CSV (Comma-Separated Values) is widely recognized for its simplicity and widespread acceptance in data storage and exchange. While the format's foundational concept is straightforward, representing data entries separated by commas, real-world applications introduce complexities that stem from the format's very simplicity and the lack of a strict standard.

One primary issue arises from the handling of values that contain commas or special characters themselves. In such cases, the value must be enclosed in quotes to prevent misinterpretation as field separators. This scenario underscores the first level of complexity: different systems or applications employ slightly varying conventions for writing CSV files. Some might quote every single value, others quote only those values containing commas, special characters, or whitespace, and others still apply quotes based on their own internal rules.

Further nuances include the treatment of quotes within quoted fields. Various applications may either use escape characters like a backslash or double up quotes to signify a literal quote character within a field. This lack of uniformity extends to other aspects of the CSV format as well. For example, while commas are standard delimiters, it's not uncommon to encounter files using alternative delimiters like the TAB character (ASCII value 9), particularly when the data itself includes commas.

The challenges don't end there. Depending on the system—Windows, or Unix/Linux/Mac—the end of a line can be marked differently, either by a carriage return and line feed (ASCII 13 and 10) or just a line feed (ASCII 10). These differences can affect how records are read and written across different systems. Additionally, storing multiline text data in CSV files is problematic due to the risk of premature end-of-record detection, making data parsing less predictable.

In recognition of these intricacies, we have invested in developing our own CSV parser, designed to accommodate the common variations we've encountered in CSV files. Our approach goes beyond basic parsing; it involves a comprehensive test suite ensuring robust handling of diverse file characteristics. Even though the CSV format inherently lacks metadata for field types, our system takes the initiative to analyze field contents and intelligently determine the most suitable data types for each field. This analysis is crucial for maintaining data integrity and optimizing subsequent data processing tasks.

By acknowledging the CSV format's subtleties and preparing for the challenges it presents, we leverage its convenience while ensuring reliability and accuracy in data operations. Our adaptive handling strategies for this deceptively simple format underline our commitment to offering versatile, dependable solutions in the dynamic realm of data management.


data types we support

double (decimal)
Large objects: