Metrics

The cortex get and cortex get API_NAME commands display the request time (averaged over the past 2 weeks) and response code counts (summed over the past 2 weeks) for your APIs:

cortex get

env      api                         status   up-to-date   requested   last update   avg request   2XX
cortex   iris-classifier             live     1            1           17m           24ms          1223
cortex   text-generator              live     1            1           8m            180ms         433
cortex   image-classifier-resnet50   live     2            2           1h            32ms          1121126

The cortex get API_NAME command also provides a link to a Grafana dashboard:

dashboard

Metrics in the dashboard

Panel	Description	Note
Request Rate	Request rate, computed over every minute, of an API
In Flight Request	Active in-flight requests for an API.	In-flight requests are recorded every 10 seconds, which will correspond to the minimum resolution.
Active Replicas	Active replicas for an API
2XX Responses	Request rate, computed over a minute, for responses with status code 2XX of an API
4XX Responses	Request rate, computed over a minute, for responses with status code 4XX of an API
5XX Responses	Request rate, computed over a minute, for responses with status code 5XX of an API
p99 Latency	99th percentile latency, computed over a minute, for an API	Value might not be accurate because the histogram buckets are not dynamically set.
p90 Latency	90th percentile latency, computed over a minute, for an API	Value might not be accurate because the histogram buckets are not dynamically set.
p50 Latency	50th percentile latency, computed over a minute, for an API	Value might not be accurate because the histogram buckets are not dynamically set.
Average Latency	Average latency, computed over a minute, for an API

Custom user metrics

It is possible to export custom user metrics by adding the metrics_client argument to the handler constructor. Below there is an example of how to use the metrics client. The implementation is similar to all handler types.

class Handler:
    def __init__(self, config, metrics_client):
        self.metrics = metrics_client

    def handle_post(self, payload):
        # --- my handler code here ---
        result = ...

        # increment a counter with name "my_metric" and tags model:v1
        self.metrics.increment(metric="my_counter", value=1, tags={"model": "v1"})

        # set the value for a gauge with name "my_gauge" and tags model:v1
        self.metrics.gauge(metric="my_gauge", value=42, tags={"model": "v1"})

        # set the value for an histogram with name "my_histogram" and tags model:v1
        self.metrics.histogram(metric="my_histogram", value=100, tags={"model": "v1"})

Note: The metrics client uses the UDP protocol to push metrics, so if it fails during a metrics push, no exception is thrown.