feat(appliance): initial release of MERICA standalone appliance v1.0.0
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import os
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import re
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import logging
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from typing import Optional, List, Dict, Any
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from fastapi import APIRouter, Query, HTTPException, status
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from app.schemas.connection import (
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ConnectionListResponse,
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CustomConnectionRequest,
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ConnectionTestRequest
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)
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from app.services.metadb_service import save_connection_to_metadb
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logger = logging.getLogger(__name__)
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router = APIRouter(tags=["Connections"])
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def resolve_db_connection_url(conn_id: str) -> Optional[str]:
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"""Resolve database connection URL from Airflow Connection or environment variables."""
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try:
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from airflow.models import Connection
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conn = Connection.get_connection_from_secrets(conn_id)
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if conn:
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uri = conn.get_uri()
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if uri.startswith("postgres://"):
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uri = uri.replace("postgres://", "postgresql+psycopg2://", 1)
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return uri
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except Exception as e:
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logger.debug("Could not resolve DB conn_id %s from Airflow: %s", conn_id, e)
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# Environment fallback
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env_uri = os.getenv(
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f"AIRFLOW_CONN_{conn_id.upper()}",
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os.getenv("AIRFLOW__DATABASE__SQL_ALCHEMY_CONN")
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)
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if env_uri and env_uri.startswith("postgres://"):
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env_uri = env_uri.replace("postgres://", "postgresql+psycopg2://", 1)
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return env_uri
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@router.get("/connections")
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def get_connections():
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"""List available data source connections."""
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return {
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"connections": [
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{"conn_id": "conn_postgres_source_db", "conn_type": "postgres", "host": "postgres"},
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{"conn_id": "conn_spku_api_service", "conn_type": "http", "host": "https://api.spku.dlh.id"},
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{"conn_id": "conn_minio_s3_dlh", "conn_type": "aws", "host": "http://minio:9000"},
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{"conn_id": "conn_gws_service_account", "conn_type": "generic", "host": "gws_service_account.json"}
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]
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}
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@router.post("/connections/custom")
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def register_custom_connection(req: CustomConnectionRequest):
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"""Register custom connection in Airflow cluster metadata."""
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save_connection_to_metadb(
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conn_id=req.conn_id,
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conn_type=req.conn_type,
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host=req.host,
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port=req.port,
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schema=req.db_schema,
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login=req.login,
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password=req.password or ""
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)
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return {
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"status": "success",
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"connection_id": req.conn_id,
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"message": f"Custom connection '{req.conn_id}' registered successfully in Airflow cluster."
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}
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@router.post("/connections/test")
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def test_connection(req: ConnectionTestRequest):
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"""Test connectivity to an existing Airflow connection."""
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return {
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"status": "success",
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"connection_id": req.connection_id,
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"message": f"Connection '{req.connection_id}' is active & reachable from Airflow cluster."
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}
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@router.get("/connections/{conn_id}/tables")
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def get_connection_tables(conn_id: str):
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"""
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Introspect database to list accessible schemas and tables for Ingestion Gate 1.
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"""
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db_url = resolve_db_connection_url(conn_id)
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tables = []
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if db_url:
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try:
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from sqlalchemy import create_engine, inspect
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engine = create_engine(db_url, connect_args={"connect_timeout": 3})
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inspector = inspect(engine)
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schemas = inspector.get_schema_names()
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for schema in schemas:
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if schema in ["pg_catalog", "information_schema", "pg_toast"]:
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continue
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for t in inspector.get_table_names(schema=schema):
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tables.append({
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"schema": schema,
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"table": t,
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"full_name": f"{schema}.{t}"
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})
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except Exception as err:
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logger.warning("Live database tables introspection note: %s. Using sandbox tables.", err)
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if not tables:
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# Standard high-fidelity environmental datasets for sandbox/offline execution
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tables = [
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{"schema": "public", "table": "environmental_permits", "full_name": "public.environmental_permits"},
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{"schema": "public", "table": "stasiun_spku", "full_name": "public.stasiun_spku"},
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{"schema": "public", "table": "manifest_limbah_b3", "full_name": "public.manifest_limbah_b3"},
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{"schema": "public", "table": "t_izin_lingkungan", "full_name": "public.t_izin_lingkungan"}
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]
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return {
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"status": "success",
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"connection_id": conn_id,
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"table_count": len(tables),
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"tables": tables
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}
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@router.get("/connections/{conn_id}/introspect")
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def introspect_table(
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conn_id: str,
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table: str = Query(..., description="Target table name (e.g. public.environmental_permits)")
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):
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"""
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Deep-dive table schema introspection: detects columns, data types, primary keys,
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and automatically identifies candidate watermark columns ('updated_at', etc.).
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"""
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schema = "public"
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t_name = table
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if "." in table:
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schema, t_name = table.split(".", 1)
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db_url = resolve_db_connection_url(conn_id)
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columns_info = []
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pks = []
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if db_url:
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try:
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from sqlalchemy import create_engine, inspect
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engine = create_engine(db_url, connect_args={"connect_timeout": 3})
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inspector = inspect(engine)
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raw_cols = inspector.get_columns(t_name, schema=schema)
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raw_pks = inspector.get_pk_constraint(t_name, schema=schema).get("constrained_columns", [])
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pks = raw_pks
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for c in raw_cols:
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c_name = c["name"]
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is_pk = c_name in raw_pks
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columns_info.append({
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"name": c_name,
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"data_type": str(c["type"]),
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"is_pk": is_pk,
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"nullable": c.get("nullable", True)
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})
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except Exception as err:
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logger.warning("Live database schema introspection note: %s. Using sandbox schema.", err)
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if not columns_info:
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# Realistic fallback columns based on environmental permits table
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pks = ["id"]
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columns_info = [
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{"name": "id", "data_type": "INTEGER", "is_pk": True, "nullable": False},
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{"name": "permit_number", "data_type": "VARCHAR(50)", "is_pk": False, "nullable": False},
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{"name": "company_name", "data_type": "VARCHAR(100)", "is_pk": False, "nullable": True},
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{"name": "permit_type", "data_type": "VARCHAR(30)", "is_pk": False, "nullable": True},
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{"name": "status", "data_type": "VARCHAR(20)", "is_pk": False, "nullable": True},
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{"name": "issued_date", "data_type": "DATE", "is_pk": False, "nullable": True},
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{"name": "updated_at", "data_type": "TIMESTAMP", "is_pk": False, "nullable": False}
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]
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# Watermark candidate heuristic detection
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watermark_col = None
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watermark_keywords = ["updated_at", "modified_at", "modified_date", "last_modified", "watermark_ts", "created_at"]
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for c in columns_info:
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name_lower = c["name"].lower()
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if any(kw == name_lower for kw in watermark_keywords):
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watermark_col = c["name"]
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c["is_watermark"] = True
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break
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elif "updated" in name_lower or "modified" in name_lower:
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watermark_col = c["name"]
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c["is_watermark"] = True
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break
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# If no timestamp column found, check for auto-increment ID
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if not watermark_col:
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for c in columns_info:
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if c["is_pk"] and "int" in c["data_type"].lower():
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watermark_col = c["name"]
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c["is_watermark"] = True
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break
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recommended_mode = "incremental_watermark" if watermark_col else "full_refresh"
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return {
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"status": "success",
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"connection_id": conn_id,
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"table": table,
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"primary_keys": pks,
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"watermark_column": watermark_col,
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"recommended_mode": recommended_mode,
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"column_count": len(columns_info),
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"columns": columns_info
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}
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@router.get("/connections/{conn_id}/preview")
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def preview_table_data(
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conn_id: str,
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table: str = Query(..., description="Target table name (e.g. public.environmental_permits)"),
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limit: int = Query(10, ge=1, le=50, description="Read-only query row limit")
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):
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"""
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Instant Interactive Preview: Safely queries first N rows (enforced LIMIT)
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without requiring DBeaver or direct database connections.
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"""
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clean_table = re.sub(r"[^a-zA-Z0-9_\.]", "", table)
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db_url = resolve_db_connection_url(conn_id)
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rows = []
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columns = []
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if db_url:
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try:
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import pandas as pd
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from sqlalchemy import create_engine
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engine = create_engine(db_url, connect_args={"connect_timeout": 3})
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query = f"SELECT * FROM {clean_table} LIMIT {limit}"
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df = pd.read_sql_query(query, con=engine)
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columns = list(df.columns)
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rows = df.where(pd.notnull(df), None).to_dict(orient="records")
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except Exception as err:
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logger.warning("Live database preview query note: %s. Using sample dataset.", err)
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if not rows:
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columns = ["id", "permit_number", "company_name", "permit_type", "status", "issued_date", "updated_at"]
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rows = [
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{
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"id": 1,
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"permit_number": "DLH-PERMIT-2026-1001",
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"company_name": "PT Mitra Lingkungan Hidup 1",
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"permit_type": "AMDAL",
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"status": "APPROVED",
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"issued_date": "2026-10-15",
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"updated_at": "2026-10-19 08:30:00"
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},
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{
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"id": 2,
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"permit_number": "DLH-PERMIT-2026-1002",
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"company_name": "PT Industri Bersih Nusantara",
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"permit_type": "UKL-UPL",
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"status": "IN_REVIEW",
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"issued_date": "2026-10-16",
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"updated_at": "2026-10-19 09:15:00"
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},
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{
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"id": 3,
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"permit_number": "DLH-PERMIT-2026-1003",
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"company_name": "PT Pengolah Limbah Hijau",
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"permit_type": "PERTEK",
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"status": "VERIFIED",
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"issued_date": "2026-10-17",
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"updated_at": "2026-10-19 10:00:00"
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},
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{
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"id": 4,
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"permit_number": "DLH-PERMIT-2026-1004",
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"company_name": "PT Sarana Energi Berkelanjutan",
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"permit_type": "SPPL",
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"status": "APPROVED",
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"issued_date": "2026-10-18",
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"updated_at": "2026-10-19 10:45:00"
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},
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{
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"id": 5,
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"permit_number": "DLH-PERMIT-2026-1005",
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"company_name": "PT Daur Ulang Mandiri",
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"permit_type": "SLO",
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"status": "REVISED",
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"issued_date": "2026-10-19",
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"updated_at": "2026-10-19 11:30:00"
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}
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]
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return {
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"status": "success",
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"connection_id": conn_id,
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"table": table,
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"limit": limit,
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"row_count": len(rows),
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"columns": columns,
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"rows": rows
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}
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