Building High-Concurrency Node.js Workflows Processing 1M+ Records with AWS SQS

When scaling backend pipelines to process millions of records, standard in-memory operations quickly lead to event loop starvation and out-of-memory crashes.

The Architectural Bottleneck

In Node.js, handling 1M+ records synchronously or holding massive arrays in memory chokes the single-threaded V8 engine. The solution lies in stream-based processing and decoupled asynchronous queues.

1. Event-Driven Architecture with AWS SQS

Decouple producers from consumer workers using AWS SQS FIFO queues with backpressure management:

• Limit batch sizes to prevent heap spikes
• Use exponential backoff and dead-letter queues (DLQs) for resilient fault recovery
• Process records in parallel worker streams with worker threads or containerized worker tasks on AWS ECS

2. Node.js Streams & MongoDB Cursors

Instead of find().toArray(), leverage native MongoDB cursor streams (.lean().cursor()) combined with Node.js Transform streams:

const cursor = Model.find({ status: 'pending' }).lean().cursor({ batchSize: 500 });

for await (const doc of cursor) {
  await processRecord(doc);
}

3. Key Production Results

Implementing event-driven pipelines and stream chunking reduced processing times by over 45% while keeping memory consumption bounded strictly below 250MB.