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// Copyright 2014 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
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package template
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import (
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"context"
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"math"
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"net/url"
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"reflect"
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"testing"
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"time"
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"github.com/stretchr/testify/require"
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"github.com/prometheus/prometheus/model/labels"
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"github.com/prometheus/prometheus/promql"
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)
func TestTemplateExpansion ( t * testing . T ) {
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testTemplateExpansion ( t , [ ] scenario {
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{
// No template.
text : "plain text" ,
output : "plain text" ,
} ,
{
// Simple value.
text : "{{ 1 }}" ,
output : "1" ,
} ,
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{
// Non-ASCII space (not allowed in text/template, see https://github.com/golang/go/blob/master/src/text/template/parse/lex.go#L98)
text : "{{ }}" ,
shouldFail : true ,
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errorMsg : "error parsing template test: template: test:1: unrecognized character in action: U+00A0" ,
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} ,
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{
// HTML escaping.
text : "{{ \"<b>\" }}" ,
output : "<b>" ,
html : true ,
} ,
{
// Disabling HTML escaping.
text : "{{ \"<b>\" | safeHtml }}" ,
output : "<b>" ,
html : true ,
} ,
{
// HTML escaping doesn't apply to non-html.
text : "{{ \"<b>\" }}" ,
output : "<b>" ,
} ,
{
// Pass multiple arguments to templates.
text : "{{define \"x\"}}{{.arg0}} {{.arg1}}{{end}}{{template \"x\" (args 1 \"2\")}}" ,
output : "1 2" ,
} ,
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{
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text : "{{ query \"1.5\" | first | value }}" ,
output : "1.5" ,
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>
2022-10-28 07:58:40 -07:00
queryResult : promql . Vector { { T : 0 , F : 1.5 } } ,
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} ,
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{
// Get value from query.
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text : "{{ query \"metric{instance='a'}\" | first | value }}" ,
queryResult : promql . Vector {
{
Metric : labels . FromStrings ( labels . MetricName , "metric" , "instance" , "a" ) ,
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>
2022-10-28 07:58:40 -07:00
T : 0 ,
F : 11 ,
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} ,
} ,
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output : "11" ,
} ,
{
// Get label from query.
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text : "{{ query \"metric{instance='a'}\" | first | label \"instance\" }}" ,
queryResult : promql . Vector {
{
Metric : labels . FromStrings ( labels . MetricName , "metric" , "instance" , "a" ) ,
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>
2022-10-28 07:58:40 -07:00
T : 0 ,
F : 11 ,
2021-10-22 01:06:44 -07:00
} ,
} ,
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output : "a" ,
} ,
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{
// Get label "__value__" from query.
text : "{{ query \"metric{__value__='a'}\" | first | strvalue }}" ,
queryResult : promql . Vector {
{
Metric : labels . FromStrings ( labels . MetricName , "metric" , "__value__" , "a" ) ,
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>
2022-10-28 07:58:40 -07:00
T : 0 ,
F : 11 ,
2021-10-22 01:06:44 -07:00
} ,
} ,
2020-07-09 01:43:32 -07:00
output : "a" ,
} ,
2015-11-28 05:45:32 -08:00
{
// Missing label is empty when using label function.
2017-11-23 04:04:54 -08:00
text : "{{ query \"metric{instance='a'}\" | first | label \"foo\" }}" ,
queryResult : promql . Vector {
{
Metric : labels . FromStrings ( labels . MetricName , "metric" , "instance" , "a" ) ,
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>
2022-10-28 07:58:40 -07:00
T : 0 ,
F : 11 ,
2021-10-22 01:06:44 -07:00
} ,
} ,
2015-11-28 05:45:32 -08:00
output : "" ,
} ,
{
// Missing label is empty when not using label function.
