This saves memory in other kinds of aggregation.
We don't need `orderedResult` in `aggregationCountValues`; the ordering
is not guaranteed.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
They aggregate results in different ways.
topk/bottomk don't consider histograms so can simplify data collection.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
This is a cleaner split of responsibilities.
We now check the sample count after calling rangeEvalAgg.
Changed re-use of samples to use `Clone` and `defer`.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
Pass it as a float64 not as interface{}.
Make k a simple int, since that is the parameter to make().
Pull invalid quantile warning out of the loop.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
The new function `rangeEvalAgg` is mostly a copy of `rangeEval`, but
without `initSeries` which we don't need and inlining the callback to
`aggregation()`.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
The existing aggregation function is very long and covers very different
cases.
`aggregationCountValues` is just for `count_values`, which differs from
other aggregations in that it outputs as many series per group as there
are values in the input.
Remove the top-level switch on string parameter type; use the same `Op`
check there as elswehere.
Pull checking parameters out to caller, where it is only executed once.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* add custom buckets to native histogram model
* simple copy for custom bounds
* return errors for unsupported add/sub operations
* add test cases for string and update appendhistogram in scrape to account for new schema
* check fields which are supposed to be unused but may affect results in equals
* allow appending custom buckets histograms regardless of max schema
Signed-off-by: Jeanette Tan <jeanette.tan@grafana.com>
Aggregations discard the metric name, so don't try to
include it in the error message.
Add a test that generates this warning.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
Fixes#11708.
If a range vector is fixen in time with the @ modifier, it gets still
moved around for different steps in a range query. Since no additional
points are retrieved from the TSDB, this leads to steadily emptying
the range, leading to the weird behavior described in isse #11708.
This only happens for functions listed in `AtModifierUnsafeFunctions`,
and the only of those that takes a range vector is `predict_linear`,
which is the reason why we see it only for this particular function.
Signed-off-by: beorn7 <beorn@grafana.com>
These functions act on the labels only, so don't need to go step by step
over the samples in a range query.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
* Reusing points slice from previous series when the slice is under utilized
* Adding comments on the bench test
Signed-off-by: Alan Protasio <alanprot@gmail.com>
This function is called very frequently when executing PromQL functions,
and we can do it much more efficiently inside Labels.
In the common case that `__name__` comes first in the labels, we simply
re-point to start at the next label, which is nearly free.
`DropMetricName` is now so cheap I removed the cache - benchmarks show
everything still goes faster.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
Optimize histogram iterators
Histogram iterators allocate new objects in the AtHistogram and
AtFloatHistogram methods, which makes calculating rates over long
ranges expensive.
In #13215 we allowed an existing object to be reused
when converting an integer histogram to a float histogram. This commit follows
the same idea and allows injecting an existing object in the AtHistogram and
AtFloatHistogram methods. When the injected value is nil, iterators allocate
new histograms, otherwise they populate and return the injected object.
The commit also adds a CopyTo method to Histogram and FloatHistogram which
is used in the BufferedIterator to overwrite items in the ring instead of making
new copies.
Note that a specialized HPoint pool is needed for all of this to work
(`matrixSelectorHPool`).
---------
Signed-off-by: Filip Petkovski <filip.petkovsky@gmail.com>
Co-authored-by: George Krajcsovits <krajorama@users.noreply.github.com>
Restore more efficient version of NewPossibleNonCounterInfo annotation
Signed-off-by: Jeanette Tan <jeanette.tan@grafana.com>
---------
Signed-off-by: Jeanette Tan <jeanette.tan@grafana.com>
In https://github.com/prometheus/prometheus/pull/13276 we started reusing float histogram objects to reduce allocations in PromQL.
That PR introduces a bug where histogram pointers gets copied to the beginning of the histograms slice,
but are still kept in the end of the slice. When a new histogram is read into the last element,
it can overwrite a previous element because the pointer is the same.
This commit fixes the issue by moving outdated points to the end of the slice
so that we don't end up with duplicate pointers in the same buffer. In other words,
the slice gets rotated so that old objects can get reused.
Signed-off-by: Filip Petkovski <filip.petkovsky@gmail.com>
This commit reduces the memory needed to query native histogram objects
by reusing existing HPoint instances.
Signed-off-by: Filip Petkovski <filip.petkovsky@gmail.com>
This function is useful to analyze promQL queries. We want to use this in Mimir to record the time range which the query touches.
I also chose to remove the `Engine` receiver because it was unnecessary, and it makes it easier to use, but happy to refactor that if you disagree.
The function is untested on its own. If you prefer to have unit tests now that its exported, I can look into adding some.
Signed-off-by: Dimitar Dimitrov <dimitar.dimitrov@grafana.com>
* Remove NewPossibleNonCounterInfo until it can be made more efficient, and avoid creating empty annotations as much as possible
Signed-off-by: Jeanette Tan <jeanette.tan@grafana.com>
Return annotations (warnings and infos) from PromQL queries
This generalizes the warnings we have already used before (but only for problems with remote read) as "annotations".
Annotations can be warnings or infos (the latter could be false positives). We do not treat them different in the API for now and return them all as "warnings". It would be easy to distinguish them and return infos separately, should that appear useful in the future.
The new annotations are then used to create a lot of warnings or infos during PromQL evaluations. Partially these are things we have wanted for a long time (e.g. inform the user that they have applied `rate` to a metric that doesn't look like a counter), but the new native histograms have created even more needs for those annotations (e.g. if a query tries to aggregate float numbers with histograms).
The annotations added here are not yet complete. A prominent example would be a warning about a range too short for a rate calculation. But such a warnings is more tricky to create with good fidelity and we will tackle it later.
Another TODO is to take annotations into account when evaluating recording rules.
---------
Signed-off-by: Jeanette Tan <jeanette.tan@grafana.com>
promql engine: check unique labels using existing map
ContainsSameLabelset constructs a map with the same hash key as the one used to compile the output of rangeEval, so we can use that one and save work.
Need to hold the timestamp so we can be sure we saw the same series in the same evaluation.
`ContainsSameLabelset` constructs a map with the same hash key as
the one used to compile the output of `rangeEval`, so we can use that
one and save work.
Need to hold the timestamp so we can be sure we saw the same series
in the same evaluation.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
The operator changes the meaning of the metric, so the metric name should
be dropped. Technically this would be a breaking change, but it's also very
obviously a bug and not likely that anyone depends on it.
Signed-off-by: Julius Volz <julius.volz@gmail.com>
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>
Bryan Boreham