README.md
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# 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/reports.html "Reports User Guide")
* [API Contract](https://maestro-server.github.io/report-app/ "API Contract")
# Maestro Server - Reports API #
Application to aggregate, filter and generate reports.
* Parse complex queries and generate reports
* Manage storage and control each technical flow
* Transform reports on artifacts such as pdf, csv or json
* Save results on database
**Reports API, organized by modules:**
* API Rest
* Worker - General Query
* Worker - Pivot Query
* Worker - Upload Json - Filters data
* Worker - Webhook - Copy data to reports database
* Worker - Aggr - Execute aggregation task and save in report collections
* Worker - Audit - Send aggr data to audit service
* Worker - Notification (Events)
* Worker - WS (Websocket Events)
## Setup ##
#### Installation by docker ####
```bash
version: '2'
services:
reports:
image: maestroserver/reports-maestro
environment:
- "CELERY_BROKER_URL=amqp://rabbitmq:5672"
- "MAESTRO_REPORT_URI=http://localhost:5005"
- "MAESTRO_MONGO_URI=mongodb"
- "MAESTRO_MONGO_DATABASE=maestro-reports"
reports_worker:
image: maestroserver/reports-maestro-celery
environment:
- "MAESTRO_REPORT_URI=http://reports:5005"
- "MAESTRO_DATA_URI=http://data:5010"
- "CELERY_BROKER_URL=amqp://rabbitmq:5672"
```
#### Install run api ####
```bash
python -m flask run.py --port 5005
or
FLASK_APP=run.py FLASK_DEBUG=1 flask run --port 5005
or
npm run server
// ping
curl http://localhost:5005
```
#### Install run rabbit workers ####
```bash
celery -A app.celery worker -E -Q report --hostname=report@%h --loglevel=info
or
npm run celery
```
## TechStack ##
* Python < 3.6
* Flask
* Pandas
* NumPy
* Celery
* RabbitMq
### Env variables ###
| Env Variables | Example | Description |
|---------------------------|-----------------------|--------------------------------------------|
| MAESTRO_PORT | 5005 | Reports API Port |
| MAESTRO_MONGO_URI | localhost | Mongo Url conn |
| MAESTRO_MONGO_DATABASE | maestro-reports | Db name, its differente of servers-app |
| MAESTRO_DATA_URI | http://localhost:5010 | Data APP - API URL |
| MAESTRO_REPORT_URI | http://localhost:5005 | Report App - API URL |
| MAESTRO_AUDIT_URI | http://localhost:10900| Audit App - API URL |
| MAESTRO_WEBSOCKET_URI | http://localhost:8000 | Webosocket App - API URL |
| MAESTRO_INSERT_QTD | 200 | Throughput insert in reports collection |
| | | |
| MAESTRO_WEBSOCKET_SECRET | XXXX | Secret Key - JWT Websocket connections |
| MAESTRO_SECRETJWT_PRIVATE | XXX | Secret Key - JWT private connections |
| MAESTRO_NOAUTH | XXX | Secret Pass - validate private connections |
| | | |
| CELERY_BROKER_URL | amqp://rabbitmq:5672 | RabbitMQ connection or SQS connection |
### 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.
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### Sponsor ###
[<img src="docs/_imgs/jetbrains.png" width="100">](https://www.jetbrains.com/?from=maestroserver)