2017-11-23 04:04:54 -08:00
text : "{{ $x := query \"metric\" | first }}{{ $x.Labels.foo }}" ,
queryResult : promql . Vector {
{
Metric : labels . FromStrings ( labels . MetricName , "metric" , "instance" , "a" ) ,
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>
2022-10-28 07:58:40 -07:00
T : 0 ,
F : 11 ,
2021-10-22 01:06:44 -07:00
} ,
} ,
2015-11-28 05:45:32 -08:00
output : "" ,
} ,
{
2017-11-23 04:04:54 -08:00
text : "{{ $x := query \"metric\" | first }}{{ $x.Labels.foo }}" ,
queryResult : promql . Vector {
{
Metric : labels . FromStrings ( labels . MetricName , "metric" , "instance" , "a" ) ,
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>
2022-10-28 07:58:40 -07:00
T : 0 ,
F : 11 ,
2021-10-22 01:06:44 -07:00
} ,
} ,
2015-11-28 05:45:32 -08:00
output : "" ,
html : true ,
} ,
2014-05-28 10:44:54 -07:00
{
2014-08-05 11:56:05 -07:00
// Range over query and sort by label.
2017-11-23 04:04:54 -08:00
text : "{{ range query \"metric\" | sortByLabel \"instance\" }}{{.Labels.instance}}:{{.Value}}: {{end}}" ,
queryResult : promql . Vector {
{
Metric : labels . FromStrings ( labels . MetricName , "metric" , "instance" , "b" ) ,
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>
2022-10-28 07:58:40 -07:00
T : 0 ,
F : 21 ,
2020-07-09 01:43:32 -07:00
} , {
Metric : labels . FromStrings ( labels . MetricName , "metric" , "instance" , "a" ) ,
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>
2022-10-28 07:58:40 -07:00
T : 0 ,
F : 11 ,
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} ,
} ,
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output : "a:11: b:21: " ,
} ,
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{
// Simple hostname.
text : "{{ \"foo.example.com\" | stripPort }}" ,
output : "foo.example.com" ,
} ,
{
// Hostname with port.
text : "{{ \"foo.example.com:12345\" | stripPort }}" ,
output : "foo.example.com" ,
} ,
{
// Simple IPv4 address.
text : "{{ \"192.0.2.1\" | stripPort }}" ,
output : "192.0.2.1" ,
} ,
{
// IPv4 address with port.
text : "{{ \"192.0.2.1:12345\" | stripPort }}" ,
output : "192.0.2.1" ,
} ,
{
// Simple IPv6 address.
text : "{{ \"2001:0DB8::1\" | stripPort }}" ,
output : "2001:0DB8::1" ,
} ,
{
// IPv6 address with port.
text : "{{ \"[2001:0DB8::1]:12345\" | stripPort }}" ,
output : "2001:0DB8::1" ,
} ,
{
// Value can't be split into host and port.
text : "{{ \"[2001:0DB8::1]::12345\" | stripPort }}" ,
output : "[2001:0DB8::1]::12345" ,
} ,
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{
// Missing value is no value for nil options.
text : "{{ .Foo }}" ,
output : "<no value>" ,
} ,
{
// Missing value is no value for no options.
text : "{{ .Foo }}" ,
options : make ( [ ] string , 0 ) ,
output : "<no value>" ,
} ,
{
// Assert that missing value returns error with missingkey=error.
text : "{{ .Foo }}" ,
options : [ ] string { "missingkey=error" } ,
shouldFail : true ,
errorMsg : ` error executing template test: template: test:1:3: executing "test" at <.Foo>: nil data; no entry for key "Foo" ` ,
} ,
{
// Missing value is "" for nil options in ExpandHTML.
text : "{{ .Foo }}" ,
output : "" ,
html : true ,
} ,
{
// Missing value is "" for no options in ExpandHTML.
text : "{{ .Foo }}" ,
options : make ( [ ] string , 0 ) ,
output : "" ,
html : true ,
} ,
{
// Assert that missing value returns error with missingkey=error in ExpandHTML.
text : "{{ .Foo }}" ,
options : [ ] string { "missingkey=error" } ,
shouldFail : true ,
errorMsg : ` error executing template test: template: test:1:3: executing "test" at <.Foo>: nil data; no entry for key "Foo" ` ,
html : true ,
} ,
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{
// Unparsable template.
text : "{{" ,
shouldFail : true ,
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errorMsg : "error parsing template test: template: test:1: unclosed action" ,
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} ,
{
// Error in function.
