prometheus/promql
beorn7 c0879d64cf promql: Separate Point into FPoint and HPoint
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>
2023-04-13 19:25:16 +02:00
..
fuzz-data textparse: Add fuzzing and fix bug caught 2017-07-07 11:12:17 +02:00
parser labels: simplify call to get Labels from Builder 2023-03-22 17:05:20 +00:00
testdata Adds day_of_year function to PromQL 2022-05-20 14:08:34 +02:00
bench_test.go promql: add a benchmark for topk with k > 1 2023-03-28 11:07:29 +00:00
engine.go promql: Separate Point into FPoint and HPoint 2023-04-13 19:25:16 +02:00
engine_test.go promql: Separate Point into FPoint and HPoint 2023-04-13 19:25:16 +02:00
functions.go promql: Separate Point into FPoint and HPoint 2023-04-13 19:25:16 +02:00
functions_test.go promql: Separate Point into FPoint and HPoint 2023-04-13 19:25:16 +02:00
fuzz.go refactor (promql): move from github.com/pkg/errors to 'errors' and 'fmt' (#10817) 2022-06-08 10:47:52 +02:00
fuzz_test.go error for invalid media type should not be completely swallowed (#10186) 2022-02-08 10:57:56 +01:00
promql_test.go promql: disable some slow cases in TestConcurrentRangeQueries 2023-03-08 14:28:30 +00:00
quantile.go promql: Separate Point into FPoint and HPoint 2023-04-13 19:25:16 +02:00
query_logger.go Close file descriptor in query logging (#11148) 2022-08-17 15:27:27 +05:30
query_logger_test.go refactor: move from io/ioutil to io and os packages (#10528) 2022-04-27 11:24:36 +02:00
test.go promql: Separate Point into FPoint and HPoint 2023-04-13 19:25:16 +02:00
test_test.go promql: Separate Point into FPoint and HPoint 2023-04-13 19:25:16 +02:00
value.go promql: Separate Point into FPoint and HPoint 2023-04-13 19:25:16 +02:00
value_test.go Shortcut ContainsSameLabelset() (#11000) 2022-07-13 15:18:10 +05:30