Reads and writes CSV, fixed-length and other flat file formats with a focus on schema definition, configuration and speed.
Clone or download


Reads and writes CSV, fixed-length and other flat file formats with a focus on schema definition, configuration and speed. Supports mapping directly between files and classes.

Download using NuGet: FlatFiles

You can check out all of the awesome enhancements and new features in the CHANGELOG.


A lot of us still need to work with flat files (e.g. CSV, fixed-length, etc.) either because we're interfacing with older systems or because we're running one-time migration scripts. As common as these legacy file formats are, it's surprising there's nothing built-in to .NET for handling them. Worse, it seems like each system has its own little quirks. People have a pretty easy time reading most flat file formats but we as developers spend an enormous amount of time tweaking our code to handle every oddball edge case.

FlatFiles focuses on schema configuration, allowing you to very clearly define the schema of your file, whether reading or writing. It gives complete control over the way dates, numbers, enums, etc. are interpreted in your files. This means less time chasing down edge cases and dealing with conversion issues.

FlatFiles makes it easy to work with flat files in many different ways. It supports type mappers for directly reading and writing with data objects. It even has support for DataTables and IDataReader if you need to interface with ADO.NET classes. If you really want to, you can read and write values using raw object arrays.

Table of Contents

Type Mappers

Using the type mappers, you can directly read file contents into your classes:

var mapper = SeparatedValueTypeMapper.Define<Customer>();
mapper.Property(c => c.CustomerId).ColumnName("customer_id");
mapper.Property(c => c.Name).ColumnName("name");
mapper.Property(c => c.Created).ColumnName("created").InputFormat("yyyyMMdd");
mapper.Property(c => c.AverageSales).ColumnName("avg_sales");
using (var reader = new StreamReader(File.OpenRead(@"C:\path\to\file.csv")))
    var customers = mapper.Read(reader).ToList();

To define the schema when working with type mappers, call Property in the order that the fields appear in the file. The type of the column is determined by the type of the mapped property. Each property configuration provides options for controlling the way FlatFiles handles strings, numbers, date/times, GUIDs, enums and more. Once the properties are configured, you can call Read or Write on the type mapper.

Note The Read method only retrieves records from the underlying file on-demand. To bring the entire file into memory at once, just call ToList or ToArray, or loop over the records inside of a foreach.

Writing to a file is just as easily:

mapper.Property(c => c.Created).OutputFormat("yyyyMMdd");
mapper.Property(c => c.AverageSales).OutputFormat("N2");
using (var writer = new StreamWriter(File.OpenCreate(@"C:\path\to\file2.csv")))
    mapper.Write(writer, customers);

Note I was able to customize the OutputFormat of properties that were previously configured. The first time Property is called on a property, FlatFiles assumes it's the next column to appear in the flat file. However, subsequent configuration on the property doesn't change the order of the columns or reset any other settings.


If your delimited file (CSV, TSV, etc.) has a schema with column names that match your class's property names, you can use the GetAutoMappedReader method as a shortcut. This method will read the schema from your file and map the columns to the properties automatically, returning a reader for retrieving the data. It's important to note that you cannot customize the parsing behavior of any of the columns, at which point you are better off explicitly defining the schema. Fortunately, FlatFiles uses pretty liberal parsing out-of-the-box, so most common formats will work.

By default, columns and properties are matched by name (case-insensitive). If you need more control over how columns and properties are matched, you can pass in your own IAutoMapMatcher. Given an IColumnDefinition and a MemberInfo, a matcher must determine whether the two map to one another. For convenience, you can also use the AutoMapMatcher.For method to pass a Func<IColumnDefinition, MemberInfo, bool> delegate rather than implement the interface.

Similarly, use the GetAutoMappedWriter method to automatically write out a delimited file. Note that there's no way to control the column formatting. However, you can control the name and position of the columns by passing an IAutoMapResolver. The IAutoMapResolver interface provides the GetPosition and a GetColumnName methods, both accepting a MemberInfo. For convenience, you can also use the AutoMapResolver.For method to pass delegates for determining the names/positions, rather than implement the interface.


Under the hood, type mapping internally defines a schema, giving each column a name, order and type in the flat file. You can get access to the schema by calling GetSchema on the mapper.

