mirror of
https://github.com/VictoriaMetrics/VictoriaMetrics.git
synced 2024-12-15 16:30:55 +01:00
7acc54025e
This reverts commit 9e99f2f5b3.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4068
Reason for revert: this breaks valid use cases:
- If timestamps aren't specified in the incoming samples on purpose. For example, if stream aggregation is used
as StatsD replacement. StatsD protocol has no timestamp concept for incoming samples.
See https://github.com/b/statsd_spec
- If all the samples must be aggregated, even if they contain stale timestamps.
for example, if the stream aggregation produces some counter of some events,
it may be better to count all the events even if they were delayed before
being ingested into VictoriaMetrics.
Is is also unclear how to determine whether the sample becomes stale.
For example, if the aggregation interval equals to 1h, and the previous
aggregation cycle just finished 10 minutes ago, what to do with the newly
incoming sample with the timestamp 30 minutes older than the current time?
The answer highly depends on the context, so it is unsafe to uncoditionally
use a single logic for dropping the old samples here.
796 lines
17 KiB
Go
796 lines
17 KiB
Go
package streamaggr
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import (
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"fmt"
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"sort"
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"strings"
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"sync"
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"testing"
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"time"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/prompbmarshal"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/promrelabel"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/protoparser/prometheus"
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)
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func TestAggregatorsFailure(t *testing.T) {
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f := func(config string) {
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t.Helper()
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pushFunc := func(tss []prompbmarshal.TimeSeries) {
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panic(fmt.Errorf("pushFunc shouldn't be called"))
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}
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a, err := NewAggregatorsFromData([]byte(config), pushFunc, 0)
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if err == nil {
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t.Fatalf("expecting non-nil error")
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}
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if a != nil {
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t.Fatalf("expecting nil a")
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}
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}
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// Invalid config
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f(`foobar`)
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// Unknown option
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f(`
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- interval: 1m
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outputs: [total]
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foobar: baz
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`)
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// missing interval
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f(`
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- outputs: [total]
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`)
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// missing outputs
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f(`
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- interval: 1m
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`)
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// Invalid output
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f(`
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- interval: 1m
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outputs: [foobar]
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`)
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// Negative interval
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f(`- interval: -5m`)
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// Too small interval
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f(`- interval: 10ms`)
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// Invalid input_relabel_configs
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f(`
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- interval: 1m
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outputs: [total]
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input_relabel_configs:
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- foo: bar
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`)
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f(`
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- interval: 1m
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outputs: [total]
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input_relabel_configs:
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- action: replace
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`)
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// Invalid output_relabel_configs
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f(`
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- interval: 1m
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outputs: [total]
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output_relabel_configs:
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- foo: bar
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`)
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f(`
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- interval: 1m
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outputs: [total]
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output_relabel_configs:
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- action: replace
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`)
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// Both by and without are non-empty
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f(`
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- interval: 1m
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outputs: [total]
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by: [foo]
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without: [bar]
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`)
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// Invalid quantiles()
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f(`
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- interval: 1m
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outputs: ["quantiles("]
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`)
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f(`
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- interval: 1m
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outputs: ["quantiles()"]
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`)
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f(`
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- interval: 1m
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outputs: ["quantiles(foo)"]
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`)
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f(`
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- interval: 1m
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outputs: ["quantiles(-0.5)"]
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`)
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f(`
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- interval: 1m
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outputs: ["quantiles(1.5)"]
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`)
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}
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func TestAggregatorsEqual(t *testing.T) {
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f := func(a, b string, expectedResult bool) {
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t.Helper()
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pushFunc := func(tss []prompbmarshal.TimeSeries) {}
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aa, err := NewAggregatorsFromData([]byte(a), pushFunc, 0)
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if err != nil {
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t.Fatalf("cannot initialize aggregators: %s", err)
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}
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ab, err := NewAggregatorsFromData([]byte(b), pushFunc, 0)
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if err != nil {
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t.Fatalf("cannot initialize aggregators: %s", err)
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}
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result := aa.Equal(ab)
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if result != expectedResult {
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t.Fatalf("unexpected result; got %v; want %v", result, expectedResult)
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}
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}
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f("", "", true)
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f(`
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- outputs: [total]
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interval: 5m
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`, ``, false)
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f(`
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- outputs: [total]
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interval: 5m
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`, `
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- outputs: [total]
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interval: 5m
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`, true)
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f(`
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- outputs: [total]
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interval: 3m
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`, `
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- outputs: [total]
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interval: 5m
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`, false)
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}
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func TestAggregatorsSuccess(t *testing.T) {
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f := func(config, inputMetrics, outputMetricsExpected string) {
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t.Helper()
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// Initialize Aggregators
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var tssOutput []prompbmarshal.TimeSeries
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var tssOutputLock sync.Mutex
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pushFunc := func(tss []prompbmarshal.TimeSeries) {
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tssOutputLock.Lock()
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for _, ts := range tss {
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labelsCopy := append([]prompbmarshal.Label{}, ts.Labels...)