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text : "{{ query \"missing\" | first }}" ,
queryResult : promql . Vector { } ,
shouldFail : true ,
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errorMsg : "error executing template test: template: test:1:21: executing \"test\" at <first>: error calling first: first() called on vector with no elements" ,
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} ,
{
// Panic.
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text : "{{ (query \"missing\").banana }}" ,
queryResult : promql . Vector { } ,
shouldFail : true ,
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errorMsg : "error executing template test: template: test:1:10: executing \"test\" at <\"missing\">: can't evaluate field banana in type template.queryResult" ,
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} ,
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{
// Regex replacement.
text : "{{ reReplaceAll \"(a)b\" \"x$1\" \"ab\" }}" ,
output : "xa" ,
} ,
{
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// Humanize - float64.
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text : "{{ range . }}{{ humanize . }}:{{ end }}" ,
input : [ ] float64 { 0.0 , 1.0 , 1234567.0 , .12 } ,
output : "0:1:1.235M:120m:" ,
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} ,
{
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// Humanize - string.
text : "{{ range . }}{{ humanize . }}:{{ end }}" ,
input : [ ] string { "0.0" , "1.0" , "1234567.0" , ".12" } ,
output : "0:1:1.235M:120m:" ,
} ,
{
// Humanize - string with error.
text : ` {{ humanize "one" }} ` ,
shouldFail : true ,
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errorMsg : ` error executing template test: template: test:1:3: executing "test" at <humanize "one">: error calling humanize: strconv.ParseFloat: parsing "one": invalid syntax ` ,
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} ,
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{
// Humanize - int.
text : "{{ range . }}{{ humanize . }}:{{ end }}" ,
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input : [ ] int64 { 0 , - 1 , 1 , 1234567 , math . MaxInt64 } ,
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output : "0:-1:1:1.235M:9.223E:" ,
} ,
{
// Humanize - uint.
text : "{{ range . }}{{ humanize . }}:{{ end }}" ,
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input : [ ] uint64 { 0 , 1 , 1234567 , math . MaxUint64 } ,
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output : "0:1:1.235M:18.45E:" ,
} ,
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{
// Humanize1024 - float64.
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text : "{{ range . }}{{ humanize1024 . }}:{{ end }}" ,
input : [ ] float64 { 0.0 , 1.0 , 1048576.0 , .12 } ,
output : "0:1:1Mi:0.12:" ,
} ,
{
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// Humanize1024 - string.
text : "{{ range . }}{{ humanize1024 . }}:{{ end }}" ,
input : [ ] string { "0.0" , "1.0" , "1048576.0" , ".12" } ,
output : "0:1:1Mi:0.12:" ,
} ,
{
// Humanize1024 - string with error.
text : ` {{ humanize1024 "one" }} ` ,
shouldFail : true ,
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errorMsg : ` error executing template test: template: test:1:3: executing "test" at <humanize1024 "one">: error calling humanize1024: strconv.ParseFloat: parsing "one": invalid syntax ` ,
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} ,
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{
// Humanize1024 - int.
text : "{{ range . }}{{ humanize1024 . }}:{{ end }}" ,
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input : [ ] int64 { 0 , - 1 , 1 , 1234567 , math . MaxInt64 } ,
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output : "0:-1:1:1.177Mi:8Ei:" ,
} ,
{
// Humanize1024 - uint.
text : "{{ range . }}{{ humanize1024 . }}:{{ end }}" ,
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input : [ ] uint64 { 0 , 1 , 1234567 , math . MaxUint64 } ,
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output : "0:1:1.177Mi:16Ei:" ,
} ,
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{
// HumanizeDuration - seconds - float64.
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text : "{{ range . }}{{ humanizeDuration . }}:{{ end }}" ,
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input : [ ] float64 { 0 , 1 , 60 , 3600 , 86400 , 86400 + 3600 , - ( 86400 * 2 + 3600 * 3 + 60 * 4 + 5 ) , 899.99 } ,
output : "0s:1s:1m 0s:1h 0m 0s:1d 0h 0m 0s:1d 1h 0m 0s:-2d 3h 4m 5s:14m 59s:" ,
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} ,
{
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// HumanizeDuration - seconds - string.
text : "{{ range . }}{{ humanizeDuration . }}:{{ end }}" ,
input : [ ] string { "0" , "1" , "60" , "3600" , "86400" } ,
output : "0s:1s:1m 0s:1h 0m 0s:1d 0h 0m 0s:" ,
} ,
{
// HumanizeDuration - subsecond and fractional seconds - float64.
