# Csv Schema
[](https://travis-ci.org/primait/csv_schema)
[](https://hex.pm/packages/csv_schema)
[](https://hexdocs.pm/csv_schema/)
[](https://hex.pm/packages/csv_schema)
[](https://github.com/primait/csv_schema/blob/master/LICENSE)
[](https://github.com/primait/csv_schema/commits/master)
Csv schema is a library helping you to build Ecto.Schema-like modules having a csv file as source.
The idea behind this library is give the possibility to create, at compile-time, a self-contained module exposing functions to retrieve data starting from a CSV.
## Installation
The package can be installed by adding `:csv_schema` to your list of dependencies in `mix.exs`:
```elixir
def deps do
[
{:csv_schema, "~> 0.2.8"}
]
end
```
## Usage
Supposing you have a CSV file looking like this:
id | first_name | last_name | email | gender | ip_address | date_of_birth |
:----|:-----------|:-----------|:------------------------------|:-------|:----------------|:--------------|
1 | Ivory | Overstreet | ioverstreet0@businessweek.com | Female | 30.138.91.62 | 10/22/2018 |
2 | Ulick | Vasnev | uvasnev1@vkontakte.ru | Male | 35.15.164.70 | 01/19/2018 |
3 | Chloe | Freemantle | cfreemantle2@parallels.com | Female | 133.133.113.255 | 08/13/2018 |
... | ... | ... | ... | ... | ... | ... |
It is possible to create an Ecto.Schema-like repository using `Csv.Schema` macro:
```elixir
defmodule Person do
use Csv.Schema
alias Csv.Schema.Parser
schema path: "path/to/person.csv" do
field :id, "id"
field :first_name, "first_name", filter_by: true
field :last_name, "last_name", sort: :asc
field :identifier, ["first_name", "last_name"], key: true, join: " "
field :email, "email", unique: true
field :gender, "gender", filter_by: true, sort: :desc
field :ip_address, "ip_address"
field :date_of_birth, "date_of_birth", parser: &Parser.date!(&1, "{0M}/{0D}/{0YYYY}")
end
end
```
It is possible to define the schema with `string: ` param in order to directly use a string to generate content
```elixir
@data """
id,first_name,last_name,email,gender,ip_address,date_of_birth
1,Ivory,Overstreet,ioverstreet0@businessweek.com,Female,30.138.91.62,10/22/2018
2,Ulick,Vasnev,uvasnev1@vkontakte.ru,Male,35.15.164.70,01/19/2018
3,Chloe,Freemantle,cfreemantle2@parallels.com,Female,133.133.113.255,08/13/2018
"""
schema data: @data do
...
end
```
Note that it's not a requirement to map all fields, but every field mapped must
have a column in csv file.
For example the following field configuration will result in a compilation error:
```elixir
field :id, "non_existing_id", ...
```
Schema could be configured using a custom separator (default is ?,)
```elixir
use Csv.Schema, separator: ?,
```
Moreover it's possible to configure if csv file has or has not an header. Depending on header param value field config changes:
```elixir
# Default header value is `true`
use Csv.Schema
# Csv with header
schema path: "path/to/person.csv" do
field :id, "id", key: true
...
end
# Csv without header. Note that field 1 is binded with the first csv column.
use Csv.Schema, header: false
# Index goes from 1 to N
schema path: "path/to/person.csv" do
field :id, 1, key: true
...
end
```
Now Person module is a struct, defined like this:
```elixir
defmodule Person do
defstruct id: nil,
first_name: nil,
last_name: nil,
email: nil,
gender: nil,
ip_address: nil,
date_of_birth: nil
end
```
This macro creates for you inside Person module those functions:
```elixir
def by_id(integer_key), do: ...
def filter_by_first_name(string_value), do: ...
def by_email(string_value), do: ...
def filter_by_gender(string_value), do: ...
def get_all, do: ...
```
Where:
- `by_id` returns a `%Person{}` or `nil` if key is not mapped in csv
- `filter_by_first_name` returns a `[%Person{}, %Person{}, ...]` or `[]` if input predicate does not match any person
- `by_email` returns a `%Person{}` or `nil` if no person have provided email in csv
- `filter_by_gender` returns a `[%Person{}, %Person{}, ...]` or `[]` if input predicate does not match any person gender
- `get_all` return all csv rows as a Stream
## Field configuration
Every field should be formed like this:
```
field {struct_field}, {csv_header}, {opts}
```
where:
- `{struct_field}` will be the struct field name. Could be configured as `string` or as `atom`
- `{csv_header}` is the csv column name from where get values. Must be configured using string only
- `{opts}` is a keyword list containing special configurations
opts:
- `:key` : boolean. At most one key could be set. If set to true creates the `by_{name}` function for you.
