mirror of
https://github.com/prometheus/statsd_exporter.git
synced 2024-11-29 02:31:00 +00:00
dae5d782a6
The Go client for prometheus aggregates summary metrics over 10 minutes by default, in 5 buckets. This is not always the behaviour we want. Allow tweaking those settings in `statsd_exporter`, so we can aggregate summary metrics over more or less time, with more or fewer buckets, and set the cap for the bucket as well. Signed-off-by: Thomas Gummerer <t.gummerer@gmail.com>
473 lines
18 KiB
Markdown
473 lines
18 KiB
Markdown
# statsd exporter [][travis]
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[][circleci]
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[][quay]
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[][hub]
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`statsd_exporter` receives StatsD-style metrics and exports them as Prometheus metrics.
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## Overview
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### With StatsD
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To pipe metrics from an existing StatsD environment into Prometheus, configure
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StatsD's repeater backend to repeat all received metrics to a `statsd_exporter`
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process. This exporter translates StatsD metrics to Prometheus metrics via
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configured mapping rules.
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+----------+ +-------------------+ +--------------+
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| StatsD |---(UDP/TCP repeater)--->| statsd_exporter |<---(scrape /metrics)---| Prometheus |
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+----------+ +-------------------+ +--------------+
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### Without StatsD
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Since the StatsD exporter uses the same line protocol as StatsD itself, you can
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also configure your applications to send StatsD metrics directly to the exporter.
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In that case, you don't need to run a StatsD server anymore.
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We recommend this only as an intermediate solution and recommend switching to
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[native Prometheus instrumentation](http://prometheus.io/docs/instrumenting/clientlibs/)
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in the long term.
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### Tagging Extensions
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The exporter supports Librato, InfluxDB, and DogStatsD-style tags,
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which will be converted into Prometheus labels.
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For Librato-style tags, they must be appended to the metric name with a
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delimiting `#`, as so:
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```
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metric.name#tagName=val,tag2Name=val2:0|c
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```
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See the [statsd-librato-backend README](https://github.com/librato/statsd-librato-backend#tags)
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for a more complete description.
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For InfluxDB-style tags, they must be appended to the metric name with a
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delimiting comma, as so:
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```
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metric.name,tagName=val,tag2Name=val2:0|c
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```
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See [this InfluxDB blog post](https://www.influxdata.com/blog/getting-started-with-sending-statsd-metrics-to-telegraf-influxdb/#introducing-influx-statsd)
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for a larger overview.
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For DogStatsD-style tags, they're appended as a `|#` delimited section at the
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end of the metric, as so:
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```
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metric.name:0|c|#tagName:val,tag2Name:val2
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```
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See [Tags](https://docs.datadoghq.com/developers/dogstatsd/data_types/#tagging)
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in the DogStatsD documentation for the concept description and
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[Datagram Format](https://docs.datadoghq.com/developers/dogstatsd/datagram_shell/).
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If you encounter problems, note that this tagging style is incompatible with
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the original `statsd` implementation.
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Be aware: If you mix tag styles (e.g., Librato/InfluxDB with DogStatsD), the
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exporter will consider this an error and the sample will be discarded. Also,
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tags without values (`#some_tag`) are not supported and will be ignored.
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## Building and Running
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NOTE: Version 0.7.0 switched to the [kingpin](https://github.com/alecthomas/kingpin) flags library. With this change, flag behaviour is POSIX-ish:
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* long flags start with two dashes (`--version`)
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* multiple short flags can be combined (but there currently is only one)
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* flag processing stops at the first `--`
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```
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$ go build
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$ ./statsd_exporter --help
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usage: statsd_exporter [<flags>]
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Flags:
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-h, --help Show context-sensitive help (also try --help-long and --help-man).
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--web.listen-address=":9102"
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The address on which to expose the web interface and generated Prometheus metrics.
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--web.telemetry-path="/metrics"
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Path under which to expose metrics.
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--statsd.listen-udp=":9125"
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The UDP address on which to receive statsd metric lines. "" disables it.
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--statsd.listen-tcp=":9125"
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The TCP address on which to receive statsd metric lines. "" disables it.
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--statsd.listen-unixgram=""
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The Unixgram socket path to receive statsd metric lines in datagram. "" disables it.
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--statsd.unixsocket-mode="755"
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The permission mode of the unix socket.
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--statsd.mapping-config=STATSD.MAPPING-CONFIG
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Metric mapping configuration file name.
