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# Applications overview
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The application can be interfaced with via HTTP, its endpoints providing the following functionality:
use "apiKey": "4ACFE3202A5FF5CF467898FC58AAB1D615029441" as headers in your requests
This is local deployment details.
Python : 3.6, Ubunut 16.04

    ○ get a value (GET /keys/{id})
      http://127.0.0.1:5000/keys/4

    ○ get all keys and values (GET /keys)
      http://127.0.0.1:5000/keys

    ○ set a value (PUT /keys)
      http://127.0.0.1:5000/keys
      payload Body/raw value "test_value"

    ○ check if a value exists (HEAD /keys/{id})
      http://127.0.0.1:5000/keys/5

    ○ delete a value (DELETE /keys/{id})
      http://127.0.0.1:5000/keys/4

    ○ delete all values (DELETE /keys)
      http://127.0.0.1:5000/keys

    ○ set an expiry time when adding a value (PUT /keys?expire_in=60)
      http://127.0.0.1:5000/keys?expire_in=100

    ○ support wildcard keys when getting all values (GET /keys?filter=wo$d)
      http://127.0.0.1:5000/keys?filter=vik%

The application logs stdout with use of python logger with format, used to forward to ELK(live monitoring purpose)

Application Status code:
    200 : success response
    400 : Bad request
    403 : Unauthorized user
    404 : Not found key/url/method not found
    500 : Internal server error(on interal failure)


Sqlite3 DB data stored used in backend (since small application)

Prometheus integration to monitor(/metrics endpoint) the applications

Grafana integration for data Visualization solutions and beautiful graph monitoring

Docker-compose : start application
    docker-compose up -d
    Note: make sure configuration related prometheus, grafana etc

<snap>
vikas@vikas-pc:~/workspace/Platform_service/native_instruments_sol$ docker-compose ps
              Name                            Command               State           Ports
--------------------------------------------------------------------------------------------------
backend-engine                     /bin/sh -c python ./main.py      Up      0.0.0.0:5000->5000/tcp
native_instruments_sol_grafana_1   /run.sh                          Up      0.0.0.0:3000->3000/tcp
prometheus                         /bin/prometheus --config.f ...   Up      0.0.0.0:9090->9090/tcp
vikas@vikas-pc:~/workspace/Platform_service/native_instruments_sol$
<snap>

prometheus URL:
http://127.0.0.1:9090

Graphana URL:
http:/127.0.0.1:3000

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## Build, Installations steps Individual images ##
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Docker build and run:
    docker build -t native_instruments_sol .
    docker run -d -p 5000:5000 native_instruments_sol

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## Prometheus setup ##
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Docker run for prometheus:
    docker run -d -p 9090:9090 --volume <full_path>/config/prometheus.yml:/etc/prometheus/prometheus.yml --network=host prom/prometheus

Note: Please update prometheus config file accordingly (ip addr, or localhost)

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## Grafana setup ##
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default, Grafana will be listening on http://localhost:3000. The default login is admin/admin
To create a Prometheus data source:
1. Click on the grafana logo to open the sidebar menu.
2. Click on "Data Sources" in the sidebar.
3. Click on "Add New".
4. Select "Prometheus" as the type.
4. Set the appropriate Prometheus server URL (for example, http://localhost:9090/)
5. Adjust other data source settings as desired (for example, turning the proxy access off).
6. Click "Add" to save the new data source.

Start exploring the graphs and live monitoring.

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## Application directory structure ##
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vikas@vikas-pc:~/workspace/Platform_service/native_instruments_sol$ tree
.
├── app.py
├── config
│   ├── __init__.py
│   ├── prometheus
│   └── prometheus.yml
├── DATABASE.sqlite3
├── docker-compose.yml
├── Dockerfile
├── helpers
│   ├── __init__.py
│   └── middleware.py
├── __init__.py
├── main.py
├── manage.py
├── README.md
├── requirements.txt
└── utils
    ├── auth.py
    ├── config.py
    ├── constants.py
    ├── errors.py
    ├── __init__.py
    └── log.py

4 directories, 19 files
vikas@vikas-pc:~/workspace/Platform_service/native_instruments_sol$

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## app features/advantage in SDLC
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1. Integrate with any cloud technology (GCP, AWS, etc)
2. maintainable, reusable, reconfigurable (microservice)
3. CICD easy setup using docker compose
3. can be integrate with Kuberenetes
4. reused open source code and tools
5. easy DB migration, upgrade

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##  current limitations ##
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1. Optimised more in sense of scalability, performance (kubernetes solution)
2. integrate with ELK (monitoring, alerting, pagerduety etc)
3. instead of flask can be written in django(if more coplex in strucutre)
4. better database (mysql etc)