# Drools + Quarkus + Runtime metrics

## Description

This example demonstrates how to enable and consume the runtime metrics monitoring feature in Kogito. 

### Prerequisites
 
You will need:
  - Java 11+ installed 
  - Environment variable JAVA_HOME set accordingly
  - Maven 3.8.1+ installed
  - Docker 19+ (only if you want to run the integration tests and/or you want to use the `docker-compose` script provided in this example).
  
### How to enable the feature

Import the following dependency in your `pom.xml`:

```XML
<dependency>
 <groupId>org.kie.kogito</groupId>
 <artifactId>kogito-addons-quarkus-monitoring-prometheus</artifactId>
</dependency>
```

### Architecture

Once you compile your Quarkus project, a dashboard for each available endpoint will be generated under the path `target/classes/META-INF/resources/monitoring/dashboards/`. You can then inject those grafana dashboards during the deployment of the grafana instance.

The use case is summarized in the following schema:
 
![RuntimeMetrics](https://user-images.githubusercontent.com/18282531/76740726-a0cbdd80-676e-11ea-8cc3-63ed5cbb3ac8.png)

To summarize, the kogito app will expose by default an endpoint `/metrics` with the prometheus variables, and a prometheus instance will simply fetch the data from there.

### Dashboards

Kogito currently exports two types of dashboards depending on the model used on the endpoint:
1. Operational dashboard: this dashboard is generated for DMN and DRL endpoints and it contains
    * Total number of requests on the endpoint.
    * Average per minute of the number of requests on the endpoint.
    * Quantiles on the elapsed time to evaluate the requests.
    * Exception details.
![Screenshot from 2020-05-19 15-20-03](https://user-images.githubusercontent.com/18282531/82339837-ca171d00-99ee-11ea-85bc-2681878fb6ab.png)
2. Domain specific dashboard: currently this dashboard is exported only for DMN endpoints. In particular, the domain specific dashboard contains a graph for each type of decision in the DMN model. At the moment, only the built-in types `number`, `string` and `boolean` are supported:
   * if the output of the decision is a number, the graph contains the quantiles for that metric (on a sliding window of 3 minutes).
   * If the output is a boolean or a string, the graph contains the number of occurrences for each output (10 minutes average).
![Screenshot from 2020-05-19 15-19-48](https://user-images.githubusercontent.com/18282531/82339828-c71c2c80-99ee-11ea-85b6-b5d4a0337f0b.png)

You can use these default dashboards, or you can personalize them and use your custom dashboards.

#### Custom dashboards
User may add custom dashboards defined as `json` files. Format specification and details are provided in the [Official documentation](https://grafana.com/docs/grafana/latest/dashboards/json-model/) page.
There are some conventions to be followed to successfully add such dashboards inside a `Kogito` project:

1. dashboard files must be stored inside `/src/main/resources/META-INF/dashboards` directory
2. dashboard file names must start with `domain-dashoboard` (for domain specific dashboards) or `operational-dashboard` (for operational ones)
3. dashboard file names must end with `.json`
4. dashboard file names should not conflict with auto-generated ones
5. the `title` attribute of custom dashboards should not conflict with auto-generated ones.

Custom dashboards will be available in the grafana panel, together with auto-generated ones.

### Compile and Run in Local Dev Mode

It is possible to use `docker-compose` to demonstrate how to inject the generated dashboards in the volume of the grafana container:
1. Run `mvn clean package` to build the project and generate dashboards. A docker image tagged `org.kie.kogito.examples/dmn-drools-quarkus-metrics-example:1.0` will be built (docker must be installed on your system).
2. Run `docker-compose up` to start the applications. 

The volumes of the grafana container are properly set in the `docker-compose.yml` file, so that the dashboards are properly loaded at startup.

## Example Usage

Once the service is up and running, you can use the following example to interact with the service.

### POST /hello

Post "hello":

```sh
curl -H "Content-Type: application/json" -X POST -d '{"strings":["world"]}' http://localhost:8080/hello
```

the service will return `["hello", "world"]`

### POST /LoanEligibility

Post:

```sh
curl -X POST 'http://localhost:8080/LoanEligibility' -H 'Content-Type: application/json' \
    -d '{
        "Client": {"age": 43,"salary": 1950, "existing payments": 100},
        "Loan": {"duration": 15,"installment": 180}, 
        "SupremeDirector" : "Yes", 
        "Bribe": 1000
    }'
```

the service will return the decision results.  

If you are using the `docker-compose` script we provided, go to `localhost:3000` and have a look at your dashboards.