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--statsd.read-buffer=STATSD.READ-BUFFER
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Size (in bytes) of the operating system's transmit read buffer associated with the UDP or Unixgram connection. Please make sure the kernel parameters net.core.rmem_max is set to
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a value greater than the value specified.
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--statsd.cache-size=1000 Maximum size of your metric mapping cache. Relies on least recently used replacement policy if max size is reached.
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--statsd.event-queue-size=10000
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Size of internal queue for processing events
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--statsd.event-flush-threshold=1000
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Number of events to hold in queue before flushing
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--statsd.event-flush-interval=200ms
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Number of events to hold in queue before flushing
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--debug.dump-fsm="" The path to dump internal FSM generated for glob matching as Dot file.
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--log.level="info" Only log messages with the given severity or above. Valid levels: [debug, info, warn, error, fatal]
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--log.format="logger:stderr"
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Set the log target and format. Example: "logger:syslog?appname=bob& local=7" or "logger:stdout?json=true"
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--version Show application version.
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```
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## Tests
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$ go test
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## Metric Mapping and Configuration
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The `statsd_exporter` can be configured to translate specific dot-separated StatsD
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metrics into labeled Prometheus metrics via a simple mapping language. The config
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file is reloaded on SIGHUP.
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A mapping definition starts with a line matching the StatsD metric in question,
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with `*`s acting as wildcards for each dot-separated metric component. The
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lines following the matching expression must contain one `label="value"` pair
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each, and at least define the metric name (label name `name`). The Prometheus
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metric is then constructed from these labels. `$n`-style references in the
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label value are replaced by the n-th wildcard match in the matching line,
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starting at 1. Multiple matching definitions are separated by one or more empty
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lines. The first mapping rule that matches a StatsD metric wins.
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Metrics that don't match any mapping in the configuration file are translated
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into Prometheus metrics without any labels and with any non-alphanumeric
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characters, including periods, translated into underscores.
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In general, the different metric types are translated as follows:
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StatsD gauge -> Prometheus gauge
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StatsD counter -> Prometheus counter
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StatsD timer -> Prometheus summary <-- indicates timer quantiles
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-> Prometheus counter (suffix `_total`) <-- indicates total time spent
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-> Prometheus counter (suffix `_count`) <-- indicates total number of timer events
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An example mapping configuration:
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```yaml
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mappings:
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- match: "test.dispatcher.*.*.*"
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name: "dispatcher_events_total"
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labels:
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processor: "$1"
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action: "$2"
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outcome: "$3"
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job: "test_dispatcher"
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- match: "*.signup.*.*"
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name: "signup_events_total"
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labels:
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provider: "$2"
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outcome: "$3"
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job: "${1}_server"
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```
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This would transform these example StatsD metrics into Prometheus metrics as
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follows:
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test.dispatcher.FooProcessor.send.success
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=> dispatcher_events_total{processor="FooProcessor", action="send", outcome="success", job="test_dispatcher"}
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foo_product.signup.facebook.failure
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=> signup_events_total{provider="facebook", outcome="failure", job="foo_product_server"}
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test.web-server.foo.bar
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=> test_web_server_foo_bar{}
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Each mapping in the configuration file must define a `name` for the metric. The
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metric's name can contain `$n`-style references to be replaced by the n-th
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wildcard match in the matching line. That allows for dynamic rewrites, such as:
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```yaml
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mappings:
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- match: "test.*.*.counter"
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name: "${2}_total"
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labels:
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provider: "$1"
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```
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The metric name can also contain references to regex matches. The mapping above
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could be written as:
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```yaml
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mappings:
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- match: "test\\.(\\w+)\\.(\\w+)\\.counter"
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match_type: regex
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name: "${2}_total"
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labels:
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provider: "$1"
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```
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Be aware about yaml escape rules as a mapping like the following one will not work.
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```yaml
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mappings:
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- match: "test\.(\w+)\.(\w+)\.counter"
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match_type: regex
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name: "${2}_total"
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labels:
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provider: "$1"
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```
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Please note that metrics with the same name must also have the same set of
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label names.
