Analytics MaestroΒΆ

Accountant to get and create a application dependency tree and build diagrams:

  • Create business graphs
  • Drawing diagrams
Maestro Server - Analytics maestro architecture

Analytics app use Flask, on python >3.5.

Setup dev env

cd devtool/

docker-compose up -d

It will be set a rabbitmq and a redis

Highlights

  • The diagram lookup and draw process are compound by:

    • entry: The first task, they get all entries application and send to graphlookup.
    • graphlookup: Requesting the db data over Data App, doing an application lookup using a MongoDB $graphLookup feature.
    • network business: Do a grid tree, and then send to enrichment task and info task.
    • enrichment: Getting servers.
    • info business: Calculate histogram, counts, density and connections.
    • network client: Getting clients.
    • draw business: Draw svgs.
    • notification: Send updates to Data App.
    • send front app: Send the svg to Analytics Front app.

Flower - Debug Celery

Real-time monitoring using Celery Events

  • Task progress and history
  • Ability to show task details (arguments, start time, runtime, and more)
  • Graphs and statistics
pip install flower

flower -A app.celery

npm run flower

Installation guide

  • Python >3.4
  • RabbitMQ

Download the repository

git clone https://github.com/maestro-server/discovery-api.git

Installing dependencies

pip install -r requeriments.txt

Running

python -m flask run.py

or

FLASK_APP=run.py FLASK_DEBUG=1 flask run

or

npm run server

Running workers

celery -A app.celery worker -E -Q analytics --loglevel=info

or

npm run celery

Warning

On production we use gunicorn to handle multiple threads.

# gunicorn_config.py

import os

bind = "0.0.0.0:" + str(os.environ.get("MAESTRO_PORT", 5020))
workers = os.environ.get("MAESTRO_GWORKERS", 2)