Scheduler
Purpose
The scheduler is responsible for scheduling the execution of tasks. The execution of those tasks are being prioritized / scored by a ranker. The tasks are then pushed onto a priority queue.
Within the project of KAT, the scheduler is tasked with scheduling boefje and normalizer tasks.
Architecture
See architecture document for the architecture and the extending document for the extending the scheduler with your own custom schedulers, and rankers.
Stack, packages and libraries
Name |
Version |
Description |
---|---|---|
Python |
^3.8 |
|
FastAPI |
^0.109.0 |
Used for api server |
SQLAlchemy |
^2.0.23 |
|
pydantic |
^2.5.2 |
External services
The scheduler interfaces with the following services:
Service |
Usage |
---|---|
[Octopoes] |
Retrieving random OOI’s of organizations |
[Katalogus] |
Used for referencing available plugins and organizations |
[Bytes] |
Retrieve last run boefje for organization and OOI |
[RabbitMQ] |
Used for retrieving scan profile changes, and created raw data in bytes |
Project structure
$ tree -L 3 --dirsfirst
.
├── docs/ # additional documentation
├── scheduler/ # scheduler python module
│ ├── config # application settings configuration
│ ├── connectors # external service connectors
│ │ ├── listeners # channel/socket listeners
│ │ ├── services # rest api connectors
│ │ └── __init__.py
│ ├── context/ # shared application context
│ ├── models/ # internal model definitions
│ ├── queues/ # priority queue
│ ├── rankers/ # priority/score calculations
│ ├── storage/ # data abstraction layer
│ ├── schedulers/ # schedulers
│ ├── server/ # http rest api server
│ ├── utils/ # common utility functions
│ ├── __init__.py
│ ├── __main__.py
│ ├── app.py # kat scheduler app implementation
│ └── version.py # version information
└─── tests/
├── factories/
├── integration/
├── mocks/
├── scripts/
├── simulation/
├── unit/
├── utils/
└── __init__.py
Running / Developing
Typically the scheduler will be run from the overarching nl-kat-coordination project. When you want to run and the scheduler individually you can use the following setup. We are using docker to setup our development environment, but you are free to use whatever you want.
Prerequisites
By the use of environment variables we load in the configuration of the scheduler. See the environment settings section under Installation and Deployment for more information.
Running
# Build and run the scheduler in the background
$ docker compose up --build -d scheduler
Testing
# Run integration tests
$ make itest
# Run unit tests
$ make utest
# Individually test a file
$ make file=test_file.py utest
# Individually test a function
$ make file=test_file.py function='test_function' utest