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If the default metric help text is insufficient for your needs you may use the YAML
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configuration to specify a custom help text for each mapping:
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```yaml
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mappings:
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- match: "http.request.*"
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help: "Total number of http requests"
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name: "http_requests_total"
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labels:
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code: "$1"
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```
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### StatsD timers
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By default, statsd timers are represented as a Prometheus summary with
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quantiles. You may optionally configure the [quantiles and acceptable
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error](https://prometheus.io/docs/practices/histograms/#quantiles), as
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well as adjusting how the summary metric is aggregated:
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```yaml
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mappings:
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- match: "test.timing.*.*.*"
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timer_type: summary
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name: "my_timer"
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labels:
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provider: "$2"
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outcome: "$3"
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job: "${1}_server"
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summary_options:
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quantiles:
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- quantile: 0.99
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error: 0.001
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- quantile: 0.95
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error: 0.01
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- quantile: 0.9
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error: 0.05
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- quantile: 0.5
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error: 0.005
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max_summary_age: 30s
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summary_age_buckets: 3
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stream_buffer_size: 1000
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```
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The default quantiles are 0.99, 0.9, and 0.5.
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The default summary age is 10 minutes, the default number of buckets
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is 5 and the default buffer size is 500. See also the
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[`golang_client` docs](https://godoc.org/github.com/prometheus/client_golang/prometheus#SummaryOpts).
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The `max_summary_age` corresponds to `SummaryOptions.MaxAge`, `summary_age_buckets`
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to `SummaryOptions.AgeBuckets` and `stream_buffer_size` to `SummaryOptions.BufCap`.
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In the configuration, one may also set the timer type to "histogram". The
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default is "summary" as in the plain text configuration format. For example,
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to set the timer type for a single metric:
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```yaml
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mappings:
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- match: "test.timing.*.*.*"
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timer_type: histogram
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histogram_options:
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buckets: [ 0.01, 0.025, 0.05, 0.1 ]
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name: "my_timer"
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labels:
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provider: "$2"
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outcome: "$3"
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job: "${1}_server"
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```
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Note that timers will be accepted with the `ms`, `h`, and `d` statsd types. The first two are timers and histograms and the `d` type is for DataDog's "distribution" type. The distribution type is treated identically to timers and histograms.
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It should be noted that whereas timers in statsd expects the unit of timing data to be in milliseconds,
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prometheus expects the unit to be seconds. Hence, the exporter converts all timers to seconds
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before exporting them.
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### DogStatsD Client Behavior
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#### `timed()` decorator
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If you are using the DogStatsD client's [timed](https://datadogpy.readthedocs.io/en/latest/#datadog.threadstats.base.ThreadStats.timed) decorator,
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it emits the metric in seconds, set [use_ms](https://datadogpy.readthedocs.io/en/latest/index.html?highlight=use_ms) to `True` to fix this.
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### Regular expression matching
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Another capability when using YAML configuration is the ability to define matches
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using raw regular expressions as opposed to the default globbing style of match.
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This may allow for pulling structured data from otherwise poorly named statsd
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metrics AND allow for more precise targetting of match rules. When no `match_type`
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paramter is specified the default value of `glob` will be assumed:
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```yaml
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mappings:
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- match: "(.*)\.(.*)--(.*)\.status\.(.*)\.count"
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match_type: regex
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name: "request_total"
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labels:
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hostname: "$1"
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exec: "$2"
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protocol: "$3"
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code: "$4"
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```
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Note, that one may also set the histogram buckets. If not set, then the default
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[Prometheus client values](https://godoc.org/github.com/prometheus/client_golang/prometheus#pkg-variables) are used: `[.005, .01, .025, .05, .1, .25, .5, 1, 2.5, 5, 10]`. `+Inf` is added
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automatically.
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`timer_type` is only used when the statsd metric type is a timer. `buckets` is
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only used when the statsd metric type is a timerand the `timer_type` is set to
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"histogram."
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### Global defaults
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One may also set defaults for the timer type, buckets or quantiles, and match_type. These will be used
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by all mappings that do not define these.
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An option that can only be configured in `defaults` is `glob_disable_ordering`, which is `false` if omitted. By setting this to `true`, `glob` match type will not honor the occurance of rules in the mapping rules file and always treat `*` as lower priority than a general string.
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```yaml
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defaults:
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timer_type: histogram
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buckets: [.005, .01, .025, .05, .1, .25, .5, 1, 2.5 ]
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match_type: glob
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glob_disable_ordering: false
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ttl: 0 # metrics do not expire
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mappings:
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# This will be a histogram using the buckets set in `defaults`.
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- match: "test.timing.*.*.*"
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name: "my_timer"
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labels:
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provider: "$2"
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outcome: "$3"
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job: "${1}_server"
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# This will be a summary timer.
