Principle:Apache Airflow Task Execution TEI
| Knowledge Sources | |
|---|---|
| Domains | Execution, Architecture |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
A decoupled task execution architecture where tasks run in isolated environments communicating with the Airflow core via a defined API protocol.
Description
The Task Execution Interface (TEI) is an Airflow 3.0 architectural change that decouples task execution from the core Airflow process. Tasks execute in isolated environments (subprocesses, containers, or pods) using the Task SDK, which communicates with the Airflow core via an HTTP-based Execution API. This design enables multi-language support (Python SDK, Go SDK), improved isolation, and better resource management. The executor sends an ExecuteTask workload to the isolated environment, which uses the Task SDK to run the task and report results.
Usage
TEI is the default execution model in Airflow 3.x. It applies to all task execution regardless of executor type. Understanding TEI is important for custom operator development and troubleshooting task execution issues.
Theoretical Basis
TEI Architecture:
- Scheduler creates ExecuteTask workload with task details and JWT token
- Executor dispatches workload to execution environment
- Task SDK loads the DAG file and locates the task
- Task SDK executes the task with supervision
- Results, XCom values, and state updates are communicated via Execution API
- Executor collects completion status
Key Properties:
- Isolation: Tasks run in separate processes/containers
- Authentication: JWT tokens authenticate task-to-API communication
- Multi-language: Protocol supports Python and Go SDKs
- Supervision: Task SDK monitors and controls task execution