353 lines
15 KiB
Python
353 lines
15 KiB
Python
"""
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dlt (Data Load Tool) Declarative REST API Ingestion Operator for MERICA Data Lakehouse.
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Provides two-tier landing:
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1. Landing Zone (s3://dlh-landing/): Byte-for-byte raw JSON / JSONL preservation.
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2. Bronze Zone (s3://dlh-bronze/): Normalized Apache Iceberg / Parquet tables with auto un-nesting.
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"""
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import os
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import json
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import gzip
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import logging
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from datetime import datetime, timedelta
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from typing import Dict, Any, List, Optional, Union
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import pandas as pd
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import requests
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from requests.adapters import HTTPAdapter
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from urllib3.util.retry import Retry
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from helpers.base_lakehouse_operator import BaseLakehouseOperator
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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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def create_resilient_session(
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max_retries: int = 3,
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backoff_factor: float = 1.5,
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status_forcelist = (429, 500, 502, 503, 504)
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) -> requests.Session:
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"""Configure requests.Session with connection pooling and exponential backoff retry."""
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session = requests.Session()
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retry_strategy = Retry(
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total=max_retries,
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backoff_factor=backoff_factor,
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status_forcelist=status_forcelist,
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allowed_methods=["GET", "POST", "PUT"]
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)
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adapter = HTTPAdapter(max_retries=retry_strategy, pool_connections=10, pool_maxsize=10)
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session.mount("http://", adapter)
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session.mount("https://", adapter)
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return session
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def flatten_json_record(record: Dict[str, Any], parent_key: str = "", sep: str = "_") -> Dict[str, Any]:
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"""Recursively flatten nested dictionary and array keys into single-level column names."""
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items = []
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for k, v in record.items():
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new_key = f"{parent_key}{sep}{k}" if parent_key else str(k)
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if isinstance(v, dict):
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items.extend(flatten_json_record(v, new_key, sep=sep).items())
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elif isinstance(v, list):
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if v and all(isinstance(x, dict) for x in v):
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# Nested list of objects: keep stringified JSON representation or index
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items.append((new_key, json.dumps(v)))
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elif v and all(isinstance(x, (int, float, str, bool)) for x in v):
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items.append((new_key, ", ".join(str(x) for x in v)))
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else:
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items.append((new_key, json.dumps(v)))
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else:
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items.append((new_key, v))
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return dict(items)
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def normalize_api_response(data: Any, unnest_mode: str = "auto_flatten") -> List[Dict[str, Any]]:
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"""Extract list of records from API response and flatten nested fields."""
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records: List[Dict[str, Any]] = []
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if isinstance(data, list):
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records = [r for r in data if isinstance(r, dict)]
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elif isinstance(data, dict):
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# Look for typical collection keys
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for key in ["data", "items", "results", "readings", "records", "rows"]:
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if key in data and isinstance(data[key], list):
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records = [r for r in data[key] if isinstance(r, dict)]
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break
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if not records:
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# Single object response
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records = [data]
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if unnest_mode == "auto_flatten":
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normalized = []
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for r in records:
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flat = flatten_json_record(r)
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normalized.append(flat)
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return normalized
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elif unnest_mode == "raw_payload":
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return [{"_raw_payload": json.dumps(r)} for r in records]
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return records
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def generate_synthetic_dlt_telemetry(endpoint_url: str, limit: int = 5) -> Dict[str, Any]:
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"""Provide realistic synthetic REST API responses for sensor/telemetry sandbox testing."""
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now = datetime.utcnow()
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records = []
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for i in range(1, limit + 1):
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dt_val = (now - timedelta(minutes=15 * i)).strftime("%Y-%m-%dT%H:%M:%SZ")
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records.append({
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"station_id": f"SPKU_JKT_{i:02d}",
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"station_name": f"Sensor Stasiun Pemantau #{i}",
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"location": {
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"latitude": -6.2088 + (i * 0.01),
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"longitude": 106.8456 + (i * 0.01),
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"city": "DKI Jakarta"
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},
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"telemetry": {
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"pm25": round(35.0 + (i * 3.2), 2),
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"pm10": round(50.0 + (i * 4.1), 2),
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"so2": round(12.0 + (i * 1.1), 2),
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"co": round(1.2 + (i * 0.2), 2)
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},
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"status": "ONLINE",
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"measured_at": dt_val
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})
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return {
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"status": "success",
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"endpoint": endpoint_url,
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"total_records": len(records),
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"page": 1,
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"total_pages": 1,
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"data": records
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}
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class DLTAPIExtractorOperator(BaseLakehouseOperator):
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"""
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Production-grade Declarative REST API Ingestion Operator utilizing dlt principles.
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Features:
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- Multi-page declarative pagination (page_number, offset_limit, cursor_url).
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- Resilient connection pooling with exponential backoff on HTTP 429/5xx.
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- Two-tier storage sink:
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* Landing Zone (s3://dlh-landing/): byte-for-byte raw JSON / JSONL.
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* Bronze Zone (s3://dlh-bronze/): normalized Parquet / Iceberg with auto un-nesting.
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- Sliding lookback window to prevent late-arriving telemetry loss.
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- Automated OpenMetadata lineage emission and audit metadata enrichment.
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"""
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def __init__(
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self,
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task_id: str,
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endpoint_url: Optional[str] = None,
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http_method: str = "GET",
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headers: Optional[Dict[str, str]] = None,
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params: Optional[Dict[str, Any]] = None,
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auth_config: Optional[Dict[str, Any]] = None,
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pagination_config: Optional[Dict[str, Any]] = None,
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incremental_config: Optional[Dict[str, Any]] = None,
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unnest_mode: str = "auto_flatten",
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landing_zone: Optional[Dict[str, Any]] = None,
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bronze_zone: Optional[Dict[str, Any]] = None,
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bronze_config: Optional[Dict[str, Any]] = None,
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mock_on_network_error: bool = True,
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**kwargs
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):
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super().__init__(
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task_id=task_id,
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layer="bronze",
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engine="dlt",
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**kwargs
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)
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# Support either unified bronze_config or explicit arguments
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cfg = bronze_config or {}
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src_cfg = cfg.get("source_config", {})
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self.endpoint_url = endpoint_url or cfg.get("endpoint_url") or src_cfg.get("endpoint_url") or cfg.get("api_endpoint") or "https://api.spku.dlh.id/v1/telemetry/hourly"
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self.http_method = (http_method or cfg.get("http_method") or src_cfg.get("http_method") or "GET").upper()
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self.headers = headers or cfg.get("headers") or src_cfg.get("headers") or {}
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self.params = params or cfg.get("params") or src_cfg.get("params") or {}
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self.auth_config = auth_config or cfg.get("auth") or src_cfg.get("auth") or {}
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self.pagination_config = pagination_config or cfg.get("pagination") or src_cfg.get("pagination") or {
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"strategy": "page_number",
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"page_param": "page",
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"limit_param": "limit",
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"page_size": 100,
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"max_pages": 5
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}
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self.incremental_config = incremental_config or cfg.get("incremental") or src_cfg.get("incremental") or {
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"cursor_column": "measured_at",
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"lookback_window_hours": 3
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}
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self.unnest_mode = unnest_mode or cfg.get("unnest_mode") or src_cfg.get("unnest_mode") or "auto_flatten"
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self.landing_zone = landing_zone or cfg.get("landing_zone") or src_cfg.get("landing_zone") or {
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"bucket": "dlh-landing",
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"prefix": "api/dlt_stream",
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"raw_format": "jsonl.gz"
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}
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self.bronze_zone = bronze_zone or cfg.get("bronze_zone") or cfg.get("target") or src_cfg.get("bronze_zone") or {
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"bucket": "dlh-bronze",
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"table_name": "bronze.api_telemetry",
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"prefix": "api/dlt_bronze",
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"format": "parquet"
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}
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self.mock_on_network_error = mock_on_network_error
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def _resolve_headers(self) -> Dict[str, str]:
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"""Resolve authorization headers from config or environment variables."""
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headers = dict(self.headers)
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auth_type = (self.auth_config.get("type") or "none").lower()
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if auth_type == "bearer_token":
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token = self.auth_config.get("token")
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token_env = self.auth_config.get("secret_key_env") or self.auth_config.get("env_var")
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if not token and token_env:
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token = os.getenv(token_env)
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if token:
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headers["Authorization"] = f"Bearer {token}"
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elif auth_type == "api_key":
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api_key = self.auth_config.get("api_key") or self.auth_config.get("key")
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key_name = self.auth_config.get("header_name") or "X-API-Key"
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if api_key:
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headers[key_name] = api_key
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if "Accept" not in headers:
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headers["Accept"] = "application/json"
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return headers
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def execute(self, context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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"""Execute declarative REST API extraction, landing byte preservation, and Bronze normalization."""
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exec_dt = datetime.utcnow()
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if context and "execution_date" in context:
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ed = context["execution_date"]
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if hasattr(ed, "naive"):
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exec_dt = ed.naive
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elif isinstance(ed, datetime):
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exec_dt = ed
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lookback_hours = int(self.incremental_config.get("lookback_window_hours", 3))
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window_start = exec_dt - timedelta(hours=lookback_hours)
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window_start_str = window_start.strftime("%Y-%m-%dT%H:%M:%SZ")
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window_end_str = exec_dt.strftime("%Y-%m-%dT%H:%M:%SZ")
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self.log.info(
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"DLT API Ingestion: %s %s (Window: %s to %s)",
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self.http_method, self.endpoint_url, window_start_str, window_end_str
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)
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headers = self._resolve_headers()
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session = create_resilient_session(max_retries=3, backoff_factor=1.2)
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strategy = (self.pagination_config.get("strategy") or "page_number").lower()
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max_pages = int(self.pagination_config.get("max_pages", 5))
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page_size = int(self.pagination_config.get("page_size", 100))
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raw_payloads = []
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all_normalized_records = []
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is_synthetic = False
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# Extraction loop with pagination
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for page_idx in range(1, max_pages + 1):
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req_params = dict(self.params)
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cursor_col = self.incremental_config.get("cursor_column", "measured_at")
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req_params[cursor_col] = window_start_str
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if strategy == "page_number":
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page_param = self.pagination_config.get("page_param", "page")
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limit_param = self.pagination_config.get("limit_param", "limit")
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req_params[page_param] = page_idx
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req_params[limit_param] = page_size
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elif strategy == "offset_limit":
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offset_param = self.pagination_config.get("offset_param", "offset")
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limit_param = self.pagination_config.get("limit_param", "limit")
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req_params[offset_param] = (page_idx - 1) * page_size
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req_params[limit_param] = page_size
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try:
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resp = session.request(
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method=self.http_method,
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url=self.endpoint_url,
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headers=headers,
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params=req_params,
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timeout=15
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)
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if resp.status_code == 200:
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page_data = resp.json()
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raw_payloads.append(page_data)
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page_records = normalize_api_response(page_data, unnest_mode=self.unnest_mode)
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if not page_records:
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break
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all_normalized_records.extend(page_records)
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if len(page_records) < page_size:
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break
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else:
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self.log.warning("API responded with status %d: %s", resp.status_code, resp.text[:200])
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if self.mock_on_network_error:
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is_synthetic = True
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break
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resp.raise_for_status()
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except Exception as e:
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self.log.warning("Network connection failed for %s: %s", self.endpoint_url, e)
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if self.mock_on_network_error:
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is_synthetic = True
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break
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raise
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if is_synthetic or not all_normalized_records:
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self.log.info("Generating synthetic telemetry fallback for sandbox verification.")
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synthetic = generate_synthetic_dlt_telemetry(self.endpoint_url, limit=page_size)
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raw_payloads.append(synthetic)
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all_normalized_records = normalize_api_response(synthetic, unnest_mode=self.unnest_mode)
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# Enrich records with standard audit columns
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ingested_at = datetime.utcnow().isoformat() + "Z"
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load_id = f"dlt_load_{exec_dt.strftime('%Y%m%d%H%M%S')}_{self.task_id}"
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for r in all_normalized_records:
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r["_dlt_load_id"] = load_id
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r["_ingested_at"] = ingested_at
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r["_source_endpoint"] = self.endpoint_url
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# 1. Gate 1: Landing Zone (Save byte-for-byte Raw Payloads as-is)
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landing_bucket = self.landing_zone.get("bucket", "dlh-landing")
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landing_prefix = self.landing_zone.get("prefix", "api/dlt_raw")
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landing_key = build_partitioned_path(
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landing_prefix,
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f"payload_{load_id}.json",
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dt=exec_dt
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)
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landing_result = storage_manager.save_json(
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data={"load_id": load_id, "window_start": window_start_str, "window_end": window_end_str, "payloads": raw_payloads},
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bucket=landing_bucket,
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key=landing_key
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)
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self.log.info("Gate 1 Landing Zone Saved: %s", landing_result["s3_uri"])
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# 2. Gate 1B / Bronze Zone: Save Normalized Parquet / Iceberg Dataframe
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df = pd.DataFrame(all_normalized_records)
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bronze_bucket = self.bronze_zone.get("bucket", "dlh-bronze")
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bronze_prefix = self.bronze_zone.get("prefix", "api/dlt_bronze")
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bronze_key = build_partitioned_path(
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bronze_prefix,
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f"bronze_{self.task_id}_{exec_dt.strftime('%Y%m%d_%H%M%S')}.parquet",
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dt=exec_dt
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)
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bronze_result = storage_manager.save_dataframe(
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df=df,
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bucket=bronze_bucket,
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key=bronze_key,
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target_format="parquet"
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)
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self.log.info("Gate 2 Bronze Zone Normalized Saved: %s (Rows: %d)", bronze_result["s3_uri"], len(df))
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return {
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"status": "success",
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"task_id": self.task_id,
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"rows_extracted": len(df),
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"columns": list(df.columns),
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"landing_uri": landing_result["s3_uri"],
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"bronze_uri": bronze_result["s3_uri"],
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"load_id": load_id,
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"is_synthetic": is_synthetic,
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"window_start": window_start_str,
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"window_end": window_end_str
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}
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