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text : "{{ range . }}{{ humanizeDuration . }}:{{ end }}" ,
input : [ ] float64 { .1 , .0001 , .12345 , 60.1 , 60.5 , 1.2345 , 12.345 } ,
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output : "100ms:100us:123.5ms:1m 0s:1m 0s:1.234s:12.35s:" ,
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} ,
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{
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// HumanizeDuration - subsecond and fractional seconds - string.
text : "{{ range . }}{{ humanizeDuration . }}:{{ end }}" ,
input : [ ] string { ".1" , ".0001" , ".12345" , "60.1" , "60.5" , "1.2345" , "12.345" } ,
output : "100ms:100us:123.5ms:1m 0s:1m 0s:1.234s:12.35s:" ,
} ,
{
// HumanizeDuration - string with error.
text : ` {{ humanizeDuration "one" }} ` ,
shouldFail : true ,
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errorMsg : ` error executing template test: template: test:1:3: executing "test" at <humanizeDuration "one">: error calling humanizeDuration: strconv.ParseFloat: parsing "one": invalid syntax ` ,
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} ,
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{
// HumanizeDuration - int.
text : "{{ range . }}{{ humanizeDuration . }}:{{ end }}" ,
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input : [ ] int { 0 , - 1 , 1 , 1234567 } ,
output : "0s:-1s:1s:14d 6h 56m 7s:" ,
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} ,
{
// HumanizeDuration - uint.
text : "{{ range . }}{{ humanizeDuration . }}:{{ end }}" ,
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input : [ ] uint { 0 , 1 , 1234567 } ,
output : "0s:1s:14d 6h 56m 7s:" ,
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} ,
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{
// Humanize* Inf and NaN - float64.
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text : "{{ range . }}{{ humanize . }}:{{ humanize1024 . }}:{{ humanizeDuration . }}:{{humanizeTimestamp .}}:{{ end }}" ,
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input : [ ] float64 { math . Inf ( 1 ) , math . Inf ( - 1 ) , math . NaN ( ) } ,
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output : "+Inf:+Inf:+Inf:+Inf:-Inf:-Inf:-Inf:-Inf:NaN:NaN:NaN:NaN:" ,
} ,
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{
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// Humanize* Inf and NaN - string.
text : "{{ range . }}{{ humanize . }}:{{ humanize1024 . }}:{{ humanizeDuration . }}:{{humanizeTimestamp .}}:{{ end }}" ,
input : [ ] string { "+Inf" , "-Inf" , "NaN" } ,
output : "+Inf:+Inf:+Inf:+Inf:-Inf:-Inf:-Inf:-Inf:NaN:NaN:NaN:NaN:" ,
} ,
{
// HumanizePercentage - model.SampleValue input - float64.
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text : "{{ -0.22222 | humanizePercentage }}:{{ 0.0 | humanizePercentage }}:{{ 0.1234567 | humanizePercentage }}:{{ 1.23456 | humanizePercentage }}" ,
output : "-22.22%:0%:12.35%:123.5%" ,
} ,
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{
// HumanizePercentage - int.
text : "{{ range . }}{{ humanizePercentage . }}:{{ end }}" ,
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input : [ ] int64 { 0 , - 1 , 1 , 1234567 , math . MaxInt64 } ,
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output : "0%:-100%:100%:1.235e+08%:9.223e+20%:" ,
} ,
{
// HumanizePercentage - uint.
text : "{{ range . }}{{ humanizePercentage . }}:{{ end }}" ,
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input : [ ] uint64 { 0 , 1 , 1234567 , math . MaxUint64 } ,
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output : "0%:100%:1.235e+08%:1.845e+21%:" ,
} ,
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{
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// HumanizePercentage - model.SampleValue input - string.
