Using TinyCsvParser and FluentValidation

In my last post I have described TinyCsvParser, which is an easy to extend, easy to use library for parsing CSV data. This post shows how to perform an additional validation on the CSV parse results with FluentValidation.

FluentValidation is "... a small validation library for .NET that uses a fluent interface and lambda expressions for building validation rules". It's a great library, because it is open source, easy to extend and easy to use.

I think both libraries are fun to use and they are easy to combine.

Example Scenario

Imagine a client sends us a CSV file with peoples data, that we need to import into our system. We don't want to have invalid data in our application, because it could lead to unexpected behavior and unexpected crashes.

Real life data is never perfect. So what we get might look like this.


There are obviously invalid records in the data. One record has a wrong mail address, someone is born in the future and someone doesn't have a first name. This isn't a problem for tiny datasets, which could be fixed manually, but it's a problem for larger files.

Installing the Packages

Create a class library project and install TinyCsvParser, FluentValidation and NUnit.

Install-Package TinyCsvParser
Install-Package FluentValidation
Install-Package NUnit

Domain Model

First of all we are defining the domain model in the application. Create a new class Person, that holds the first name, last name, mail address and birth date.

public class Person
    public string FirstName { get; set; }

    public string LastName { get; set; }

    public string MailAddress { get; set; }

    public DateTime BirthDate { get; set; }

    public override string ToString()
        return string.Format("Person (FirstName = {0}, LastName = {1}, MailAddress = {2}, BirthDate = {3})",
            FirstName, LastName, MailAddress, BirthDate.ToShortDateString());

CSV Mapping

Then we need to define how the CSV file and the domain model match. This is done by using a CsvMapping of the TinyCsvParser library. See how easy it is to write the mapping.

public class CsvPersonMapping : CsvMapping<Person>
    public CsvPersonMapping()
        MapProperty(0, x => x.FirstName);
        MapProperty(1, x => x.LastName);
        MapProperty(2, x => x.BirthDate);
        MapProperty(3, x => x.MailAddress);

Enter FluentValidation

The basic idea of FluentValidation is to define rules on each property of your domain model. You can chain multiple rules by using its Fluent interface, which makes it easy to understand the validation rules.

Every Validator in FluentValidation is an AbstractValidator. The AbstractValidator has a method RuleFor, which takes an expression (the property) and exposes a Fluent interface.

public class PersonValidator : AbstractValidator<Person>
    public PersonValidator()

        RuleFor(x => x.FirstName)
            .Length(1, 255);

        RuleFor(x => x.LastName)
            .Length(1, 255);

        RuleFor(x => x.MailAddress)
            .Length(1, 255)

        RuleFor(x => x.BirthDate)
            .GreaterThan(new DateTime(1870, 1, 1, 0, 0, 0, DateTimeKind.Utc));

Combining TinyCsvParser and FluentValidation

Now we want to read the CSV data and filter the invalid results. The CsvParser in TinyCsvParser returns a ParallelQuery, which can be used to perform additional processing on the data.

For the problem at hand, we are first instantiating a new PersonValidator. Then we construct the CsvParser, read the CSV data and validate the records with the PersonValidator. The last step is to filter for the valid records only.

Problem solved in a few lines of code!

public void CsvParseAndValidateTest()
    // Create the CSV Data. You could also read from file with TinyCsvParser:
    var csvData = new StringBuilder()

    // Instantiate the Validator:
    var validator = new PersonValidator();

    // Create the CsvParser:
    CsvParserOptions csvParserOptions = new CsvParserOptions(true, new[] { ';' });
    CsvReaderOptions csvReaderOptions = new CsvReaderOptions(new[] { Environment.NewLine });
    CsvPersonMapping csvMapper = new CsvPersonMapping();
    CsvParser<Person> csvParser = new CsvParser<Person>(csvParserOptions, csvMapper);

    // LINQ to rescue:
    var results = csvParser
        .ReadFromString(csvReaderOptions, csvData)
        .Where(x => x.IsValid)
        .Select(x => new { Entity = x.Result, ValidationResult = validator.Validate(x.Result) }) 
        .Where(x => x.ValidationResult.IsValid)

    Assert.AreEqual(2, results.Count);

    Assert.AreEqual("LastNameA", results[0].Entity.LastName);
    Assert.AreEqual("LastNameB", results[1].Entity.LastName);

How to contribute

One of the easiest ways to contribute is to participate in discussions. You can also contribute by submitting pull requests.

General feedback and discussions?

Do you have questions or feedback on this article? Please create an issue on the GitHub issue tracker.

Something is wrong or missing?

There may be something wrong or missing in this article. If you want to help fixing it, then please make a Pull Request to this file on GitHub.