The CsvReader library is an extended version of Sébastien Lorion's fast CSV Reader project and provides fast parsing and reading of CSV files
To this end it is a straight drop-in replacement for the existing NuGet package LumenWork.Framework.IO and LumenWorksCsvReader, but with additional capabilities; the other rationale for the project is that the code is not available elsewhere in a public source repository, making it difficult to extend/contribute to.
Welcome to contributions from anyone.
You can see the version history here.
- Install Fake
- In the command line run dotnet fake build
The library is available under the MIT License, for more information see the License file in the GitHub repository.
A good starting point is to look at Sébastien's article on Code Project.
A basic use of the reader something like this...
using System.IO;
using LumenWorks.Framework.IO.Csv;
void ReadCsv()
{
// open the file "data.csv" which is a CSV file with headers
using (var csv = new CachedCsvReader(new StreamReader("data.csv"), true))
{
// Field headers will automatically be used as column names
myDataGrid.DataSource = csv;
}
}Having said that, there are some extensions built into this version of the library that it is worth mentioning.
One addition is the addition of a Column list which holds the names and types of the data in the CSV file. If there are no headers present, we default the column names to Column1, Column2 etc; this can be overridden by setting the DefaultColumnHeader property e.g.
using System.IO;
using LumenWorks.Framework.IO.Csv;
void ReadCsv()
{
// open the file "data.csv" which is a CSV file with headers
using (var csv = new CachedCsvReader(new StreamReader("data.csv"), false))
{
csv.DefaultColumnHeader = "Fred"
// Field headers will now be Fred1, Fred2, etc
myDataGrid.DataSource = csv;
}
}You can specify the columns yourself if there are none, and also specify the expected type; this is especially important when using against SqlBulkCopy which we will come back to later.
using System.IO;
using LumenWorks.Framework.IO.Csv;
void ReadCsv()
{
// open the file "data.csv" which is a CSV file with headers
using (var csv = new CachedCsvReader(new StreamReader("data.csv"), false))
{
csv.Columns.Add(new Column { Name = "PriceDate", Type = typeof(DateTime) });
csv.Columns.Add(new Column { Name = "OpenPrice", Type = typeof(decimal) });
csv.Columns.Add(new Column { Name = "HighPrice", Type = typeof(decimal) });
csv.Columns.Add(new Column { Name = "LowPrice", Type = typeof(decimal) });
csv.Columns.Add(new Column { Name = "ClosePrice", Type = typeof(decimal) });
csv.Columns.Add(new Column { Name = "Volume", Type = typeof(int) });
// Field headers will now be picked from the Columns collection
myDataGrid.DataSource = csv;
}
}One use of CSV Reader is to have a nice .NET way of using SQL Bulk Copy (SBC) rather than bcp for bulk loading of data into SQL Server.
A couple of issues arise when using SBC 1. SBC wants the data presented as the correct type rather than as string 2. You need to map between the table destination columns and the CSV if the order does not match exactly
Below is a example using the Columns collection to set up the correct metadata for SBC
public void Import(string fileName, string connectionString)
{
using (var reader = new CsvReader(new StreamReader(fileName), false))
{
reader.Columns = new List<LumenWorks.Framework.IO.Csv.Column>
{
new LumenWorks.Framework.IO.Csv.Column { Name = "PriceDate", Type = typeof(DateTime) },
new LumenWorks.Framework.IO.Csv.Column { Name = "OpenPrice", Type = typeof(decimal) },
new LumenWorks.Framework.IO.Csv.Column { Name = "HighPrice", Type = typeof(decimal) },
new LumenWorks.Framework.IO.Csv.Column { Name = "LowPrice", Type = typeof(decimal) },
new LumenWorks.Framework.IO.Csv.Column { Name = "ClosePrice", Type = typeof(decimal) },
new LumenWorks.Framework.IO.Csv.Column { Name = "Volume", Type = typeof(int) },
new LumenWorks.Framework.IO.Csv.Column { Name = "IsActive", Type = typeof(bool) },
};
// With the help of CustomBooleanReplacer you can define a mapping between string values in the CSV file and boolean values
// In this example, 'Y' and 'Yes' will be treated as true; 'N' and 'No' - as false value.