text : ` {{ "-0.22222" | humanizePercentage }} : {{ "0.0" | humanizePercentage }} : {{ "0.1234567" | humanizePercentage }} : {{ "1.23456" | humanizePercentage }} ` ,
output : "-22.22%:0%:12.35%:123.5%" ,
} ,
{
// HumanizePercentage - model.SampleValue input - string with error.
text : ` {{ "one" | humanizePercentage }} ` ,
shouldFail : true ,
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errorMsg : ` error executing template test: template: test:1:11: executing "test" at <humanizePercentage>: error calling humanizePercentage: strconv.ParseFloat: parsing "one": invalid syntax ` ,
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} ,
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{
// HumanizeTimestamp - int.
text : "{{ range . }}{{ humanizeTimestamp . }}:{{ end }}" ,
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input : [ ] int64 { 0 , - 1 , 1 , 1234567 , 9223372036 } ,
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output : "1970-01-01 00:00:00 +0000 UTC:1969-12-31 23:59:59 +0000 UTC:1970-01-01 00:00:01 +0000 UTC:1970-01-15 06:56:07 +0000 UTC:2262-04-11 23:47:16 +0000 UTC:" ,
} ,
{
// HumanizeTimestamp - uint.
text : "{{ range . }}{{ humanizeTimestamp . }}:{{ end }}" ,
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input : [ ] uint64 { 0 , 1 , 1234567 , 9223372036 } ,
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output : "1970-01-01 00:00:00 +0000 UTC:1970-01-01 00:00:01 +0000 UTC:1970-01-15 06:56:07 +0000 UTC:2262-04-11 23:47:16 +0000 UTC:" ,
} ,
{
// HumanizeTimestamp - int with error.
text : "{{ range . }}{{ humanizeTimestamp . }}:{{ end }}" ,
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input : [ ] int64 { math . MinInt64 , math . MaxInt64 } ,
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shouldFail : true ,
errorMsg : ` error executing template test: template: test:1:16: executing "test" at <humanizeTimestamp .>: error calling humanizeTimestamp: -9.223372036854776e+18 cannot be represented as a nanoseconds timestamp since it overflows int64 ` ,
} ,
{
// HumanizeTimestamp - uint with error.
text : "{{ range . }}{{ humanizeTimestamp . }}:{{ end }}" ,
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input : [ ] uint64 { math . MaxUint64 } ,
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shouldFail : true ,
errorMsg : ` error executing template test: template: test:1:16: executing "test" at <humanizeTimestamp .>: error calling humanizeTimestamp: 1.8446744073709552e+19 cannot be represented as a nanoseconds timestamp since it overflows int64 ` ,
} ,
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{
// HumanizeTimestamp - model.SampleValue input - float64.
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text : "{{ 1435065584.128 | humanizeTimestamp }}" ,
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output : "2015-06-23 13:19:44.128 +0000 UTC" ,
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} ,
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{
// HumanizeTimestamp - model.SampleValue input - string.
text : ` {{ "1435065584.128" | humanizeTimestamp }} ` ,
output : "2015-06-23 13:19:44.128 +0000 UTC" ,
} ,
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{
// ToTime - model.SampleValue input - float64.
text : ` {{ ( 1435065584 .128 | toTime ) .Format "2006" }} ` ,
output : "2015" ,
} ,
{
// ToTime - model.SampleValue input - string.
text : ` {{ ( "1435065584.128" | toTime ) .Format "2006" }} ` ,
output : "2015" ,
} ,
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{
// Title.
text : "{{ \"aa bb CC\" | title }}" ,
output : "Aa Bb CC" ,
} ,
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{
// toUpper.
text : "{{ \"aa bb CC\" | toUpper }}" ,
output : "AA BB CC" ,
} ,
{
// toLower.
text : "{{ \"aA bB CC\" | toLower }}" ,
output : "aa bb cc" ,
} ,
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{
// Match.
text : "{{ match \"a+\" \"aa\" }} {{ match \"a+\" \"b\" }}" ,
output : "true false" ,
} ,
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{
// graphLink.
text : "{{ graphLink \"up\" }}" ,
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output : "/graph?g0.expr=up&g0.tab=0" ,
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} ,
{
// tableLink.
