telegraf/plugins/inputs/statsd/statsd.go

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package statsd
import (
"errors"
"fmt"
"log"
"net"
"sort"
"strconv"
"strings"
"sync"
"time"
"github.com/influxdata/telegraf/plugins/parsers/graphite"
"github.com/influxdata/telegraf"
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"github.com/influxdata/telegraf/plugins/inputs"
)
const (
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// UDP packet limit, see
// https://en.wikipedia.org/wiki/User_Datagram_Protocol#Packet_structure
UDP_MAX_PACKET_SIZE int = 64 * 1024
defaultFieldName = "value"
defaultSeparator = "_"
)
var dropwarn = "ERROR: Message queue full. Discarding line [%s] " +
"You may want to increase allowed_pending_messages in the config\n"
var prevInstance *Statsd
type Statsd struct {
// Address & Port to serve from
ServiceAddress string
// Number of messages allowed to queue up in between calls to Gather. If this
// fills up, packets will get dropped until the next Gather interval is ran.
AllowedPendingMessages int
// Percentiles specifies the percentiles that will be calculated for timing
// and histogram stats.
Percentiles []int
PercentileLimit int
DeleteGauges bool
DeleteCounters bool
DeleteSets bool
DeleteTimings bool
ConvertNames bool
// MetricSeparator is the separator between parts of the metric name.
MetricSeparator string
// This flag enables parsing of tags in the dogstatsd extention to the
// statsd protocol (http://docs.datadoghq.com/guides/dogstatsd/)
ParseDataDogTags bool
// UDPPacketSize is deprecated, it's only here for legacy support
// we now always create 1 max size buffer and then copy only what we need
// into the in channel
// see https://github.com/influxdata/telegraf/pull/992
UDPPacketSize int `toml:"udp_packet_size"`
sync.Mutex
wg sync.WaitGroup
// Channel for all incoming statsd packets
in chan []byte
done chan struct{}
// Cache gauges, counters & sets so they can be aggregated as they arrive
// gauges and counters map measurement/tags hash -> field name -> metrics
// sets and timings map measurement/tags hash -> metrics
gauges map[string]cachedgauge
counters map[string]cachedcounter
sets map[string]cachedset
timings map[string]cachedtimings
// bucket -> influx templates
Templates []string
listener *net.UDPConn
}
// One statsd metric, form is <bucket>:<value>|<mtype>|@<samplerate>
type metric struct {
name string
field string
bucket string
hash string
intvalue int64
floatvalue float64
mtype string
additive bool
samplerate float64
tags map[string]string
}
type cachedset struct {
name string
fields map[string]map[int64]bool
tags map[string]string
}
type cachedgauge struct {
name string
fields map[string]interface{}
tags map[string]string
}
type cachedcounter struct {
name string
fields map[string]interface{}
tags map[string]string
}
type cachedtimings struct {
name string
fields map[string]RunningStats
tags map[string]string
}
func (_ *Statsd) Description() string {
return "Statsd Server"
}
const sampleConfig = `
## Address and port to host UDP listener on
service_address = ":8125"
## Delete gauges every interval (default=false)
delete_gauges = false
## Delete counters every interval (default=false)
delete_counters = false
## Delete sets every interval (default=false)
delete_sets = false
## Delete timings & histograms every interval (default=true)
delete_timings = true
## Percentiles to calculate for timing & histogram stats
percentiles = [90]
## separator to use between elements of a statsd metric
metric_separator = "_"
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## Parses tags in the datadog statsd format
## http://docs.datadoghq.com/guides/dogstatsd/
parse_data_dog_tags = false
## Statsd data translation templates, more info can be read here:
## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md#graphite
# templates = [
# "cpu.* measurement*"
# ]
## Number of UDP messages allowed to queue up, once filled,
## the statsd server will start dropping packets
allowed_pending_messages = 10000
## Number of timing/histogram values to track per-measurement in the
## calculation of percentiles. Raising this limit increases the accuracy
## of percentiles but also increases the memory usage and cpu time.
percentile_limit = 1000
`
func (_ *Statsd) SampleConfig() string {
return sampleConfig
}
func (s *Statsd) Gather(acc telegraf.Accumulator) error {
s.Lock()
defer s.Unlock()
now := time.Now()
for _, metric := range s.timings {
// Defining a template to parse field names for timers allows us to split
// out multiple fields per timer. In this case we prefix each stat with the
// field name and store these all in a single measurement.
