External Integrations

# PostgreSQL integration
from luigi.contrib.postgres import PostgresTarget, CopyToTable, PostgresQuery

class PostgresTarget:
    """Target for PostgreSQL database tables."""
    
    def __init__(self, host: str, database: str, user: str, password: str,
                 table: str, update_id: str, port: int = 5432):
        """
        Initialize PostgreSQL target.
        
        Args:
            host: Database host
            database: Database name
            user: Username
            password: Password
            table: Table name
            update_id: Unique identifier for this update
            port: Database port (default 5432)
        """
    
    def exists(self) -> bool:
        """Check if the target exists in database."""

class CopyToTable(Task):
    """Task that copies data to PostgreSQL table."""
    
    host: str
    database: str
    user: str
    password: str
    table: str
    
    def copy(self, file_object):
        """Copy data from file object to table."""
    
    def output(self):
        """Return PostgresTarget for the table."""

# MySQL integration  
from luigi.contrib.mysqldb import MySqlTarget

class MySqlTarget:
    """Target for MySQL database tables."""
    
    def __init__(self, host: str, database: str, user: str, password: str,
                 table: str, update_id: str, port: int = 3306):
        """Initialize MySQL target."""

# MongoDB integration
from luigi.contrib.mongodb import MongoTarget

class MongoTarget:
    """Target for MongoDB collections."""
    
    def __init__(self, host: str, port: int, database: str, collection: str,
                 mongo_client=None):
        """Initialize MongoDB target."""

# Amazon S3 integration
from luigi.contrib.s3 import S3Target, S3Client, S3FlagTarget, S3PathTask

class S3Target:
    """Target for Amazon S3 objects."""
    
    def __init__(self, path: str, format=None, client=None, 
                 is_tmp: bool = False):
        """
        Initialize S3 target.
        
        Args:
            path: S3 path (s3://bucket/key)
            format: File format handler
            client: S3 client instance
            is_tmp: Whether this is a temporary file
        """
    
    def exists(self) -> bool:
        """Check if S3 object exists."""
    
    def open(self, mode: str = 'r'):
        """Open S3 object for reading/writing."""
    
    def remove(self):
        """Delete S3 object."""

class S3Client:
    """Client for S3 operations."""
    
    def exists(self, path: str) -> bool:
        """Check if S3 object exists."""
    
    def put(self, local_path: str, destination_s3_path: str):
        """Upload local file to S3."""
    
    def get(self, s3_path: str, local_path: str):
        """Download S3 object to local file."""
    
    def list(self, path: str) -> list:
        """List S3 objects with path prefix."""

# Google Cloud Storage integration
from luigi.contrib.gcs import GCSTarget, GCSClient

class GCSTarget:
    """Target for Google Cloud Storage objects."""
    
    def __init__(self, path: str, format=None, client=None):
        """Initialize GCS target."""

class GCSClient:
    """Client for GCS operations."""
    
    def exists(self, path: str) -> bool:
        """Check if GCS object exists."""
    
    def put(self, local_path: str, destination_gcs_path: str):
        """Upload local file to GCS."""

# Azure Blob Storage integration  
from luigi.contrib.azureblob import AzureBlobTarget

class AzureBlobTarget:
    """Target for Azure Blob Storage."""
    
    def __init__(self, container: str, blob: str, 
                 account_name: str, account_key: str):
        """Initialize Azure Blob target."""

# HDFS integration
from luigi.contrib.hdfs import HdfsTarget, HdfsClient

class HdfsTarget:
    """Target for HDFS files."""
    
    def __init__(self, path: str, format=None, client=None, is_tmp: bool = False):
        """Initialize HDFS target."""
    
    def exists(self) -> bool:
        """Check if HDFS file exists."""
    
    def open(self, mode: str = 'r'):
        """Open HDFS file."""

class HdfsClient:
    """Client for HDFS operations."""
    
    def exists(self, path: str) -> bool:
        """Check if HDFS path exists."""
    
    def put(self, local_path: str, destination_hdfs_path: str):
        """Upload local file to HDFS."""
    
    def get(self, hdfs_path: str, local_path: str):
        """Download HDFS file to local path."""

# Apache Spark integration
from luigi.contrib.spark import SparkSubmitTask, PySparkTask

class SparkSubmitTask(Task):
    """Task for submitting Spark applications."""
    
    app: str  # Spark application path
    master: str = "local[*]"  # Spark master URL
    deploy_mode: str = "client"  # Deploy mode
    executor_memory: str = "1g"  # Executor memory
    driver_memory: str = "1g"  # Driver memory
    
    def app_options(self) -> list:
        """Return application-specific options."""
    
    def run(self):
        """Submit Spark application."""

