Principle:Eventual Inc Daft Data Aggregation
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Data_Analysis |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Data aggregation is the technique for computing summary statistics over groups of rows in a DataFrame, producing condensed analytical results.
Description
Data aggregation groups rows by one or more key columns and applies aggregate functions (such as sum, mean, count, min, max, list, and concat) to produce summary results. This is the foundation for analytical queries and reporting in Daft. The operation first partitions the data by the specified group-by columns, then computes the requested aggregate expressions within each group. The result is a new DataFrame with one row per unique combination of group-by keys. Daft supports both simple aggregations (single function per column) and complex aggregations (multiple functions, expressions, and aliases).
Usage
Use data aggregation when you need to compute grouped summaries such as totals, averages, counts, or other statistical measures. Common scenarios include sales reporting by region, user activity metrics by time period, and feature engineering for machine learning pipelines.
Theoretical Basis
Data aggregation corresponds to the relational GROUP BY operation combined with aggregate functions. The theoretical model is:
Given a relation R and grouping attributes G = {g1, g2, ..., gn}:
1. Partition R into groups where all tuples share the same values for G
2. For each group, apply aggregate functions F = {f1, f2, ..., fm}
3. Produce one output tuple per group containing G values and F results
In a distributed setting, aggregation is typically performed in two phases:
- Partial aggregation: Each partition computes local aggregates.
- Final aggregation: Partial results are shuffled by group keys and combined into final results.
This two-phase approach minimizes data movement across the network while producing correct results.