fields := make(map[string]interface{})
for fieldName, stats := range metric.fields {
var prefix string
if fieldName != defaultFieldName {
prefix = fieldName + "_"
}
fields[prefix+"mean"] = stats.Mean()
fields[prefix+"stddev"] = stats.Stddev()
fields[prefix+"upper"] = stats.Upper()
fields[prefix+"lower"] = stats.Lower()
fields[prefix+"count"] = stats.Count()
for _, percentile := range s.Percentiles {
name := fmt.Sprintf("%s%v_percentile", prefix, percentile)
fields[name] = stats.Percentile(percentile)
}
}
acc.AddFields(metric.name, fields, metric.tags, now)
}
if s.DeleteTimings {
s.timings = make(map[string]cachedtimings)
}
for _, metric := range s.gauges {
acc.AddFields(metric.name, metric.fields, metric.tags, now)
}
if s.DeleteGauges {
s.gauges = make(map[string]cachedgauge)
}
for _, metric := range s.counters {
acc.AddFields(metric.name, metric.fields, metric.tags, now)
}
if s.DeleteCounters {
s.counters = make(map[string]cachedcounter)
}
for _, metric := range s.sets {
fields := make(map[string]interface{})
for field, set := range metric.fields {
fields[field] = int64(len(set))
}
acc.AddFields(metric.name, fields, metric.tags, now)
}
if s.DeleteSets {
s.sets = make(map[string]cachedset)
}
return nil
}
func (s *Statsd) Start(_ telegraf.Accumulator) error {
// Make data structures
s.done = make(chan struct{})
s.in = make(chan []byte, s.AllowedPendingMessages)
if prevInstance == nil {
s.gauges = make(map[string]cachedgauge)
s.counters = make(map[string]cachedcounter)
s.sets = make(map[string]cachedset)
s.timings = make(map[string]cachedtimings)
} else {
s.gauges = prevInstance.gauges
s.counters = prevInstance.counters
s.sets = prevInstance.sets
s.timings = prevInstance.timings
}
if s.ConvertNames {
log.Printf("WARNING statsd: convert_names config option is deprecated," +
" please use metric_separator instead")
}
if s.MetricSeparator == "" {
s.MetricSeparator = defaultSeparator
}
s.wg.Add(2)
// Start the UDP listener
go s.udpListen()
// Start the line parser
go s.parser()
log.Printf("Started the statsd service on %s\n", s.ServiceAddress)
prevInstance = s
return nil
}
// udpListen starts listening for udp packets on the configured port.
func (s *Statsd) udpListen() error {
defer s.wg.Done()
var err error
address, _ := net.ResolveUDPAddr("udp", s.ServiceAddress)
s.listener, err = net.ListenUDP("udp", address)
if err != nil {
log.Fatalf("ERROR: ListenUDP - %s", err)
}
log.Println("Statsd listener listening on: ", s.listener.LocalAddr().String())
buf := make([]byte, UDP_MAX_PACKET_SIZE)
for {
select {
case <-s.done:
return nil
default:
n, _, err := s.listener.ReadFromUDP(buf)
if err != nil && !strings.Contains(err.Error(), "closed network") {
log.Printf("ERROR READ: %s\n", err.Error())
continue
}
bufCopy := make([]byte, n)
copy(bufCopy, buf[:n])
select {
case s.in <- bufCopy:
default:
log.Printf(dropwarn, string(buf[:n]))
}
}
}
}
// parser monitors the s.in channel, if there is a packet ready, it parses the
// packet into statsd strings and then calls parseStatsdLine, which parses a
// single statsd metric into a struct.
func (s *Statsd) parser() error {
defer s.wg.Done()
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var packet []byte
for {
select {
case <-s.done:
return nil
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case packet = <-s.in:
lines := strings.Split(string(packet), "\n")
for _, line := range lines {
line = strings.TrimSpace(line)
if line != "" {
s.parseStatsdLine(line)
}
}
}
}
}
// parseStatsdLine will parse the given statsd line, validating it as it goes.