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samplesCopy := append([]prompbmarshal.Sample{}, ts.Samples...)
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tssOutput = append(tssOutput, prompbmarshal.TimeSeries{
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Labels: labelsCopy,
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Samples: samplesCopy,
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})
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}
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tssOutputLock.Unlock()
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}
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a, err := NewAggregatorsFromData([]byte(config), pushFunc, 0)
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if err != nil {
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t.Fatalf("cannot initialize aggregators: %s", err)
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}
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// Push the inputMetrics to Aggregators
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tssInput := mustParsePromMetrics(inputMetrics)
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a.Push(tssInput)
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a.MustStop()
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// Verify the tssOutput contains the expected metrics
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tsStrings := make([]string, len(tssOutput))
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for i, ts := range tssOutput {
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tsStrings[i] = timeSeriesToString(ts)
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}
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sort.Strings(tsStrings)
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outputMetrics := strings.Join(tsStrings, "")
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if outputMetrics != outputMetricsExpected {
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t.Fatalf("unexpected output metrics;\ngot\n%s\nwant\n%s", outputMetrics, outputMetricsExpected)
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}
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}
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// Empty config
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f(``, ``, ``)
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f(``, `foo{bar="baz"} 1`, ``)
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f(``, "foo 1\nbaz 2", ``)
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// Empty by list - aggregate only by time
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f(`
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- interval: 1m
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outputs: [count_samples, sum_samples, count_series, last]
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, `bar:1m_count_samples 1
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bar:1m_count_series 1
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bar:1m_last 5
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bar:1m_sum_samples 5
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foo:1m_count_samples{abc="123"} 2
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foo:1m_count_samples{abc="456",de="fg"} 1
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foo:1m_count_series{abc="123"} 1
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foo:1m_count_series{abc="456",de="fg"} 1
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foo:1m_last{abc="123"} 8.5
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foo:1m_last{abc="456",de="fg"} 8
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foo:1m_sum_samples{abc="123"} 12.5
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foo:1m_sum_samples{abc="456",de="fg"} 8
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`)
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// Special case: __name__ in `by` list - this is the same as empty `by` list
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f(`
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- interval: 1m
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by: [__name__]
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outputs: [count_samples, sum_samples, count_series]
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, `bar:1m_count_samples 1
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bar:1m_count_series 1
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bar:1m_sum_samples 5
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foo:1m_count_samples 3
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foo:1m_count_series 2
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foo:1m_sum_samples 20.5
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`)
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// Non-empty `by` list with non-existing labels
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f(`
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- interval: 1m
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by: [foo, bar]
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outputs: [count_samples, sum_samples, count_series]
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, `bar:1m_by_bar_foo_count_samples 1
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bar:1m_by_bar_foo_count_series 1
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bar:1m_by_bar_foo_sum_samples 5
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foo:1m_by_bar_foo_count_samples 3
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foo:1m_by_bar_foo_count_series 2
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foo:1m_by_bar_foo_sum_samples 20.5
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`)
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// Non-empty `by` list with existing label
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f(`
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- interval: 1m
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by: [abc]
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outputs: [count_samples, sum_samples, count_series]
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, `bar:1m_by_abc_count_samples 1
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bar:1m_by_abc_count_series 1
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bar:1m_by_abc_sum_samples 5
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foo:1m_by_abc_count_samples{abc="123"} 2
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foo:1m_by_abc_count_samples{abc="456"} 1
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foo:1m_by_abc_count_series{abc="123"} 1
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foo:1m_by_abc_count_series{abc="456"} 1
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foo:1m_by_abc_sum_samples{abc="123"} 12.5
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foo:1m_by_abc_sum_samples{abc="456"} 8
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`)
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// Non-empty `by` list with duplicate existing label
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f(`
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- interval: 1m
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by: [abc, abc]
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outputs: [count_samples, sum_samples, count_series]
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, `bar:1m_by_abc_count_samples 1
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bar:1m_by_abc_count_series 1
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bar:1m_by_abc_sum_samples 5
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foo:1m_by_abc_count_samples{abc="123"} 2
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foo:1m_by_abc_count_samples{abc="456"} 1
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foo:1m_by_abc_count_series{abc="123"} 1
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foo:1m_by_abc_count_series{abc="456"} 1
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foo:1m_by_abc_sum_samples{abc="123"} 12.5
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foo:1m_by_abc_sum_samples{abc="456"} 8
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`)
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// Non-empty `without` list with non-existing labels
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f(`
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- interval: 1m
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without: [foo]
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outputs: [count_samples, sum_samples, count_series]
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, `bar:1m_without_foo_count_samples 1
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bar:1m_without_foo_count_series 1
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bar:1m_without_foo_sum_samples 5
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foo:1m_without_foo_count_samples{abc="123"} 2
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foo:1m_without_foo_count_samples{abc="456",de="fg"} 1
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foo:1m_without_foo_count_series{abc="123"} 1
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foo:1m_without_foo_count_series{abc="456",de="fg"} 1
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foo:1m_without_foo_sum_samples{abc="123"} 12.5
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foo:1m_without_foo_sum_samples{abc="456",de="fg"} 8
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`)
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// Non-empty `without` list with existing labels
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f(`
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- interval: 1m
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without: [abc]
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outputs: [count_samples, sum_samples, count_series]
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, `bar:1m_without_abc_count_samples 1
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bar:1m_without_abc_count_series 1
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bar:1m_without_abc_sum_samples 5
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foo:1m_without_abc_count_samples 2
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foo:1m_without_abc_count_samples{de="fg"} 1
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foo:1m_without_abc_count_series 1
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foo:1m_without_abc_count_series{de="fg"} 1
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foo:1m_without_abc_sum_samples 12.5
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foo:1m_without_abc_sum_samples{de="fg"} 8
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`)
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// Special case: __name__ in `without` list
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f(`
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- interval: 1m
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without: [__name__]
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outputs: [count_samples, sum_samples, count_series]
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, `:1m_count_samples 1
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:1m_count_samples{abc="123"} 2
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:1m_count_samples{abc="456",de="fg"} 1
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:1m_count_series 1
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:1m_count_series{abc="123"} 1
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:1m_count_series{abc="456",de="fg"} 1
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:1m_sum_samples 5
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:1m_sum_samples{abc="123"} 12.5
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:1m_sum_samples{abc="456",de="fg"} 8
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`)
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// drop some input metrics
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f(`
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- interval: 1m
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without: [abc]
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outputs: [count_samples, sum_samples, count_series]
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input_relabel_configs:
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- if: 'foo'
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action: drop
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, `bar:1m_without_abc_count_samples 1
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bar:1m_without_abc_count_series 1
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bar:1m_without_abc_sum_samples 5
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`)
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// rename output metrics
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f(`
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- interval: 1m
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without: [abc]
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outputs: [count_samples, sum_samples, count_series]
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output_relabel_configs:
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- action: replace_all
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source_labels: [__name__]
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regex: ":|_"
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replacement: "-"
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target_label: __name__
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- action: drop
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source_labels: [de]
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regex: fg
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, `bar-1m-without-abc-count-samples 1
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bar-1m-without-abc-count-series 1
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bar-1m-without-abc-sum-samples 5
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foo-1m-without-abc-count-samples 2
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foo-1m-without-abc-count-series 1
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foo-1m-without-abc-sum-samples 12.5
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`)
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// match doesn't match anything
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f(`
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- interval: 1m
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without: [abc]
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outputs: [count_samples, sum_samples, count_series]
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match: '{non_existing_label!=""}'
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, ``)
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// match matches foo series with non-empty abc label
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f(`
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- interval: 1m
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by: [abc]
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outputs: [count_samples, sum_samples, count_series]