text : "{{ tableLink \"up\" }}" ,
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output : "/graph?g0.expr=up&g0.tab=1" ,
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} ,
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{
// tmpl.
text : "{{ define \"a\" }}x{{ end }}{{ $name := \"a\"}}{{ tmpl $name . }}" ,
output : "x" ,
html : true ,
} ,
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{
// pathPrefix.
text : "{{ pathPrefix }}" ,
output : "/path/prefix" ,
} ,
{
// externalURL.
text : "{{ externalURL }}" ,
output : "http://testhost:9090/path/prefix" ,
} ,
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{
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// parseDuration (using printf to ensure the return is a string).
text : "{{ printf \"%0.2f\" (parseDuration \"1h2m10ms\") }}" ,
output : "3720.01" ,
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} ,
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{
// Simple hostname.
text : "{{ \"foo.example.com\" | stripDomain }}" ,
output : "foo" ,
} ,
{
// Hostname with port.
text : "{{ \"foo.example.com:12345\" | stripDomain }}" ,
output : "foo:12345" ,
} ,
{
// Simple IPv4 address.
text : "{{ \"192.0.2.1\" | stripDomain }}" ,
output : "192.0.2.1" ,
} ,
{
// IPv4 address with port.
text : "{{ \"192.0.2.1:12345\" | stripDomain }}" ,
output : "192.0.2.1:12345" ,
} ,
{
// Simple IPv6 address.
text : "{{ \"2001:0DB8::1\" | stripDomain }}" ,
output : "2001:0DB8::1" ,
} ,
{
// IPv6 address with port.
text : "{{ \"[2001:0DB8::1]:12345\" | stripDomain }}" ,
output : "[2001:0DB8::1]:12345" ,
} ,
{
// Value can't be split into host and port.
text : "{{ \"[2001:0DB8::1]::12345\" | stripDomain }}" ,
output : "[2001:0DB8::1]::12345" ,
} ,
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} )
}
type scenario struct {
text string
output string
input interface { }
options [ ] string
queryResult promql . Vector
shouldFail bool
html bool
errorMsg string
}
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func testTemplateExpansion ( t * testing . T , scenarios [ ] scenario ) {
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extURL , err := url . Parse ( "http://testhost:9090/path/prefix" )
if err != nil {
panic ( err )
}
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for _ , s := range scenarios {
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queryFunc := func ( _ context . Context , _ string , _ time . Time ) ( promql . Vector , error ) {
return s . queryResult , nil
}
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var result string
var err error
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expander := NewTemplateExpander ( context . Background ( ) , s . text , "test" , s . input , 0 , queryFunc , extURL , s . options )
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if s . html {
result , err = expander . ExpandHTML ( nil )
} else {
result , err = expander . Expand ( )
}
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if s . shouldFail {
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require . Error ( t , err , "%v" , s . text )
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require . EqualError ( t , err , s . errorMsg )
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continue
}
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require . NoError ( t , err )
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if err == nil {
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require . Equal ( t , s . output , result )
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}
}
}
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func Test_floatToTime ( t * testing . T ) {
type args struct {
v float64
}
tests := [ ] struct {
name string
args args
want * time . Time
wantErr bool
} {
{
"happy path" ,
args {
v : 1657155181 ,
} ,
func ( ) * time . Time {
tm := time . Date ( 2022 , 7 , 7 , 0 , 53 , 1 , 0 , time . UTC )
return & tm
} ( ) ,
false ,
} ,
{
"more than math.MaxInt64" ,
args {
v : 1.79769313486231570814527423731704356798070e+300 ,
} ,
nil ,
true ,
} ,
{
"less than math.MinInt64" ,
args {
v : - 1.79769313486231570814527423731704356798070e+300 ,
} ,
nil ,
true ,
} ,
}
for _ , tt := range tests {
t . Run ( tt . name , func ( t * testing . T ) {
got , err := floatToTime ( tt . args . v )
if ( err != nil ) != tt . wantErr {
t . Errorf ( "floatToTime() error = %v, wantErr %v" , err , tt . wantErr )
return
}
if ! reflect . DeepEqual ( got , tt . want ) {
t . Errorf ( "floatToTime() got = %v, want %v" , got , tt . want )
}
} )
}
}