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https://github.com/vincentlaucsb/csv-parser

A high-performance, fully-featured CSV parser and serializer for modern C++.
https://github.com/vincentlaucsb/csv-parser

c-plus-plus c-plus-plus-11 c-plus-plus-14 c-plus-plus-17 csv csv-parser csv-reader json parser statistics tab-separated

Last synced: 19 days ago
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A high-performance, fully-featured CSV parser and serializer for modern C++.

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README 

          
# Vince's CSV Parser

[![CMake on Windows](https://github.com/vincentlaucsb/csv-parser/actions/workflows/cmake-multi-platform.yml/badge.svg)](https://github.com/vincentlaucsb/csv-parser/actions/workflows/cmake-multi-platform.yml) [![Memory and Thread Sanitizers](https://github.com/vincentlaucsb/csv-parser/actions/workflows/sanitizers.yml/badge.svg)](https://github.com/vincentlaucsb/csv-parser/actions/workflows/sanitizers.yml)



- [Vince's CSV Parser](#vinces-csv-parser)

  - [Motivation](#motivation)

    - [Performance and Memory Requirements](#performance-and-memory-requirements)

      - [Show me the numbers](#show-me-the-numbers)

    - [Robust Yet Flexible](#robust-yet-flexible)

      - [RFC 4180 and Beyond](#rfc-4180-and-beyond)

      - [Encoding](#encoding)

    - [Well Tested](#well-tested)

  - [Documentation](#documentation)

  - [Sponsors](#sponsors)

  - [Integration](#integration)

    - [C++ Version](#c-version)

    - [Single Header](#single-header)

    - [CMake Instructions](#cmake-instructions)

      - [Avoid cloning with FetchContent](#avoid-cloning-with-fetchcontent)

  - [Features \& Examples](#features--examples)

    - [Reading an Arbitrarily Large File (with Iterators)](#reading-an-arbitrarily-large-file-with-iterators)

      - [Memory-Mapped Files vs. Streams](#memory-mapped-files-vs-streams)

    - [Indexing by Column Names](#indexing-by-column-names)

    - [Numeric Conversions](#numeric-conversions)

    - [Converting to JSON](#converting-to-json)

    - [Specifying the CSV Format](#specifying-the-csv-format)

      - [Trimming Whitespace](#trimming-whitespace)

      - [Handling Variable Numbers of Columns](#handling-variable-numbers-of-columns)

      - [Setting Column Names](#setting-column-names)

    - [Parsing an In-Memory String](#parsing-an-in-memory-string)

    - [Writing CSV Files](#writing-csv-files)



## Motivation

There's plenty of other CSV parsers in the wild, but I had a hard time finding what I wanted. Inspired by Python's `csv` module, I wanted a library with **simple, intuitive syntax**. Furthermore, I wanted support for special use cases such as calculating statistics on very large files. Thus, this library was created with these following goals in mind.



### Performance and Memory Requirements

A high performance CSV parser allows you to take advantage of the deluge of large datasets available. By using overlapped threads, memory mapped IO, and 

minimal memory allocation, this parser can quickly tackle large CSV files--even if they are larger than RAM.



In fact, [according to Visual Studio's profier](https://github.com/vincentlaucsb/csv-parser/wiki/Microsoft-Visual-Studio-CPU-Profiling-Results) this

CSV parser **spends almost 90% of its CPU cycles actually reading your data** as opposed to getting hung up in hard disk I/O or pushing around memory.