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match: 'foo{abc=~".+"}'
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`, `
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foo{abc="123"} 4
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bar 5
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foo{abc="123"} 8.5
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foo{abc="456",de="fg"} 8
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`, `foo:1m_by_abc_count_samples{abc="123"} 2
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foo:1m_by_abc_count_samples{abc="456"} 1
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foo:1m_by_abc_count_series{abc="123"} 1
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foo:1m_by_abc_count_series{abc="456"} 1
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foo:1m_by_abc_sum_samples{abc="123"} 12.5
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foo:1m_by_abc_sum_samples{abc="456"} 8
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`)
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// total output for non-repeated series
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f(`
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- interval: 1m
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outputs: [total]
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`, `
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foo 123
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bar{baz="qwe"} 4.34
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`, `bar:1m_total{baz="qwe"} 0
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foo:1m_total 0
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`)
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// total output for repeated series
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f(`
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- interval: 1m
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outputs: [total]
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`, `
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foo 123
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bar{baz="qwe"} 1.32
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bar{baz="qwe"} 4.34
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bar{baz="qwe"} 2
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foo{baz="qwe"} -5
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bar{baz="qwer"} 343
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bar{baz="qwer"} 344
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foo{baz="qwe"} 10
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`, `bar:1m_total{baz="qwe"} 5.02
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bar:1m_total{baz="qwer"} 1
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foo:1m_total 0
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foo:1m_total{baz="qwe"} 15
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`)
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// total output for repeated series with group by __name__
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f(`
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- interval: 1m
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by: [__name__]
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outputs: [total]
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`, `
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foo 123
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bar{baz="qwe"} 1.32
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bar{baz="qwe"} 4.34
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bar{baz="qwe"} 2
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foo{baz="qwe"} -5
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bar{baz="qwer"} 343
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bar{baz="qwer"} 344
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foo{baz="qwe"} 10
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`, `bar:1m_total 6.02
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foo:1m_total 15
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`)
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|
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// increase output for non-repeated series
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f(`
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- interval: 1m
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outputs: [increase]
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`, `
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foo 123
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bar{baz="qwe"} 4.34
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`, `bar:1m_increase{baz="qwe"} 0
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foo:1m_increase 0
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`)
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|
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// increase output for repeated series
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f(`