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- match: "other.timing.*.*.*"
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timer_type: summary
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name: "other_timer"
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labels:
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provider: "$2"
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outcome: "$3"
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job: "${1}_server_other"
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```
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### Choosing between glob or regex match type
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Despite from the missing flexibility of using regular expression in mapping and
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formatting labels, `glob` matching is optimized to have better performance than
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`regex` in certain use cases. In short, glob will have best performance if the
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rules amount is not so less and captures (using of `*`) is not to much in a
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single rule. Whether disabling ordering in glob or not won't have a noticable
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effect on performance in general use cases. In edge cases like the below however,
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disabling ordering will be beneficial:
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a.*.*.*.*
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a.b.*.*.*
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a.b.c.*.*
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a.b.c.d.*
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The reason is that the list assignment of captures (using of `*`) is the most
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expensive operation in glob. Honoring ordering will result in up to 10 list
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assignments, while without ordering it will need only 4 at most.
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For details, see [pkg/mapper/fsm/README.md](pkg/mapper/fsm/README.md).
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Running `go test -bench .` in **pkg/mapper** directory will produce
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a detailed comparison between the two match type.
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### `drop` action
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You may also drop metrics by specifying a "drop" action on a match. For
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example:
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```yaml
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mappings:
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# This metric would match as normal.
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- match: "test.timing.*.*.*"
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name: "my_timer"
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labels:
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provider: "$2"
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outcome: "$3"
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job: "${1}_server"
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# Any metric not matched will be dropped because "." matches all metrics.
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- match: "."
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match_type: regex
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action: drop
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name: "dropped"
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```
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You can drop any metric using the normal match syntax.
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The default action is "map" which does the normal metrics mapping.
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### Explicit metric type mapping
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StatsD allows emitting of different metric types under the same metric name,
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but the Prometheus client library can't merge those. For this use-case the
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mapping definition allows you to specify which metric type to match:
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```
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mappings:
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- match: "test.foo.*"
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name: "test_foo"
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match_metric_type: counter
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labels:
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provider: "$1"
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```
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Possible values for `match_metric_type` are `gauge`, `counter` and `timer`.
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### Mapping cache size and cache replacement policy
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There is a cache used to improve the performance of the metric mapping, that can greatly improvement performance.
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The cache has a default maximum of 1000 unique statsd metric names -> prometheus metrics mappings that it can store.
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This maximum can be adjust using the `statsd.cache-size` flag.
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If the maximum is reached, entries are rotated using the [least recently used replacement policy](https://en.wikipedia.org/wiki/Cache_replacement_policies#Least_recently_used_(LRU)).
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If you are using this exporter to reduce the cardinality of your data, a high maximum cache size can be a costly use of memory.
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### Time series expiration
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The `ttl` parameter can be used to define the expiration time for stale metrics.
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The value is a time duration with valid time units: "ns", "us" (or "µs"),
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"ms", "s", "m", "h". For example, `ttl: 1m20s`. `0` value is used to indicate
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metrics that do not expire.
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TTL configuration is stored for each mapped metric name/labels combination
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whenever new samples are received. This means that you cannot immediately
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expire a metric only by changing the mapping configuration. At least one
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sample must be received for updated mappings to take effect.
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### Event flushing configuration
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Internally `statsd_exporter` runs a goroutine for each network listener (UDP, TCP & Unix Socket). These each receive and parse metrics received into an event. For performance purposes, these events are queued internally and flushed to the main exporter goroutine periodically in batches. The size of this queue and the flush criteria can be tuned with the `--statsd.event-queue-size`, `--statsd.event-flush-threshold` and `--statsd.event-flush-interval`. However, the defaults should perform well even for very high traffic environments.
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## Using Docker
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You can deploy this exporter using the [prom/statsd-exporter](https://registry.hub.docker.com/u/prom/statsd-exporter/) Docker image.
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For example:
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```bash
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docker pull prom/statsd-exporter
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docker run -d -p 9102:9102 -p 9125:9125 -p 9125:9125/udp \
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-v $PWD/statsd_mapping.yml:/tmp/statsd_mapping.yml \
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prom/statsd-exporter --statsd.mapping-config=/tmp/statsd_mapping.yml
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```
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[travis]: https://travis-ci.org/prometheus/statsd_exporter
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[circleci]: https://circleci.com/gh/prometheus/statsd_exporter
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[quay]: https://quay.io/repository/prometheus/statsd-exporter
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[hub]: https://hub.docker.com/r/prom/statsd-exporter/
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