Principle:Apache Paimon Ray Cluster Initialization
| Knowledge Sources | |
|---|---|
| Domains | Data_Lake, Distributed_Computing |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Mechanism for initializing a distributed computing runtime to enable parallel data processing across multiple nodes.
Description
Ray cluster initialization establishes the distributed execution environment required for parallel data processing. ray.init() starts or connects to a Ray cluster, configuring available compute resources (CPUs, GPUs, memory). The initialization is idempotent with ignore_reinit_error=True, allowing safe calls in scripts that may re-execute. This is a prerequisite for all Ray-based Paimon operations including distributed reads and writes.
Usage
Use this principle when distributed processing is required for large datasets that exceed single-machine capacity, or when parallel execution can speed up data transformations.
Theoretical Basis
Distributed computing follows the master-worker paradigm. Ray uses a shared-memory object store and task scheduler to distribute work across available workers. The initialization step configures the resource pool available for task scheduling.
The Ray runtime consists of:
- A head node that runs the Global Control Store (GCS) and scheduler
- Worker nodes that execute tasks and store objects in shared memory
- An object store (Plasma) for zero-copy data sharing between tasks
When ray.init() is called, the driver process either starts a new local cluster or connects to an existing remote cluster. The resource configuration (CPUs, GPUs, memory) determines how many concurrent tasks can be scheduled on each node.