Principle:Eventual Inc Daft Data Sorting
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Data_Analysis |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Data sorting is the technique for ordering DataFrame rows by one or more column values, with configurable sort direction and null placement.
Description
Data sorting reorders all rows in a DataFrame based on column values in ascending or descending order, with configurable null placement (first or last). It supports multi-column sorting where each column can have an independent sort direction and null positioning. Since Daft is a distributed DataFrame library, sorting is a global operation that requires an expensive repartition to produce a fully ordered result across all partitions. Sort columns can be specified as column names, expressions, or combinations thereof.
Usage
Use data sorting when you need ordered results for display, top-N queries, report generation, or downstream operations that require sorted input. It is also useful for producing deterministic output ordering for testing and validation purposes.
Theoretical Basis
Data sorting implements a comparison-based global sort across distributed partitions. The general approach is:
1. Sample data across partitions to determine sort key distribution
2. Compute partition boundaries (range boundaries) from the sample
3. Repartition data by range so that each partition contains a contiguous key range
4. Sort each partition locally using a stable comparison-based sort
5. Concatenate sorted partitions to produce the global result
Key properties:
- Stable sort: Equal elements maintain their relative order from the input.
- Multi-column sort: Columns are compared in order; ties in the first column are broken by the second, and so on.
- Null ordering: Nulls can be placed first or last independently for each column. By default, nulls are treated as the greatest value (last for ascending, first for descending).