Principle:Eventual Inc Daft CSV Writing
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Data_Storage |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Technique for persisting DataFrame contents to CSV text files.
Description
CSV writing exports tabular data to a widely-compatible text format. CSV (Comma-Separated Values) is a row-based text serialization where each row is a line and fields are separated by a delimiter character (typically a comma). While less efficient than columnar formats like Parquet, CSV is universally readable by spreadsheets, databases, and programming languages. Daft supports partitioning, multiple write modes, and configurable delimiters, quoting, and date/timestamp formatting.
Usage
Use CSV writing when you need to export data in a universally readable text format. Common scenarios include sharing data with non-technical stakeholders, importing into spreadsheet applications, loading into legacy systems, or producing human-readable output files.
Theoretical Basis
Row-based text serialization with configurable formatting:
CSV Structure:
header_row = field_name_1, field_name_2, ...
data_row = value_1, value_2, ...
Serialization Rules:
- Fields containing delimiters are quoted
- Fields containing quotes use escape characters
- Null values are represented as empty fields
- Date/timestamp values use configurable format strings
Write Modes:
- append: add new files alongside existing data
- overwrite: replace all existing data
- overwrite-partitions: replace only affected partition directories
Partitioning (Hive-style):
root_dir/
partition_col=value1/
data_file_1.csv
partition_col=value2/
data_file_2.csv
CSV writing is a blocking operation that triggers full materialization of the DataFrame query plan before writing.