You can work directly with schemas if you don't plan on using the type mappers. For instance, this is how we would define a CSV file schema:

var schema = new SeparatedValueSchema();
schema.AddColumn(new Int64Column("customer_id"))
      .AddColumn(new StringColumn("name"))
      .AddColumn(new DateTimeColumn("created") { InputFormat = "yyyyMMdd", OutputFormat = "yyyyMMdd" })
      .AddColumn(new DoubleColumn("avg_sales") { OutputFormat = "N2" });

Or, if the schema is for a fixed-length file:

var schema = new FixedLengthSchema();
schema.AddColumn(new Int64Column("customer_id"), 10)
  .AddColumn(new StringColumn("name"), 255)
  .AddColumn(new DateTimeColumn("created") { InputFormat = "yyyyMMdd", OutputFormat = "yyyyMMdd" }, 8)
  .AddColumn(new DoubleColumn("avg_sales") { OutputFormat = "N2" }, 10);

The FixedLengthSchema class is the same as the SeparatedValueSchema class, except it associates a Window to each column. A Window records the Width of the column in the file. It also allows you to specify the Alignment (left or right) in cases where the value doesn't fill the entire width of the column (the default is left aligned). The FillCharacter property can be used to say what character is used as padding. You can also set the TruncationPolicy to say whether to chop off the front or the back of values that exceed their width.

Note Some fixed-length files may have columns that are not used. The fixed-length schema doesn't provide a way to specify a starting index for a column. Look at the Ignored Fields section below to learn about ways to to handle this.

Delimited Files

If you are working with delimited files, such as comma-separated (CSV) or tab-separated (TSV) files, you want to use the SeparatedValueTypeMapper. Internally, the mapper uses the SeparatedValueReader and SeparatedValueWriter classes, both of which work in terms of raw object arrays. In effect, all the mapper does is map the values in the array to the properties in your data objects. These classes read data from a TextReader, such as a StreamReader or a StringReader, and write data to a TextWriter, such as a StreamWriter or a StringWriter. Internally, the mapper will build a SeparatedValueSchema based on the property/column configuration; this is where you customize the schema to match your file format. For more global settings, there is also a SeparatedValueOptions object that allows you to customize the read/write behavior to suit your needs.

Within separated value files, fields can be surrounded with double quotes ("). This way they can include the separator within the field. You can override the "quote" character in the SeparatedValueOptions class, if needed. The SeparatedValueOptions class supports a Separator property for specifying the string/character that separates your fields. A comma (,) is the default separator; you'd obviously want to change this to tab (\t) for a TSV file.

The RecordSeparator property specifies what string/character is used to separate records. By default, FlatFiles will look for \r, \n or \r\n, which are the default line separators for Mac, Linux and Windows, respectively. When writing files, Environment.NewLine is used by default; this means by default you'll get different output if you run the same code on different platforms. If you need to target a specific platform, be sure to set the RecordSeparator property explicitly.

When working directly with the SeparatedValueReader class, the IsFirstRecordSchema option tells the reader to treat the first record in the file as the schema. This is useful when you don't know the schema ahead of time or you are just dumping files into staging tables. If you provide a schema, this setting tells the reader to simply skip the first record. If you are using it to determine the schema from the file, the reader treats every column as a string - it doesn't try to interpret the type from the data. While occasionally useful, you will normally want to provide a schema to give you the most control over the parsing process - that's what FlatFiles is good at!

When working directly with the SeparatedValueWriter class, setting IsFirstRecordSchema to true option causes a header to be written to the file upon writing the first record.

Fixed Length Files

If you have a file with fixed length columns, you will want to use the FixedLengthTypeMapper class. Internally, the mapper uses the FixedLengthReader and FixedLengthWriter classes, both of which work in terms of raw object arrays. In effect, all the mapper does is map the values in the array to the properties in your data objects. These classes read data from a TextReader, such as StreamReader or StringReader, and write data to a TextWriter, such as StreamWriter or StringWriter. Internally, the mapper will build a FixedLengthSchema based on the property/column configuration; this is where you customize the schema to match your file format. For more global settings, there is also a FixedLengthOptions object that allows you to customize the read/write behavior to suit your needs.

Since each column has a fixed length, FlatFiles provides configuration options to specify how to handle values that are too short or too long: FillCharacter, Alignment and TruncationPolicy. FillCharacter specifies what character is used to pad values on the left or the right, using space () by default. You can configure whether the padding should go to the left or the right using the Alignment property, putting padding to the right by default (LeftAligned). TruncationPolicy tells FlatFiles how to crop values that exceed the width of their column when writing out to a file, removing leading characters by default. These options can be specified globally in the FixedLengthOptions object or overridden at the column level using the Window object.

The RecordSeparator property specifies what string/character is used to separate records. By default, FlatFiles will look for \r, \n or \r\n, which are the default line separators for Mac, Linux and Windows, respectively. When writing files, Environment.NewLine is used by default; this means by default you'll get different output if you run the same code on different platforms. If you need to target a specific platform, be sure to set the RecordSeparator property explicitly.

By default, FlatFiles assumes there is a separator string/character between each record. If you set the HasRecordSeparator to false, FlatFiles will read the next record immediately following the last character of the previous record. When writing, it will not insert a separator, writing immediately after the last character of the previous record.

If the FixedLengthOptions's IsFirstRecordHeader property is set to true, the first record in the file will be skipped when reading. Unlike the SeparatedValueReader, you must always provide a schema for fixed-length files, since the width of the columns cannot be determined from the file format. When writing, a header will be written to the file upon writing the first record.

Handling Nulls

Each column can be marked as "nullable", using the IsNullable property. By default, all columns are nullable, meaning null is considered a valid value. Setting IsNullable to false will cause FlatFiles to throw an exception whenever a null is encountered.

Type mappers will automatically configure columns to be nullable based on the type of the property. If you need to support nulls, make sure you use nullable types for your properties, for example int? instead of int.

Default Values

When working with non-nullable columns, you can specify a default value to use whenever a null is encountered, rather than throw an exception. You simply set a custom IDefaultValue on the column. The DefaultValue class provides helper methods for generating default values. For example:

column.DefaultValue = DefaultValue.Use(0);

Or, if you are using type mappings, you can simply use the DefaultValue method:

mapper.Property(c => c.Amount)

Null Formatters

By default, FlatFiles will treat blank or empty strings as null. If nulls are represented differently in your file, you can set a custom INullFormatter on the column. If it is a fixed value, you can use the NullFormatter.ForValue method.

var dateColumn = new DateTimeColumn("created") 
    NullFormatter = NullFormatter.ForValue("NULL") 

Or, if you are using type mappers, you can simply use the NullFormatter method:

mapper.Property(c => c.Created)

You can implement the INullFormatter interface if you need to support something more complex.

Ignored Fields

Most of the time, we don't have control over the flat file we're working with. This usually leads to columns that our code just doesn't care about. Both type mappers and schemas expect columns to be listed in the order they appear in the document. For type mappers, this would mean defining properties in your classes that you'd never use (e.g., Ignored1, Ignored2, etc.). Another common problem with fixed-length files is that they will separate fields with pipes (|), or other characters, even though they are fixed-length.

Types mappers support the Ignored method that will tell FlatFiles to simply ignore the next column in the file. Unlike other mapper methods, it does not take a property, since the value is simply thrown away. You can optionally call ColumnName if you want to provide a column name when writing out schemas.

When working with the IFixedLengthTypeMapper, Ignored takes a Window. This is a great way to skip unused sections within the document. You can even set the FillCharacter property to insert pipes (|), or another character, between fields.

Under the hood, type mappers is adding an IgnoredColumn to the underlying schema. IgnoredColumn has a constructor to optionally specify a column name. IgnoredColumn affects the way you work with readers and writers. The readers will initially retrieve all columns from the document and then throw away any ignored values. You'll only see values for the columns that aren't ignored. Also, you do not need to provide values to the writers for ignored columns; the schema will automatically take care of writing out blanks for them. From a development perspective, it's as if those columns didn't exist in the underlying document.


It is often useful to incorporate metadata with the records you are reading from a file. The most common example is tracking a record's line number, so users can be informed where to look in their files when something goes wrong.

Currently, the only out-of-the-box metadata column is RecordNumberColumn; however, it's really easy to create your own custom metadata columns (more on that below).

