308 lines
12 KiB
Python
308 lines
12 KiB
Python
"""
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GSC Insights Task Executor
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Handles execution of GSC insights fetch tasks for connected platforms.
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"""
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import logging
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import os
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import time
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import json
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from datetime import datetime, timedelta
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from typing import Dict, Any, Optional
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from sqlalchemy.orm import Session
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import sqlite3
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from ..core.executor_interface import TaskExecutor, TaskExecutionResult
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from ..core.exception_handler import TaskExecutionError, DatabaseError, SchedulerExceptionHandler
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from models.platform_insights_monitoring_models import PlatformInsightsTask, PlatformInsightsExecutionLog
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from services.gsc_service import GSCService
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from utils.logger_utils import get_service_logger
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logger = get_service_logger("gsc_insights_executor")
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class GSCInsightsExecutor(TaskExecutor):
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"""
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Executor for GSC insights fetch tasks.
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Handles:
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- Fetching GSC insights data weekly
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- On first run: Loads existing cached data
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- On subsequent runs: Fetches fresh data from GSC API
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- Logging results and updating task status
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"""
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def __init__(self):
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self.logger = logger
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self.exception_handler = SchedulerExceptionHandler()
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self.gsc_service = GSCService()
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async def execute_task(self, task: PlatformInsightsTask, db: Session) -> TaskExecutionResult:
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"""
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Execute a GSC insights fetch task.
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Args:
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task: PlatformInsightsTask instance
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db: Database session
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Returns:
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TaskExecutionResult
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"""
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start_time = time.time()
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user_id = task.user_id
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site_url = task.site_url
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try:
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self.logger.info(
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f"Executing GSC insights fetch: task_id={task.id} | "
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f"user_id={user_id} | site_url={site_url}"
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)
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# Create execution log
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execution_log = PlatformInsightsExecutionLog(
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task_id=task.id,
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execution_date=datetime.utcnow(),
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status='running'
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)
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db.add(execution_log)
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db.flush()
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# Fetch insights
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result = await self._fetch_insights(task, db)
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# Update execution log
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execution_time_ms = int((time.time() - start_time) * 1000)
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execution_log.status = 'success' if result.success else 'failed'
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execution_log.result_data = result.result_data
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execution_log.error_message = result.error_message
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execution_log.execution_time_ms = execution_time_ms
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execution_log.data_source = result.result_data.get('data_source') if result.success else None
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# Update task based on result
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task.last_check = datetime.utcnow()
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if result.success:
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task.last_success = datetime.utcnow()
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task.status = 'active'
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task.failure_reason = None
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# Schedule next check (7 days from now)
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task.next_check = self.calculate_next_execution(
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task=task,
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frequency='Weekly',
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last_execution=task.last_check
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)
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else:
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task.last_failure = datetime.utcnow()
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task.failure_reason = result.error_message
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task.status = 'failed'
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# Schedule retry in 1 day
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task.next_check = datetime.utcnow() + timedelta(days=1)
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task.updated_at = datetime.utcnow()
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db.commit()
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return result
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except Exception as e:
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execution_time_ms = int((time.time() - start_time) * 1000)
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# Set database session for exception handler
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self.exception_handler.db = db
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error_result = self.exception_handler.handle_task_execution_error(
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task=task,
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error=e,
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execution_time_ms=execution_time_ms,
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context="GSC insights fetch"
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)
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# Update task
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task.last_check = datetime.utcnow()
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task.last_failure = datetime.utcnow()
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task.failure_reason = str(e)
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task.status = 'failed'
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task.next_check = datetime.utcnow() + timedelta(days=1)
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task.updated_at = datetime.utcnow()
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db.commit()
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return error_result
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async def _fetch_insights(self, task: PlatformInsightsTask, db: Session) -> TaskExecutionResult:
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"""
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Fetch GSC insights data.
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On first run (no last_success), loads cached data.
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On subsequent runs, fetches fresh data from API.
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"""
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user_id = task.user_id
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site_url = task.site_url
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try:
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# Check if this is first run (no previous success)
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is_first_run = task.last_success is None
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if is_first_run:
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# First run: Try to load from cache
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self.logger.info(f"First run for GSC insights task {task.id} - loading cached data")
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cached_data = self._load_cached_data(user_id, site_url)
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if cached_data:
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self.logger.info(f"Loaded cached GSC data for user {user_id}")
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return TaskExecutionResult(
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success=True,
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result_data={
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'data_source': 'cached',
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'insights': cached_data,
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'message': 'Loaded from cached data (first run)'
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}
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)
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else:
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# No cached data - try to fetch from API
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self.logger.info(f"No cached data found, fetching from GSC API")
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return await self._fetch_fresh_data(user_id, site_url)
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else:
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# Subsequent run: Always fetch fresh data
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self.logger.info(f"Subsequent run for GSC insights task {task.id} - fetching fresh data")
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return await self._fetch_fresh_data(user_id, site_url)
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except Exception as e:
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self.logger.error(f"Error fetching GSC insights for user {user_id}: {e}", exc_info=True)
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return TaskExecutionResult(
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success=False,
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error_message=f"Failed to fetch GSC insights: {str(e)}",
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result_data={'error': str(e)}
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)
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def _load_cached_data(self, user_id: str, site_url: Optional[str]) -> Optional[Dict[str, Any]]:
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"""Load most recent cached GSC data from database."""
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try:
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db_path = self.gsc_service.db_path
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with sqlite3.connect(db_path) as conn:
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cursor = conn.cursor()
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# Find most recent cached data
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if site_url:
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cursor.execute('''
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SELECT data_json, created_at
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FROM gsc_data_cache
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WHERE user_id = ? AND site_url = ? AND data_type = 'analytics'
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ORDER BY created_at DESC
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LIMIT 1
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''', (user_id, site_url))
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else:
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cursor.execute('''
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SELECT data_json, created_at
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FROM gsc_data_cache
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WHERE user_id = ? AND data_type = 'analytics'
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ORDER BY created_at DESC
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LIMIT 1
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''', (user_id,))
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result = cursor.fetchone()
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if result:
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data_json, created_at = result
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insights_data = json.loads(data_json) if isinstance(data_json, str) else data_json
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self.logger.info(
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f"Found cached GSC data from {created_at} for user {user_id}"
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)
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return insights_data
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return None
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except Exception as e:
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self.logger.warning(f"Error loading cached GSC data: {e}")
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return None
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async def _fetch_fresh_data(self, user_id: str, site_url: Optional[str]) -> TaskExecutionResult:
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"""Fetch fresh GSC insights from API."""
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try:
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# If no site_url, get first site
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if not site_url:
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sites = self.gsc_service.get_site_list(user_id)
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if not sites:
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return TaskExecutionResult(
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success=False,
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error_message="No GSC sites found for user",
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result_data={'error': 'No sites found'}
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)
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site_url = sites[0]['siteUrl']
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# Get analytics for last 30 days
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end_date = datetime.now().strftime('%Y-%m-%d')
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start_date = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d')
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# Fetch search analytics
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search_analytics = self.gsc_service.get_search_analytics(
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user_id=user_id,
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site_url=site_url,
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start_date=start_date,
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end_date=end_date
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)
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if 'error' in search_analytics:
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return TaskExecutionResult(
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success=False,
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error_message=search_analytics.get('error', 'Unknown error'),
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result_data=search_analytics
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)
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# Format insights data
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insights_data = {
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'site_url': site_url,
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'date_range': {
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'start': start_date,
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'end': end_date
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},
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'overall_metrics': search_analytics.get('overall_metrics', {}),
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'query_data': search_analytics.get('query_data', {}),
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'fetched_at': datetime.utcnow().isoformat()
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}
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self.logger.info(
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f"Successfully fetched GSC insights for user {user_id}, site {site_url}"
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)
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return TaskExecutionResult(
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success=True,
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result_data={
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'data_source': 'api',
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'insights': insights_data,
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'message': 'Fetched fresh data from GSC API'
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}
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)
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except Exception as e:
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self.logger.error(f"Error fetching fresh GSC data: {e}", exc_info=True)
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return TaskExecutionResult(
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success=False,
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error_message=f"API fetch failed: {str(e)}",
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result_data={'error': str(e)}
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)
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def calculate_next_execution(
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self,
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task: PlatformInsightsTask,
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frequency: str,
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last_execution: Optional[datetime] = None
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) -> datetime:
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"""
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Calculate next execution time based on frequency.
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For platform insights, frequency is always 'Weekly' (7 days).
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"""
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if last_execution is None:
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last_execution = datetime.utcnow()
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if frequency == 'Weekly':
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return last_execution + timedelta(days=7)
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elif frequency == 'Daily':
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return last_execution + timedelta(days=1)
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else:
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# Default to weekly
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return last_execution + timedelta(days=7)
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