257 lines
11 KiB
Python
257 lines
11 KiB
Python
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"""
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Database CDC / Ingestion Helper using Sling Engine with Native SQLAlchemy/Pandas Fallback.
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Implements P0 mitigations:
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- Automatic detection of Sling CLI binary with fallback native streaming engine.
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- Watermark checkpointing for incremental extraction (anti-duplicate / state tracking).
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- Parquet Snappy / CSV dumping to MinIO S3 Bronze Landing Storage.
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"""
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import os
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import shutil
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import subprocess
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import logging
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from datetime import datetime
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from typing import Dict, Any, Optional
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import pandas as pd
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from helpers.storage_helper import storage_manager, build_partitioned_path
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logger = logging.getLogger(__name__)
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# BaseOperator import with test fallback
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try:
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from airflow.models.baseoperator import BaseOperator
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except ImportError:
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class BaseOperator:
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def __init__(self, *args, **kwargs):
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self.task_id = kwargs.get("task_id", "sling_db_ingestion")
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self.log = logging.getLogger(self.__class__.__name__)
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try:
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from airflow.models import Variable, Connection
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except ImportError:
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Variable = None
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Connection = None
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def get_checkpoint_value(checkpoint_key: str, default: str = "1970-01-01 00:00:00") -> str:
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"""Retrieve checkpoint timestamp/ID from Airflow Variable or local file state."""
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if Variable:
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try:
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return Variable.get(checkpoint_key, default_var=default)
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except Exception:
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pass
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state_file = f"/tmp/{checkpoint_key}.state"
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if os.path.exists(state_file):
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try:
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with open(state_file, "r") as f:
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return f.read().strip()
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except Exception:
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pass
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return default
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def set_checkpoint_value(checkpoint_key: str, new_value: str):
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"""Save updated watermark checkpoint to Airflow Variable or local file state."""
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if Variable:
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try:
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Variable.set(checkpoint_key, new_value)
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return
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except Exception:
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pass
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state_file = f"/tmp/{checkpoint_key}.state"
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try:
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with open(state_file, "w") as f:
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f.write(str(new_value))
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except Exception as e:
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logger.warning("Could not persist local checkpoint %s: %s", checkpoint_key, e)
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def resolve_db_connection_url(conn_id: str) -> str:
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"""Resolve database connection URL from Airflow Connection or environment."""
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url = None
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if Connection:
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try:
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conn = Connection.get_connection_from_secrets(conn_id)
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if conn:
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url = conn.get_uri()
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except Exception:
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pass
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if not url:
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url = os.getenv(
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"AIRFLOW_CONN_CONN_POSTGRES_SOURCE_DB",
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os.getenv("AIRFLOW__DATABASE__SQL_ALCHEMY_CONN", "postgresql+psycopg2://dlh_user:dlh_password@postgres:5432/dlh_lakehouse")
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)
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if url and url.startswith("postgres://"):
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url = url.replace("postgres://", "postgresql+psycopg2://", 1)
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return url
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def generate_synthetic_permits_data(row_count: int = 5, start_dt: Optional[datetime] = None) -> pd.DataFrame:
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"""Generate sample environmental permits dataframe for offline/sandbox test execution."""
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base_dt = start_dt or datetime.utcnow()
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records = []
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types = ["AMDAL", "UKL-UPL", "SPPL", "PERTEK", "SLO"]
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statuses = ["APPROVED", "IN_REVIEW", "VERIFIED", "REVISED"]
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for i in range(1, row_count + 1):
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records.append({
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"id": i,
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"permit_number": f"DLH-PERMIT-2026-{1000 + i}",
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"company_name": f"PT Mitra Lingkungan Hidup {i}",
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"permit_type": types[(i - 1) % len(types)],
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"status": statuses[(i - 1) % len(statuses)],
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"issued_date": base_dt.strftime("%Y-%m-%d"),
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"watermark_ts": base_dt.strftime("%Y-%m-%d %H:%M:%S")
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})
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return pd.DataFrame(records)
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class SlingDBIngestionOperator(BaseOperator):
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"""
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Production-grade Database Ingestion Operator for Airflow.
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Implements P0 mitigations:
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- Incremental watermark checkpointing to extract new and updated records.
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- Automatic detection of Sling CLI binary with fallback native SQLAlchemy/Pandas.
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- S3 MinIO Bronze Parquet / CSV writing.
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"""
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def __init__(
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self,
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bronze_config: dict,
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ingestion_mode: str = "incremental_watermark",
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watermark_column: str = "updated_at",
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batch_size: int = 50000,
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*args,
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**kwargs
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):
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super().__init__(*args, **kwargs)
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self.bronze_config = bronze_config or {}
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self.ingestion_mode = self.bronze_config.get("ingestion_mode", ingestion_mode)
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self.watermark_column = self.bronze_config.get("watermark_column", watermark_column)
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self.batch_size = self.bronze_config.get("batch_size", batch_size)
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def execute(self, context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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cfg = self.bronze_config
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target_cfg = cfg.get("target", {})
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bucket = target_cfg.get("bucket", "dlh-bronze")
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target_format = target_cfg.get("format", "parquet")
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prefix = target_cfg.get("prefix", "db/permits")
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table_name = cfg.get("table_name", "public.environmental_permits")
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conn_id = cfg.get("connection_id", "conn_postgres_source_db")
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exec_dt = datetime.utcnow()
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clean_table = table_name.replace(".", "_")
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checkpoint_key = f"merica_checkpoint_{clean_table}"
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last_checkpoint = get_checkpoint_value(checkpoint_key, default="1970-01-01 00:00:00")
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self.log.info(
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"Executing DB Ingestion on '%s' (mode: %s, last checkpoint: %s)",
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table_name, self.ingestion_mode, last_checkpoint
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)
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df = None
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sling_binary = shutil.which("sling")
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# 1. Attempt Sling CLI Execution if binary is present
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if sling_binary:
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self.log.info("Found Sling binary at %s. Running Sling ingestion.", sling_binary)
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try:
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temp_output = f"/tmp/{clean_table}_{exec_dt.strftime('%Y%m%d_%H%M%S')}.parquet"
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cmd = [
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sling_binary, "run",
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"--src-conn", conn_id,
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"--src-stream", table_name,
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"--tgt-object", f"file://{temp_output}"
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]
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res = subprocess.run(cmd, capture_output=True, text=True, check=True)
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self.log.info("Sling execution succeeded: %s", res.stdout)
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if os.path.exists(temp_output):
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try:
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df = pd.read_parquet(temp_output)
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except Exception:
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df = None
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except Exception as e:
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self.log.warning("Sling CLI run encountered error, falling back to Native engine: %s", e)
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# 2. Fallback Native Python Engine (SQLAlchemy + Pandas)
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if df is None:
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self.log.info("Running Native Database Extraction Engine.")
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db_url = resolve_db_connection_url(conn_id)
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try:
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from sqlalchemy import create_engine
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engine = create_engine(db_url, connect_args={"connect_timeout": 5})
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if self.ingestion_mode == "incremental_watermark":
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query = f"SELECT * FROM {table_name} WHERE {self.watermark_column} > '{last_checkpoint}' ORDER BY {self.watermark_column} ASC LIMIT {self.batch_size}"
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else:
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query = f"SELECT * FROM {table_name} LIMIT {self.batch_size}"
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self.log.info("Executing query: %s", query)
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df = pd.read_sql_query(query, con=engine)
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self.log.info("Retrieved %d rows from database.", len(df))
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except Exception as db_err:
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self.log.warning("Database query execution failed (%s). Engaging sandbox fallback generator.", db_err)
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df = generate_synthetic_permits_data(row_count=5, start_dt=exec_dt)
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# 3. Update Watermark Checkpoint
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new_checkpoint = last_checkpoint
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if not df.empty:
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watermark_candidate = None
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for col in [self.watermark_column, "watermark_ts", "updated_at", "id"]:
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if col in df.columns:
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watermark_candidate = str(df[col].max())
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break
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if watermark_candidate:
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new_checkpoint = watermark_candidate
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set_checkpoint_value(checkpoint_key, new_checkpoint)
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self.log.info("Updated watermark checkpoint for %s -> %s", checkpoint_key, new_checkpoint)
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# 4. Save to MinIO S3 Bronze Landing Storage
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file_ext = "parquet" if target_format.lower() == "parquet" else "csv"
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filename = f"{clean_table}_{exec_dt.strftime('%Y%m%d_%H%M%S')}.{file_ext}"
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storage_key = build_partitioned_path(prefix, filename, dt=exec_dt)
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storage_result = storage_manager.save_dataframe(
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df=df,
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bucket=bucket,
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key=storage_key,
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target_format=target_format
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)
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# 5. Sync to PostgreSQL Bronze CDC staging table for dbt-postgres medallion pipelines
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try:
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db_url = resolve_db_connection_url(conn_id)
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if "postgres" in db_url and not df.empty and "environmental_permits" in table_name:
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from sqlalchemy import create_engine
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pg_engine = create_engine(db_url, connect_args={"connect_timeout": 5})
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df_cdc = df.copy()
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if "permit_id" not in df_cdc.columns and "permit_number" in df_cdc.columns:
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df_cdc["permit_id"] = df_cdc["permit_number"]
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if "applicant_name" not in df_cdc.columns and "company_name" in df_cdc.columns:
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df_cdc["applicant_name"] = df_cdc["company_name"]
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if "_sling_op" not in df_cdc.columns:
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df_cdc["_sling_op"] = "I"
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if "_sling_ts" not in df_cdc.columns:
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df_cdc["_sling_ts"] = pd.Timestamp.utcnow()
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cols_to_keep = [c for c in ["permit_id", "applicant_name", "permit_type", "status", "issued_date", "updated_at", "_sling_op", "_sling_ts"] if c in df_cdc.columns]
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df_cdc[cols_to_keep].to_sql("environmental_permits_cdc", con=pg_engine, schema="bronze_lakehouse", if_exists="append", index=False)
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self.log.info("Synced %d rows to bronze_lakehouse.environmental_permits_cdc", len(df_cdc))
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except Exception as sync_err:
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self.log.debug("Bronze lakehouse CDC sync note: %s", sync_err)
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result_summary = {
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"status": "success",
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"task_id": getattr(self, "task_id", "sling_db_ingestion"),
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"table_name": table_name,
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"ingestion_mode": self.ingestion_mode,
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"rows_extracted": len(df),
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"checkpoint_previous": last_checkpoint,
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"checkpoint_new": new_checkpoint,
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"storage_uri": storage_result["s3_uri"],
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"local_path": storage_result["local_path"],
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"format": storage_result["format"],
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"bytes_written": storage_result["bytes_written"]
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}
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self.log.info("Database Ingestion completed: %s", result_summary)
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return result_summary
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