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Queue payload offloading proxy for Redis-based queues
Build a lightweight proxy/middleware that sits between BullMQ/Redis and the application, automatically offloading large job payloads to disk or S3-compatible storage and passing only references through Redis. This prevents OOM crashes during burst spikes without requiring developers to manually restructure their code.
Why this matters
You're running BullMQ on Redis for your Node.js application, and most of the time the queue sits at 2% capacity. But once a month, a customer triggers a massive batch upload and your queue spikes to millions of items. Redis gobbles up memory trying to hold all those payloads, hits OOM, and crashes—taking your queued events with it. You know the fix is to store payloads externally and pass only IDs through the queue, but refactoring your entire producer and consumer code to do that feels like a big project you keep deprioritizing. You wish there were a drop-in solution that handled this automatically.
- · Built for Engineering teams using BullMQ or Redis-based queues who experience OOM crashes or high Redis memory costs during burst traffic, particularly those with large job payloads stored in the queue..
- · Most likely monetization: SaaS subscription (managed proxy) or open-core with paid enterprise features.
The Pain · Narrative
You're running BullMQ on Redis for your Node.js application, and most of the time the queue sits at 2% capacity. But once a month, a customer triggers a massive batch upload and your queue spikes to millions of items. Redis gobbles up memory trying to hold all those payloads, hits OOM, and crashes—taking your queued events with it. You know the fix is to store payloads externally and pass only IDs through the queue, but refactoring your entire producer and consumer code to do that feels like a big project you keep deprioritizing. You wish there were a drop-in solution that handled this automatically.
Score Breakdown
Market Signal
Go-to-Market
Node.js engineering teams using BullMQ in production who have experienced OOM crashes or spiraling Redis costs during traffic bursts
~10,000-20,000 teams actively using BullMQ in production with payload sizes large enough to cause memory pressure
r/node and r/redis organic posts, BullMQ GitHub issues/Discussions, technical blog post about Redis OOM prevention
$29/month for hosted proxy, $149/month for multi-region with S3 backend
50 GitHub stars and 10 paying users within 30 days of launch
MVP Scope · 1–2 weeks
- Build a Node.js middleware/proxy that wraps BullMQ's add() and process() methods to intercept payloads
- Implement S3-compatible storage backend for offloaded payloads with automatic ID generation
- Add configurable threshold logic: payloads above N bytes get offloaded, smaller ones stay in Redis
- Implement transparent deserialization on the consumer side so application code sees full payloads
- Write unit tests verifying end-to-end job processing with offloaded payloads
- Add automatic payload cleanup after job completion (with configurable retention period)
- Implement local disk storage backend as alternative to S3 for simpler deployments
- Build a simple monitoring dashboard showing offload count, storage usage, and Redis memory saved
- Create BullMQ integration tests simulating 1M+ item burst to verify no OOM occurs
- Write documentation and a quick-start guide showing drop-in replacement for existing BullMQ code
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1The solution is relatively simple to implement, so engineering teams may build their own version rather than paying for a product
- 2BullMQ maintainers could add native payload offloading as a built-in feature, especially if the community demand is visible
- 3Teams may solve the problem by switching to disk-persisted queue solutions (NATS JetStream, Kafka) rather than adding a proxy layer to their existing Redis setup
Evidence Summary
How AI synthesized this insight — no verbatim quotes
Multiple commenters identified payload storage in Redis as the root cause of OOM during burst spikes. One commenter explicitly recommended storing payloads in Postgres or S3 and passing only IDs through the queue, noting this prevents memory issues during 2.5M-item spikes. Another commenter noted Redis memory costs scale quickly with payload size. The original poster confirmed that OOM events caused loss of queue data, which is a critical reliability issue. The pattern of multiple commenters independently suggesting the same workaround indicates both the pain and the lack of a turnkey solution.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Validate
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Landing Page Copy Kit
Ready-to-paste copy based on real Reddit community language — no editing required
Headline
Queue payload offloading proxy for Redis-based queues
Sub-headline
Build a lightweight proxy/middleware that sits between BullMQ/Redis and the application, automatically offloading large job payloads to disk or S3-compatible storage and passing only references through Redis. This prevents OOM crashes during burst spikes without requiring developers to manually restructure their code.
Who It's For
For Engineering teams using BullMQ or Redis-based queues who experience OOM crashes or high Redis memory costs during burst traffic, particularly those with large job payloads stored in the queue.
Feature List
✓ Transparent proxy that intercepts job payloads and automatically stores them in S3/disk, passing only IDs through Redis ✓ Configurable payload size threshold (only offload payloads above N bytes) ✓ Automatic cleanup of stored payloads after job completion ✓ Support for multiple backends: local disk, S3, MinIO, Postgres large objects ✓ Drop-in compatibility with BullMQ API—no application code changes required ✓ Monitoring dashboard showing Redis memory savings and offload statistics
Where to Validate
Share your landing page in r/r/webdev — that's exactly where these pain points were discovered.
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