With this microservice, you will be able to monitor yours tasks, theirs executions and steps. This document will explain to you how to install and use it.
Note that if you use docker toolbox, some modifications on the path must be made. the localhost in this document must be replaced by your docker machine IP address (default is 192.168.99.100).
To run the database and the server, you will need docker. If you don't already have it, you can found some informations here
With your docker, you will need to do a
$ docker-compose up --build
if it's the first time, or without the --build if not.
Note that the docker-compose up will populate the mongo database with some data for the tests. If you want only an empty database, you can use the following command:
$ docker-compose up --build mongo server panel
To run the tests and verify that everything is ok, you only need to run the test project with maven using the following command from the Code/tasks-specs/ folder:
$ mvn install
If you use docker toolbox, you have to replace the server address in the pom.xml file by replacing the localhost with your docker machine IP address
<io.heig.tasks.server.url>http://localhost:8080/api/</io.heig.tasks.server.url>
You will see the results just after the run.
An admin panel is available at the address localhost:3000
This panel shows the status of your tasks and the details of the executions of each tasks when you click on details
To have more details on this panel please see the readme in the folder Code/AdminPanel
The API documentation is available at the address http://localhost:8080/api
The interest of this service is to do that directly in your code, to do so follow this steps :
1 - You need to add a Task which is generally the same for a long period of time. To do so, send a POST request to http://localhost:8080/api/tasks with a name, a description and a unique task_id.
You can use the interactif doc to go faster => http://localhost:8080/api
2 - Set the task_id as a constant in the class/function you want to monitor. Add an execution for this task by sending a POST request to http://localhost:8080/api/executions using the task_id when you start a new execution of your task. You can give the execution a name if you have the need to identify the differents executions.
Use the api's response to get the unique execution id and store it as a temporary variable accessible from all the functions used in the execution.
3 - Finally use the execution id to add the differents step of your execution. Each time you want to mark a step of your code send a POST request to http://localhost:8080/api/steps with the actual execution_id. You can specify a status that can be Running, Error, Log or Success. You can also give the step a name and a context to help you debug the code in case of an error.