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metrics.go
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metrics.go
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// Licensed to the Apache Software Foundation (ASF) under one or more
// contributor license agreements. See the NOTICE file distributed with
// this work for additional information regarding copyright ownership.
// The ASF licenses this file to You 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 dataflowlib
import (
"encoding/json"
"fmt"
"github.com/apache/beam/sdks/go/pkg/beam/core/metrics"
df "google.golang.org/api/dataflow/v1b3"
)
// FromMetricUpdates extracts metrics from a slice of MetricUpdate objects and
// groups them into counters, distributions and gauges.
//
// Dataflow currently only reports Counter and Distribution metrics to Cloud
// Monitoring. Gauge metrics are not supported. The output metrics.Results will
// not contain any gauges.
func FromMetricUpdates(allMetrics []*df.MetricUpdate, job *df.Job) *metrics.Results {
ac, ad := groupByType(allMetrics, job, true)
cc, cd := groupByType(allMetrics, job, false)
return metrics.NewResults(metrics.MergeCounters(ac, cc), metrics.MergeDistributions(ad, cd), make([]metrics.GaugeResult, 0))
}
func groupByType(allMetrics []*df.MetricUpdate, job *df.Job, tentative bool) (
map[metrics.StepKey]int64,
map[metrics.StepKey]metrics.DistributionValue) {
counters := make(map[metrics.StepKey]int64)
distributions := make(map[metrics.StepKey]metrics.DistributionValue)
for _, metric := range allMetrics {
isTentative := metric.Name.Context["tentative"] == "true"
if isTentative != tentative {
continue
}
key, err := extractKey(metric, job)
if err != nil {
continue
}
if metric.Scalar != nil {
v, err := extractCounterValue(metric.Scalar)
if err != nil {
continue
}
counters[key] = v
} else if metric.Distribution != nil {
v, err := extractDistributionValue(metric.Distribution)
if err != nil {
continue
}
distributions[key] = v
}
}
return counters, distributions
}
func extractKey(metric *df.MetricUpdate, job *df.Job) (metrics.StepKey, error) {
stepName, ok := metric.Name.Context["step"]
if !ok {
return metrics.StepKey{}, fmt.Errorf("could not find the internal step name")
}
userStepName := ""
for _, step := range job.Steps {
if step.Name == stepName {
properties := make(map[string]string)
json.Unmarshal(step.Properties, &properties)
userStepName = properties["user_name"]
break
}
}
if userStepName == "" {
return metrics.StepKey{}, fmt.Errorf("could not translate the internal step name %v", stepName)
}
namespace := metric.Name.Context["namespace"]
if namespace == "" {
namespace = "dataflow/v1b3"
}
return metrics.StepKey{Step: userStepName, Name: metric.Name.Name, Namespace: namespace}, nil
}
func extractCounterValue(obj interface{}) (int64, error) {
v, ok := obj.(float64)
if !ok {
return -1, fmt.Errorf("expected float64, got data of type %T instead", obj)
}
return int64(v), nil
}
func extractDistributionValue(obj interface{}) (metrics.DistributionValue, error) {
m := obj.(map[string]interface{})
propertiesToVisit := []string{"count", "sum", "min", "max"}
var values [4]int64
for i, p := range propertiesToVisit {
v, ok := m[p].(float64)
if !ok {
return metrics.DistributionValue{}, fmt.Errorf("expected float64, got data of type %T instead", m[p])
}
values[i] = int64(v)
}
return metrics.DistributionValue{Count: values[0], Sum: values[1], Min: values[2], Max: values[3]}, nil
}