pybrook 0.1.6

PyBrook - a real-time cloud computing framework for the Internet of Things.

Introduction

PyBrook - a real-time cloud computing framework for the Internet of Things. PyBrook enables users to define complex data processing models declaratively using the Python programming language. The framework also provides a generic web interface that presents the collected data in real-time.

PyBrook aims to make the development of real-time data processing services as easy as possible by utilising powerful mechanisms of the Python programming language and modern concepts like hot-reloading or deploying software in Linux Containers.

A simple docker-compose up is enough to start playing with the framework.

Run demo with Docker

It is recommended to use docker-compose for learning (you can use the docker-compose.yml from the project repository:

docker-compose up

This command will start all the services, including Redis with Redis Gears enabled.

The following services will be available:

• OpenAPI docs (ReDoc): http://localhost:8000/redoc
• OpenAPI docs (Swagger UI): http://localhost:8000/docs
• PyBrook frontend: http://localhost:8000/panel
• Locust panel for load testing: http://localhost:8089

You should probably visit the Locust panel first and start sending some reports.

Using your own model

The configured model is pybrook.examples.demo, but replacing it with your own is very easy.
First, you have to save your custom model somewhere. For now, you can just copy the source of pybrook.examples.demo (attached below) and save it as mymodel.py in your working directory.

??? example "Source of pybrook.examples.demo"

```python linenums="1"
from datetime import datetime
from math import atan2, degrees
from typing import Optional, Sequence

from pybrook.models import (
    InReport,
    OutReport,
    PyBrook,
    ReportField,
    dependency,
    historical_dependency,
)

brook = PyBrook('redis://localhost')
app = brook.app


@brook.input('ztm-report', id_field='vehicle_number')
class ZTMReport(InReport):
    vehicle_number: int
    time: datetime
    lat: float
    lon: float
    brigade: str
    line: str


@brook.output('location-report')
class LocationReport(OutReport):
    vehicle_number = ReportField(ZTMReport.vehicle_number)
    lat = ReportField(ZTMReport.lat)
    lon = ReportField(ZTMReport.lon)
    line = ReportField(ZTMReport.line)
    time = ReportField(ZTMReport.time)
    brigade = ReportField(ZTMReport.brigade)


@brook.artificial_field()
def direction(lat_history: Sequence[float] = historical_dependency(
    ZTMReport.lat, history_length=1),
                    lon_history: Sequence[float] = historical_dependency(
                        ZTMReport.lon, history_length=1),
                    lat: float = dependency(ZTMReport.lat),
                    lon: float = dependency(ZTMReport.lon)) -> Optional[float]:
    prev_lat, = lat_history
    prev_lon, = lon_history
    if prev_lat and prev_lon:
        return degrees(atan2(lon - prev_lon, lat - prev_lat))
    else:
        return None


@brook.output('direction-report')
class DirectionReport(OutReport):
    direction = ReportField(direction)


@brook.artificial_field()
async def counter(prev_values: Sequence[int] = historical_dependency(
    'counter', history_length=1),
                  time: datetime = dependency(ZTMReport.time)) -> int:
    prev_value, = prev_values
    if prev_value is None:
        prev_value = -1
    prev_value += 1
    return prev_value


@brook.output('counter-report')
class CounterReport(OutReport):
    counter = ReportField(counter)


brook.set_meta(latitude_field=LocationReport.lat,
               longitude_field=LocationReport.lon,
               time_field=LocationReport.time,
               group_field=LocationReport.line,
               direction_field=DirectionReport.direction)

if __name__ == '__main__':
    brook.run()
```

After creating mymodel.py, you should add it to the api and worker containers, using a Docker volume. To make PyBrook use mymodel instead of pybrook.examples.demo, you should also alter the arguments passed to gunicorn and pybrook. You can simply add it to the default docker-compose.yml:

services:
  api:
    image: pybrook:latest
    build:
      context: .
    environment:
      REDIS_URL: redis://redis
    ports:
      - 8000:8000
    volumes:
      - ./mymodel.py:/src/mymodel.py
    command: gunicorn mymodel:app 
          -w 4 -k uvicorn.workers.UvicornWorker 
          -b 0.0.0.0:8000
  worker:
    image: pybrook:latest
    depends_on:
      - api
    environment:
      REDIS_URL: redis://redis
      DEFAULT_WORKERS: 8
    volumes:
      - ./mymodel.py:/src/mymodel.py
    command: pybrook mymodel:brook
  locust:
    image: pybrook:latest
    depends_on:
      - api
    ports:
      - 8089:8089
    command: locust -H http://api:8000
  redis:
    image: redislabs/redisgears:1.0.9

Then run docker-compose up --build again, to start PyBrook - this time using your own model.

Setup & Development

You can install the PyBrook from PyPi using pip:

pip install pybrook

Running all services manually, without Docker

To run the pybrook.examples.demo model, you have to start all the required services manually:

# Redis + Redis Gears
docker run --net=host -d redislabs/redisgears:1.0.9
# HTTP API based on pybrook.examples.demo - uvicorn
uvicorn pybrook.examples.demo:app --reload  
# PyBrook workers based on pybrook.examples.demo 
pybrook pybrook.examples.demo:brook -rg 
# Locust - load testing
locust -H http://localhost:8000

Contributing

PyBrook uses poetry for dependency management. To install all its development dependencies, simply run this command:

poetry install

Tests

make test

Code quality

The source code of PyBrook is formatted using yapf and isort.
To run them with the correct settings, use the following command:

make format

PyBrook uses mypy for type checking and flake8 for linting. Use the following command to run them with the appropriate settings:

make lint