airbnb/airflow
Airflow
Airflow is a workflow orchestration platform for authoring, scheduling, and monitoring complex data pipelines as code using Python. It employs a DAG-based task scheduler to manage execution timing and dependencies via directed acyclic graphs, utilizing a distributed task execution engine to run workloads across a cluster of worker nodes.
The platform provides a data pipeline monitor for tracking the health and execution history of programmatic workflows. This includes a web interface for workflow progress visualization and health monitoring to identify and troubleshoot pipeline failures.
The system covers broad capabilities for data pipeline orchestration, scheduled job execution, and distributed task processing. It includes mechanisms for programmatic workflow definition, historical data backfilling, and the sharing of task metadata.
Features
- DAG-Based Dependency Resolution - Uses Directed Acyclic Graphs to determine the exact execution order and dependency mapping of complex workflows.
- Metadata Databases - Provides a centralized relational database to store the state, history, and configuration of all workflows and tasks.
- Data Orchestration Platforms - Serves as a comprehensive platform for authoring, scheduling, and monitoring the entire lifecycle of data processing workflows.
- Data Pipeline Orchestration - Provides a comprehensive system for defining, scheduling, and monitoring complex sequences of data processing tasks.
- Workflow Schedulers - Includes mechanisms to trigger workflows based on defined timetables, cron expressions, or specific external events.
- Distributed Processing - Offloads and distributes heavy computational workloads across a cluster of worker nodes for parallel processing.
- Distributed Task Queues - Implements a distributed task queue to execute workloads across multiple worker nodes via a message broker.
- Distributed Task Workers - Runs sequences of tasks across a cluster of distributed workers based on defined dependencies and schedules.
- Scheduled Job Execution - Automates the regular execution of Python scripts and tasks based on timetables or specific trigger events.
- Scheduling Loops - Employs a continuous loop that monitors the metadata database to trigger tasks based on time and dependency status.
- Task Execution Engines - Features an execution engine that manages background tasks using distributed processing models and persistent worker pools.
- Task Scheduling - Implements a scheduler that calculates task execution order based on Directed Acyclic Graph dependencies.
- Workflow Task Definitions - Enables the definition of task sequences and dependencies using Python code for maintainable and versionable pipelines.
- Parallel Historical Backfilling - Provides capabilities to trigger workflow executions for past date ranges to populate missing or updated historical data.
- Inter-Task Metadata Sharing - Facilitates the sharing of small data packets and metadata between tasks within a single workflow execution.
- Dynamic Task Graphs - Enables the construction and modification of execution paths at runtime using Python code to handle variable workloads.
- Metadata Exchange - Allows the exchange of small state fragments between tasks using key-value pairs stored in the central database.
- Automation Workflow Monitors - Offers a visual interface to track the success, timing, and failure states of automated data pipelines.
- Data Pipeline Monitoring - Provides a web-based monitor for tracking real-time status, throughput, and execution history of data integration jobs.
- Application Health Monitors - Includes a monitoring system to track the operational health and progress of running tasks to identify failures.
- Real-time Pipeline Progress Visualization - Provides a web interface for visualizing real-time execution progress and task status to troubleshoot data pipeline failures.
- Workflow Frameworks - Python-based system for orchestrating complex workflow dependencies.