The RecordNumberColumn class provides options for controlling how the record number is generated. The IncludeSchema property indicates whether the schema or header row should be included in the count. The IncludeSkippedRecords property specifies whether to count records that are skipped.

By default, RecordNumberColumn will only count records that are actually returned, starting from 1, then 2, 3, 4, 5 and so on. If you count the schema, records will always start at 2. Including the schema and skipped records is what you probably want if you're trying to simulate line number. The only time the record # wouldn't be the same as the line # is if a record spanned multiple lines. Here's an example showing how to capture this pseudo line number:

var mapper = SeparatedValueTypeMapper.Define(() => new Person());
mapper.Property(x => x.Name);
mapper.CustomMapping(new RecordNumberColumn("RecordNumber")
    IncludeSchema = true,
    IncludeSkippedRecords = true
}).WithReader(p => p.RecordNumber);

var options = new SeparatedValueOptions() { IsFirstRecordSchema = true };
var results = mapper.Read(reader, options).ToArray();

Writing metadata

I've not yet come up with a reason why you'd want to write out metadata, but I provide support for it anyway (feel free to provide me an example!).

var mapper = FixedLengthTypeMapper.Define(() => new Person());
mapper.Property(x => x.Name, 10);
// No need to define a reader or a writer - the underlying schema handles writing the metadata 
mapper.CustomMapping(new RecordNumberColumn("RecordNumber") { IncludeSchema = true }, 10);
mapper.Property(x => x.CreatedOn, 10).OutputFormat("MM/dd/yyyy");

Creating your own metadata columns

FlatFiles provides the MetadataColumn<T> abstract base class to allow you to create your own metadata columns. To implement this interface, you must implement the methods:

T OnParse(IColumnContext context);

string OnFormat(IColumnContext context);

Within IColumnContext, the following information is currently provided:

  • PhysicalIndex - The index of the column in the file.
  • LogicalIndex - The index of the column, excluding ignored columns.
  • RecordContext - Details about the record this column pertains to.
    • PhysicalRecordNumber - The actual number of records read from the file.
    • LogicalRecordNumber - The number of records that have not been skipped. This count does not yet include the current record.
    • ExecutionContext - Details about the current read/write operation.
      • Schema - The schema being used to parse the file.
      • Options - The options passed to the reader/writer.

Skipping Records

If you work directly with SeparatedValueReader or FixedLengthReader, you can call Skip to arbitrarily skip records in the input file. However, you often need the ability to inspect the record to determine whether it needs skipped. But what if you are trying to skip records because they can't be parsed? If you need more control over what records to skip, FlatFiles provides events for inspecting records during the parsing process. These events can be wired up whether you use type mappers or are working directly with readers.

Parsing a record goes through the following life-cycle:

  1. Read text until a record terminator (usually a newline) is found.
  2. For fixed-length records, partition the text into string columns based on the configured windows.
  3. Convert the string columns to the designated column types, as defined in the schema.

For CSV files, breaking a record into columns is automatically performed while searching for the record terminator. Prior to trying to convert the text to ints, date/times, etc., FlatFiles provides you the opportunity to inspect the raw string values and skip records.

The SeparatedValueReader class provides a RecordRead event, which allows you to skip unwanted records. For example, you could use the code below to find and skip CSV records missing the necessary number of columns:

reader.RecordRead += (sender, e) =>
    e.IsSkipped = e.Values.Length < 10;

Fixed-length files come in two flavors: those with and without record terminators. If there is no record terminator, the assumption is all records are the same length. Otherwise, each record can be a different length. For that reason, FlatFiles provides an extra opportunity to filter out records prior to splitting the text into columns. This is useful for filtering out records not meeting a minimum length requirement or those using a character to indicate something like comments.

The FixedLengthReader class provides the RecordRead event, which allows you to skip unwanted records. For example, you could use the code below to find and skip records starting with a # symbol:

reader.RecordRead += (sender, e) => 
    e.IsSkipped = e.Record.StartsWith("#");

Similar to CSV files, you can also filter out fixed-length records after they are broken into columns. However, it is important to note that the record is expected to fit the configured windows.

