prometheus/retrieval/format/processor0_0_1_test.go
Julius Volz a1ba23038e Fix scrape timestamps to reduce sample time jitter.
We're currently timestamping samples with the time at the end of a scrape
iteration. It makes more sense to use a timestamp from the beginning of the
scrape for two reasons:

a) this time is more relevant to the scraped values than the time at the
   end of the HTTP-GET + JSON decoding work.
b) it reduces sample timestamp jitter if we measure at the beginning, and
   not at the completion of a scrape.
2013-04-13 03:45:37 +02:00

264 lines
7.5 KiB
Go

// Copyright 2013 Prometheus Team
// 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.
package format
import (
"container/list"
"fmt"
"github.com/prometheus/prometheus/model"
"github.com/prometheus/prometheus/utility/test"
"io/ioutil"
"strings"
"testing"
"time"
)
func testProcessor001Process(t test.Tester) {
var scenarios = []struct {
in string
out []Result
err error
}{
{
err: fmt.Errorf("unexpected end of JSON input"),
},
{
in: "[{\"baseLabels\":{\"name\":\"rpc_calls_total\"},\"docstring\":\"RPC calls.\",\"metric\":{\"type\":\"counter\",\"value\":[{\"labels\":{\"service\":\"zed\"},\"value\":25},{\"labels\":{\"service\":\"bar\"},\"value\":25},{\"labels\":{\"service\":\"foo\"},\"value\":25}]}},{\"baseLabels\":{\"name\":\"rpc_latency_microseconds\"},\"docstring\":\"RPC latency.\",\"metric\":{\"type\":\"histogram\",\"value\":[{\"labels\":{\"service\":\"foo\"},\"value\":{\"0.010000\":15.890724674774395,\"0.050000\":15.890724674774395,\"0.500000\":84.63044031436561,\"0.900000\":160.21100853053224,\"0.990000\":172.49828748957728}},{\"labels\":{\"service\":\"zed\"},\"value\":{\"0.010000\":0.0459814091918713,\"0.050000\":0.0459814091918713,\"0.500000\":0.6120456642749681,\"0.900000\":1.355915069887731,\"0.990000\":1.772733213161236}},{\"labels\":{\"service\":\"bar\"},\"value\":{\"0.010000\":78.48563317257356,\"0.050000\":78.48563317257356,\"0.500000\":97.31798360385088,\"0.900000\":109.89202084295582,\"0.990000\":109.99626121011262}}]}}]",
out: []Result{
{
Sample: model.Sample{
Metric: model.Metric{"service": "zed", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"service": "bar", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"service": "foo", model.MetricNameLabel: "rpc_calls_total"},
Value: 25,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.04598141,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 78.485634,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.010000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 15.890724674774395,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.04598141,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 78.485634,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.050000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 15.890724674774395,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 0.61204565,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 97.317986,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.500000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 84.63044,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 1.3559151,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 109.89202,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.900000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 160.21101,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "zed"},
Value: 1.7727332,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "bar"},
Value: 109.99626,
},
},
{
Sample: model.Sample{
Metric: model.Metric{"percentile": "0.990000", model.MetricNameLabel: "rpc_latency_microseconds", "service": "foo"},
Value: 172.49829,
},
},
},
},
}
for i, scenario := range scenarios {
inputChannel := make(chan Result, 1024)
defer func(c chan Result) {
close(c)
}(inputChannel)
reader := strings.NewReader(scenario.in)
err := Processor001.Process(ioutil.NopCloser(reader), time.Now(), model.LabelSet{}, inputChannel)
if !test.ErrorEqual(scenario.err, err) {
t.Errorf("%d. expected err of %s, got %s", i, scenario.err, err)
continue
}
if scenario.err != nil && err != nil {
if scenario.err.Error() != err.Error() {
t.Errorf("%d. expected err of %s, got %s", i, scenario.err, err)
}
} else if scenario.err != err {
t.Errorf("%d. expected err of %s, got %s", i, scenario.err, err)
}
delivered := make([]Result, 0)
for len(inputChannel) != 0 {
delivered = append(delivered, <-inputChannel)
}
if len(delivered) != len(scenario.out) {
t.Errorf("%d. expected output length of %d, got %d", i, len(scenario.out), len(delivered))
continue
}
expectedElements := list.New()
for _, j := range scenario.out {
expectedElements.PushBack(j)
}
for j := 0; j < len(delivered); j++ {
actual := delivered[j]
found := false
for element := expectedElements.Front(); element != nil && found == false; element = element.Next() {
candidate := element.Value.(Result)
if !test.ErrorEqual(candidate.Err, actual.Err) {
continue
}
if candidate.Sample.Value != actual.Sample.Value {
continue
}
if len(candidate.Sample.Metric) != len(actual.Sample.Metric) {
continue
}
labelsMatch := false
for key, value := range candidate.Sample.Metric {
actualValue, ok := actual.Sample.Metric[key]
if !ok {
break
}
if actualValue == value {
labelsMatch = true
break
}
}
if !labelsMatch {
continue
}
// XXX: Test time.
found = true
expectedElements.Remove(element)
}
if !found {
t.Errorf("%d.%d. expected to find %s among candidate, absent", i, j, actual.Sample)
}
}
}
}
func TestProcessor001Process(t *testing.T) {
testProcessor001Process(t)
}
func BenchmarkProcessor001Process(b *testing.B) {
for i := 0; i < b.N; i++ {
testProcessor001Process(b)
}
}