#csv #csv-tsv #type #codegen #cargo-subcommand #input-file #read

app cargo-generate-type

Cargo subcommand to generate Rust code from and for input data

5 releases

0.1.4 Apr 9, 2024
0.1.3 Dec 27, 2023
0.1.2 Feb 22, 2023
0.1.1 Jan 12, 2023
0.1.0 Jan 8, 2023

#65 in Cargo plugins

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This binary, used as a cargo custom command, analyzes delimited (CSV, TSV) files with headers and generates appropriate Rust code to cleanly parse the data. The generated code wraps the csv crate to do the CSV parsing. You can install this utility now by running:

cargo install cargo-generate-type

The output of this tool is meant to make your life much easier when dealing with delimited data -- just point it to your file (or one representative of what you plan to process "in production") and integrate the generated code into your project.

cargo-generate-type reads the specified file; decides on the appropriate type for each column and, for integers, restricts the type to the minimum size required to accommodate the range; and generates code that makes reading those data ergonomic:

$ head -5 iris.csv
sepal length in cm,sepal width in cm,petal length in cm,petal width in cm,class

$ cargo generate-type iris.csv
Generated "iris.rs"

This generates an Iris struct:

#[derive(Clone, Debug)]
pub struct Iris {
    pub sepal_length_in_cm: f64,
    pub sepal_width_in_cm: f64,
    pub petal_length_in_cm: f64,
    pub petal_width_in_cm: f64,
    pub class: String,

It also generates the impl Iris that knows how to load and parse the data and deal with errors in input.


You must pass in the path to a delimited file. By default, the name of this file becomes the name of the generated struct (albeit with some normalization) and the output source file. For example, "iris.csv" would generate an Iris struct contained in iris.rs. "shareholder_report.csv" will generate ShareholderReport. You can override this behavior using the --typename name argument to specify the struct name and --output-file outfile to specify the file to create. If the file already exists, it will not be overwritten unless you use the --force flag.

The presence of a header row allows this tool to generate appropriate column names. If your data lack a header row, you may pass in --no-header. This will count the number of columns in the first row of input and stub in column names in the format column_{index}. You may then rename these columns if you so choose from your IDE.

Schema detection

When you run this tool, it will process (part of) your input file to try to understand the schema, including the column names and the type for all columns.

By default, it will process the first 100 rows of input to guess what types are. You may override this by specifying --rows n for any non-negative integer value n. If you use --rows 0, it will use the entire input file.

Type inference for columns roughly follows this process, in order:

  • If a column is always blank, it will be treated as unit (()).
  • If a column ever has a blank value, it will be treated as an Option<T> for whatever type T is decided on.
  • If values are always "true" or "false", the column will be bool
  • If values are always integral types, the range of values seen will be tracked, and according to the min and max values, the column will be one of: i8, i16, i32, i64, u8, u16, u32, u64.
  • If values are always numeric, the column will be f64.
  • Otherwise, String is used.

Currently, string values will be allocated instead of using a &'a str referring to the underlying csv::StringRecord. This may be replaced soon with a Cow<'_, str> to reduce allocations.

Error Handling

Generated code can handle unexpected input in three different ways, specified with the --error handling-type argument:

  1. --error ignore will silently ignore any errors in input, skipping rows where a column isn't present or is of an unexpected type.
  2. --error panic will panic for any row with invalid input.
  3. --error result will return a Result<T, E> for every row with T being the type representing your data and E covering underlying csv errors as well as unexpected input types. This is the default behavior if unspecified.

String Handling

By default, text columns will be processed as owned Strings. For large inputs or for columns that have low cardinality, the memory allocation that is incurred may be excessive. Version 0.1.3 provides for the --strings argument with possible values owned (default), static, and enum.

With --strings static, the set of distinct strings seen are profiled, and generated code will give you a &'static str instead of String, thereby reducing memory allocations (and permitting the generated struct to #[derive(Copy)]).

The same thing is done with --strings enum, except an auxiliary enum is defined with impl std::str::FromStr to parse the input text as that enum. This enum and thus the type's struct are also Copy.

There is also a --max-strings n argument (where n defaults to 20); if more than n distinct values are seen, code generation fails so as to avoid excessive code for high cardinality data.

Note on use

Because this binary is meant to be a Cargo custom command, it is called as cargo generate-type. Note that this is the cargo program with the generate-type subcommand. Cargo passes this subcommand as an argument to the binary, which it ignores. Because this argument is expected, if you try running cargo-generate-type itself, you must pass a dummy argument to it:

./cargo-generate-type generate-type shareholder_report.csv --error result
#                     ^^^^^^^^^^^^^ or any bogus argument is fine

Extra documentation

You can create a ".def" file alongside your input ".csv" that will produce documentation for the type's fields. For example, given the above Iris datatype generated from "iris.csv", you might create an "iris.def" file that looks like this:

This represents [Fisher's Iris data set](https://en.wikipedia.org/wiki/Iris_flower_data_set)

[sepal length in cm]
The length of the flower's sepal, in centimeters.

[sepal width in cm]
The width of the flower's sepal, in centimeters.

[petal length in cm]
The length of the flower's petal, in centimeters.

[petal width in cm]
The width of the flower's petal, in centimeters.

The species of flower, one of: Iris-setosa, Iris-virginica, Iris-versicolor.

When generating code, the following type is generated with documentation:

#[derive(Clone, Debug)]
/// This represents [Fisher's Iris data set](https://en.wikipedia.org/wiki/Iris_flower_data_set)
pub struct Iris {
    /// The length of the flower's sepal, in centimeters.
    pub sepal_length_in_cm: f64,
    /// The width of the flower's sepal, in centimeters.
    pub sepal_width_in_cm: f64,
    /// The length of the flower's petal, in centimeters.
    pub petal_length_in_cm: f64,
    /// The width of the flower's petal, in centimeters.
    pub petal_width_in_cm: f64,
    /// The species of flower, one of: Iris-setosa, Iris-virginica, Iris-versicolor.
    pub class: String,

Trimming input

Some input files might have space-padded content despite being delimited. For example:

sepal length in cm,sepal width in cm,petal length in cm,petal width in cm,class
5.1               ,3.5              ,1.4               ,0.2              ,Iris-setosa
4.9               ,3.0              ,1.4               ,0.2              ,Iris-setosa
4.7               ,3.2              ,1.3               ,0.2              ,Iris-setosa
4.6               ,3.1              ,1.5               ,0.2              ,Iris-setosa
5.0               ,3.6              ,1.4               ,0.2              ,Iris-setosa

In this case, because the values won't parse as floats, they would incorrectly be seen as Strings. If you pass the --trim-input switch, the values will be trimmed before trying to parse. This switch is not enabled by default.


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