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- interval: 1m
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outputs: [increase]
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`, `
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foo 123
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bar{baz="qwe"} 1.32
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bar{baz="qwe"} 4.34
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bar{baz="qwe"} 2
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foo{baz="qwe"} -5
|
|
bar{baz="qwer"} 343
|
|
bar{baz="qwer"} 344
|
|
foo{baz="qwe"} 10
|
|
`, `bar:1m_increase{baz="qwe"} 5.02
|
|
bar:1m_increase{baz="qwer"} 1
|
|
foo:1m_increase 0
|
|
foo:1m_increase{baz="qwe"} 15
|
|
`)
|
|
|
|
// multiple aggregate configs
|
|
f(`
|
|
- interval: 1m
|
|
outputs: [count_series, sum_samples]
|
|
- interval: 5m
|
|
by: [bar]
|
|
outputs: [sum_samples]
|
|
`, `
|
|
foo 1
|
|
foo{bar="baz"} 2
|
|
foo 3.3
|
|
`, `foo:1m_count_series 1
|
|
foo:1m_count_series{bar="baz"} 1
|
|
foo:1m_sum_samples 4.3
|
|
foo:1m_sum_samples{bar="baz"} 2
|
|
foo:5m_by_bar_sum_samples 4.3
|
|
foo:5m_by_bar_sum_samples{bar="baz"} 2
|
|
`)
|
|
|
|
// min and max outputs
|
|
f(`
|
|
- interval: 1m
|
|
outputs: [min, max]
|
|
`, `
|
|
foo{abc="123"} 4
|
|
bar 5
|
|
foo{abc="123"} 8.5
|
|
foo{abc="456",de="fg"} 8
|
|
`, `bar:1m_max 5
|
|
bar:1m_min 5
|
|
foo:1m_max{abc="123"} 8.5
|
|
foo:1m_max{abc="456",de="fg"} 8
|
|
foo:1m_min{abc="123"} 4
|
|
foo:1m_min{abc="456",de="fg"} 8
|
|
`)
|
|
|
|
// avg output
|
|
f(`
|
|
- interval: 1m
|
|
outputs: [avg]
|
|
`, `
|
|
foo{abc="123"} 4
|
|
bar 5
|
|
foo{abc="123"} 8.5
|
|
foo{abc="456",de="fg"} 8
|
|
`, `bar:1m_avg 5
|
|
foo:1m_avg{abc="123"} 6.25
|
|
foo:1m_avg{abc="456",de="fg"} 8
|
|
`)
|
|
|
|
// stddev output
|
|
f(`
|
|
- interval: 1m
|
|
outputs: [stddev]
|
|
`, `
|
|
foo{abc="123"} 4
|
|
bar 5
|
|
foo{abc="123"} 8.5
|
|
foo{abc="456",de="fg"} 8
|
|
`, `bar:1m_stddev 0
|
|
foo:1m_stddev{abc="123"} 2.25
|
|
foo:1m_stddev{abc="456",de="fg"} 0
|
|
`)
|
|
|
|
// stdvar output
|
|
f(`
|
|
- interval: 1m
|
|
outputs: [stdvar]
|
|
`, `
|
|
foo{abc="123"} 4
|
|
bar 5
|
|
foo{abc="123"} 8.5
|
|
foo{abc="456",de="fg"} 8
|
|
`, `bar:1m_stdvar 0
|
|
foo:1m_stdvar{abc="123"} 5.0625
|
|
foo:1m_stdvar{abc="456",de="fg"} 0
|
|
`)
|
|
|
|
// histogram_bucket output
|
|
f(`
|
|
- interval: 1m
|
|
outputs: [histogram_bucket]
|
|
`, `
|
|
cpu_usage{cpu="1"} 12.5
|
|
cpu_usage{cpu="1"} 13.3
|
|
cpu_usage{cpu="1"} 13
|
|
cpu_usage{cpu="1"} 12
|
|
cpu_usage{cpu="1"} 14
|
|
cpu_usage{cpu="1"} 25
|
|
cpu_usage{cpu="2"} 90
|
|
`, `cpu_usage:1m_histogram_bucket{cpu="1",vmrange="1.136e+01...1.292e+01"} 2
|
|
cpu_usage:1m_histogram_bucket{cpu="1",vmrange="1.292e+01...1.468e+01"} 3
|
|
cpu_usage:1m_histogram_bucket{cpu="1",vmrange="2.448e+01...2.783e+01"} 1
|
|
cpu_usage:1m_histogram_bucket{cpu="2",vmrange="8.799e+01...1.000e+02"} 1
|
|
`)
|
|
|
|
// histogram_bucket output without cpu
|
|
f(`
|
|
- interval: 1m
|
|
without: [cpu]
|
|
outputs: [histogram_bucket]
|
|
`, `
|
|
cpu_usage{cpu="1"} 12.5
|
|
cpu_usage{cpu="1"} 13.3
|
|
cpu_usage{cpu="1"} 13
|
|
cpu_usage{cpu="1"} 12
|
|
cpu_usage{cpu="1"} 14
|
|
cpu_usage{cpu="1"} 25
|
|
cpu_usage{cpu="2"} 90
|
|
`, `cpu_usage:1m_without_cpu_histogram_bucket{vmrange="1.136e+01...1.292e+01"} 2
|
|
cpu_usage:1m_without_cpu_histogram_bucket{vmrange="1.292e+01...1.468e+01"} 3
|
|
cpu_usage:1m_without_cpu_histogram_bucket{vmrange="2.448e+01...2.783e+01"} 1
|
|
cpu_usage:1m_without_cpu_histogram_bucket{vmrange="8.799e+01...1.000e+02"} 1
|
|
`)
|
|
|
|
// quantiles output
|
|
f(`
|
|
- interval: 1m
|
|
outputs: ["quantiles(0, 0.5, 1)"]
|
|
`, `
|
|
cpu_usage{cpu="1"} 12.5
|
|
cpu_usage{cpu="1"} 13.3
|
|
cpu_usage{cpu="1"} 13
|
|
cpu_usage{cpu="1"} 12
|
|
cpu_usage{cpu="1"} 14
|
|
cpu_usage{cpu="1"} 25
|
|
cpu_usage{cpu="2"} 90
|
|
`, `cpu_usage:1m_quantiles{cpu="1",quantile="0"} 12
|
|
cpu_usage:1m_quantiles{cpu="1",quantile="0.5"} 13.3
|
|
cpu_usage:1m_quantiles{cpu="1",quantile="1"} 25
|
|
cpu_usage:1m_quantiles{cpu="2",quantile="0"} 90
|
|
cpu_usage:1m_quantiles{cpu="2",quantile="0.5"} 90
|
|
cpu_usage:1m_quantiles{cpu="2",quantile="1"} 90
|
|
`)
|
|
|
|
// quantiles output without cpu
|
|
f(`
|
|
- interval: 1m
|
|
without: [cpu]
|
|
outputs: ["quantiles(0, 0.5, 1)"]
|
|
`, `
|
|
cpu_usage{cpu="1"} 12.5
|
|
cpu_usage{cpu="1"} 13.3
|
|
cpu_usage{cpu="1"} 13
|
|
cpu_usage{cpu="1"} 12
|
|
cpu_usage{cpu="1"} 14
|
|
cpu_usage{cpu="1"} 25
|
|
cpu_usage{cpu="2"} 90
|
|
`, `cpu_usage:1m_without_cpu_quantiles{quantile="0"} 12
|
|
cpu_usage:1m_without_cpu_quantiles{quantile="0.5"} 13.3
|
|
cpu_usage:1m_without_cpu_quantiles{quantile="1"} 90
|
|
`)
|
|
}
|
|
|
|
func TestAggregatorsWithDedupInterval(t *testing.T) {
|
|
f := func(config, inputMetrics, outputMetricsExpected string) {
|
|
t.Helper()
|
|
|
|
// Initialize Aggregators
|
|
var tssOutput []prompbmarshal.TimeSeries
|
|
var tssOutputLock sync.Mutex
|
|
pushFunc := func(tss []prompbmarshal.TimeSeries) {
|
|
tssOutputLock.Lock()
|
|
for _, ts := range tss {
|
|
labelsCopy := append([]prompbmarshal.Label{}, ts.Labels...)
|
|
samplesCopy := append([]prompbmarshal.Sample{}, ts.Samples...)