Again, the FixedLengthReader class provides the RecordPartitioned event, which allows you to skip unwanted records. For example, you could use the code below to find and skip records whose third column has a flag:

reader.RecordPartitioned += (sender, e) => 
    e.IsSkipped = e.Values[2] == "ERROR";

Error Handling

The reader and writer classes support two events for handling errors: RecordError and ColumnError. The ColumnError event is raised whenever an error occurs while reading/writing a column; for example, when a value can't be parsed. In that case, an instance of ColumnErrorEventArgs will be sent to the listener(s), which provides access to the context (ColumnContext), the value that caused the error (ColumnValue) and the exception that was thrown (Exception).

Furthermore, the IsHandled property can be set to true to inform FlatFiles that the exception should not be propagated. In that case, a value should be provided for the Substitution property. This property is an object, so it's important to make sure the substituted value is the same type as the column or a runtime error may occur.

The following code could be used to detect issues with a file so that errors can be reported back to a user:

public class ErrorDetail
    public int RecordNumber { get; set; }
    public string ColumnName { get; set; }
    public string ErrorValue { get; set; }
    public string ErrorMessage { get; set; }
var details = new List<ErrorDetail>();
var csvReader = new SeparatedValueReader(textReader, schema);
csvReader.ColumnError += (sender, e) =>
    var columnContext = e.ColumnContext;
    var detail = new ErrorDetail()
        RecordNumber = columnContext.RecordContext.PhysicalRecordNumber,
        ColumnName = columnContext.ColumnDefinition.ColumnName,
        ErrorValue = e.ColumnValue?.ToString(),
        ErrorMessage = e.Exception.InnerException?.Message ?? e.Exception.Message
    e.IsHandled = true;
    e.Substitution = null;  // May not work for non-nullable value types

If a column-level exception is not handled, the exception propagates. This, along with other record-level errors, will cause the RecordError event to be raised. Listeners will be sent an instance of RecordErrorEventArgs, which provides access to the context (RecordContext) and the exception that was thrown (Exception). Similarly, it provides an IsHandled property, that when set to true, will prevent the exception from propagating. In the case of record-level errors, this causes the record to be skipped while reading or writing.

Files Containing Multiple Schemas

Some flat file formats will contain multiple schemas. Often, data appears within "blocks" with a header and perhaps a footer. It can be extremely useful to parse each type of record using a different schema and return them in the same order they appear in the file.

FlatFiles provides support for these file formats using the SchemaSelector and TypeMapperSelector classes. Once you define the schemas or type mappers, you can register them with the selector.

var selector = new SeparatedValueTypeMapperSelector();
var dataMapper = getDataTypeMapper();
selector.When(x => x.Length == 10).Use(dataMapper);
selector.When(x => x.Length == 2).Use(getHeaderTypeMapper());
selector.When(x => x.Length == 3).Use(getFooterTypeMapper());

Each type mapper is associated with a predicate, which accepts the preprocessed record. If the predicate succeeds, that type mapper will be used to parse the record. Each predicate is tested in the order it is registered, so be sure to make your predicates smart enough to correctly identify the record type.

Note: You'll might have noticed dataMapper is registered twice, once up-front with a specific condition and then as the default. This isn't necessary; however, making sure the most common schema is tried first may help performance in some cases.

Once your selector is configured, you can call GetReader, passing in the TextReader and the options object. From there you can register events and read the objects from the file. Since different types can be returned, the reader will return instances of object.

If you want to work directly with schemas and readers, you can build a SchemaSelector by registering schemas with predicates in a similar fashion. The SeparatedValueReader and FixedLengthReader classes provide a constructor accepting a SchemaSelector. Note that whenever you work with selectors, calls to GetSchema will return null.

var selector = new SeparatedValueSchemaSelector();
var recordSchema = getDataSchema();
selector.When(values => values.Length == 10).Use(recordSchema);
selector.When(values => values.Length == 2).Use(getHeaderSchema());
selector.When(values => values.Length == 3).Use(getFooterSchema());

var reader = new SeparatedValueReader(fileStream, selector);
while (reader.Read())
    object[] values = reader.GetValues();

