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Refactor to reflect modern ways of charming #14

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Refactor to reflect modern ways of charming #14

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130 changes: 50 additions & 80 deletions observed/README.md
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## Description

This charm demonstrates how to integrate with [Canonical Observability Stack (COS)](https://charmhub.io/cos-lite).
This charm demonstrates how to integrate with COS observability stack.

It ships a dashboard, a prometheus alert rule and a loki alert rule.
The general steps for a clean start are:

## Usage

Deploy the [Canonical Observability Stack (COS)](https://charmhub.io/cos-lite) and offer the three needed integrations. This guide does not cover setting up COS lite but leaves that up to the reader.
1. Initialize your charm you wish to integrate.

```charmcraft init --profile machine```

2. Fetch the [COS agent libs] with charmcraft:

```charmcraft fetch-lib charms.grafana_agent.v0.cos_agent```

3. Create directories for grafana dashboards, prometheus alert rules & loki rules. We will reference those in the code.
```mkdir -p ./src/alert_rules/loki ./src/alert_rules/prometheus ./src/grafana_dashboards```

4. Add to metadata.yaml

```
provides:
cos-agent:
interface: cos_agent
```

5. Then in your charm.py

```
def __init__(self, *args):
super().__init__(*args)

# Define the data to send to grafana-agent
self._grafana_agent = COSAgentProvider(
self, metrics_endpoints=[
{"path": "/metrics", "port": self.config.get('port')},
],
metrics_rules_dir="./src/alert_rules/prometheus",
logs_rules_dir="./src/alert_rules/loki"
```

Full guide here: https://charmhub.io/topics/canonical-observability-stack/tutorials/instrumenting-machine-charms

![Alt text](overview.png)
## Usage

Lets say your url:s from the COS stack are available as:
First, make sure you have a COS stack deployed and operational which offers the three needed integrations. Lets say your url:s are available as:

admin/cos.cos-grafana-dashboard
admin/admin/cos.prometheus-remote
admin/admin/cos.cos-loki-logging

Then deploy this charm with the grafana-agent as a subordinate in your own test model like this:

Then deploy this charm with the grafana-agent as a subordinate like this:

juju depoy ./observed.charm
juju deploy grafana-agent
juju deploy grafana-agent # You might need to add --channel edge.

Now, relate with the COS offers you have in your test model.
Now, relate with the COS offers you have in your model

juju relate grafana-agent cos.cos-grafana-dashboard

Expand All @@ -33,80 +67,16 @@ Now, relate with the COS offers you have in your test model.

Juju will now take care of sending dashboards, alert-rules & loki rules to COS and you can access them in COS grafana dashboard.

![The deployed model](deployed-and-related.png)

![The dashboard](grafana-dashboard-view.png)

## Dashboard with juju topoligy.

When Juju ships the supplied [dashboard](src/grafana_dashboards/microsample_dashboard.json), variables from the juju topolgy are injected by the grafana-agent library such that they can be used directly in the dashboard json.

For example in the loki query:

```"expr": "{juju_application=\"$juju_application\", juju_unit=~\"$juju_unit\"} |~ \".* 404 .*\""```

Read about [Juju topology](https://discourse.charmhub.io/t/juju-topology-labels/8874) for how to extend this to your own charms.

## Alert rules examples
## Customize dashboards etc.

The charm ships a [prometheus alert rule](src/alert_rules/prometheus/microsample_prometheus.rule) that is triggered once you have called the microsample API more than 3 times like below:
Read here: https://discourse.charmhub.io/t/juju-topology-labels/8874

curl http://microsample.ip:8080/api/info
## Relations

The charm shipps a Loki alert rule, that is triggered if API is called incorrecty which causes Loki to see a 404 code in the logs)
Relate this to https://charmhub.io/grafana-agent

curl http://microsample.ip:8080/api/XXX
## Contributing

## Alertmanager examples
Place a PR

There is 4 different examples of [alertmanager configurations](src/alertmanager_configs/) that shows how to intergrate pagerduty and slack with alertmanager.
If other providers or configuration is needed, more information can be found at [prometheus documentation](https://prometheus.io/docs/alerting/latest/configuration/) site.

Inject configuration file with juju using following command:

juju config alertmanager config_file=@/path/to/file

To show or check current configuration:

juju run-action alertmanager/0 show-config
juju run-action alertmanager/0 check-config

### Development process

This charm is developed following this guide: https://charmhub.io/topics/canonical-observability-stack/tutorials/instrumenting-machine-charms

The general steps are:

1. Initialize your charm you wish to integrate.

```charmcraft init --profile machine```

2. Fetch the grafana-agent libs with charmcraft:

```charmcraft fetch-lib charms.grafana_agent.v0.cos_agent```

3. Create directories for grafana dashboards, prometheus alert rules & loki rules. We will reference those in the code.
```mkdir -p ./src/alert_rules/loki ./src/alert_rules/prometheus ./src/grafana_dashboards```

4. Add to metadata.yaml

```
provides:
cos-agent:
interface: cos_agent
```

5. Then in your charm.py

```
def __init__(self, *args):
super().__init__(*args)

# Define the data to send to grafana-agent
self._grafana_agent = COSAgentProvider(
self, metrics_endpoints=[
{"path": "/metrics", "port": self.config.get('port')},
],
metrics_rules_dir="./src/alert_rules/prometheus",
logs_rules_dir="./src/alert_rules/loki"
```
[COS agent libs]: https://canonical.github.io/charm-relation-interfaces/interfaces/cos_agent/v0/
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31 changes: 25 additions & 6 deletions observed/charmcraft.yaml
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# Learn more about charmcraft.yaml configuration at:
# https://juju.is/docs/sdk/charmcraft-config
type: "charm"
name: observed
type: charm
title: The observed charm
summary: An educational charm that can be observed with the COS stack.
description: |
A charm that allows a user to observe the microsample service with the COS stack.

bases:
- build-on:
- name: "ubuntu"
- name: ubuntu
channel: "22.04"
run-on:
- name: "ubuntu"
channel: "22.04"
- name: ubuntu
channel: "22.04"

config:
options:
port:
type: string
default: "8080"
description: "Listen port (-p) passed to flask run."
channel:
type: string
default: "edge"
description: "Channel for microsample snap."

provides:
cos-agent:
interface: cos_agent
9 changes: 0 additions & 9 deletions observed/config.yaml
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