Hi all,
I’ve just read the Ecto.Schema docs. While I appreciate the tools at our disposal to define the various structs to map to different sources and destination, I fail to understand how to do complex data mapping.
For example, imagine that José purchased something with my frontend which then sends this to my backend:
%{
customer: %{
email: "jose@valim.com",
firstname: "José",
lastname: "Valim",
address: %{
first_line: "1 Picadilly Circus",
second_line: "",
city: "London",
postcode: "SE1GE2",
country: "UK"
}
},
payment: %{
type: "credit_card",
status: "success",
amount: 5403,
transaction_id: "f8usdefkljnds902lfsdjkljjj20"
}
basket: %{
products: [
%{
id: 123,
quantity: 3,
discount_code: "J05E_R0CK5_2019"
},
%{
id: 29,
quantity: 1,
discount_code: ""
}
]
}
}
```
One of the duties of my backend is to update our CRM with some of this data. Following is the data I need to extract, transform and load into the CRM based on the previous example:
```
%{
name: "José Valim",
address: "1 Picadilly Circus, London SE1GE2, UK",
purchase_amount: 5403,
}
```
I'm thinking that Ecto could shine here. I could define an embedded schema for the second shape and cast the received data into it but
1. how is Ecto helping to extract `customer.firstname` and `customer.lastname`, concat them and put the result into `name`?
2. how is Ecto helping to take all address fields from the purchase and join them into a single string?
3. how is Ecto helping to put `payment.amount` into `purchase_amount`?
As you can see now, there are multiple issues: the keys don't map because the name don't match and because they are also sometimes nested differently, sometimes a transformation is necessary (concat X Y and Z).
