Hooks and Monitoring

class BeamHook(BaseHook):
    def __init__(self, runner: str) -> None:
        """
        Initialize Beam hook.
        
        Parameters:
        - runner (str): Runner type for pipeline execution
        """

    def start_python_pipeline(
        self,
        variables: dict,
        py_file: str,
        py_options: list[str],
        py_interpreter: str = "python3",
        py_requirements: list[str] | None = None,
        py_system_site_packages: bool = False,
        process_line_callback: Callable[[str], None] | None = None,
        is_dataflow_job_id_exist_callback: Callable[[], bool] | None = None,
    ):
        """
        Start Apache Beam python pipeline.
        
        Parameters:
        - variables (dict): Pipeline execution variables and options
        - py_file (str): Path to Python pipeline file
        - py_options (list[str]): Additional Python command-line options
        - py_interpreter (str): Python interpreter version
        - py_requirements (list[str]): Python packages for virtual environment
        - py_system_site_packages (bool): Include system packages in venv
        - process_line_callback (Callable): Optional callback for output processing
        - is_dataflow_job_id_exist_callback (Callable): Optional callback for job ID detection
        """

    def start_java_pipeline(
        self,
        variables: dict,
        jar: str,
        job_class: str | None = None,
        process_line_callback: Callable[[str], None] | None = None,
        is_dataflow_job_id_exist_callback: Callable[[], bool] | None = None,
    ) -> None:
        """
        Start Apache Beam Java pipeline.
        
        Parameters:
        - variables (dict): Pipeline execution variables and options
        - jar (str): Path to JAR file containing pipeline
        - job_class (str): Java class name for pipeline execution
        - process_line_callback (Callable): Optional callback for output processing  
        - is_dataflow_job_id_exist_callback (Callable): Optional callback for job ID detection
        """

    def start_go_pipeline(
        self,
        variables: dict,
        go_file: str,
        process_line_callback: Callable[[str], None] | None = None,
        should_init_module: bool = False,
    ) -> None:
        """
        Start Apache Beam Go pipeline with source file.
        
        Parameters:
        - variables (dict): Pipeline execution variables and options
        - go_file (str): Path to Go source file
        - process_line_callback (Callable): Optional callback for output processing
        - should_init_module (bool): Initialize Go module and dependencies
        """

    def start_go_pipeline_with_binary(
        self,
        variables: dict,
        launcher_binary: str,
        worker_binary: str,
        process_line_callback: Callable[[str], None] | None = None,
    ) -> None:
        """
        Start Apache Beam Go pipeline with pre-compiled binary.
        
        Parameters:
        - variables (dict): Pipeline execution variables and options
        - launcher_binary (str): Path to launcher binary
        - worker_binary (str): Path to worker binary
        - process_line_callback (Callable): Optional callback for output processing
        """

class BeamAsyncHook(BeamHook):
    def __init__(self, runner: str) -> None:
        """
        Initialize asynchronous Beam hook.
        
        Parameters:
        - runner (str): Runner type for pipeline execution
        """

    async def start_python_pipeline_async(
        self,
        variables: dict,
        py_file: str,
        py_options: list[str] | None = None,
        py_interpreter: str = "python3",
        py_requirements: list[str] | None = None,
        py_system_site_packages: bool = False,
        process_line_callback: Callable[[str], None] | None = None,
    ):
        """
        Start Apache Beam python pipeline asynchronously.
        
        Parameters:
        - variables (dict): Pipeline execution variables and options
        - py_file (str): Path to Python pipeline file
        - py_options (list[str]): Additional Python command-line options
        - py_interpreter (str): Python interpreter version
        - py_requirements (list[str]): Python packages for virtual environment
        - py_system_site_packages (bool): Include system packages in venv
        - process_line_callback (Callable): Optional callback for output processing
        
        Returns:
        - int: Pipeline execution return code
        """

    async def start_java_pipeline_async(
        self,
        variables: dict,
        jar: str,
        job_class: str | None = None,
        process_line_callback: Callable[[str], None] | None = None,
    ) -> int:
        """
        Start Apache Beam Java pipeline asynchronously.
        
        Parameters:
        - variables (dict): Pipeline execution variables and options
        - jar (str): Path to JAR file containing pipeline
        - job_class (str): Java class name for pipeline execution
        - process_line_callback (Callable): Optional callback for output processing
        
        Returns:
        - int: Pipeline execution return code
        """

    async def start_pipeline_async(
        self,
        variables: dict,
        command_prefix: list[str],
        working_directory: str | None = None,
        process_line_callback: Callable[[str], None] | None = None,
    ) -> int:
        """
        Start Apache Beam pipeline with custom command asynchronously.
        
        Parameters:
        - variables (dict): Pipeline execution variables and options
        - command_prefix (list[str]): Command prefix for pipeline execution
        - working_directory (str): Directory for command execution
        - process_line_callback (Callable): Optional callback for output processing
        
        Returns:
        - int: Pipeline execution return code
        """

class BeamRunnerType:
    """Helper class for listing available runner types."""
    DataflowRunner = "DataflowRunner"
    DirectRunner = "DirectRunner" 
    SparkRunner = "SparkRunner"
    FlinkRunner = "FlinkRunner"
    SamzaRunner = "SamzaRunner"
    NemoRunner = "NemoRunner"
    JetRunner = "JetRunner"
    Twister2Runner = "Twister2Runner"

def beam_options_to_args(options: dict) -> list[str]:
    """
    Convert pipeline options dictionary to command line arguments.
    
    Parameters:
    - options (dict): Dictionary with pipeline options
    
    Returns:
    - list[str]: List of formatted command line arguments
    """

def run_beam_command(
    cmd: list[str],
    log: logging.Logger,
    process_line_callback: Callable[[str], None] | None = None,
    working_directory: str | None = None,
    is_dataflow_job_id_exist_callback: Callable[[], bool] | None = None,
) -> None:
    """
    Run pipeline command in subprocess with monitoring.
    
    Parameters:
    - cmd (list[str]): Command parts to execute
    - log (logging.Logger): Logger for output
    - process_line_callback (Callable): Optional output processor
    - working_directory (str): Execution directory
    - is_dataflow_job_id_exist_callback (Callable): Optional job ID detector
    """

from airflow.providers.apache.beam.hooks.beam import BeamHook, BeamRunnerType

# Initialize hook
beam_hook = BeamHook(runner=BeamRunnerType.DirectRunner)

# Execute Python pipeline
beam_hook.start_python_pipeline(
    variables={
        'output': '/tmp/beam_output',
        'temp_location': '/tmp/beam_temp',
    },
    py_file='/path/to/pipeline.py',
    py_options=['-u'],
    py_interpreter='python3',
)

def log_processor(line: str) -> None:
    """Custom processor for pipeline output."""
    if 'ERROR' in line:
        logger.error(f"Pipeline error: {line}")
    elif 'INFO' in line:
        logger.info(f"Pipeline info: {line}")

def job_id_detector() -> bool:
    """Check if Dataflow job ID has been extracted."""
    return hasattr(beam_hook, 'dataflow_job_id') and beam_hook.dataflow_job_id

beam_hook.start_python_pipeline(
    variables=pipeline_options,
    py_file='gs://bucket/pipeline.py',
    process_line_callback=log_processor,
    is_dataflow_job_id_exist_callback=job_id_detector,
)

import asyncio
from airflow.providers.apache.beam.hooks.beam import BeamAsyncHook

async def run_async_pipeline():
    """Execute pipeline asynchronously."""
    async_hook = BeamAsyncHook(runner='DataflowRunner')
    
    return_code = await async_hook.start_python_pipeline_async(
        variables={
            'project': 'my-gcp-project',
            'region': 'us-central1',
            'temp_location': 'gs://my-bucket/temp',
        },
        py_file='gs://my-bucket/pipeline.py',
        py_requirements=['apache-beam[gcp]>=2.60.0'],
    )
    
    if return_code == 0:
        print("Pipeline completed successfully")
    else:
        print(f"Pipeline failed with return code: {return_code}")

# Run the async pipeline
asyncio.run(run_async_pipeline())

# Custom Python environment with specific packages
beam_hook.start_python_pipeline(
    variables=pipeline_options,
    py_file='/path/to/pipeline.py',
    py_requirements=[
        'apache-beam[gcp]==2.60.0',
        'pandas==2.1.0',
        'numpy==1.24.0',
        'google-cloud-bigquery==3.11.0',
    ],
    py_system_site_packages=False,  # Isolated environment
    py_interpreter='python3.10',
)

beam_hook.start_java_pipeline(
    variables={
        'runner': 'DataflowRunner',
        'project': 'my-gcp-project',
        'region': 'us-central1',
        'tempLocation': 'gs://my-bucket/temp',
    },
    jar='/path/to/pipeline.jar',
    job_class='com.company.DataProcessingPipeline',
)

from airflow.providers.apache.beam.hooks.beam import beam_options_to_args

options = {
    'runner': 'DataflowRunner',
    'project': 'my-project',
    'streaming': True,  # Boolean flag
    'labels': {'env': 'prod', 'team': 'data'},  # Dictionary
    'experiments': ['enable_google_cloud_profiler', 'enable_streaming_engine'],  # List
    'numWorkers': 4,  # Numeric value
    'skipValidation': False,  # False boolean (skipped)
    'tempLocation': None,  # None value (skipped)
}

args = beam_options_to_args(options)
# Results in: ['--runner=DataflowRunner', '--project=my-project', '--streaming', 
#              '--labels={"env":"prod","team":"data"}', 
#              '--experiments=enable_google_cloud_profiler', '--experiments=enable_streaming_engine',
#              '--numWorkers=4']

def comprehensive_monitor(line: str) -> None:
    """Comprehensive pipeline output monitoring."""
    import re
    
    # Extract job ID from Dataflow output
    job_id_match = re.search(r'Submitted job: ([a-zA-Z0-9\-]+)', line)
    if job_id_match:
        job_id = job_id_match.group(1)
        print(f"Extracted Dataflow job ID: {job_id}")
    
    # Monitor for errors
    if any(keyword in line.lower() for keyword in ['error', 'exception', 'failed']):
        logger.error(f"Pipeline error detected: {line}")
    
    # Track progress indicators
    if 'Processing bundle' in line:
        logger.info(f"Pipeline progress: {line}")

beam_hook.start_python_pipeline(
    variables=pipeline_options,
    py_file='pipeline.py',
    process_line_callback=comprehensive_monitor,
)

from airflow.exceptions import AirflowException

try:
    beam_hook.start_python_pipeline(
        variables=pipeline_options,
        py_file='pipeline.py',
    )
except AirflowException as e:
    if "Apache Beam process failed" in str(e):
        logger.error(f"Beam pipeline execution failed: {e}")
        # Handle pipeline failure
    else:
        logger.error(f"Hook error: {e}")
        # Handle other errors