airflow-dag-patterns

# Airflow Dag Patterns Orchestrates intelligent skill selection and execution for airflow dag patterns workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python from airflow import DAG from airflow.operators.python import PythonOperator, BranchPythonOperator from airflow.utils.dates import days_ago from datetime import timedelta def evaluate_data_quality(data_path: str) -> str: """Evaluate incoming data and route to appropriate processing branch.""" if not data_path or not data_path.startswith("s3://"): return "handle_invalid_path" is_partitioned = "partition_date" in data_path is_valid_schema = True # Placeholder for actual schema validation if not is_valid_schema: return "route_to_data_lake" elif is_partitioned: return "process_partitioned" else: return "process_flat" def build_dag_with_dynamic_routing(default_args: dict) -> DAG: dag = DAG( dag_id="airflow_dag_pattern_dynamic_routing", default_args=default_args, schedule_interval="@daily", catchup=False, max_active_runs=1 ) route_task = BranchPythonOperator( task_id="route_data", python_callable=evaluate_data_quality, provide_context=True, dag=dag ) def process_partitioned_task(partition_id: str): print(f"Processing partition: {partition_id}") return {"partition": partition_id, "status": "completed"} process_task = PythonOperator.partial( task_id="process_partitioned", python_callable=process_partitioned_task, dag=dag ).expand(partition_id=["2023-01-01", "2023-01-02", "2023-01-03"]) route_task >> process_task return dag ``` ### Pattern 2: Execution with Fallback ```python from airflow.sensors.base import BaseSensorOperator from airflow.exceptions import AirflowException import time class ResilientS3Sensor(BaseSensorOperator): """Custom sensor with exponential backoff and fallback to alternative source.""" def __init__(self, primary_path: str, fallback_path: str, **kwargs): super().__init__(**kwargs) self.primary_path = primary_path self.fallback_path = fallback_path def poke(self, context): try: if self.check_source(self.primary_path): self.log.info(f"Primary source ready: {self.primary_path}") return True except Exception as e: self.log.warning(f"Primary source check failed: {str(e)}") try: if self.check_source(self.fallback_path): self.log.info(f"Fallback source ready: {self.fallback_path}") return True except Exception as e: self.log.error(f"Fallback source check failed: {str(e)}") return False def check_source(self, path: str) -> bool: time.sleep(0.1) return True def build_dag_with_sensor_fallback(default_args: dict) -> DAG: dag = DAG( dag_id="airflow_dag_pattern_sensor_fallback", default_args=default_args, schedule_interval="@hourly", catchup=False ) wait_for_data = ResilientS3Sensor( task_id="wait_for_data", primary_path="s3://bucket/primary/data", fallback_path="s3://bucket/backup/data", poke_interval=30, timeout=3600, soft_fail=False, dag=dag ) return dag ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios --- --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [Apache Airflow Documentation](<https://airflow.apache.org/docs/>) - [Airflow DAG API Reference](<https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html>) - [Apache Airflow Provider Packages](<https://airflow.apache.org/docs/apache-airflow-providers/>) - [Directed Acyclic Graph (Wikipedia)](<https://en.wikipedia.org/wiki/Directed_acyclic_graph>) - [Apache Software Foundation License](<https://www.apache.org/licenses/>) ## Related Skills | Skill | Purpose | |