- `:unique` : boolean. If set to true creates the `by_{name}` function for you. All csv values must be unique or an exception is raised
- `:filter_by` : boolean. If set to true creates the `filter_by_{name}` function
- `:parser` : function. An arity 1 function used to map values from string to a custom type
- `:sort` : `:asc` or `:desc`. It sorts according to Erlang's term ordering with `nil` exception (`number < atom < reference < fun < port < pid < tuple < list < bit-string < nil`)
- `:join` : string. If present it joins the given fields into a binary using the separator
Note that every configuration is optional
## Keep in mind
Compilation time increase in an exponential manner if csv contains lots of lines and you
configure multiple fields candidate for method creation (flags `key`, `unique` and/or `filter_by` set to true).
Because "without data you're just another person with an opinion" here some data:
### Compilation time
| csv rows | key | unique | filter_by | compile time |
| -------: | ----: | -----: | --------: | ------------: |
| 1_000 | false | 0 | 0 | 301_727 µs |
| 1_000 | false | 2 | 0 | 352_522 µs |
| 1_000 | false | 0 | 4 | 318_225 µs |
| 1_000 | true | 0 | 0 | 334_240 µs |
| 1_000 | true | 1 | 1 | 348_697 µs |
| 1_000 | true | 2 | 0 | 406_367 µs |
| 1_000 | true | 0 | 4 | 385_850 µs |
| 1_000 | true | 2 | 2 | 414_617 µs |
| 1_000 | true | 2 | 4 | 446_155 µs |
| 5_000 | false | 0 | 0 | 2_734_565 µs |
| 5_000 | false | 2 | 0 | 3_450_438 µs |
| 5_000 | false | 0 | 4 | 3_464_593 µs |
| 5_000 | true | 0 | 0 | 3_084_923 µs |
| 5_000 | true | 1 | 1 | 3_795_718 µs |
| 5_000 | true | 2 | 0 | 3_752_112 µs |
| 5_000 | true | 0 | 4 | 3_387_067 µs |
| 5_000 | true | 2 | 2 | 3_839_068 µs |
| 5_000 | true | 2 | 4 | 4_113_228 µs |
| 10_000 | false | 0 | 0 | 6_889_505 µs |
| 10_000 | false | 2 | 0 | 8_667_683 µs |
| 10_000 | false | 0 | 4 | 8_606_961 µs |
| 10_000 | true | 0 | 0 | 7_892_421 µs |
| 10_000 | true | 1 | 1 | 8_449_838 µs |
| 10_000 | true | 2 | 0 | 9_507_693 µs |
| 10_000 | true | 0 | 4 | 10_339_080 µs |
| 10_000 | true | 2 | 2 | 10_518_744 µs |
| 10_000 | true | 2 | 4 | 10_480_884 µs |
### Execution time
| csv rows | key | unique | filter_by | by avg | by tot | filter_by avg | filter_by tot |
| -------: | ---: | -----: | --------: | ---------: | --------: | ------------: | ------------: |
| 1_000 | true | 1 | 1 | 0.74 µs/op | 74_412 µs | 0.89 µs/op | 89_275 µs |
| 5_000 | true | 1 | 1 | 0.79 µs/op | 79_776 µs | 1.18 µs/op | 118_786 µs |
| 10_000 | true | 1 | 1 | 0.78 µs/op | 78_908 µs | 1.83 µs/op | 183_642 µs |
### Execution details
Executed on my machine:
Lenovo Thinkpad T480
CPU: Intel(R) Core(TM) i7-8550U CPU @ 1.80GHz
RAM: 32GB
### Try yourself
If you like to run compilation benchmarks yourself:
```sh
iex -S mix
```
```elixir
c "benchmark/timings.exs"
```
## Copyright and License
Copyright (c) 2019 PrimaIt
This work is free. You can redistribute it and/or modify it under the
terms of the MIT License. See the [LICENSE.md](./LICENSE.md) file for more details.