reader.CustomBooleanReplacer = new Dictionary<string, bool>
{
{"Y", true},
{"N", false},
{"Yes", true},
{"No", false},
};
// Now use SQL Bulk Copy to move the data
using (var sbc = new SqlBulkCopy(connectionString))
{
sbc.DestinationTableName = "dbo.DailyPrice";
sbc.BatchSize = 1000;
sbc.AddColumnMapping("PriceDate", "PriceDate");
sbc.AddColumnMapping("OpenPrice", "OpenPrice");
sbc.AddColumnMapping("HighPrice", "HighPrice");
sbc.AddColumnMapping("LowPrice", "LowPrice");
sbc.AddColumnMapping("ClosePrice", "ClosePrice");
sbc.AddColumnMapping("Volume", "Volume");
sbc.AddColumnMapping("IsActive", "IsActive");
sbc.WriteToServer(reader);
}
}
}The method AddColumnMapping is an extension I wrote to simplify adding mappings to SBC
public static class SqlBulkCopyExtensions
{
public static SqlBulkCopyColumnMapping AddColumnMapping(this SqlBulkCopy sbc, int sourceColumnOrdinal, int targetColumnOrdinal)
{
var map = new SqlBulkCopyColumnMapping(sourceColumnOrdinal, targetColumnOrdinal);
sbc.ColumnMappings.Add(map);
return map;
}
public static SqlBulkCopyColumnMapping AddColumnMapping(this SqlBulkCopy sbc, string sourceColumn, string targetColumn)
{
var map = new SqlBulkCopyColumnMapping(sourceColumn, targetColumn);
sbc.ColumnMappings.Add(map);
return map;
}
}One other issue recently arose where we wanted to use SBC but some of the data was not in the file itself, but metadata that needed to be included on every row. The solution was to amend the CSV reader and Columns collection to allow default values to be provided that are not in the data.
The additional columns should be added at the end of the Columns collection to avoid interfering with the parsing, see the amended example below...
public void Import(string fileName, string connectionString)
{
using (var reader = new CsvReader(new StreamReader(fileName), false))
{
reader.Columns = new List<LumenWorks.Framework.IO.Csv.Column>
{
...
new LumenWorks.Framework.IO.Csv.Column { Name = "Volume", Type = typeof(int) },
// NB Fake column so bulk import works
new LumenWorks.Framework.IO.Csv.Column { Name = "Ticker", Type = typeof(string) },
};
// Fix up the column defaults with the values we need
reader.UseColumnDefaults = true;
reader.Columns[reader.GetFieldIndex("Ticker")] = Path.GetFileNameWithoutExtension(fileName);
// Now use SQL Bulk Copy to move the data
using (var sbc = new SqlBulkCopy(connectionString))
{
...
sbc.AddColumnMapping("Ticker", "Ticker");
sbc.WriteToServer(reader);
}
}
}It may happen that your database table where you would like to import a CSV contains more or different columns than your CSV file. As SqlBulkCopy requires to define all column mappings from the target table, you can use the VirtualColumns functionality:
csv.VirtualColumns.Add(new Column { Name = "SourceTypeId", Type = typeof(int), DefaultValue = "1", NumberStyles = NumberStyles.Integer });
csv.VirtualColumns.Add(new Column { Name = "DataBatchId", Type = typeof(int), DefaultValue = dataBatchId.ToString(), NumberStyles = NumberStyles.Integer });In this case you define 2 additional columns that do not exist in the source CSV file, but exist in the target table. Also you can set the DefaultValue that will be bulk-copied to the target table together with the CSV file content. Do not forget to include the defined virtual columns to the SqlBulkCopy column mapping!
In case if your CSV file is big enough and you do not want to import a whole file but some set of data, you can set the ExcludeFilter action:
csv.ExcludeFilter = () => ((csv["Fmly"] ?? "") + (csv["Group"] ?? "") + (csv["Type"] ?? "")).ToUpperInvariant() == "EQDEQUIT";In this case all rows that fit the defined criteria will not be imported to the database.
Calling this method returns you an IEnumerable where T is the type of an entity/DTO you want to map your CSV file. Before calling this method you should define Columns passing names and data type of all columns within CSV file.
var expected = new List<SampleData3>
{
new SampleData3("John", "Doe", "120 jefferson st.", "Riverside", "NJ", 8075, true, null),
new SampleData3("Jack", "McGinnis", "220 hobo Av.", "Phila", "PA", 9119, false, null),
new SampleData3("John \"Da Man\"", "Repici", "120 Jefferson St.", "Riverside", "NJ", 8075, false, null),
new SampleData3("Stephen", "Tyler", "7452 Terrace \"At the Plaza\" road", "SomeTown", "SD", 91234, false, null),
new SampleData3(null, "Blankman", null, "SomeTown", "SD", 298, false, null),
new SampleData3("Joan \"the bone\", Anne", "Jet", "9th, at Terrace plc", "Desert City", "CO", 123, false, null),
};
/// using propertyToColumnMapping parameter you can map column names from CSV file to property names of your entity/DTO
var propertyToColumnMapping = new Dictionary<string, string>
{
{ "FirstName", "First Name" },
{ "LastName", "Last Name" },
{ "ZipCode", "Zip Code" }
};
using (CsvReader csv = new CsvReader(new StringReader(CsvReaderSampleData.SampleData1), true))
{
/// you should define Columns before calling MapDataToDto<T>!
csv.Columns = new ColumnCollection
{
{"First Name", typeof(string)},
{"Last Name", typeof(string)},
{"Address", typeof(string)},
{"City", typeof(string)},
{"State", typeof(string)},
{"Zip Code", typeof(int)},
{"IsActive", typeof(bool)},
};
csv.CustomBooleanReplacer = new Dictionary<string, bool> { { "Y", true }, { "N", false } };
var result = csv.MapDataToDto<SampleData3>(propertyToColumnMapping).ToList();
result.Should().BeEquivalentTo(expected);
}
To give an idea of performance, this took a native sample app using an ORM from 2m 27s to 1.37s using SBC and the full import took just over 11m to import 9.8m records.
