Deployment

1. Create venv and install dependencies from requirements.txt
2. Create the database and the user
3. Migrate the database with the command flask db upgrade
4. Import the dataset with python import_dataset.py
5. Run the app with python app.py

Test requests

1. Show the number of impressions and clicks that occurred before the 1st of June 2017, broken down by channel and country, sorted by clicks in descending order.

curl --location --request POST 'http://127.0.0.1:5000/metrics' \
--header 'Content-Type: application/json' \
--data-raw '{
    "filter": {
        "date_to": "01-06-2017"
    },
    "group_by": ["channel", "country"],
    "sort": {
        "clicks": "DESC"
    }
}'

2. Show the number of installs that occurred in May of 2017 on iOS, broken down by date, sorted by date in ascending order.

curl --location --request POST 'http://127.0.0.1:5000/metrics' \
--header 'Content-Type: application/json' \
--data-raw '{
    "filter": {
        "date_from": "01-05-2017",
        "date_to": "31-05-2017",
        "os": ["ios"]
    },
    "group_by": ["date"],
    "sort": {
        "date": "ASC"
    }
}'

3. Show revenue, earned on June 1, 2017 in US, broken down by operating system and sorted by revenue in descending order.

curl --location --request POST 'http://127.0.0.1:5000/metrics' \
--header 'Content-Type: application/json' \
--data-raw '{
    "filter": {
        "date_from": "01-06-2017",
        "date_to": "01-06-2017",
        "country": "US",
        "os": ["ios"]
    },
    "group_by": ["os"],
    "sort": {
        "revenue": "DESC"
    }
}'

4. Show CPI and spend for Canada (CA) broken down by channel ordered by CPI in descending order. Please think carefully which is an appropriate aggregate function for CPI.

curl --location --request POST 'http://127.0.0.1:5000/metrics' \
--header 'Content-Type: application/json' \
--data-raw '{
    "filter": {
        "country": "CA"
    },
    "group_by": ["channel"],
    "sort": {
        "cpi": "DESC"
    }
}'