Principle:Apache Airflow DAG Deployment
| Knowledge Sources | |
|---|---|
| Domains | DAG_Processing, Deployment |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
A continuous process for discovering, parsing, and serializing DAG files into the metadata database for scheduler consumption.
Description
DAG Deployment is the process by which DAG Python files are transformed from source code into serialized representations in the metadata database. The DagFileProcessorManager orchestrates parallel parsing of DAG files using DagFileProcessorProcess workers. Each worker loads a file via DagBag, serializes the resulting DAGs, and persists them to the database. The manager handles file discovery, scheduling of re-parses, and tracking of file statistics and import errors.
Usage
This process runs continuously as the dag-processor component. It automatically detects new, modified, or deleted DAG files in configured bundle directories and updates the metadata database accordingly. No manual intervention is needed for standard deployments.
Theoretical Basis
Parsing Pipeline:
- File Discovery: Scan configured DAG bundle directories for Python files
- Change Detection: Compare file modification timestamps with last parse time
- Parallel Parsing: Distribute files across worker processes (bounded by parallelism setting)
- Serialization: Convert DAG objects to JSON for database storage
- Persistence: Upsert serialized DAGs into metadata database
- Cleanup: Remove stale DAGs that no longer exist in source files
File Processing Scheduling:
# Pseudo-code for file processing priority
for file in files_to_process:
if file.last_parse_time + file_process_interval < now():
schedule_parse(file, priority=file.run_count)