Principle:Apache Airflow Executor Dispatch
| Knowledge Sources | |
|---|---|
| Domains | Execution, Distributed_Computing |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
An abstraction layer for dispatching scheduled tasks to execution backends with pluggable executor implementations.
Description
Executor Dispatch defines how the scheduler sends tasks for execution. The executor is the component that actually runs tasks — it receives task instances from the scheduler and manages their execution lifecycle. Airflow supports multiple executor backends: LocalExecutor (multiprocessing), CeleryExecutor (distributed message queue), KubernetesExecutor (pod-per-task), and CeleryKubernetesExecutor (hybrid). The ExecutorLoader handles dynamic loading and configuration of executors.
Usage
Choose an executor based on deployment requirements: LocalExecutor for single-machine deployments, CeleryExecutor for distributed execution with a message broker, KubernetesExecutor for dynamic pod scaling, or CeleryKubernetesExecutor for hybrid workloads.
Theoretical Basis
Executor Interface Contract:
# Pseudo-code for executor dispatch cycle
class Executor:
def execute_async(task_instance, command):
"""Submit task for async execution."""
...
def sync(self):
"""Check status of running tasks and update event_buffer."""
...
def heartbeat(self):
"""Called by scheduler on each loop iteration."""
self.sync()
self.trigger_tasks()
Dispatch Flow:
- Scheduler marks TaskInstance as queued
- Executor picks up queued tasks (bounded by parallelism)
- Executor launches task in execution environment
- Executor monitors completion and reports back via event_buffer
- Scheduler updates TaskInstance state based on executor result