Data EngineeringHigh-Throughput

High-Throughput Data Pipeline with RabbitMQ

Scaling a verification workload from ~40K to 2M+ records a day with an event-driven worker fleet

Re-architected a bottlenecked, per-request processing system into a distributed, message-driven pipeline on RabbitMQ. A fleet of stateless async workers offloads the heavy I/O off the request path, lifting sustained throughput from tens of thousands of records a day to more than two million, with zero lost work across crashes.

Python 3.12asyncioRabbitMQaio-pikaMongoDBMySQL 8httpxdnspythonStreamlit

Problem Statement

A high-volume data-verification platform was capped at tens of thousands of records a day. Verification ran a full browser session per record, synchronously, so throughput was bounded by how many heavy, I/O-bound checks a single process could grind through in sequence.

A per-request browser-automation step made every check heavy, synchronous and slow.
Throughput hit a hard ceiling near 30 to 40K records a day and could not grow with demand.
A crash or restart risked losing in-flight work, with no durable record of what was already done.

Headline Outcomes

2M+/day~50×

Daily throughput

~30–40K/day

Zerodurable settlement

Lost records across crashes & outages

Horizontalmore workers, more throughput

Scaling model

The Solution

A message-driven micro-worker architecture with RabbitMQ as the single durable backbone. Work, results and telemetry each travel on their own queues, and a horizontally-scalable fleet of stateless async workers consumes them, so adding workers adds throughput and the heavy I/O runs off the request path.

RabbitMQ carries three separated flows (work, results and events), decoupling producers from a fleet of stateless consumers.

Heavy per-record I/O is offloaded to async workers, so throughput scales with worker count instead of one process.

Exactly-processed settlement: durable queues, persistent messages, publisher confirms and ack-after-downstream-confirm mean a result is never lost and never acknowledged before it lands.

A prefetch of one message per worker adds backpressure, and a dedicated retry queue re-queues rate-limited work, so a slow or crashed worker never hoards or drops records.

Polyglot persistence keeps hot operational state in MongoDB and analytics in a separate MySQL warehouse fed by an event stream, so the analytics plane can never slow production.

System Architecture

How the data flows

Enqueue

Durable RabbitMQ work queue

Worker Fleet

Stateless async, prefetch 1

I/O Offload

Heavy work off request path

Settle

Confirms + retry, zero loss

Event Stream

MySQL rollups + dashboard

Result 01

Broke a hard throughput ceiling, lifting sustained processing from tens of thousands to millions of records a day.

Result 02

Guaranteed zero lost work across worker crashes and datastore outages through broker-anchored settlement.

Result 03

Delivered elastic, horizontal scale, so throughput grows with worker count rather than a single process.

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From the blog

Data EngineeringJul 29, 2026

RabbitMQ Data Pipelines: Millions of Records a Day

How to scale a data pipeline with RabbitMQ: work queues, a horizontal worker pool, durability and backpressure. The pattern that took one pipeline past 2M records a day.

RabbitMQMessage QueueData EngineeringDistributed Systems

Read Article11 min read

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