#### Show me the numbers

On my computer (12th Gen Intel(R) Core(TM) i5-12400 @ 2.50 GHz/Western Digital Blue 5400RPM HDD), this parser can read

 * the [69.9 MB 2015_StateDepartment.csv](https://github.com/vincentlaucsb/csv-data/tree/master/real_data) in 0.19 seconds (360 MBps)

 * a [1.4 GB Craigslist Used Vehicles Dataset](https://www.kaggle.com/austinreese/craigslist-carstrucks-data/version/7) in 1.18 seconds (1.2 GBps)

 * a [2.9GB Car Accidents Dataset](https://www.kaggle.com/sobhanmoosavi/us-accidents) in 8.49 seconds (352 MBps)



### Robust Yet Flexible

#### RFC 4180 and Beyond

This CSV parser is much more than a fancy string splitter, and parses all files following [RFC 4180](https://www.rfc-editor.org/rfc/rfc4180.txt).



However, in reality we know that RFC 4180 is just a suggestion, and there's many "flavors" of CSV such as tab-delimited files. Thus, this library has:

 * Automatic delimiter guessing

 * Ability to ignore comments in leading rows and elsewhere

 * Ability to handle rows of different lengths

 * Ability to handle arbitrary line endings (as long as they are some combination of carriage return and newline)



By default, rows of variable length are silently ignored, although you may elect to keep them or throw an error.



#### Encoding

This CSV parser is encoding-agnostic and will handle ANSI and UTF-8 encoded files.

It does not try to decode UTF-8, except for detecting and stripping UTF-8 byte order marks.



### Well Tested

This CSV parser has:

 * An extensive Catch2 test suite

 * Address, thread safety, and undefined behavior checks with ASan, TSan, and Valgrind (see [GitHub Actions](https://github.com/vincentlaucsb/csv-parser/actions))



If you still manage to find a bug, do not hesitate to report it.



## Documentation



In addition to the [Features & Examples](#features--examples) below, a [fully-fledged online documentation](https://vincela.com/csv/) contains more examples, details, interesting features, and instructions for less common use cases.



## Sponsors

If you use this library for work, please [become a sponsor](https://github.com/sponsors/vincentlaucsb). Your donation

will fund continued maintenance and development of the project.



## Integration



This library was developed with Microsoft Visual Studio and is compatible with >g++ 7.5 and clang.

All of the code required to build this library, aside from the C++ standard library, is contained under `include/`.



### C++ Version

While C++17 is recommended, C++11 is the minimum version required. This library makes extensive use of string views, and uses

[Martin Moene's string view library](https://github.com/martinmoene/string-view-lite) if `std::string_view` is not available.



### Single Header

This library is available as a single `.hpp` file under [`single_include/csv.hpp`](single_include/csv.hpp).



### CMake Instructions

If you're including this in another CMake project, you can simply clone this repo into your project directory, 

and add the following to your CMakeLists.txt:



```

# Optional: Defaults to C++ 17

# set(CSV_CXX_STANDARD 11)

add_subdirectory(csv-parser)



# ...



add_executable( ...)

target_link_libraries( csv)



```



#### Avoid cloning with FetchContent

Don't want to clone? No problem. There's also [a simple example documenting how to use CMake's FetchContent module to integrate this library](https://github.com/vincentlaucsb/csv-parser/wiki/Example:-Using-csv%E2%80%90parser-with-CMake-and-FetchContent).



## Features & Examples

### Reading an Arbitrarily Large File (with Iterators)

With this library, you can easily stream over a large file without reading its entirety into memory.



**C++ Style**

```cpp

# include "csv.hpp"



using namespace csv;



...



CSVReader reader("very_big_file.csv");



for (CSVRow& row: reader) { // Input iterator

    for (CSVField& field: row) {

        // By default, get<>() produces a std::string.

        // A more efficient get() is also available, where the resulting

        // string_view is valid as long as the parent CSVRow is alive

        std::cout << field.get<>() << ...

    }

}



...

```



**Old-Fashioned C Style Loop**

```cpp

...



CSVReader reader("very_big_file.csv");

CSVRow row;

 

while (reader.read_row(row)) {

    // Do stuff with row here

}



...

```



#### Memory-Mapped Files vs. Streams

By default, passing in a file path string to the constructor of `CSVReader`

causes memory-mapped IO to be used. In general, this option is the most

performant.



