Principle:Apache Paimon Distributed Data Operations
| Knowledge Sources | |
|---|---|
| Domains | Data_Lake, Distributed_Computing |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Mechanism for applying parallel transformations, filters, and aggregations on distributed datasets.
Description
Once data is loaded into a Ray Dataset, standard distributed data operations can be applied. These include row-level filtering (filter), element-wise transformations (map), and group-based aggregations (groupby). All operations execute in parallel across Ray workers, leveraging the distributed nature of the dataset. These operations are part of Ray Data's API but are documented here in the context of processing Paimon table data.
The primary operation types are:
- filter() - row-level selection based on a predicate function
- map() - element-wise transformation of individual records
- groupby() - group-based aggregations (sum, count, mean, etc.)
Usage
Use this principle after creating a Ray Dataset from Paimon to perform data transformations. Choose filter() for row selection, map() for transformations, and groupby() for aggregations.
Theoretical Basis
Distributed data operations follow the MapReduce paradigm extended with lazy evaluation. Ray Data implements these as DAG-based transformations that are optimized and executed by the Ray scheduler.
From the perspective of relational algebra:
- Filter operations are selection operators (sigma) that reduce row count
- Map operations are projection/transformation operators (pi) that modify columns
- Groupby operations are aggregation operators (gamma) that collapse groups
Ray Data applies operator fusion to combine consecutive operations (e.g., a filter followed by a map) into a single pass over the data, reducing materialization overhead.