Quick Start

Get Maestro up in just a few minutes, we recommend to use docker, but if you like to install direcly read the installation section.

Overview

List of micro service:

Client App FrontEnd client Vue2 + Bootstrap 3
Server App Primary API, authetication, crud and manager NodeJs 8.11 Kraken
Discovery App Auto discovery and crawlers Python 3.6, flask
Scheduler App Jobs manager with celery beat Python 3.6, celery
Reports App Reports generetor Python 3.6, flask
Analytics App Analytics Maestro - Graphs Generator Python 3.6, flask
Analytics Front Analytics Front NodeJs 8.11 Kraken
Data DB App Data layer Python 3.6, flask
WebSocket APP WebSocket - Events Go, Centrifugo

Running locally

We recommend to use docker, if you like to see demo version, copy and execute docker-compose below, you need to change only two variable in client-app, url, and port.

Note

PS: Docker will be created and manager all networks and communication between services.

PS: The containers its prepared for run in production ready, but its recommend to create a separate database environment and export the volume (remember all storage inside of docker its temporary)

Warning

This is quickstart, it’s a docker compose to setup fast in local machines, if you like to install in production env, go to installing guide.

version: '2'

services:
    client:
        image: maestroserver/client-maestro
        ports:
        - "80:80"
        environment:
        - "API_URL=http://localhost:8888"
        - "STATIC_URL=http://localhost:8888/static/"
        - "ANALYTICS_URL=http://localhost:9999"
        - "WEBSOCKET_URL=ws://localhost:8000"
        depends_on:
        - server

    server:
        image: maestroserver/server-maestro
        ports:
        - "8888:8888"
        environment:
        - "MAESTRO_MONGO_URI=mongodb"
        - "MAESTRO_MONGO_DATABASE=maestro-client"
        - "MAESTRO_DISCOVERY_URI=http://discovery:5000"
        - "MAESTRO_ANALYTICS_URI=http://analytics:5020"
        - "MAESTRO_ANALYTICS_FRONT_URI=http://analytics_front:9999"
        - "MAESTRO_REPORT_URI=http://reports:5005"
        - "SMTP_PORT=25"
        - "SMTP_HOST=maildev"
        - "SMTP_SENDER=myemail@gmail.com"
        - "SMTP_IGNORE=true"
        depends_on:
        - mongodb
        - discovery
        - reports

    discovery:
        image: maestroserver/discovery-maestro
        ports:
        - "5000:5000"
        environment:
        - "CELERY_BROKER_URL=amqp://rabbitmq:5672"
        - "MAESTRO_DATA_URI=http://data:5010"
        depends_on:
        - rabbitmq
        - data

    discovery_worker:
        image: maestroserver/discovery-maestro-celery
        environment:
        - "MAESTRO_DATA_URI=http://data:5010"
        - "MAESTRO_WEBSOCKET_URI=http://ws:8000"
        - "CELERY_BROKER_URL=amqp://rabbitmq:5672"
        depends_on:
        - rabbitmq
        - data

    reports:
        image: maestroserver/reports-maestro
        environment:
        - "CELERY_BROKER_URL=amqp://rabbitmq:5672"
        - "MAESTRO_MONGO_URI=mongodb"
        - "MAESTRO_MONGO_DATABASE=maestro-reports"
        depends_on:
        - rabbitmq
        - mongodb

    reports_worker:
        image: maestroserver/reports-maestro-celery
        environment:
        - "MAESTRO_REPORT_URI=http://reports:5005"
        - "MAESTRO_DATA_URI=http://data:5010"
        - "MAESTRO_WEBSOCKET_URI=http://ws:8000"
        - "CELERY_BROKER_URL=amqp://rabbitmq:5672"
        depends_on:
        - rabbitmq
        - data

    scheduler:
        image: maestroserver/scheduler-maestro
        environment:
        - "MAESTRO_DATA_URI=http://data:5010"
        - "CELERY_BROKER_URL=amqp://rabbitmq:5672"
        - "MAESTRO_MONGO_URI=mongodb"
        - "MAESTRO_MONGO_DATABASE=maestro-client"
        depends_on:
        - mongodb
        - rabbitmq

    scheduler_worker:
        image: maestroserver/scheduler-maestro-celery
        environment:
        - "MAESTRO_DATA_URI=http://data:5010"
        - "MAESTRO_DISCOVERY_URI=http://discovery:5000"
        - "MAESTRO_ANALYTICS_URI=http://analytics:5020"
        - "MAESTRO_REPORT_URI=http://reports:5005"
        - "CELERY_BROKER_URL=amqp://rabbitmq:5672"
        depends_on:
        - rabbitmq
        - data

    analytics:
        image: maestroserver/analytics-maestro
        ports:
        - "5020:5020"
        environment:
        - "CELERY_BROKER_URL=amqp://rabbitmq:5672"
        - "MAESTRO_DATA_URI=http://data:5010"
        depends_on:
        - rabbitmq
        - data

    analytics_worker:
        image: maestroserver/analytics-maestro-celery
        environment:
        - "MAESTRO_DATA_URI=http://data:5010"
        - "MAESTRO_ANALYTICS_FRONT_URI=http://analytics_front:9999"
        - "MAESTRO_WEBSOCKET_URI=http://ws:8000"
        - "CELERY_BROKER_URL=amqp://rabbitmq:5672"
        - "CELERYD_MAX_TASKS_PER_CHILD=2"
        depends_on:
        - rabbitmq
        - data

    analytics_front:
        image: maestroserver/analytics-front-maestro
        ports:
        - "9999:9999"
        environment:
        - "MAESTRO_MONGO_URI=mongodb"
        - "MAESTRO_MONGO_DATABASE=maestro-client"

    data:
        image: maestroserver/data-maestro
        environment:
        - "MAESTRO_MONGO_URI=mongodb"
        - "MAESTRO_MONGO_DATABASE=maestro-client"
        depends_on:
        - mongodb

    ws:
        image: maestroserver/websocket-maestro
        ports:
        - "8000:8000"

    rabbitmq:
        hostname: "discovery-rabbit"
        image: rabbitmq:3-management
        ports:
        - "15672:15672"
        - "5672:5672"

    mongodb:
        image: mongo
        volumes:
        - mongodata:/data/db
        ports:
        - "27017:27017"

    maildev:
        image: djfarrelly/maildev
        mem_limit: 80m
        ports:
        - "1025:25"
        - "1080:80"


volumes:
    mongodata: {}

Vagrant

We have VagrantFile, its good for visualization (demo) or the best way to create a development environment.

Note

HA - High availability and critical system

If your necessity is, HA, critical situation, go in Ha session.