However, `std::ifstream` may also be used as well as in-memory sources via `std::stringstream`.



**Note**: Currently CSV guessing only works for memory-mapped files. The CSV dialect

must be manually defined for other sources.



**⚠️ IMPORTANT - Iterator Type and Memory Safety**:  

`CSVReader::iterator` is an **input iterator** (`std::input_iterator_tag`), NOT a forward iterator.

This design enables streaming large CSV files (50+ GB) without loading them entirely into memory.



**Why Forward Iterator Algorithms Don't Work**:

- As the iterator advances, underlying data chunks are automatically freed to bound memory usage

- Algorithms like `std::max_element` require ForwardIterator semantics (multi-pass, hold multiple positions)

- Using such algorithms directly on `CSVReader::iterator` will cause **heap-use-after-free** when the

  algorithm tries to access iterators pointing to already-freed data chunks

- While it may appear to work with small files that fit in a single chunk, it WILL fail with larger files



**✅ Correct Approach for ForwardIterator Algorithms**:

```cpp

// Copy rows to vector first (enables multi-pass iteration)

CSVReader reader("large_file.csv");

std::vector rows(reader.begin(), reader.end());



// Now safely use any algorithm requiring ForwardIterator

auto max_row = std::max_element(rows.begin(), rows.end(), 

    [](const CSVRow& a, const CSVRow& b) { 

        return a["salary"].get() < b["salary"].get(); 

    });

```



```cpp

CSVFormat format;

// custom formatting options go here



CSVReader mmap("some_file.csv", format);



std::ifstream infile("some_file.csv", std::ios::binary);

CSVReader ifstream_reader(infile, format);



std::stringstream my_csv;

CSVReader sstream_reader(my_csv, format);

```



### Indexing by Column Names

Retrieving values using a column name string is a cheap, constant time operation.



```cpp

# include "csv.hpp"



using namespace csv;



...



CSVReader reader("very_big_file.csv");

double sum = 0;



for (auto& row: reader) {

    // Note: Can also use index of column with [] operator

    sum += row["Total Salary"].get();

}



...

```



### Numeric Conversions

If your CSV has lots of numeric values, you can also have this parser (lazily)

convert them to the proper data type.



 * Type checking is performed on conversions to prevent undefined behavior and integer overflow

   * Negative numbers cannot be blindly converted to unsigned integer types

 * `get()`, `get()`, and `get()` are capable of parsing numbers written in scientific notation.

 * **Note:** Conversions to floating point types are not currently checked for loss of precision.



```cpp

# include "csv.hpp"



using namespace csv;



...



CSVReader reader("very_big_file.csv");



for (auto& row: reader) {

    if (row["timestamp"].is_int()) {

        // Can use get<>() with any integer type, but negative

        // numbers cannot be converted to unsigned types

        row["timestamp"].get();

        

        // You can also attempt to parse hex values

        long long value;

        if (row["hexValue"].try_parse_hex(value)) {

            std::cout << "Hex value is " << value << std::endl;

        }



        // Or specify a different integer type

        int smallValue;

        if (row["smallHex"].try_parse_hex(smallValue)) {

            std::cout << "Small hex value is " << smallValue << std::endl;

        }



        // Non-imperial decimal numbers can be handled this way

        long double decimalValue;

        if (row["decimalNumber"].try_parse_decimal(decimalValue, ',')) {

            std::cout << "Decimal value is " << decimalValue << std::endl;

        }



        // ..

    }

}



```



### Converting to JSON

You can serialize individual rows as JSON objects, where the keys are column names, or as 

