Yes, you can definitely run subflows in parallel when querying MSSQL! Here's how you can implement this, along with some important considerations:

python
from prefect import flow, task
from typing import List
import asyncio
from prefect.futures import as_completed

@task
async def query_database(query: str):
    # Your MSSQL connection and query logic here
    # Make sure to use async database drivers if possible
    ...
    return results

@flow
async def subflow(query: str):
    return await query_database(query)

@flow
async def main_flow(queries: List[str]):
    # Create futures for all subflows
    futures = [subflow.submit(query) for query in queries]
    
    # Process results as they complete
    results = []
    async for future in as_completed(futures):
        result = await future
        results.append(result)
    
    return results

# Example usage
queries = [
    "SELECT * FROM table1 WHERE condition1",
    "SELECT * FROM table2 WHERE condition2",
    "SELECT * FROM table3 WHERE condition3"
]
asyncio.run(main_flow(queries))

Important considerations: 1. Connection Pool Management: When running parallel queries, make sure your MSSQL connection pool is properly configured to handle multiple concurrent connections. Your db.t4g.small instance type might be a limiting factor here. 2. Resource Constraints: Monitor your database performance. While Prefect can handle parallel execution, your MSSQL server might become a bottleneck, especially on a t4g.small instance. 3. Error Handling: Add appropriate error handling for database connection issues and query failures. Each subflow should handle its own errors appropriately. 4. Alternative Approach: If you're dealing with a large number of queries, you might want to use task mapping instead:

python
from prefect import flow, task

@task
async def execute_query(query: str):
    # Your MSSQL query logic here
    ...
    return results

@flow
async def parallel_queries(queries: List[str]):
    results = await execute_query.map(queries)
    return results

5. Performance Monitoring: Keep an eye on both Prefect's task execution metrics and your database server's performance metrics to ensure optimal operation. Remember that while Prefect can execute tasks in parallel, the actual performance improvement will depend on: - Your database server's capacity (particularly important for your t4g.small instance) - The nature of your queries - Network latency - Connection pool settings For optimal performance, you might want to experiment with the number of concurrent queries to find the sweet spot for your specific setup. You can control this using Prefect's task runners or by batching your queries into smaller groups. Would you like me to provide more specific examples for any of these aspects or would you like to know more about optimizing the parallel execution for your specific MSSQL setup?

For example, to create a global limit:

bash
prefect gcl create database-queries --slots 3

Would you like me to elaborate on any of these methods or show more specific examples for your use case?

I notice you've said "yes" but I'm not sure which aspect you'd like me to elaborate on. I can provide more details about: 1. Global concurrency limits (GCL) and how to manage them 2. Tag-based concurrency with more complex examples 3. TaskRunner configurations and different options 4. The batch processing approach with

as_completed

Could you please specify which aspect you'd like to learn more about? This will help me provide the most relevant and helpful information for your needs.

Here's a comprehensive example showing different patterns for batch processing with

as_completed

, including error handling, monitoring, and progress tracking: ```python from prefect import flow, task from prefect.futures import as_completed from prefect.logging import get_run_logger from typing import List, Dict, Any import asyncio from datetime import timedelta @task( retries=3, # Retry up to 3 times retry_delay_seconds=30, # Wait 30 seconds between retries timeout_seconds=300, # Maximum time for single query tags=["database", "mssql"] ) async def query_database(query: str, params: Dict[str, Any] = None) -> Dict: logger = get_run_logger() try: # Your database connection/query logic here # result = await db.execute(query, params) # For example purposes, we'll simulate a query await asyncio.sleep(2) # Simulate query time return {"query": query, "status": "success", "data": []} except Exception as e: logger.error(f"Query failed: {query[:100]}... Error: {str(e)}") raise @flow(name="Database Batch Processing") async def process_queries_in_batches( queries: List[str], max_concurrent: int = 3, batch_timeout: int = 600, total_timeout: int = 3600 ) -> List[Dict]: logger = get_run_logger() results = [] failed_queries = [] total_queries = len(queries) processed_count = 0 # Process queries in batches async with asyncio.timeout(total_timeout): try: query_queue = queries.copy() while query_queue: batch = query_queue[:max_concurrent] query_queue = query_queue[max_concurrent:] # Submit batch of queries futures = [query_database.submit(query) for query in batch] # Process results as they complete with timeout try: async with asyncio.timeout(batch_timeout): async for future in as_completed(futures): try: result = await future results.append(result) processed_count += 1 # Log progress logger.info(f"Processed {processed_count}/{total_queries} queries") except Exception as e: failed_query = batch[futures.index(future)] failed_queries.append({ "query": failed_query, "error": str(e) }) logger.error(f"Query failed: {str(e)}") except asyncio.TimeoutError: logger.error(f"Batch timeout after {batch_timeout} seconds") # Add unprocessed queries from this batch to failed queries for f in futures: if not f.done(): failed_queries.append({ "query": batch[futures.index(f)], "error": "Timeout" }) # Optional: Add delay between batches if needed await asyncio.sleep(1) except asyncio.TimeoutError: logger.error(f"Total execution timeout after {total_timeout} seconds") # Handle any remaining queries failed_queries.extend([ {"query": q, "error": "Total timeout"} for q in query_queue ]) # Create summary summary = { "total_queries": total_queries, "successful_queries": len(results),

"failed_queries": len(failed_queries), "success_rate": f"{(len(results)/total_queries)*100:.2f}%" } logger.info(f"Processing complete. Summary: {summary}") # You can store failed queries for later retry if needed if failed_queries: logger.warning(f"Failed queries: {failed_queries}") return results, failed_queries, summary # Example usage async def main(): # Example queries queries = [ f"SELECT * FROM table{i}" for i in range(10) ] results, failures, summary = await process_queries_in_batches( queries=queries, max_concurrent=3, batch_timeout=600, # 10 minutes per batch total_timeout=3600 # 1 hour total ) # You could implement retry logic for failed queries if failures: print(f"Failed queries to retry later: {len(failures)}") # Run the flow if name == "__main__": asyncio.run(main())

This enhanced version includes several important features:

1. *Batch Control*:
   - Processes queries in batches of `max_concurrent` size
   - Includes both batch-level and total execution timeouts
   - Allows for rate limiting between batches

2. *Error Handling*:
   - Task-level retries for transient errors
   - Comprehensive error capturing and logging
   - Separate tracking of failed queries
   - Timeout handling at multiple levels

3. *Monitoring and Logging*:
   - Progress tracking with completion percentages
   - Detailed logging of successes and failures
   - Summary statistics at completion
   - Task tagging for better observability

4. *Flexibility*:
   - Configurable concurrency limits
   - Adjustable timeout values
   - Easy to modify for different types of database operations

You can extend this pattern further by:

1. Adding result validation:
```python
@task
async def validate_result(result: Dict) -> bool:
    # Add your validation logic
    return True

2. Implementing automatic retries for failed queries:

python
@flow
async def retry_failed_queries(failed_queries: List[Dict]):
    # Implement retry logic with exponential backoff
    pass

3. Adding metrics collection:

python
@task
async def record_metrics(summary: Dict):
    # Send metrics to your monitoring system
    pass

Would you like me to elaborate on any of these aspects or show how to implement any of the extensions?