VictoriaMetrics/lib/streamaggr/streamaggr_test.go

package streamaggr

import (
	"fmt"
	"sort"
	"strings"
	"sync"
	"testing"
	"time"

	"github.com/VictoriaMetrics/VictoriaMetrics/lib/fasttime"
	"github.com/VictoriaMetrics/VictoriaMetrics/lib/prompbmarshal"
	"github.com/VictoriaMetrics/VictoriaMetrics/lib/promrelabel"
	"github.com/VictoriaMetrics/VictoriaMetrics/lib/protoparser/prometheus"
)

func TestAggregatorsFailure(t *testing.T) {
	f := func(config string) {
		t.Helper()
		pushFunc := func(tss []prompbmarshal.TimeSeries) {
			panic(fmt.Errorf("pushFunc shouldn't be called"))
		}
		a, err := NewAggregatorsFromData([]byte(config), pushFunc, 0)
		if err == nil {
			t.Fatalf("expecting non-nil error")
		}
		if a != nil {
			t.Fatalf("expecting nil a")
		}
	}

	// Invalid config
	f(`foobar`)

	// Unknown option
	f(`
- interval: 1m
  outputs: [total]
  foobar: baz
`)

	// missing interval
	f(`
- outputs: [total]
`)

	// missing outputs
	f(`
- interval: 1m
`)

	// Invalid output
	f(`
- interval: 1m
  outputs: [foobar]
`)

	// Negative interval
	f(`- interval: -5m`)
	// Too small interval
	f(`- interval: 10ms`)

	// Invalid input_relabel_configs
	f(`
- interval: 1m
  outputs: [total]
  input_relabel_configs:
  - foo: bar
`)
	f(`
- interval: 1m
  outputs: [total]
  input_relabel_configs:
  - action: replace
`)

	// Invalid output_relabel_configs
	f(`
- interval: 1m
  outputs: [total]
  output_relabel_configs:
  - foo: bar
`)
	f(`
- interval: 1m
  outputs: [total]
  output_relabel_configs:
  - action: replace
`)

	// Both by and without are non-empty
	f(`
- interval: 1m
  outputs: [total]
  by: [foo]
  without: [bar]
`)

	// Invalid quantiles()
	f(`
- interval: 1m
  outputs: ["quantiles("]
`)
	f(`
- interval: 1m
  outputs: ["quantiles()"]
`)
	f(`
- interval: 1m
  outputs: ["quantiles(foo)"]
`)
	f(`
- interval: 1m
  outputs: ["quantiles(-0.5)"]
`)
	f(`
- interval: 1m
  outputs: ["quantiles(1.5)"]
`)
}

func TestAggregatorsEqual(t *testing.T) {
	f := func(a, b string, expectedResult bool) {
		t.Helper()

		pushFunc := func(tss []prompbmarshal.TimeSeries) {}
		aa, err := NewAggregatorsFromData([]byte(a), pushFunc, 0)
		if err != nil {
			t.Fatalf("cannot initialize aggregators: %s", err)
		}
		ab, err := NewAggregatorsFromData([]byte(b), pushFunc, 0)
		if err != nil {
			t.Fatalf("cannot initialize aggregators: %s", err)
		}
		result := aa.Equal(ab)
		if result != expectedResult {
			t.Fatalf("unexpected result; got %v; want %v", result, expectedResult)
		}
	}
	f("", "", true)
	f(`
- outputs: [total]
  interval: 5m
`, ``, false)
	f(`
- outputs: [total]
  interval: 5m
`, `
- outputs: [total]
  interval: 5m
`, true)
	f(`
- outputs: [total]
  interval: 3m
`, `
- outputs: [total]
  interval: 5m
`, false)
}

func TestAggregatorsSuccess(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()
		}
		a, err := NewAggregatorsFromData([]byte(config), pushFunc, 0)
		if err != nil {
			t.Fatalf("cannot initialize aggregators: %s", err)
		}

		// Push the inputMetrics to Aggregators
		tssInput := mustParsePromMetrics(inputMetrics)
		a.Push(tssInput)
		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)
		}
	}

	// Empty config
	f(``, ``, ``)
	f(``, `foo{bar="baz"} 1`, ``)
	f(``, "foo 1\nbaz 2", ``)

	// Empty by list - aggregate only by time
	f(`
- interval: 1m
  outputs: [count_samples, sum_samples, count_series, last]
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, `bar:1m_count_samples 1
bar:1m_count_series 1
bar:1m_last 5
bar:1m_sum_samples 5
foo:1m_count_samples{abc="123"} 2
foo:1m_count_samples{abc="456",de="fg"} 1
foo:1m_count_series{abc="123"} 1
foo:1m_count_series{abc="456",de="fg"} 1
foo:1m_last{abc="123"} 8.5
foo:1m_last{abc="456",de="fg"} 8
foo:1m_sum_samples{abc="123"} 12.5
foo:1m_sum_samples{abc="456",de="fg"} 8
`)

