examples/sample-signals/README.md
# Example dashboard
With this example, you can bring up a simple Flask application that has 4 endpoints, plus one that always returns an error response. The 4 main endpoints simply return `ok` after some random delay.
The example Compose project also includes a random load generator, a Prometheus server, and a Grafana instance with preconfigured dashboards, feeding data from Prometheus, which is also configured to scrape metrics from the example application automatically. All you need to do is:
```shell
docker-compose up -d
```
Then open <http://localhost:3000/d/_eX4mpl3> in your browser to see the dashboard. You can edit each panel to check what metric it uses, but here is a quick rundown of what's going on in there.
## Requests per second
Number of successful Flask requests per second. Shown per path.
```json
rate(
flask_http_request_duration_seconds_count{status="200"}[30s]
)
```
## Errors per second
Number of failed (non HTTP 200) responses per second.
```json
sum(
rate(
flask_http_request_duration_seconds_count{status!="200"}[30s]
)
)
```
## Total requests per minute
The total number of requests measured over one minute intervals. Shown per HTTP response status code.
```json
increase(
flask_http_request_total[1m]
)
```
## Average response time [30s]
The average response time measured over 30 seconds intervals for successful requests. Shown per path.
```json
rate(
flask_http_request_duration_seconds_sum{status="200"}[30s]
)
/
rate(
flask_http_request_duration_seconds_count{status="200"}[30s]
)
```
## Requests under 250ms
The percentage of successful requests finished within 1/4 second. Shown per path.
```json
increase(
flask_http_request_duration_seconds_bucket{status="200",le="0.25"}[30s]
)
/ ignoring (le)
increase(
flask_http_request_duration_seconds_count{status="200"}[30s]
)
```
## Request duration [s] - p50
The 50th percentile of request durations over the last 30 seconds. In other words, half of the requests finish in (min/max/avg) these times. Shown per path.
```json
histogram_quantile(
0.5,
rate(
flask_http_request_duration_seconds_bucket{status="200"}[30s]
)
)
```
## Request duration [s] - p90
The 90th percentile of request durations over the last 30 seconds. In other words, 90 percent of the requests finish in (min/max/avg) these times. Shown per path.
```json
histogram_quantile(
0.9,
rate(
flask_http_request_duration_seconds_bucket{status="200"}[30s]
)
)
```
## Memory usage
The memory usage of the Flask app. Based on data from the underlying Prometheus client library, not Flask specific.
```json
process_resident_memory_bytes{job="example"}
```
## CPU usage
The CPU usage of the Flask app as measured over 30 seconds intervals. Based on data from the underlying Prometheus client library, not Flask specific.
```json
rate(
process_cpu_seconds_total{job="example"}[30s]
)
```
## Cleaning up
Don't forget to shut the demo down, once finished:
```shell
docker-compose down -v --rmi all
```