Based on the research, I'll provide you with a practical example of how to set up and use an asyncpg connection pool with Prefect flows. Here's a complete solution that includes creating a custom block for managing the connection pool and using it in your flows:

python
from prefect import flow, task
from prefect.blocks.core import Block
import asyncpg
from typing import Optional
from contextlib import asynccontextmanager

class PostgresConnectionPool(Block):
    """Block for managing an asyncpg connection pool."""
    
    _block_type_name = "postgres-connection-pool"
    _block_schema_capabilities = ["connection-pool"]

    host: str
    port: int = 5432
    database: str
    user: str
    password: str
    min_size: int = 10
    max_size: int = 20
    _pool: Optional[asyncpg.Pool] = None

    async def get_pool(self) -> asyncpg.Pool:
        """Get or create the connection pool."""
        if self._pool is None:
            self._pool = await asyncpg.create_pool(
                host=self.host,
                port=self.port,
                database=self.database,
                user=self.user,
                password=self.password,
                min_size=self.min_size,
                max_size=self.max_size
            )
        return self._pool

    @asynccontextmanager
    async def connection(self):
        """Context manager for getting a connection from the pool."""
        pool = await self.get_pool()
        async with pool.acquire() as connection:
            yield connection

    async def close_pool(self):
        """Close the connection pool."""
        if self._pool is not None:
            await self._pool.close()
            self._pool = None

# Example task using the connection pool
@task
async def insert_data(data: dict):
    # Get the connection pool block
    pool_block = await PostgresConnectionPool.load("default")
    
    async with pool_block.connection() as conn:
        # Example insert query
        await conn.execute(
            """
            INSERT INTO your_table (column1, column2)
            VALUES ($1, $2)
            """,
            data['column1'],
            data['column2']
        )

@flow
async def insert_data_flow(data: dict):
    await insert_data(data)

To use this setup: 1. First, save the connection block configuration:

python
async def save_postgres_block():
    pool_block = PostgresConnectionPool(
        host="your_host",
        database="your_db",
        user="your_user",
        password="your_password",
        min_size=10,  # Adjust based on your needs
        max_size=20   # Adjust based on your needs
    )
    await pool_block.save(name="default")

2. Then you can use it in your flows:

python
data = {"column1": "value1", "column2": "value2"}
await insert_data_flow(data)

Some important notes about this implementation: 1. Connection Pool Sizing: Given your requirement of handling 1000 requests per minute, I've set default pool sizes of min_size=10 and max_size=20, but you should adjust these based on: - Your specific workload patterns - The capabilities of your EC2 t2.medium instance - The nature of your database operations 2. Resource Management: The implementation includes proper connection pool management with: - Lazy pool creation (only created when needed) - Connection context manager for safe connection handling - Pool cleanup method 3. Error Handling: You might want to add more robust error handling and retries depending on your specific needs. 4. Performance Considerations: For your high-throughput requirement (1000 requests/minute): - Consider implementing batch operations where possible - Monitor the pool size and adjust based on performance metrics - Implement appropriate timeouts and error handling You can enhance this basic implementation by adding: - Connection timeouts - Retry logic - Connection pool metrics - Health checks - Transaction management

Would you like me to elaborate on any of these aspects or provide more specific guidance for your use case?