StatsD Tagged Metrics

The tagged metrics observer emits StatsD-compatible time-series metrics about the performance of your application with tags in the InfluxStatsD format. The tags added to the metrics are configurable: any tags that pass through the set_tag() function are filtered through a user-supplied allowlist in the configuration file.


Make sure your service calls configure_observers() during application startup and then add the following to your configuration file to enable and configure the StatsD tagged metrics observer.



# required to enable observer
metrics.tagging = true

# optional: which span tags should be attached to metrics. see below.
# `endpoint` and `client` are always allowed
metrics.allowlist = foo, bar, baz

# optional: the percent of statsd metrics to sample.
# if not specified, it will default to 100% (all metrics sent)
metrics_observer.sample_rate = 100%


Tag Allowlist

Wavefront supports a maximum of 20 tags per cluster and 1000 distinct time series per metric. Baseplate integrations of frameworks come out of the box with some default tags set via set_tag(), but to append them to the metrics they must be present in the configuration file via metrics.allowlist.

In order to find these tags to put in the allowlist, look through the code base for calls to set_tag() or check a zipkin trace in Wavefront to see all the tags on a span.


For each span in the application, the metrics observer emits a Timer tracking how long the span took and increments a Counter for success or failure of the span (failure being an unexpected exception).

A key differentiation from the untagged StatsD metrics observer is that the emitted outputs from baseplate no longer contain a namespace prefix. Prepending the namespace must be configured in Telegraf via the name_prefix input plugin configuration.

For the ServerSpan representing the request the server is handling, the timer has a name like baseplate.server.latency,endpoint={route_or_method_name} and the counter looks like baseplate.server.rate,success={True,False},endpoint={route_or_method_name}.

For each span representing a call to a remote service or database, the timer has a name like baseplate.clients.latency,client={name},endpoint={method} and the counter baseplate.clients.rate,client={name},endpoint={method},success={True,False}.

When using baseplate-serve, various process-level runtime metrics will also be emitted. These are not tied to individual requests but instead give insight into how the whole application is functioning. See Prometheus Exporter for more information.

Direct Use

When enabled, the metrics observer also adds a Client object as an attribute named metrics to the RequestContext which can take an optional tags parameter in the form of a dict:

def my_handler(request):
    request.metrics.counter("foo", {"bar": "baz"}).increment()

To keep your application more generic, it’s better to use local spans for custom local timers and incr_tag() for custom counters.