README.md
[](https://www.codacy.com/gh/maestro-server/analytics-maestro/dashboard?utm_source=github.com&utm_medium=referral&utm_content=maestro-server/analytics-maestro&utm_campaign=Badge_Grade)
[](https://travis-ci.org/maestro-server/analytics-maestro)
[](https://codeclimate.com/github/maestro-server/analytics-maestro/maintainability)
[](https://codeclimate.com/github/maestro-server/analytics-maestro/test_coverage)
# Maestro Server #
Maestro Server is an open source software platform for management and discovery servers, apps and system for Hybrid IT. Can manage small and large environments, be able to visualize the latest multi-cloud environment state.
### Demo ###
To test out the demo, [Demo Online](http://demo.maestroserver.io "Demo Online")
## Documentation ##
* [UserGuide](http://docs.maestroserver.io/en/latest/userguide/cloud_inventory/inventory.html "User Guide")
* [API Contract](https://maestro-server.github.io/analytics-maestro/ "Analytics API Contract")
# Maestro Server - Analytics API #
Accountant to get and create a application dependency tree and build diagrams:
* Create business graphs
* Drawing diagrams
**Core API:**
* Create grids
* Create bussiness graph
* Create network graph
* Create infra graph
* Drawing
* SVGs
## TechStack ##
* Python 3.6
* Flask 1.0.2
* SvgWrite
* NetworkX
## Service dependencies ##
* Maestro Data
* Maestro Analytics Front
## Setup ##
#### Installation by docker ####
```bash
docker run -p 5020
-e "MAESTRO_DATA_URI=http://localhost:5010"
-e "CELERY_BROKER_URL=amqp://rabbitmq:5672"
-e 'MAESTRO_MONGO_URI=localhost'
maestroserver/analytics-maestro
docker run
-e "MAESTRO_DATA_URI=http://localhost:5010"
-e "MAESTRO_ANALYTICS_FRONT_URI=http://localhost:9999"
-e "CELERY_BROKER_URL=amqp://rabbitmq:5672"
maestroserver/analytics-maestro-celery
```
Or by docker-compose
```bash
version: '2'
services:
analytics:
image: maestroserver/analytics-maestro
ports:
- "5020:5020"
environment:
- "CELERY_BROKER_URL=amqp://rabbitmq:5672"
- "MAESTRO_DATA_URI=http://data:5010"
analytics_worker:
image: maestroserver/analytics-maestro-celery
environment:
- "MAESTRO_DATA_URI=http://data:5010"
- "MAESTRO_ANALYTICS_FRONT_URI=http://analytics_front:9999"
- "CELERY_BROKER_URL=amqp://rabbitmq:5672"
- "CELERYD_MAX_TASKS_PER_CHILD=2"
```
#### Dev Env ####
Run python and celery.
```bash
cd devtools/
docker-compose up -d
```
Configure rabbitmq service in .env file
```bash
CELERY_BROKER_URL="amqp://localhost:5672"
CELERYD_TASK_TIME_LIMIT=30
```
Install pip dependences
```bash
pip install -r requeriments.txt
```
Run server
```bash
python -m flask run.py
or
FLASK_APP=run.py FLASK_DEBUG=1 flask run
or
npm run server
```
Run workers
```bash
celery -A app.celery worker -E -Q discovery --hostname=discovery@%h --loglevel=info
or
npm run celery
```
Run all tests
```bash
python -m unittest discover
```
Create doc
```bash
npm install
apidoc -i app/controller/ -o docs/
or
npm run docs
```
#### Important notes ####
* Controller used only graph to start all tasks:
* The drawer process is compound by:
* **entry:** First task, figure out all entry applications accordingly system endpoint parameters, our any direct application if avalaible.
* **graphlookup:** Request for Data App a aggregate query using MongoDB $graphLookup.
* **network bussiness:** Construct Grid Map, and send to enrichment and info bussines.
* **enrichment:** Request for Data App all servers used on grid.
* **info bussiness:** Calculate histogram, counts, density and connections.
* **network client:** Request for Data App all clients used in grid.
* **draw bussiness:** Create svgs based of grid.
* **notification:** Send updates for Data App.
* **send front app:** Send svgs to Analytics Front app.
* Each step have unique task.
* Config is managed by env variables, need to be, because in production env like k8s is easier to manager the pods.
* Repository it's pymongo objects.
### Env variables ###
| Env Variables | Example | Description |
|------------------------------|--------------------------|--------------------------------------------|
| MAESTRO_PORT | 5020 | API Port |
| MAESTRO_DATA_URI | http://localhost:5010 | Data Layer API URL |
| MAESTRO_ANALYTICS_FRONT_URI | http://localhost:9999 | Analytics Front URL |
| MAESTRO_WEBSOCKET_URI | http://localhost:8000 | Webosocket App - API URL |
| | | |
| MAESTRO_WEBSOCKET_SECRET | XXXX | Secret Key - JWT Websocket connections |
| MAESTRO_SECRETJWT_PRIVATE | XXX | Secret Key - JWT private connections |
| MAESTRO_NOAUTH | XXX | Secret Pass to validate private connections|
| | | |
| MAESTRO_GWORKERS | 2 | Prefetch used in translate worker |
| CELERY_BROKER_URL | amqp://rabbitmq:5672 | Rabbitmq URL |
| CELERYD_TASK_TIME_LIMIT | 10 | Timeout - worker |
### Contribute ###
Are you interested in developing Maestro Server, creating new features or extending them?
We created a set of documentation, explaining how to set up your development environment, coding styles, standards, learn about the architecture and more. Welcome to the team and contribute with us.
[See our developer guide](http://docs.maestroserver.io/en/latest/contrib.html)
### Contact ###
We may be able to resolve support queries via email. [Please send me a message here](https://maestroserver.typeform.com/to/vf6sGR)
### Donate ###
I have made Maestro Server with my heart, think to solve a real operation IT problem. Its not easy, take time and resources.
The donation will be user to:
- Create new features, implement new providers.
- Maintenance libs, securities flaws, and technical points.
<a href="https://www.buymeacoffee.com/9lVypB7WQ" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/purple_img.png" alt="Buy Me A Coffee" style="height: 41px !important;width: 174px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;" ></a>
### Sponsor ###
[<img src="docs/_imgs/jetbrains.png" width="100">](https://www.jetbrains.com/?from=maestroserver)