If you want to create multi-schema files, there are "injector" equivalents for each "selector" class. For example:

var selector = new FixedLengthTypeMapperInjector();

var stringWriter = new StringWriter();
var writer = injector.GetWriter(stringWriter);
writer.Write(new HeaderRecord() { BatchName = "First Batch", RecordCount = 2 });
writer.Write(new DataRecord() { Id = 1, Name = "Bob Smith", CreatedOn = new DateTime(2018, 06, 04), TotalAmount = 12.34m });
writer.Write(new DataRecord() { Id = 2, Name = "Jane Doe", CreatedOn = new DateTime(2018, 06, 05), TotalAmount = 34.56m });
writer.Write(new FooterRecord() { TotalAmount = 46.9m, AverageAmount = 23.45m, IsCriteriaMet = true });

The When method accepts a predicate if you need more than just the record type to decide what type mapper/schema to use.

Custom Mapping

The Property methods are best when your file's schema pretty much matches one-to-one with your classes. One column goes to one property. Frequently, though, your classes are more structured than your flat files. Starting with FlatFiles 3.0, you can provide your own mapping logic to control serializing and deserializing your objects.

First, let's see how to use the CustomMapping method to simulate the Property methods:

var mapper = SeparatedValueTypeMapper.Define(() => new Person());
mapper.CustomMapping(new StringColumn("FirstName"))
    .WithReader(p => p.FirstName)
    .WithWriter(p => p.FirstName);

This mapping tells FlatFiles to use a StringColumm to read/write the values from the file. It then says to read the values into and write out of the FirstName property of your class.

It's important to note that the type returned by the IColumnDefinition must exactly match the type of the property. You must deal with nulls and conversions yourself. There are several other overloads that provide more control -- this particular example would be better handled sticking to the Property methods.

Here is a more complicated example where two columns are used to build a Geolocation property.

public class Geolocation { public decimal Longitude; public decimal Latitude; }
public class Person { public Geolocation Location { get; set; } }
var mapper = SeparatedValueTypeMapper.Define(() => new Person() 
    Location = new Geolocation() 
mapper.CustomMapping(new DecimalColumn("Longitude"))
    .WithReader((p, v) => p.Location.Longitude = (decimal)v)  // long way
    .WithWriter(p => p.Location.Longitude);
mapper.CustomMapping(new DecimalColumn("Latitude"))
    .WithReader(p => p.Location.Latitude)  // short way
    .WithWriter(p => p.Location.Latitude);

Finally, here is an example configuration, where multiple columns are stored in a collection.

public class Contact
    public int Id { get; set; }
    public string Name { get; set; }
    public List<string> PhoneNumbers { get; set; } = new List<string>();
var mapper = FixedLengthTypeMapper.Define(() => new Contact());
mapper.CustomMapping(new Int32Column("Id"), 10)
    .WithReader(c => c.Id)
    .WithWriter(c => c.Id);
mapper.CustomMapping(new StringColumn("Name"), 10)
    .WithReader(c => c.Name)
    .WithWriter(c => c.Name);
mapper.CustomMapping(new StringColumn("Phone1"), 12)
    .WithWriter(c => c.PhoneNumbers.Count > 0 ? c.PhoneNumbers[0] : null);
mapper.CustomMapping(new StringColumn("Phone2"), 12)
    .WithWriter(c => c.PhoneNumbers.Count > 1 ? c.PhoneNumbers[1] : null);
public void PhoneReader(Contact contact, string phoneNumber)
    if (phoneNumber != null)

In benchmarks, using CustomMapping is only slightly slower than using Property, making it a great option when you need a little extra control.

There are versions of WithReader and WithWriter that provide contextual information (IColumnContext), so you can access metadata while reading and writing. The WithWriter method also provides an overload passing along the underlying array being written to, so you can write to multiple columns simultaneously or inspect previously serialized values.

Runtime Mapping

Even if you don't know the type of a class at compile time, it can still be beneficial to use the type mappers to populate these objects from a file. Or, if you are working in a language without support for expression trees, you'll be glad to know FlatFiles provides an alternative way to configure type mappers.

The code below illustrates how you could define a mapper for a type that is only known at runtime:

// Assume there is a runtime-generated type called "entityType" and a "TextReader".
var mapper = SeparatedValueMapper.DefineDynamic(entityType);
var entities = mapper.Read(reader).ToArray();
// Do something with the entities.

Disabling Optimization

FlatFile's type mappers can serialize and deserialize extremely quickly by generating code at runtime, using classes in the System.Reflection.Emit namespace. For most of us, that's awesome news because it means mapping values to and from your entities is almost as fast as if you had done the mapping by hand. However, there are some enviornments, like Mono running on iOS, that do not support runtime JIT'ing, so FlatFiles would not work.

As of version 1.0, mappers support a new method OptimizeMapping that can be used to switch to (A.K.A., slow) reflection. For example:

var mapper = SeparatedValueTypeMapper.Define<Person>(() => new Person());
mapper.Property(x => x.Id);
mapper.Property(x => x.Name);
mapper.OptimizeMapping(false);  // Use normal reflection to get and set properties

Non-Public Classes and Members

As of FlatFiles 3.0, you can no longer map to non-public classes and members (aka., internal, protected or private) without taking additional steps. The simplest solution is to make your classes and members public. Alternatively, you can disable optimizations which will cause FlatFiles to use normal reflection, which should be able to access anything, at the cost of some runtime overhead.

Another option is to grant FlatFiles access to your internal classes and members by adding the following line to you Assembly.cs file:

[assembly: InternalsVisibleTo("FlatFiles.DynamicAssembly,PublicKey=00240000048000009400000006020000002400005253413100040000010001009b9e44f637b293021ec4d8625071e5fe1682eeb167c233b46314cca79bf2769606285d5d1225cba8ce1e75be9e8ab7251d17eaf2c3b00fde5eac50a0f7dc7fec2f70279ff71c72341ad2738661babfdc6792479f14fd64d841285644d5c09c2902e9467f574e0d369161caee632087c5d819c3c36f76622306b09a4f868230c1")]

Notice that this only grants access to internal types/members. You will still not be able to access private members.

As a final option, you can use the CustomMapping method, passing delegates to read/write your members. Since the delegates are part of your project, they can access non-public members without trouble. There is almost no runtime overhead using CustomMapping instead of Property.

ADO.NET DataTables

If you are using DataTables, you can read and write to a DataTable using the ReadFlatFile and WriteFlatFile extension methods. Just pass the corresponding reader or writer object.

var customerTable = new DataTable("Customer");
using (var streamReader = new StreamReader(File.OpenRead(@"C:\path\to\file.csv")))
    var reader = new SeparatedValueReader(streamReader, schema);


For low-level ADO.NET file reading, you can use the FlatFileDataReader class. It provides an IDataReader interface to the records in the file, making it compatible with other ADO.NET interfaces.

// The DataReader Approach
using (var fileReader = new StreamReader(File.OpenRead(@"C:\path\to\file.csv"))
    var csvReader = new SeparatedValueReader(fileReader, schema);
    var dataReader = new FlatFileDataReader(csvReader);
    var customers = new List<Customer>();
    while (dataReader.Read())
        var customer = new Customer();
        customer.CustomerId = dataReader.GetInt32(0);
        customer.Name = dataReader.GetString(1);
        customer.Created = dataReader.GetDateTime(2);
        customer.AverageSales = dataReader.GetDouble(3);
    return customers;

Usually in cases like this, it is just easier to use the type mappers. However, this could be useful if you are swapping out an actual database call with CSV data inside of a unit test.

FlatFiles also provides helpful extension methods on the IDataReader interface to make it easier to extract data. It provides GetNullable* variants of the IDataReader methods, so you don't need to constantly call IsDBNull. There are also variants of each method accepting the column name rather than the ordinal position.

There are also generic GetValue<T> methods that can deal with type conversions automatically for you. For example, anytime you read in a CSV file without providing the schema, FlatFiles assumes each column is a string. When calling GetValue<int>("Id") or GetValue<DateTime?>("CreatedOn"), FlatFiles will try to convert the values for you. This is extremely helpful when you don't want to provide (or can't provide) a schema but can still determine the column types. For example, when you know the column names and their types but their order could be different between runs.


This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or distribute this software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means.

In jurisdictions that recognize copyright laws, the author or authors of this software dedicate any and all copyright interest in the software to the public domain. We make this dedication for the benefit of the public at large and to the detriment of our heirs and successors. We intend this dedication to be an overt act of relinquishment in perpetuity of all present and future rights to this software under copyright law.


For more information, please refer to