JSON arrays (which don't contain column names). The outputted JSON contains properly escaped

strings with minimal whitespace and no quoting for numeric values. How these JSON fragments are 

assembled into a larger JSON document is an exercise left for the user.



```cpp

# include 

# include "csv.hpp"



using namespace csv;



...



CSVReader reader("very_big_file.csv");

std::stringstream my_json;



for (auto& row: reader) {

    my_json << row.to_json() << std::endl;

    my_json << row.to_json_array() << std::endl;



    // You can pass in a vector of column names to

    // slice or rearrange the outputted JSON

    my_json << row.to_json({ "A", "B", "C" }) << std::endl;

    my_json << row.to_json_array({ "C", "B", "A" }) << std::endl;

}



```



### Specifying the CSV Format

Although the CSV parser has a decent guessing mechanism, in some cases it is preferrable to specify the exact parameters of a file.



```cpp

# include "csv.hpp"

# include ...



using namespace csv;



CSVFormat format;

format.delimiter('\t')

      .quote('~')

      .header_row(2);   // Header is on 3rd row (zero-indexed)

      // .no_header();  // Parse CSVs without a header row

      // .quote(false); // Turn off quoting 



// Alternatively, we can use format.delimiter({ '\t', ',', ... })

// to tell the CSV guesser which delimiters to try out



CSVReader reader("wierd_csv_dialect.csv", format);



for (auto& row: reader) {

    // Do stuff with rows here

}



```



#### Trimming Whitespace

This parser can efficiently trim off leading and trailing whitespace. Of course,

make sure you don't include your intended delimiter or newlines in the list of characters

to trim.



```cpp

CSVFormat format;

format.trim({ ' ', '\t'  });

```



#### Handling Variable Numbers of Columns

Sometimes, the rows in a CSV are not all of the same length. Whether this was intentional or not,

this library is built to handle all use cases.



```cpp

CSVFormat format;



// Default: Silently ignoring rows with missing or extraneous columns

format.variable_columns(false); // Short-hand

format.variable_columns(VariableColumnPolicy::IGNORE_ROW);



// Case 2: Keeping variable-length rows

format.variable_columns(true); // Short-hand

format.variable_columns(VariableColumnPolicy::KEEP);



// Case 3: Throwing an error if variable-length rows are encountered

format.variable_columns(VariableColumnPolicy::THROW);

```



#### Setting Column Names

If a CSV file does not have column names, you can specify your own:



```cpp

std::vector col_names = { ... };

CSVFormat format;

format.column_names(col_names);

```



### Parsing an In-Memory String



```cpp

# include "csv.hpp"



using namespace csv;



...



// Method 1: Using parse()

std::string csv_string = "Actor,Character\r\n"

    "Will Ferrell,Ricky Bobby\r\n"

    "John C. Reilly,Cal Naughton Jr.\r\n"

    "Sacha Baron Cohen,Jean Giard\r\n";



auto rows = parse(csv_string);

for (auto& r: rows) {

    // Do stuff with row here

}

    

// Method 2: Using _csv operator

auto rows = "Actor,Character\r\n"

    "Will Ferrell,Ricky Bobby\r\n"

    "John C. Reilly,Cal Naughton Jr.\r\n"

    "Sacha Baron Cohen,Jean Giard\r\n"_csv;



for (auto& r: rows) {

    // Do stuff with row here

}



```



### Writing CSV Files



```cpp

# include "csv.hpp"

# include ...



using namespace csv;

using namespace std;



...



stringstream ss; // Can also use ofstream, etc.



auto writer = make_csv_writer(ss);

// auto writer = make_tsv_writer(ss);               // For tab-separated files

// DelimWriter writer(ss);  // Your own custom format

// set_decimal_places(2);                           // How many places after the decimal will be written for floats



writer << vector({ "A", "B", "C" })

    << deque({ "I'm", "too", "tired" })

    << list({ "to", "write", "documentation." });



writer << array({ "The quick brown", "fox", "jumps over the lazy dog" });

writer << make_tuple(1, 2.0, "Three");

...

```



You can pass in arbitrary types into `DelimWriter` by defining a conversion function

for that type to `std::string`.