|
|
tssOutput = append(tssOutput, prompbmarshal.TimeSeries{
|
|
Labels: labelsCopy,
|
|
Samples: samplesCopy,
|
|
})
|
|
}
|
|
tssOutputLock.Unlock()
|
|
}
|
|
const dedupInterval = time.Hour
|
|
a, err := NewAggregatorsFromData([]byte(config), pushFunc, dedupInterval)
|
|
if err != nil {
|
|
t.Fatalf("cannot initialize aggregators: %s", err)
|
|
}
|
|
|
|
// Push the inputMetrics to Aggregators
|
|
tssInput := mustParsePromMetrics(inputMetrics)
|
|
a.Push(tssInput)
|
|
if a != nil {
|
|
for _, aggr := range a.as {
|
|
aggr.dedupFlush()
|
|
aggr.flush()
|
|
}
|
|
}
|
|
a.MustStop()
|
|
|
|
// Verify the tssOutput contains the expected metrics
|
|
tsStrings := make([]string, len(tssOutput))
|
|
for i, ts := range tssOutput {
|
|
tsStrings[i] = timeSeriesToString(ts)
|
|
}
|
|
sort.Strings(tsStrings)
|
|
outputMetrics := strings.Join(tsStrings, "")
|
|
if outputMetrics != outputMetricsExpected {
|
|
t.Fatalf("unexpected output metrics;\ngot\n%s\nwant\n%s", outputMetrics, outputMetricsExpected)
|
|
}
|
|
}
|
|
|
|
f(`
|
|
- interval: 1m
|
|
outputs: [sum_samples]
|
|
`, `
|
|
foo 123
|
|
bar 567
|
|
`, `bar:1m_sum_samples 567
|
|
foo:1m_sum_samples 123
|
|
`)
|
|
|
|
f(`
|
|
- interval: 1m
|
|
outputs: [sum_samples]
|
|
`, `
|
|
foo 123
|
|
bar{baz="qwe"} 1.32
|
|
bar{baz="qwe"} 4.34
|
|
bar{baz="qwe"} 2
|
|
foo{baz="qwe"} -5
|
|
bar{baz="qwer"} 343
|
|
bar{baz="qwer"} 344
|
|
foo{baz="qwe"} 10
|
|
`, `bar:1m_sum_samples{baz="qwe"} 2
|
|
bar:1m_sum_samples{baz="qwer"} 344
|
|
foo:1m_sum_samples 123
|
|
foo:1m_sum_samples{baz="qwe"} 10
|
|
`)
|
|
}
|
|
|
|
func timeSeriesToString(ts prompbmarshal.TimeSeries) string {
|
|
labelsString := promrelabel.LabelsToString(ts.Labels)
|
|
if len(ts.Samples) != 1 {
|
|
panic(fmt.Errorf("unexpected number of samples for %s: %d; want 1", labelsString, len(ts.Samples)))
|
|
}
|
|
return fmt.Sprintf("%s %v\n", labelsString, ts.Samples[0].Value)
|
|
}
|
|
|
|
func mustParsePromMetrics(s string) []prompbmarshal.TimeSeries {
|
|
var rows prometheus.Rows
|
|
errLogger := func(s string) {
|
|
panic(fmt.Errorf("unexpected error when parsing Prometheus metrics: %s", s))
|
|
}
|
|
rows.UnmarshalWithErrLogger(s, errLogger)
|
|
var tss []prompbmarshal.TimeSeries
|
|
samples := make([]prompbmarshal.Sample, 0, len(rows.Rows))
|
|
for _, row := range rows.Rows {
|
|
labels := make([]prompbmarshal.Label, 0, len(row.Tags)+1)
|
|
labels = append(labels, prompbmarshal.Label{
|
|
Name: "__name__",
|
|
Value: row.Metric,
|
|
})
|
|
for _, tag := range row.Tags {
|
|
labels = append(labels, prompbmarshal.Label{
|
|
Name: tag.Key,
|
|
Value: tag.Value,
|
|
})
|
|
}
|
|
samples = append(samples, prompbmarshal.Sample{
|
|
Value: row.Value,
|
|
Timestamp: row.Timestamp,
|
|
})
|
|
ts := prompbmarshal.TimeSeries{
|
|
Labels: labels,
|
|
Samples: samples[len(samples)-1:],
|
|
}
|
|
tss = append(tss, ts)
|
|
}
|
|
return tss
|
|
}
|