Convert QueryOpts to an interface so that downstream projects like
https://github.com/thanos-community/promql-engine could extend the query
options with engine specific options that are not in the original
engine.
Will be used to enable query analysis per-query.
Signed-off-by: Giedrius Statkevičius <giedrius.statkevicius@vinted.com>
Handle more arithmetic operators and aggregators for native histograms
This includes operators for multiplication (formerly known as scaling), division, and subtraction. Plus aggregations for average and the avg_over_time function.
Stdvar and stddev will (for now) ignore histograms properly (rather than counting them but adding a 0 for them).
Signed-off-by: Jeanette Tan <jeanette.tan@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>
We haven't updated golint-ci in our CI yet, but this commit prepares
for that.
There are a lot of new warnings, and it is mostly because the "revive"
linter got updated. I agree with most of the new warnings, mostly
around not naming unused function parameters (although it is justified
in some cases for documentation purposes – while things like mocks are
a good example where not naming the parameter is clearer).
I'm pretty upset about the "empty block" warning to include `for`
loops. It's such a common pattern to do something in the head of the
`for` loop and then have an empty block. There is still an open issue
about this: https://github.com/mgechev/revive/issues/810 I have
disabled "revive" altogether in files where empty blocks are used
excessively, and I have made the effort to add individual
`// nolint:revive` where empty blocks are used just once or twice.
It's borderline noisy, though, but let's go with it for now.
I should mention that none of the "empty block" warnings for `for`
loop bodies were legitimate.
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>
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>
* Use zeropool.Pool to workaround SA6002
I built a tiny library called https://github.com/colega/zeropool to
workaround the SA6002 staticheck issue.
While searching for the references of that SA6002 staticheck issues on
Github first results was Prometheus itself, with quite a lot of ignores
of it.
This changes the usages of `sync.Pool` to `zeropool.Pool[T]` where a
pointer is not available.
Also added a benchmark for HeadAppender Append/Commit when series
already exist, which is one of the most usual cases IMO, as I didn't find
any.
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>
* Improve BenchmarkHeadAppender with more cases
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>
* A little copying is better than a little dependency
https://www.youtube.com/watch?v=PAAkCSZUG1c&t=9m28s
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>
* Fix imports order
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>
* Add license header
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>
* Copyright should be on one of the first 3 lines
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>
* Use require.Equal for testing
I don't depend on testify in my lib, but here we have it available.
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>
* Avoid flaky test
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>
* Also use zeropool for pointsPool in engine.go
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>
---------
Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.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>
We have a LabelBuilder in EvalNodeHelper; use it instead of creating a new one at every step.
Need to take some care that different uses of enh.lb do not overlap.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
We use `labels.Builder` to parse metrics, to avoid depending on the
internal implementation. This is not efficient, but the feature is only
used in tests. It wasn't efficient previously either - calling `Sort()`
after adding each label.
`createLabelsForAbsentFunction` also uses a Builder now, and gets
an extra `map` to replace the previous `Has()` usage.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
Fix up promql to compile with changes to Labels
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>
If we are populating series for a subquery then set the interval
parameter accordingly so that downstream users could use that
information.
Signed-off-by: Giedrius Statkevičius <giedrius.statkevicius@vinted.com>
Use new experimental package `golang.org/x/exp/slices`.
slices.Sort works on values that are directly comparable, like ints,
so avoids the overhad of an interface call to `.Less()`.
Left tests unchanged, because they don't need the speed and it may be
a cross-check that slices.Sort gives the same answer.
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>
We print the stacktrace of a panic when query causes one, but there's no
information about the query itself, which makes it harder to debug and
reproduce the issue.
This adds the 'expr' string to the logged panic.
Signed-off-by: Łukasz Mierzwa <l.mierzwa@gmail.com>
* Labels: create signature with/without labels
Instead of creating a new Labels slice then converting to signature,
go directly to the signature and save time.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* Labels: refactor Builder tests
Have one test with a range of cases, and have them check the final
output rather than checking the internal structure of the Builder.
Also add a couple of cases where the value is "", which should be
interpreted as 'delete'.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* Labels: add 'Keep' function to Builder
This lets us replace `Labels.WithLabels` with the more general `Builder`.
In `engine.resultMetric()` we can call `Keep()` instead of checking
and calling `Del()`.
Avoid calling `Sort()` in `Builder.Labels()` if we didn't add anything,
so that `Keep()` has the same performance as `WithLabels()`.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
For conventional histograms, we need to gather all the individual
bucket timeseries at a data point to do the quantile calculation. The
code so far mirrored this behavior for the new native
histograms. However, since a single data point contains all the
buckets alreade, that's actually not needed. This PR simplifies the
code while still detecting a mix of conventional and native
histograms.
The weird signature calculation for the conventional histograms is
getting even weirder because of that. If this PR turns out to do the
right thing, I will implement a proper fix for the signature
calculation upstream.
Signed-off-by: beorn7 <beorn@grafana.com>
This exactly corresponds to the statistic compared against MaxSamples
during the course of query execution, so users can see how close their
queries are to a limit.
Co-authored-by: Harkishen Singh <harkishensingh@hotmail.com>
Co-authored-by: Andrew Bloomgarden <blmgrdn@amazon.com>
Signed-off-by: Andrew Bloomgarden <blmgrdn@amazon.com>
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>
This follows the line of argument that the invariant of not looking
ahead of the query time was merely emerging behavior and not a
documented stable feature. Any query that looks ahead of the query
time was simply invalid before the introduction of the negative offset
and the @ modifier.
Signed-off-by: beorn7 <beorn@grafana.com>
This can happen if the aggregation starts with a float and later
encounters a histogram. In that case, the newly encountered histogram
would have been added to a nil histogram.
This should be tested, of course, but that's best done within the
PromQL testing framework, which we still need to enable for histograms
(for which we have a TODO in the code and now also a card in the GH
project).
Signed-off-by: beorn7 <beorn@grafana.com>
Charles Korn