# Google BigQuery integration
from luigi.contrib.bigquery import BigQueryTarget, BigQueryLoadTask

class BigQueryTarget:
    """Target for BigQuery tables."""
    
    def __init__(self, project_id: str, dataset_id: str, table_id: str,
                 client=None):
        """Initialize BigQuery target."""
    
    def exists(self) -> bool:
        """Check if BigQuery table exists."""

class BigQueryLoadTask(Task):
    """Task for loading data into BigQuery."""
    
    project_id: str
    dataset_id: str  
    table_id: str
    schema: list  # Table schema
    
    def run(self):
        """Load data into BigQuery table."""

# SLURM integration
from luigi.contrib.slurm import SlurmTask

class SlurmTask(Task):
    """Base class for SLURM job submission."""
    
    shared_tmp_dir: str  # Shared temporary directory
    job_name: str  # SLURM job name
    n_cpu: int = 1  # Number of CPUs
    mem: str = "1GB"  # Memory requirement
    time: str = "1:00:00"  # Time limit
    partition: str  # SLURM partition
    
    def work(self):
        """Define work to be done in SLURM job."""
    
    def run(self):
        """Submit job to SLURM scheduler."""

# LSF integration  
from luigi.contrib.lsf import LSFTask

class LSFTask(Task):
    """Base class for LSF job submission."""
    
    shared_tmp_dir: str
    job_name: str
    n_cpu: int = 1
    resource: str  # LSF resource requirements
    queue: str  # LSF queue
    
    def work(self):
        """Define work for LSF job."""

# Sun Grid Engine integration
from luigi.contrib.sge import SGETask

class SGETask(Task):
    """Base class for SGE job submission."""
    
    shared_tmp_dir: str
    n_cpu: int = 1
    run_locally: bool = False
    
    def work(self):
        """Define work for SGE job."""

# Docker integration
from luigi.contrib.docker_runner import DockerTask

class DockerTask(Task):
    """Task for running commands in Docker containers."""
    
    image: str  # Docker image name
    command: str  # Command to run
    container_options: dict = {}  # Docker container options
    
    def run(self):
        """Run command in Docker container."""

# Kubernetes integration
from luigi.contrib.kubernetes import KubernetesJobTask

class KubernetesJobTask(Task):
    """Task for running Kubernetes jobs."""
    
    name: str  # Job name
    image: str  # Container image
    command: list  # Command to run
    
    def spec_schema(self) -> dict:
        """Return Kubernetes job spec."""

# AWS Batch integration
from luigi.contrib.batch import BatchTask

class BatchTask(Task):
    """Task for AWS Batch job execution."""
    
    job_name: str
    job_queue: str
    job_definition: str
    
    def run(self):
        """Submit job to AWS Batch."""

# Google Cloud Dataproc integration
from luigi.contrib.dataproc import DataprocJobTask

class DataprocJobTask(Task):
    """Base class for Google Cloud Dataproc jobs."""
    
    cluster_name: str
    project_id: str
    region: str = "global"
    
    def run(self):
        """Submit job to Dataproc cluster."""

# Datadog metrics integration
from luigi.contrib.datadog_metric import DatadogMetric

class DatadogMetric:
    """Send metrics to Datadog."""
    
    def __init__(self, metric_name: str, value: float, tags: list = None):
        """
        Initialize Datadog metric.
        
        Args:
            metric_name: Metric name
            value: Metric value
            tags: List of tags
        """
    
    def send(self):
        """Send metric to Datadog."""

# Prometheus metrics integration  
from luigi.contrib.prometheus_metric import PrometheusMetric

class PrometheusMetric:
    """Send metrics to Prometheus pushgateway."""
    
    def __init__(self, metric_name: str, value: float, labels: dict = None):
        """Initialize Prometheus metric."""
    
    def push(self):
        """Push metric to Prometheus."""

import luigi
from luigi.contrib.postgres import PostgresTarget, CopyToTable

class LoadDataToPostgres(CopyToTable):
    """Load CSV data into PostgreSQL table."""
    
    host = "localhost"
    database = "mydb"
    user = "postgres"
    password = "password"
    table = "sales_data"
    
    def requires(self):
        return ProcessSalesData()  # Task that generates CSV
    
    def copy(self, file_object):
        """Custom copy logic if needed."""
        # Default implementation handles CSV copying
        return super().copy(file_object)

class QueryPostgresData(luigi.Task):
    """Query data from PostgreSQL."""
    
    def requires(self):
        return LoadDataToPostgres()
    
    def output(self):
        return luigi.LocalTarget("query_results.txt")
    
    def run(self):
        # Check that data was loaded
        target = PostgresTarget(
            host="localhost",
            database="mydb", 
            user="postgres",
            password="password",
            table="sales_data",
            update_id="loaded"
        )
        
        if target.exists():
            with self.output().open('w') as f:
                f.write("Data successfully loaded to PostgreSQL")

import luigi
from luigi.contrib.s3 import S3Target, S3Client

class ProcessS3Data(luigi.Task):
    """Process data stored in S3."""
    
    bucket = luigi.Parameter()
    key = luigi.Parameter()
    
    def output(self):
        return S3Target(f"s3://{self.bucket}/processed/{self.key}")
    
    def run(self):
        # Read from S3 input
        input_target = S3Target(f"s3://{self.bucket}/raw/{self.key}")
        
        with input_target.open('r') as input_file:
            data = input_file.read()
        
        # Process data
        processed_data = data.upper()
        
        # Write to S3 output
        with self.output().open('w') as output_file:
            output_file.write(processed_data)

class S3DataPipeline(luigi.Task):
    """Pipeline that processes multiple S3 files."""
    
    bucket = luigi.Parameter()
    
    def run(self):
        # List files in S3 bucket
        client = S3Client()
        files = client.list(f"s3://{self.bucket}/raw/")
        
        # Process each file
        tasks = []
        for file_path in files:
            key = file_path.split('/')[-1]  # Extract filename
            tasks.append(ProcessS3Data(bucket=self.bucket, key=key))
        
        # Build all processing tasks
        yield tasks

import luigi
from luigi.contrib.spark import SparkSubmitTask

class SparkDataProcessing(SparkSubmitTask):
    """Process large dataset using Spark."""
    
    input_path = luigi.Parameter()
    output_path = luigi.Parameter()
    
    # Spark configuration
    app = "spark_jobs/data_processing.py"
    master = "spark://localhost:7077"
    executor_memory = "4g"
    driver_memory = "2g"
    
    def app_options(self):
        """Pass parameters to Spark application."""
        return [
            "--input", self.input_path,
            "--output", self.output_path
        ]
    
    def output(self):
        return luigi.LocalTarget(f"{self.output_path}/_SUCCESS")

# The Spark application (data_processing.py) would contain:
# from pyspark.sql import SparkSession
# import argparse
# 
# parser = argparse.ArgumentParser()
# parser.add_argument("--input")
# parser.add_argument("--output")
# args = parser.parse_args()
# 
# spark = SparkSession.builder.appName("DataProcessing").getOrCreate()
# df = spark.read.csv(args.input, header=True)
# processed_df = df.filter(df.amount > 100)
# processed_df.write.csv(args.output, header=True)

import luigi
from luigi.contrib.slurm import SlurmTask

class HeavyComputation(SlurmTask):
    """Run computationally intensive task on SLURM cluster."""
    
    dataset = luigi.Parameter()
    
    # SLURM job configuration
    shared_tmp_dir = "/shared/tmp"
    job_name = "heavy_computation"
    n_cpu = 16
    mem = "32GB"
    time = "4:00:00"
    partition = "compute"
    
    def work(self):
        """Define work to be executed on cluster."""
        # This runs on the SLURM compute node
        import heavy_computation_module
        
        result = heavy_computation_module.process_dataset(self.dataset)
        
        # Write result to shared filesystem
        with open(f"{self.shared_tmp_dir}/result_{self.dataset}.txt", 'w') as f:
            f.write(str(result))
    
    def output(self):
        return luigi.LocalTarget(f"{self.shared_tmp_dir}/result_{self.dataset}.txt")

import luigi
from luigi.contrib.s3 import S3Target
from luigi.contrib.postgres import CopyToTable
from luigi.contrib.spark import SparkSubmitTask

class DataIngestionPipeline(luigi.WrapperTask):
    """Complete data pipeline using multiple integrations."""
    
    date = luigi.DateParameter()
    
    def requires(self):
        return [
            # 1. Download data from S3
            DownloadS3Data(date=self.date),
            
            # 2. Process with Spark
            SparkProcessing(date=self.date),
            
            # 3. Load to PostgreSQL
            LoadToDatabase(date=self.date),
            
            # 4. Send metrics to monitoring
            SendMetrics(date=self.date)
        ]

class DownloadS3Data(luigi.Task):
    date = luigi.DateParameter()
    
    def output(self):
        return luigi.LocalTarget(f"data/raw_{self.date}.csv")
    
    def run(self):
        s3_target = S3Target(f"s3://data-bucket/raw/{self.date}.csv")
        with s3_target.open('r') as s3_file, self.output().open('w') as local_file:
            local_file.write(s3_file.read())

class SparkProcessing(SparkSubmitTask):
    date = luigi.DateParameter()
    
    app = "spark_jobs/daily_processing.py"
    
    def requires(self):
        return DownloadS3Data(date=self.date)
    
    def app_options(self):
        return ["--date", str(self.date)]

class LoadToDatabase(CopyToTable):
    date = luigi.DateParameter()
    
    host = "localhost"
    database = "analytics"
    user = "luigi"
    password = "password"
    table = "daily_metrics"
    
    def requires(self):
        return SparkProcessing(date=self.date)