If you still manage to find a bug,ĭo not hesitate to report it. This CSV parser has an extensive test suite and is checked for memory safety with Valgrind. It does not try to decode UTF-8, except for detecting and stripping UTF-8 byte order marks. This CSV parser is encoding-agnostic and will handle ANSI and UTF-8 encoded files.

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

Ability to ignore comments in leading rows and elsewhere.

However, in reality we know that RFC 4180 is just a suggestion, and there's many 'flavors' of CSV such as tab-delimited files. This CSV parser is much more than a fancy string splitter, and parses all files following RFC 4180.

a 1.24GB Car Accidents Dataset in 5 seconds (248 MBps).

a 1.4 GB Craigslist Used Vehicles Dataset in 2.1 seconds (667 MBps).

the 69.9 MB 2015_StateDepartment.csv in 0.26 seconds (269 MBps).

On my computer (Intel Core i7-8550U 1.80GHz/Toshiba XG5 SSD), this parser can read

Furthermore, this parser has a minimal memory footprint andĬan handle larger-than-RAM files. By using overlapped threads, memory mapped IO, andĮfficient data structures, this parser can quickly tackle large CSV files. With the deluge of large datasets available, a performant CSV parser is a necessity. Thus, this library was created with these following goals in mind.

Furthermore, I wanted support for special use cases such as calculating statistics on very large files. Inspired by Python's csv module, I wanted a library with simple, intuitive syntax. There's plenty of other CSV parsers in the wild, but I had a hard time finding what I wanted. Reading an Arbitrarily Large File (with Iterators).

Modern csv license key