Saves memory allocations.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* labels.Builder: allow re-use of result slice
This reduces memory allocations where the caller has a suitable slice available.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* model/relabel: re-use source values slice
To reduce memory allocations.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* Unwind one change causing test failures
Restore original behaviour in PopulateLabels, where we must not overwrite the input set.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* relabel: simplify values optimisation
Use a stack-based array for up to 16 source labels, which will be the
vast majority of cases.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* lint
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
During shutdown TSDB is stopped before rule manager is stopped. Since TSDB shutdown can take a long time (minutes or 10s of minutes) it keeps rule manager running while parts of Prometheus are already stopped (most notebly scrape manager). This can cause false positive alerts to fire, mostly those that rely on absent() calls since new sample appends will stop while alert queries are still evaluated.
Stop rules before stopping TSDB and scrape manager to avoid this problem.
Signed-off-by: Łukasz Mierzwa <l.mierzwa@gmail.com>
* refactor: move from io/ioutil to io and os packages
* use fs.DirEntry instead of os.FileInfo after os.ReadDir
Signed-off-by: MOREL Matthieu <matthieu.morel@cnp.fr>
We always track total samples queried and add those to the standard set
of stats queries can report.
We also allow optionally tracking per-step samples queried. This must be
enabled both at the engine and query level to be tracked and rendered.
The engine flag is exposed via a Prometheus feature flag, while the
query flag is set when stats=all.
Co-authored-by: Alan Protasio <approtas@amazon.com>
Co-authored-by: Andrew Bloomgarden <blmgrdn@amazon.com>
Co-authored-by: Harkishen Singh <harkishensingh@hotmail.com>
Signed-off-by: Andrew Bloomgarden <blmgrdn@amazon.com>