	// Special case: __name__ in `by` list - this is the same as empty `by` list
	f(`
- interval: 1m
  by: [__name__]
  outputs: [count_samples, sum_samples, count_series]
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, `bar:1m_count_samples 1
bar:1m_count_series 1
bar:1m_sum_samples 5
foo:1m_count_samples 3
foo:1m_count_series 2
foo:1m_sum_samples 20.5
`)

	// Non-empty `by` list with non-existing labels
	f(`
- interval: 1m
  by: [foo, bar]
  outputs: [count_samples, sum_samples, count_series]
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, `bar:1m_by_bar_foo_count_samples 1
bar:1m_by_bar_foo_count_series 1
bar:1m_by_bar_foo_sum_samples 5
foo:1m_by_bar_foo_count_samples 3
foo:1m_by_bar_foo_count_series 2
foo:1m_by_bar_foo_sum_samples 20.5
`)

	// Non-empty `by` list with existing label
	f(`
- interval: 1m
  by: [abc]
  outputs: [count_samples, sum_samples, count_series]
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, `bar:1m_by_abc_count_samples 1
bar:1m_by_abc_count_series 1
bar:1m_by_abc_sum_samples 5
foo:1m_by_abc_count_samples{abc="123"} 2
foo:1m_by_abc_count_samples{abc="456"} 1
foo:1m_by_abc_count_series{abc="123"} 1
foo:1m_by_abc_count_series{abc="456"} 1
foo:1m_by_abc_sum_samples{abc="123"} 12.5
foo:1m_by_abc_sum_samples{abc="456"} 8
`)

	// Non-empty `by` list with duplicate existing label
	f(`
- interval: 1m
  by: [abc, abc]
  outputs: [count_samples, sum_samples, count_series]
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, `bar:1m_by_abc_count_samples 1
bar:1m_by_abc_count_series 1
bar:1m_by_abc_sum_samples 5
foo:1m_by_abc_count_samples{abc="123"} 2
foo:1m_by_abc_count_samples{abc="456"} 1
foo:1m_by_abc_count_series{abc="123"} 1
foo:1m_by_abc_count_series{abc="456"} 1
foo:1m_by_abc_sum_samples{abc="123"} 12.5
foo:1m_by_abc_sum_samples{abc="456"} 8
`)

	// Non-empty `without` list with non-existing labels
	f(`
- interval: 1m
  without: [foo]
  outputs: [count_samples, sum_samples, count_series]
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, `bar:1m_without_foo_count_samples 1
bar:1m_without_foo_count_series 1
bar:1m_without_foo_sum_samples 5
foo:1m_without_foo_count_samples{abc="123"} 2
foo:1m_without_foo_count_samples{abc="456",de="fg"} 1
foo:1m_without_foo_count_series{abc="123"} 1
foo:1m_without_foo_count_series{abc="456",de="fg"} 1
foo:1m_without_foo_sum_samples{abc="123"} 12.5
foo:1m_without_foo_sum_samples{abc="456",de="fg"} 8
`)

	// Non-empty `without` list with existing labels
	f(`
- interval: 1m
  without: [abc]
  outputs: [count_samples, sum_samples, count_series]
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, `bar:1m_without_abc_count_samples 1
bar:1m_without_abc_count_series 1
bar:1m_without_abc_sum_samples 5
foo:1m_without_abc_count_samples 2
foo:1m_without_abc_count_samples{de="fg"} 1
foo:1m_without_abc_count_series 1
foo:1m_without_abc_count_series{de="fg"} 1
foo:1m_without_abc_sum_samples 12.5
foo:1m_without_abc_sum_samples{de="fg"} 8
`)

	// Special case: __name__ in `without` list
	f(`
- interval: 1m
  without: [__name__]
  outputs: [count_samples, sum_samples, count_series]
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, `:1m_count_samples 1
:1m_count_samples{abc="123"} 2
:1m_count_samples{abc="456",de="fg"} 1
:1m_count_series 1
:1m_count_series{abc="123"} 1
:1m_count_series{abc="456",de="fg"} 1
:1m_sum_samples 5
:1m_sum_samples{abc="123"} 12.5
:1m_sum_samples{abc="456",de="fg"} 8
`)

	// drop some input metrics
	f(`
- interval: 1m
  without: [abc]
  outputs: [count_samples, sum_samples, count_series]
  input_relabel_configs:
  - if: 'foo'
    action: drop
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, `bar:1m_without_abc_count_samples 1
bar:1m_without_abc_count_series 1
bar:1m_without_abc_sum_samples 5
`)

	// rename output metrics
	f(`
- interval: 1m
  without: [abc]
  outputs: [count_samples, sum_samples, count_series]
  output_relabel_configs:
  - action: replace_all
    source_labels: [__name__]
    regex: ":|_"
    replacement: "-"
    target_label: __name__
  - action: drop
    source_labels: [de]
    regex: fg
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, `bar-1m-without-abc-count-samples 1
bar-1m-without-abc-count-series 1
bar-1m-without-abc-sum-samples 5
foo-1m-without-abc-count-samples 2
foo-1m-without-abc-count-series 1
foo-1m-without-abc-sum-samples 12.5
`)

	// match doesn't match anything
	f(`
- interval: 1m
  without: [abc]
  outputs: [count_samples, sum_samples, count_series]
  match: '{non_existing_label!=""}'
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, ``)

	// match matches foo series with non-empty abc label
	f(`
- interval: 1m
  by: [abc]
  outputs: [count_samples, sum_samples, count_series]
  match: 'foo{abc=~".+"}'
`, `
foo{abc="123"} 4
bar 5
foo{abc="123"} 8.5
foo{abc="456",de="fg"} 8
`, `foo:1m_by_abc_count_samples{abc="123"} 2
foo:1m_by_abc_count_samples{abc="456"} 1
foo:1m_by_abc_count_series{abc="123"} 1
foo:1m_by_abc_count_series{abc="456"} 1
foo:1m_by_abc_sum_samples{abc="123"} 12.5
foo:1m_by_abc_sum_samples{abc="456"} 8
`)

	// total output for non-repeated series
	f(`
- interval: 1m
  outputs: [total]
`, `
foo 123
bar{baz="qwe"} 4.34
`, `bar:1m_total{baz="qwe"} 0
foo:1m_total 0
`)

	// total output for repeated series
	f(`
- interval: 1m
  outputs: [total]
`, `
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_total{baz="qwe"} 5.02
bar:1m_total{baz="qwer"} 1
foo:1m_total 0
foo:1m_total{baz="qwe"} 15
`)

	// total output for repeated series with group by __name__
	f(`
- interval: 1m
  by: [__name__]
  outputs: [total]
`, `
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_total 6.02
foo:1m_total 15
`)

	// increase output for non-repeated series
	f(`
- interval: 1m
  outputs: [increase]
`, `
foo 123
bar{baz="qwe"} 4.34
`, `bar:1m_increase{baz="qwe"} 0
foo:1m_increase 0
`)

	// increase output for repeated series
	f(`
- interval: 1m
  outputs: [increase]
`, `
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_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 TestDiscardsSamplesWithOldTimestamps(t *testing.T) {
	f := func(interval string, inputTs int64, mustMatch bool) {
		t.Helper()

		config := fmt.Sprintf(`
- interval: %s
  outputs: ["avg"]
`, interval)
		input := `
cpu_usage{cpu="1"} 1
`
		expected := fmt.Sprintf(`cpu_usage:%s_avg{cpu="1"} 1
`, interval)
		if !mustMatch {
			expected = ""
		}

		// 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()
		}
		a, err := NewAggregatorsFromData([]byte(config), pushFunc, 0)
		if err != nil {
			t.Fatalf("cannot initialize aggregators: %s", err)
		}

		// Push the inputMetrics to Aggregators
		tssInput := mustParsePromMetricsSetTS(input, inputTs)
		a.Push(tssInput)
		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 != expected {
			t.Fatalf("unexpected output metrics;\ngot\n%s\nwant\n%s", outputMetrics, expected)
		}
	}
	currentTs := func() int64 {
		return int64(fasttime.UnixTimestamp() * 1000)
	}

	f("1m", currentTs(), true)
	f("1h", currentTs()-120*1000, true)
	f("24h", currentTs()-60*60*1000, true)

	f("1m", currentTs()-120*1000, false)
	f("1h", currentTs()-2*60*60*1000*1000, false)
	f("24h", currentTs()-25*60*60*1000, false)
}

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 {
	return mustParsePromMetricsSetTS(s, int64(fasttime.UnixTimestamp()*1000))
}

func mustParsePromMetricsSetTS(s string, timestamp int64) []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 {
		if row.Timestamp == 0 && timestamp != 0 {
			row.Timestamp = timestamp
		}

		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
}