// If the line is valid, it will be cached for the next call to Gather()
func (s *Statsd) parseStatsdLine(line string) error {
s.Lock()
defer s.Unlock()
lineTags := make(map[string]string)
if s.ParseDataDogTags {
recombinedSegments := make([]string, 0)
// datadog tags look like this:
// users.online:1|c|@0.5|#country:china,environment:production
// users.online:1|c|#sometagwithnovalue
// we will split on the pipe and remove any elements that are datadog
// tags, parse them, and rebuild the line sans the datadog tags
pipesplit := strings.Split(line, "|")
for _, segment := range pipesplit {
if len(segment) > 0 && segment[0] == '#' {
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// we have ourselves a tag; they are comma separated
tagstr := segment[1:]
tags := strings.Split(tagstr, ",")
for _, tag := range tags {
ts := strings.Split(tag, ":")
var k, v string
switch len(ts) {
case 1:
// just a tag
k = ts[0]
v = ""
case 2:
k = ts[0]
v = ts[1]
}
if k != "" {
lineTags[k] = v
}
}
} else {
recombinedSegments = append(recombinedSegments, segment)
}
}
line = strings.Join(recombinedSegments, "|")
}
// Validate splitting the line on ":"
bits := strings.Split(line, ":")
if len(bits) < 2 {
log.Printf("Error: splitting ':', Unable to parse metric: %s\n", line)
return errors.New("Error Parsing statsd line")
}
// Extract bucket name from individual metric bits
bucketName, bits := bits[0], bits[1:]
// Add a metric for each bit available
for _, bit := range bits {
m := metric{}
m.bucket = bucketName
// Validate splitting the bit on "|"
pipesplit := strings.Split(bit, "|")
if len(pipesplit) < 2 {
log.Printf("Error: splitting '|', Unable to parse metric: %s\n", line)
return errors.New("Error Parsing statsd line")
} else if len(pipesplit) > 2 {
sr := pipesplit[2]
errmsg := "Error: parsing sample rate, %s, it must be in format like: " +
"@0.1, @0.5, etc. Ignoring sample rate for line: %s\n"
if strings.Contains(sr, "@") && len(sr) > 1 {
samplerate, err := strconv.ParseFloat(sr[1:], 64)
if err != nil {
log.Printf(errmsg, err.Error(), line)
} else {
// sample rate successfully parsed
m.samplerate = samplerate
}
} else {
log.Printf(errmsg, "", line)
}
}
// Validate metric type
switch pipesplit[1] {
case "g", "c", "s", "ms", "h":
m.mtype = pipesplit[1]
default:
log.Printf("Error: Statsd Metric type %s unsupported", pipesplit[1])
return errors.New("Error Parsing statsd line")
}
// Parse the value
if strings.ContainsAny(pipesplit[0], "-+") {
if m.mtype != "g" {
log.Printf("Error: +- values are only supported for gauges: %s\n", line)
return errors.New("Error Parsing statsd line")
}
m.additive = true
}
switch m.mtype {
case "g", "ms", "h":
v, err := strconv.ParseFloat(pipesplit[0], 64)
if err != nil {
log.Printf("Error: parsing value to float64: %s\n", line)
return errors.New("Error Parsing statsd line")
}
m.floatvalue = v
case "c", "s":
var v int64
v, err := strconv.ParseInt(pipesplit[0], 10, 64)
if err != nil {
v2, err2 := strconv.ParseFloat(pipesplit[0], 64)
if err2 != nil {
log.Printf("Error: parsing value to int64: %s\n", line)
return errors.New("Error Parsing statsd line")
}
v = int64(v2)
}
// If a sample rate is given with a counter, divide value by the rate
if m.samplerate != 0 && m.mtype == "c" {
v = int64(float64(v) / m.samplerate)
}
m.intvalue = v
}
// Parse the name & tags from bucket
m.name, m.field, m.tags = s.parseName(m.bucket)
switch m.mtype {
case "c":
m.tags["metric_type"] = "counter"
case "g":
m.tags["metric_type"] = "gauge"
case "s":
m.tags["metric_type"] = "set"
case "ms":
m.tags["metric_type"] = "timing"
case "h":
m.tags["metric_type"] = "histogram"
}
if len(lineTags) > 0 {
for k, v := range lineTags {
m.tags[k] = v
}
}
// Make a unique key for the measurement name/tags
var tg []string
for k, v := range m.tags {
tg = append(tg, fmt.Sprintf("%s=%s", k, v))
}
sort.Strings(tg)
m.hash = fmt.Sprintf("%s%s", strings.Join(tg, ""), m.name)
s.aggregate(m)
}
return nil
}
// parseName parses the given bucket name with the list of bucket maps in the
// config file. If there is a match, it will parse the name of the metric and
// map of tags.
