StatsD Metrics

The metrics observer emits StatsD-compatible time-series metrics about the performance of your application. These metrics are useful to get a cross-sectional view of how your application is performing in a broad sense.


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 metrics observer.



# required: the prefix added to all metrics emitted.
# if present, the observer is enabled.
metrics.namespace = myservice

# optional: an endpoint to send the metrics datagrams to.
# if not specified, metrics will only be emitted to debug logs.
metrics.endpoint = statsd.local:8125

# optional: the percent of statsd metrics to sample
# if not specified, it will default to 100% (all metrics sent)
# config must be passed to the `Baseplate` constructor to use this option
metrics_observer.sample_rate = 100%



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).

For the ServerSpan representing the request the server is handling, the timer has a name like {namespace}.server.{route_or_method_name} and the counter looks like {namespace}.server.{route_or_method_name}.{success,failure}. If the request timed out an additional counter will be emitted with path {namespace}.server.{route_or_method_name}.timed_out.

For each span representing a call to a remote service or database, the timer has a name like {namespace}.clients.{context_name}.{method} and the counter {namespace}.clients.{context_name}.{method}.{success,failure} where context_name is the name of the client in the context configuration.

Calls to incr_tag() will increment a counter like {namespace}.{tag_name} by the amount specified.

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:

def my_handler(request):

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