Importing massive batches of records (such as migrating 100k customer accounts or bulk updating product stock databases) will choke synchronous scripts. If you process records sequentially, execution takes hours. However, launching thousands of concurrent API requests simultaneously will crash your server or saturate database connections.

Using asyncio queues, we build a producer-consumer worker pool to execute ingestion in parallel while throttling concurrency.

1. Structuring the Worker Pool

We instantiate an async queue, load the records (producer), and launch a limited number of consumer tasks (workers) that process jobs concurrently:

# Async Ingestion Worker Pool in Python
import asyncio

async def worker(queue):
    while True:
        # Fetch ingestion task from queue
        item = await queue.get()
        try:
            await process_item_ingestion(item)
        finally:
            queue.task_done()

async def run_bulk_ingestion(records, worker_count=10):
    queue = asyncio.Queue()
    # Populate the queue
    for r in records:
        await queue.put(r)
        
    # Start consumer tasks
    tasks = []
    for _ in range(worker_count):
        task = asyncio.create_task(worker(queue))
        tasks.append(task)
        
    await queue.join() # Wait for queue to empty
    for t in tasks:
        t.cancel() # Shut down workers

2. Concurrency Control

By adjusting worker_count, we control the maximum number of concurrent database operations, preventing lockups while utilizing full network bandwidth.

Bounded Asyncio Semaphores & Bulk-Index Flushing for High-Speed Ingestion

Prevent out-of-memory crashes during multi-million-row ingestion jobs by bounding queue depth (`asyncio.Queue(maxsize=1000)`) and flushing batched payloads with backpressure.