PoC: Use generics instead of code gen for vectors #1412
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ryantxu
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| // This function will write code to the console that should be copy/pasted into frame_json.gen.go | ||
| // when changes are required. Typically this function will always be skipped. | ||
| func TestGenerateGenericArrowCode(t *testing.T) { |
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🎉 really great to replace the bespoke code generation!
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@ryantxu if @grafana/grafana-datasources-core-services decides this is worth testing, do you think it makes sense to copy these changes to another folder/package and switch between them with an env var (similar to prom framing changes)? i can't think of another option at the moment to test these types of changes without introducing extra overhead 🤔
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@toddtreece what was the prom framing changes again?
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@asimpson a feature toggle was used to flip between an unmodified copy of the prometheus datasource framing code and a replacement. once the new code was determined to be ok, the old framing code was deleted
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@itsmylife might have thoughts here since he helped pushed that project to completion
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is this faster because it can avoid the casting? |
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@ryantxu there are a few improvements mixed in so it's probably a combination of things, but i think it's primarily related to avoiding extra allocations related to the use of |
There are already fast paths for certain types, but floats and various
others were missing, so we were falling back to a very allocation-heavy
interface{} method where every value was boxed then unboxed later.
Now that we have generic vectors we can read directly into them and
avoid a ton of allocations, which speeds things up dramatically,
especially for larger vectors.
Benchmarks for JSON unmarshalling:
```
goos: linux
goarch: amd64
pkg: github.com/grafana/grafana-plugin-sdk-go/data
cpu: AMD Ryzen 9 7950X 16-Core Processor
│ benchmark-baseline.txt │ benchmark-final.txt │
│ sec/op │ sec/op vs base │
FrameUnmarshalJSON-32 77.72µ ± ∞ ¹ 76.13µ ± ∞ ¹ ~ (p=0.421 n=5)
FrameUnmarshalJSON_FromFrameToJSON-32 74.86µ ± ∞ ¹ 76.81µ ± ∞ ¹ +2.60% (p=0.032 n=5)
FrameUnmarshalJSON_Sizes/Rows_10-32 5.345µ ± ∞ ¹ 5.065µ ± ∞ ¹ -5.24% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_100-32 25.44µ ± ∞ ¹ 22.27µ ± ∞ ¹ -12.44% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_1000-32 231.4µ ± ∞ ¹ 199.0µ ± ∞ ¹ -13.99% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_10000-32 2.523m ± ∞ ¹ 2.090m ± ∞ ¹ -17.19% (p=0.008 n=5)
FrameUnmarshalJSON_Parallel-32 25.92µ ± ∞ ¹ 25.42µ ± ∞ ¹ ~ (p=1.000 n=5)
geomean 73.85µ 68.36µ -7.43%
¹ need >= 6 samples for confidence interval at level 0.95
│ benchmark-baseline.txt │ benchmark-final.txt │
│ B/s │ B/s vs base │
FrameUnmarshalJSON-32 69.90Mi ± ∞ ¹ 71.37Mi ± ∞ ¹ ~ (p=0.421 n=5)
FrameUnmarshalJSON_FromFrameToJSON-32 72.45Mi ± ∞ ¹ 70.61Mi ± ∞ ¹ -2.54% (p=0.040 n=5)
FrameUnmarshalJSON_Sizes/Rows_10-32 93.67Mi ± ∞ ¹ 98.85Mi ± ∞ ¹ +5.53% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_100-32 118.8Mi ± ∞ ¹ 135.6Mi ± ∞ ¹ +14.21% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_1000-32 129.4Mi ± ∞ ¹ 150.5Mi ± ∞ ¹ +16.26% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_10000-32 125.4Mi ± ∞ ¹ 151.4Mi ± ∞ ¹ +20.75% (p=0.008 n=5)
geomean 98.52Mi 107.5Mi +9.07%
¹ need >= 6 samples for confidence interval at level 0.95
│ benchmark-baseline.txt │ benchmark-final.txt │
│ B/op │ B/op vs base │
FrameUnmarshalJSON-32 24.84Ki ± ∞ ¹ 24.54Ki ± ∞ ¹ -1.23% (p=0.008 n=5)
FrameUnmarshalJSON_FromFrameToJSON-32 24.84Ki ± ∞ ¹ 24.54Ki ± ∞ ¹ -1.23% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_10-32 3.328Ki ± ∞ ¹ 2.953Ki ± ∞ ¹ -11.27% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_100-32 15.50Ki ± ∞ ¹ 12.67Ki ± ∞ ¹ -18.25% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_1000-32 132.44Ki ± ∞ ¹ 98.73Ki ± ∞ ¹ -25.45% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_10000-32 1.822Mi ± ∞ ¹ 1.320Mi ± ∞ ¹ -27.59% (p=0.008 n=5)
FrameUnmarshalJSON_Parallel-32 24.84Ki ± ∞ ¹ 24.54Ki ± ∞ ¹ -1.22% (p=0.008 n=5)
geomean 41.02Ki 35.69Ki -13.00%
¹ need >= 6 samples for confidence interval at level 0.95
│ benchmark-baseline.txt │ benchmark-final.txt │
│ allocs/op │ allocs/op vs base │
FrameUnmarshalJSON-32 770.0 ± ∞ ¹ 757.0 ± ∞ ¹ -1.69% (p=0.008 n=5)
FrameUnmarshalJSON_FromFrameToJSON-32 770.0 ± ∞ ¹ 757.0 ± ∞ ¹ -1.69% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_10-32 100.00 ± ∞ ¹ 77.00 ± ∞ ¹ -23.00% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_100-32 373.0 ± ∞ ¹ 170.0 ± ∞ ¹ -54.42% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_1000-32 3.076k ± ∞ ¹ 1.073k ± ∞ ¹ -65.12% (p=0.008 n=5)
FrameUnmarshalJSON_Sizes/Rows_10000-32 30.09k ± ∞ ¹ 10.08k ± ∞ ¹ -66.50% (p=0.008 n=5)
FrameUnmarshalJSON_Parallel-32 770.0 ± ∞ ¹ 757.0 ± ∞ ¹ -1.69% (p=0.008 n=5)
geomean 1.067k 671.3 -37.10%
¹ need >= 6 samples for confidence interval at level 0.95
```
…ana-plugin-sdk-go into toddtreece/generics
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What this PR does / why we need it:
grafana tests
ran using k6 with two parallel queries (loki and prom). max 5000 results each.
before:
after:
Which issue(s) this PR fixes:
Fixes #
Special notes for your reviewer: