forked from cerc-io/plugeth
7dc100714d
This PR adds counter metrics for the CPU system and the Geth process. Currently the only metrics available for these items are gauges. Gauges are fine when the consumer scrapes metrics data at the same interval as Geth produces new values (every 3 seconds), but it is likely that most consumers will not scrape that often. Intervals of 10, 15, or maybe even 30 seconds are probably more common. So the problem is, how does the consumer estimate what the CPU was doing in between scrapes. With a counter, it's easy ... you just subtract two successive values and divide by the time to get a nice, accurate average. But with a gauge, you can't do that. A gauge reading is an instantaneous picture of what was happening at that moment, but it gives you no idea about what was going on between scrapes. Taking an average of values is meaningless.
222 lines
5.4 KiB
Go
222 lines
5.4 KiB
Go
package influxdb
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import (
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"context"
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"fmt"
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"time"
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"github.com/ethereum/go-ethereum/log"
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"github.com/ethereum/go-ethereum/metrics"
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influxdb2 "github.com/influxdata/influxdb-client-go/v2"
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"github.com/influxdata/influxdb-client-go/v2/api"
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)
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type v2Reporter struct {
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reg metrics.Registry
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interval time.Duration
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endpoint string
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token string
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bucket string
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organization string
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namespace string
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tags map[string]string
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client influxdb2.Client
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write api.WriteAPI
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}
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// InfluxDBWithTags starts a InfluxDB reporter which will post the from the given metrics.Registry at each d interval with the specified tags
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func InfluxDBV2WithTags(r metrics.Registry, d time.Duration, endpoint string, token string, bucket string, organization string, namespace string, tags map[string]string) {
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rep := &v2Reporter{
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reg: r,
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interval: d,
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endpoint: endpoint,
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token: token,
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bucket: bucket,
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organization: organization,
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namespace: namespace,
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tags: tags,
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}
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rep.client = influxdb2.NewClient(rep.endpoint, rep.token)
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defer rep.client.Close()
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// async write client
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rep.write = rep.client.WriteAPI(rep.organization, rep.bucket)
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errorsCh := rep.write.Errors()
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// have to handle write errors in a separate goroutine like this b/c the channel is unbuffered and will block writes if not read
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go func() {
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for err := range errorsCh {
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log.Warn("write error", "err", err.Error())
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}
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}()
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rep.run()
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}
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func (r *v2Reporter) run() {
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intervalTicker := time.NewTicker(r.interval)
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pingTicker := time.NewTicker(time.Second * 5)
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defer intervalTicker.Stop()
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defer pingTicker.Stop()
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for {
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select {
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case <-intervalTicker.C:
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r.send()
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case <-pingTicker.C:
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_, err := r.client.Health(context.Background())
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if err != nil {
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log.Warn("Got error from influxdb client health check", "err", err.Error())
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}
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}
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}
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}
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func (r *v2Reporter) send() {
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r.reg.Each(func(name string, i interface{}) {
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now := time.Now()
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namespace := r.namespace
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switch metric := i.(type) {
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case metrics.Counter:
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v := metric.Count()
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measurement := fmt.Sprintf("%s%s.count", namespace, name)
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fields := map[string]interface{}{
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"value": v,
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}
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pt := influxdb2.NewPoint(measurement, r.tags, fields, now)
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r.write.WritePoint(pt)
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case metrics.CounterFloat64:
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v := metric.Count()
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measurement := fmt.Sprintf("%s%s.count", namespace, name)
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fields := map[string]interface{}{
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"value": v,
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}
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pt := influxdb2.NewPoint(measurement, r.tags, fields, now)
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r.write.WritePoint(pt)
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case metrics.Gauge:
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ms := metric.Snapshot()
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measurement := fmt.Sprintf("%s%s.gauge", namespace, name)
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fields := map[string]interface{}{
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"value": ms.Value(),
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}
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pt := influxdb2.NewPoint(measurement, r.tags, fields, now)
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r.write.WritePoint(pt)
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case metrics.GaugeFloat64:
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ms := metric.Snapshot()
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measurement := fmt.Sprintf("%s%s.gauge", namespace, name)
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fields := map[string]interface{}{
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"value": ms.Value(),
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}
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pt := influxdb2.NewPoint(measurement, r.tags, fields, now)
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r.write.WritePoint(pt)
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case metrics.Histogram:
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ms := metric.Snapshot()
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if ms.Count() > 0 {
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ps := ms.Percentiles([]float64{0.5, 0.75, 0.95, 0.99, 0.999, 0.9999})
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measurement := fmt.Sprintf("%s%s.histogram", namespace, name)
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fields := map[string]interface{}{
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"count": ms.Count(),
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"max": ms.Max(),
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"mean": ms.Mean(),
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"min": ms.Min(),
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"stddev": ms.StdDev(),
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"variance": ms.Variance(),
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"p50": ps[0],
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"p75": ps[1],
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"p95": ps[2],
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"p99": ps[3],
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"p999": ps[4],
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"p9999": ps[5],
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}
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pt := influxdb2.NewPoint(measurement, r.tags, fields, now)
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r.write.WritePoint(pt)
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}
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case metrics.Meter:
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ms := metric.Snapshot()
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measurement := fmt.Sprintf("%s%s.meter", namespace, name)
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fields := map[string]interface{}{
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"count": ms.Count(),
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"m1": ms.Rate1(),
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"m5": ms.Rate5(),
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"m15": ms.Rate15(),
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"mean": ms.RateMean(),
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}
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pt := influxdb2.NewPoint(measurement, r.tags, fields, now)
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r.write.WritePoint(pt)
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case metrics.Timer:
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ms := metric.Snapshot()
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ps := ms.Percentiles([]float64{0.5, 0.75, 0.95, 0.99, 0.999, 0.9999})
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measurement := fmt.Sprintf("%s%s.timer", namespace, name)
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fields := map[string]interface{}{
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"count": ms.Count(),
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"max": ms.Max(),
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"mean": ms.Mean(),
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"min": ms.Min(),
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"stddev": ms.StdDev(),
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"variance": ms.Variance(),
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"p50": ps[0],
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"p75": ps[1],
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"p95": ps[2],
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"p99": ps[3],
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"p999": ps[4],
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"p9999": ps[5],
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"m1": ms.Rate1(),
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"m5": ms.Rate5(),
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"m15": ms.Rate15(),
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"meanrate": ms.RateMean(),
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}
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pt := influxdb2.NewPoint(measurement, r.tags, fields, now)
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r.write.WritePoint(pt)
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case metrics.ResettingTimer:
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t := metric.Snapshot()
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if len(t.Values()) > 0 {
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ps := t.Percentiles([]float64{50, 95, 99})
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val := t.Values()
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measurement := fmt.Sprintf("%s%s.span", namespace, name)
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fields := map[string]interface{}{
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"count": len(val),
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"max": val[len(val)-1],
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"mean": t.Mean(),
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"min": val[0],
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"p50": ps[0],
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"p95": ps[1],
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"p99": ps[2],
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}
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pt := influxdb2.NewPoint(measurement, r.tags, fields, now)
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r.write.WritePoint(pt)
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}
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}
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})
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// Force all unwritten data to be sent
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r.write.Flush()
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}
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