Telegraf Service Plugin: statsd

Configuration

# Statsd Server
[[inputs.statsd]]
  ### 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]

  ### convert measurement names, "." to "_" and "-" to "__"
  convert_names = true

  ### 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

  ### UDP packet size for the server to listen for. This will depend on the size
  ### of the packets that the client is sending, which is usually 1500 bytes.
  udp_packet_size = 1500

Description

The statsd plugin is a special type of plugin which runs a backgrounded statsd listener service while telegraf is running.

The format of the statsd messages was based on the format described in the original etsy statsd implementation. In short, the telegraf statsd listener will accept:

Gauges
- users.current.den001.myapp:32|g <- standard
- users.current.den001.myapp:+10|g <- additive
- users.current.den001.myapp:-10|g
Counters
- deploys.test.myservice:1|c <- increments by 1
- deploys.test.myservice:101|c <- increments by 101
- deploys.test.myservice:1|c|@0.1 <- with sample rate, increments by 10
Sets
- users.unique:101|s
- users.unique:101|s
- users.unique:102|s <- would result in a count of 2 for users.unique
Timings & Histograms
- load.time:320|ms
- load.time.nanoseconds:1|h
- load.time:200|ms|@0.1 <- sampled 1/10 of the time

It is possible to omit repetitive names and merge individual stats into a single line by separating them with additional colons:

users.current.den001.myapp:32|g:+10|g:-10|g
deploys.test.myservice:1|c:101|c:1|c|@0.1
users.unique:101|s:101|s:102|s
load.time:320|ms:200|ms|@0.1

This also allows for mixed types in a single line:

foo:1|c:200|ms

The string foo:1|c:200|ms is internally split into two individual metrics foo:1|c and foo:200|ms which are added to the aggregator separately.

Influx Statsd

In order to take advantage of InfluxDB's tagging system, we have made a couple additions to the standard statsd protocol. First, you can specify tags in a manner similar to the line-protocol, like this:

users.current,service=payroll,region=us-west:32|g

COMING SOON: there will be a way to specify multiple fields.

Measurements:

Meta:

tags: metric_type=<gauge|set|counter|timing|histogram>

Outputted measurements will depend entirely on the measurements that the user sends, but here is a brief rundown of what you can expect to find from each metric type:

Gauges
- Gauges are a constant data type. They are not subject to averaging, and they don’t change unless you change them. That is, once you set a gauge value, it will be a flat line on the graph until you change it again.
Counters
- Counters are the most basic type. They are treated as a count of a type of event. They will continually increase unless you set delete_counters=true.
Sets
- Sets count the number of unique values passed to a key. For example, you could count the number of users accessing your system using users:<user_id>|s. No matter how many times the same user_id is sent, the count will only increase by 1.
Timings & Histograms
- Timers are meant to track how long something took. They are an invaluable tool for tracking application performance.
- The following aggregate measurements are made for timers:
  - statsd_<name>_lower: The lower bound is the lowest value statsd saw for that stat during that interval.
  - statsd_<name>_upper: The upper bound is the highest value statsd saw for that stat during that interval.
  - statsd_<name>_mean: The mean is the average of all values statsd saw for that stat during that interval.
  - statsd_<name>_stddev: The stddev is the sample standard deviation of all values statsd saw for that stat during that interval.
  - statsd_<name>_count: The count is the number of timings statsd saw for that stat during that interval. It is not averaged.
  - statsd_<name>_percentile_<P> The Pth percentile is a value x such that P% of all the values statsd saw for that stat during that time period are below x. The most common value that people use for P is the 90, this is a great number to try to optimize.

Plugin arguments

service_address string: Address to listen for statsd UDP packets on
delete_gauges boolean: Delete gauges on every collection interval
delete_counters boolean: Delete counters on every collection interval
delete_sets boolean: Delete set counters on every collection interval
delete_timings boolean: Delete timings on every collection interval
percentiles []int: Percentiles to calculate for timing & histogram stats
allowed_pending_messages integer: Number of messages allowed to queue up waiting to be processed. When this fills, messages will be dropped and logged.
percentile_limit integer: 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.
templates []string: Templates for transforming statsd buckets into influx measurements and tags.

Statsd bucket -> InfluxDB line-protocol Templates

The plugin supports specifying templates for transforming statsd buckets into InfluxDB measurement names and tags. The templates have a measurement keyword, which can be used to specify parts of the bucket that are to be used in the measurement name. Other words in the template are used as tag names. For example, the following template:

templates = [
    "measurement.measurement.region"
]

would result in the following transformation:

cpu.load.us-west:100|g
=> cpu_load,region=us-west 100

Users can also filter the template to use based on the name of the bucket, using glob matching, like so:

templates = [
    "cpu.* measurement.measurement.region",
    "mem.* measurement.measurement.host"
]

which would result in the following transformation:

cpu.load.us-west:100|g
=> cpu_load,region=us-west 100

mem.cached.localhost:256|g
=> mem_cached,host=localhost 256

There are many more options available, More details can be found here

7.5 KiB Raw Blame History Unescape Escape