// Return values are (<name>, <field>, <tags>)
func (s *Statsd) parseName(bucket string) (string, string, map[string]string) {
tags := make(map[string]string)
bucketparts := strings.Split(bucket, ",")
// Parse out any tags in the bucket
if len(bucketparts) > 1 {
for _, btag := range bucketparts[1:] {
k, v := parseKeyValue(btag)
if k != "" {
tags[k] = v
}
}
}
var field string
name := bucketparts[0]
p, err := graphite.NewGraphiteParser(s.MetricSeparator, s.Templates, nil)
if err == nil {
p.DefaultTags = tags
name, tags, field, _ = p.ApplyTemplate(name)
}
if s.ConvertNames {
name = strings.Replace(name, ".", "_", -1)
name = strings.Replace(name, "-", "__", -1)
}
if field == "" {
field = defaultFieldName
}
return name, field, tags
}
// Parse the key,value out of a string that looks like "key=value"
func parseKeyValue(keyvalue string) (string, string) {
var key, val string
split := strings.Split(keyvalue, "=")
// Must be exactly 2 to get anything meaningful out of them
if len(split) == 2 {
key = split[0]
val = split[1]
} else if len(split) == 1 {
val = split[0]
}
return key, val
}
// aggregate takes in a metric. It then
// aggregates and caches the current value(s). It does not deal with the
// Delete* options, because those are dealt with in the Gather function.
func (s *Statsd) aggregate(m metric) {
switch m.mtype {
case "ms", "h":
// Check if the measurement exists
cached, ok := s.timings[m.hash]
if !ok {
cached = cachedtimings{
name: m.name,
fields: make(map[string]RunningStats),
tags: m.tags,
}
}
// Check if the field exists. If we've not enabled multiple fields per timer
// this will be the default field name, eg. "value"
field, ok := cached.fields[m.field]
if !ok {
field = RunningStats{
PercLimit: s.PercentileLimit,
}
}
if m.samplerate > 0 {
for i := 0; i < int(1.0/m.samplerate); i++ {
field.AddValue(m.floatvalue)
}
} else {
field.AddValue(m.floatvalue)
}
cached.fields[m.field] = field
s.timings[m.hash] = cached
case "c":
// check if the measurement exists
_, ok := s.counters[m.hash]
if !ok {
s.counters[m.hash] = cachedcounter{
name: m.name,
fields: make(map[string]interface{}),
tags: m.tags,
}
}
// check if the field exists
_, ok = s.counters[m.hash].fields[m.field]
if !ok {
s.counters[m.hash].fields[m.field] = int64(0)
}
s.counters[m.hash].fields[m.field] =
s.counters[m.hash].fields[m.field].(int64) + m.intvalue
case "g":
// check if the measurement exists
_, ok := s.gauges[m.hash]
if !ok {
s.gauges[m.hash] = cachedgauge{
name: m.name,
fields: make(map[string]interface{}),
tags: m.tags,
}
}
// check if the field exists
_, ok = s.gauges[m.hash].fields[m.field]
if !ok {
s.gauges[m.hash].fields[m.field] = float64(0)
}
if m.additive {
s.gauges[m.hash].fields[m.field] =
s.gauges[m.hash].fields[m.field].(float64) + m.floatvalue
} else {
s.gauges[m.hash].fields[m.field] = m.floatvalue
}
case "s":
// check if the measurement exists
_, ok := s.sets[m.hash]
if !ok {
s.sets[m.hash] = cachedset{
name: m.name,
fields: make(map[string]map[int64]bool),
tags: m.tags,
}
}
// check if the field exists
_, ok = s.sets[m.hash].fields[m.field]
if !ok {
s.sets[m.hash].fields[m.field] = make(map[int64]bool)
}
s.sets[m.hash].fields[m.field][m.intvalue] = true
}
}
func (s *Statsd) Stop() {
s.Lock()
defer s.Unlock()
log.Println("Stopping the statsd service")
close(s.done)
s.listener.Close()
s.wg.Wait()
close(s.in)
}
func init() {
inputs.Add("statsd", func() telegraf.Input {
return &Statsd{
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MetricSeparator: "_",
}
})
}