# Investigating Performance Issues

You deployed your application or function to Knative Serving but its performance 
doesn't meet your expectations. Knative Serving provides various dashboards and tools to 
help investigate such issues. This document reviews these dashboards and tools.

## Request metrics

Start your investigation with the "Revision - HTTP Requests" dashboard. 

1.  To open this dashboard, open the Grafana UI as described in
    [Accessing Metrics](./accessing-metrics.md) and navigate to 
    "Knative Serving - Revision HTTP Requests".

1.  Select your configuration and revision from the menu on top left of the page.
    You will see a page like this:

    ![Knative Serving - Revision HTTP Requests](./images/request_dash1.png)

    This dashboard gives visibility into the following for each revision:

    * Request volume
    * Request volume per HTTP response code
    * Response time
    * Response time per HTTP response code
    * Request and response sizes

This dashboard can show traffic volume or latency discrepancies between different revisions. 
If, for example, a revision's latency is higher than others revisions, then 
focus your investigation on the offending revision through the rest of this guide.

## Request traces

Next, look into request traces to find out where the time is spent for a single request.

1.  To access request traces, open the Zipkin UI as described in [Accessing Traces](./accessing-traces.md).

1.  Select your revision from the "Service Name" dropdown, and then click the "Find Traces" button. You'll
    get a view that looks like this:

    ![Zipkin - Trace Overview](./images/zipkin1.png)

    In this example, you can see that the request spent most of its time in the 
    [span](https://github.com/opentracing/specification/blob/master/specification.md#the-opentracing-data-model)
    right before the last, so focus your investigation on that specific span. 

1.  Click that span to see a view like the following:

    ![Zipkin - Span Details](./images/zipkin2.png)

    This view shows detailed information about the specific span, such as the
    micro service or external URL that was called. In this example, the call to a
    Grafana URL is taking the most time. Focus your investigation on why that URL
    is taking that long.

## Autoscaler metrics

If request metrics or traces do not show any obvious hot spots, or if they show
that most of the time is spent in your own code, look at autoscaler metrics next.

1.  To open the autoscaler dashboard, open Grafana UI and select
    "Knative Serving - Autoscaler" dashboard, which looks like this:

    ![Knative Serving - Autoscaler](./images/autoscaler_dash1.png)

This view shows 4 key metrics from the Knative Serving autoscaler:

* Actual pod count: # of pods that are running a given revision
* Desired pod count: # of pods that autoscaler thinks should serve the revision
* Requested pod count: # of pods that the autoscaler requested from Kubernetes
* Panic mode: If 0, the autoscaler is operating in [stable mode](https://github.com/knative/serving/blob/master/docs/scaling/DEVELOPMENT.md#stable-mode).
If 1, the autoscaler is operating in [panic mode](https://github.com/knative/serving/blob/master/docs/scaling/DEVELOPMENT.md#panic-mode).

A large gap between the actual pod count and the requested pod count
indicates that the Kubernetes cluster is unable to keep up allocating new
resources fast enough, or that the Kubernetes cluster is out of requested
resources.

A large gap between the requested pod count and the desired pod count indicates that
the Knative Serving autoscaler is unable to communicate with the Kubernetes master to make
the request.

In the preceding example, the autoscaler requested 18 pods to optimally serve the traffic
but was only granted 8 pods because the cluster is out of resources.

## CPU and memory usage

You can access total CPU and memory usage of your revision from 
the "Knative Serving - Revision CPU and Memory Usage" dashboard, which looks like this:

![Knative Serving - Revision CPU and Memory Usage](./images/cpu_dash1.png)

The first chart shows rate of the CPU usage across all pods serving the revision.
The second chart shows total memory consumed across all pods serving the revision.
Both of these metrics are further divided into per container usage.

* user-container: This container runs the user code (application, function, or container).
* [istio-proxy](https://github.com/istio/proxy): Sidecar container to form an 
[Istio](https://istio.io/docs/concepts/what-is-istio/overview.html) mesh.
* queue-proxy: Knative Serving owned sidecar container to enforce request concurrency limits.
* autoscaler: Knative Serving owned sidecar container to provide autoscaling for the revision.
* fluentd-proxy: Sidecar container to collect logs from /var/log.

## Profiling

...To be filled...

---

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[Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/),
and code samples are licensed under the
[Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
