Principle:Eventual Inc Daft Distributed Scaling Ray
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Distributed_Computing |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Technique for scaling DataFrame operations across a distributed Ray cluster.
Description
Ray integration enables distributing Daft operations across multiple machines or processes. The Ray runner handles task scheduling, data partitioning, and resource management transparently, allowing the same Daft code to run on a single machine or a multi-node cluster without modification. This is essential for processing datasets that exceed single-machine memory or CPU capacity, and for leveraging GPU resources distributed across multiple nodes.
Usage
Use Ray-based distributed scaling when data processing exceeds single-machine capacity or when GPU resources across multiple nodes are needed. This is appropriate for large-scale ETL pipelines, distributed model inference, and any workload that benefits from horizontal scaling.
Theoretical Basis
Distributed task-parallel execution using Ray's actor and task model:
Execution Model:
1. DataFrame operations build a logical plan (same as single-machine)
2. Physical plan is translated into Ray tasks
3. Each partition is processed as an independent Ray task
4. Ray scheduler handles:
- Task placement across nodes
- Data locality optimization
- Resource management (CPU, GPU, memory)
- Fault tolerance and retry
Data Flow:
Driver (plan) -> Ray Scheduler -> Worker Nodes
|-> Task 1 (partition 1)
|-> Task 2 (partition 2)
|-> Task N (partition N)
-> Collect results
Scaling Properties:
- Horizontal: add more nodes for more parallelism
- Memory: aggregate memory across cluster
- GPU: distribute GPU workloads across nodes
The Ray runner is a drop-in replacement for the native runner, requiring only a configuration change to switch between local and distributed execution.