One of the main reasons for using this library is its excellent performance on reading/parsing raw data, here's a recent run of the benchmark (which is in the source)
| Test | .NET 4.8 | .NET 6 | .NET 7 | .NET 8 |
|---|---|---|---|---|
| Test pass #1 - All fields | ||||
| CsvReader - No cache | 62.7333 | 80.1117 | 63.8059 | 37.0477 |
| CachedCsvReader - Run 1 | 35.5160 | 41.7880 | 38.2042 | 39.8874 |
| CachedCsvReader - Run 2 | 61426.7765 | 73999.3273 | 117302.0528 | 91973.2441 |
| TextFieldParser | 9.8574 | 13.9199 | 14.6264 | 16.4390 |
| Regex | 10.2663 | 17.9696 | 18.1544 | 20.7676 |
| Test pass #1 - Field #72 (middle) | ||||
| CsvReader - No cache | 67.7263 | 95.0044 | 86.0328 | 87.4732 |
| CachedCsvReader - Run 1 | 29.2253 | 36.9564 | 42.5468 | 44.7421 |
| CachedCsvReader - Run 2 | 792792.7928 | 646108.6637 | 512820.5128 | 639534.8837 |
| TextFieldParser | 9.9626 | 13.1838 | 14.5921 | 18.2339 |
| Regex | 22.5060 | 37.9651 | 46.1343 | 50.7253 |
| Test pass #2 - All fields | ||||
| CsvReader - No cache | 75.8756 | 88.6553 | 93.0768 | 109.0782 |
| CachedCsvReader - Run 1 | 28.8864 | 38.4666 | 45.3834 | 40.4266 |
| CachedCsvReader - Run 2 | 948275.8621 | 781527.5311 | 852713.1783 | 461699.8951 |
| TextFieldParser | 9.4858 | 13.8782 | 15.3555 | 17.4682 |
| Regex | 9.6566 | 18.4976 | 20.6475 | 22.6479 |
| Test pass #2 - Field #72 (middle) | ||||
| CsvReader - No cache | 72.6275 | 98.7495 | 107.8248 | 111.1179 |
| CachedCsvReader - Run 1 | 28.4391 | 35.7626 | 36.8091 | 52.7800 |
| CachedCsvReader - Run 2 | 830188.6792 | 765217.3913 | 827067.6692 | 995475.1131 |
| TextFieldParser | 8.6734 | 14.6872 | 15.4038 | 18.2108 |
| Regex | 22.1135 | 44.0567 | 46.4395 | 50.7668 |
| Test pass #3 - All fields | ||||
| CsvReader - No cache | 74.3428 | 90.2397 | 92.8137 | 111.8334 |
| CachedCsvReader - Run 1 | 30.5301 | 35.6446 | 43.8796 | 49.9862 |
| CachedCsvReader - Run 2 | 817843.8662 | 737018.4255 | 766550.5226 | 820895.5224 |
| TextFieldParser | 9.3366 | 14.4030 | 15.0148 | 17.9641 |
| Regex | 10.1904 | 19.1660 | 20.0524 | 21.7854 |
| Test pass #3 - Field #72 (middle) | ||||
| CsvReader - No cache | 76.5840 | 104.5209 | 105.9584 | 113.8155 |
| CachedCsvReader - Run 1 | 35.5272 | 38.0744 | 43.4385 | 37.0724 |
| CachedCsvReader - Run 2 | 932203.3898 | 766550.5226 | 634005.7637 | 748299.3197 |
| TextFieldParser | 9.7928 | 14.4643 | 13.6437 | 17.7131 |
| Regex | 22.5506 | 44.6435 | 45.5831 | 49.3559 |
| Average of all test passes | .NET 4.8 | .NET 6 | .NET 7 | .NET 8 |
|---|---|---|---|---|
| CsvReader - No cache | 716.483 | 928.803 | 915.854 | 950.610 |
| CachedCsvReader - Run 1 | 313.540 | 377.821 | 417.103 | 441.491 |
| CachedCsvReader - Run 2 | 7.304.552.278 | 6.284.036.436 | 6.184.099.499 | 6.263.129.964 |
| TextFieldParser | 95.181 | 140.894 | 147.727 | 176.715 |
| Regex | 162.139 | 303.831 | 328.352 | 360.082 |
As you can see, an average performance slightly increases from full .NET Framework 4.8 to .NET Core 8.
This was run on a Core i5-8400 (6 cores), 32Gb RAM and 2Tb SSD.