bullmq vs p-queue

backend

bullmq

p-queue

BullMQ and p-queue occupy different niches in the JavaScript backend ecosystem. BullMQ is a production-grade, Redis-backed distributed job queue designed for persistent task management across multiple processes and servers. It handles complex workflows with retries, priorities, scheduling, and monitoring. In contrast, p-queue is a lightweight, in-memory promise concurrency limiter that controls how many async operations run simultaneously within a single process—no persistence, no Redis, no distribution.

This comparison matters because developers often conflate general-purpose queues with concurrency limiters. BullMQ targets teams building scalable microservices, background job processors, or any system requiring durable task queues with horizontal scaling. P-queue serves developers who need simple rate limiting for API calls, controlled parallel processing in scripts, or browser-based async management without external infrastructure. Choosing the wrong tool means either over-engineering simple tasks or under-delivering on reliability requirements.

Head-to-Head

Persistence & Reliability→ bullmq wins

bullmq

Jobs persist in Redis with automatic recovery after crashes or restarts. Failed jobs can retry with exponential backoff, dead-letter queues handle permanent failures, and parent-child job relationships ensure workflow integrity. Data survives process death.

p-queue

Zero persistence—all queued promises exist only in memory. If the process crashes, queued operations vanish. No retry mechanisms, no failure recovery, no job history. Designed for ephemeral tasks where loss is acceptable.

Distribution & Scalability→ bullmq wins

bullmq

Built for horizontal scaling across multiple workers, servers, or containers sharing the same Redis instance. Atomic Redis operations prevent job duplication. Supports rate limiting across distributed workers and handles thousands of jobs per second with proper Redis configuration.

p-queue

Strictly single-process. Cannot distribute work across machines or even Node.js cluster workers. Concurrency limits apply only to the local JavaScript event loop. Scaling requires running multiple independent processes, each with separate p-queue instances.

Setup Complexity→ p-queue wins

bullmq

Requires Redis server installation, configuration, and monitoring. Connection management, Redis memory tuning, and understanding queue/worker/event emitter separation add cognitive overhead. Not trivial for small projects or developers unfamiliar with Redis.

p-queue

Install via npm, import, and use immediately—no external services. Three lines of code gets concurrency limiting working. Zero infrastructure decisions, no DevOps coordination, no connection pooling concerns. Perfect for prototypes and simple scripts.

Memory & Resource Footprint→ p-queue wins

bullmq

Package size around 500-800kB minified with ioredis dependency. Redis connection adds TCP overhead. Job data consumes Redis memory proportional to queue depth. Requires ongoing Redis resource allocation and monitoring.

p-queue

Approximately 5-10kB minified with zero dependencies. All memory usage is transient JavaScript objects in the V8 heap. No network overhead, no persistent storage costs. Negligible resource impact even in resource-constrained environments.

Feature Richness→ bullmq wins

bullmq

Job priorities, scheduled/delayed jobs, repeatable cron-like tasks, parent-child dependencies, FlowProducer for complex workflows, sandboxed processors, rate limiting per worker group, metrics/events for monitoring, and UI integrations like Taskforce.sh.

p-queue

Concurrency limiting, priority queue support, pause/resume/clear operations, promise timeouts, and interval-based rate limiting. Focused solely on controlling parallel async execution without job lifecycle management.

Developer Experience for Simple Tasks→ p-queue wins

bullmq

Overkill for basic async control. Requires defining workers, connecting to Redis, handling job serialization, and managing queue lifecycle even for trivial rate limiting. Debugging involves Redis inspection tools. Learning curve steep for simple needs.

p-queue

Intuitive promise-based API matching standard JavaScript async patterns. Add functions to queue, set concurrency, done. No serialization concerns since closures work directly. TypeScript inference just works. Debugging uses standard Node tools.

Verdict

Choose BullMQ when you need job durability, distributed processing, or any form of persistence. If your system spans multiple servers, requires retry logic, handles critical background tasks that cannot be lost, or needs job scheduling beyond immediate execution, BullMQ is the only viable option here. The Redis requirement is a feature, not a burden—it provides the foundation for reliability that production systems demand. The complexity pays for itself once you need even one advanced feature like delayed jobs or failure recovery.

Choose p-queue for in-memory concurrency control where job loss on crash is acceptable. This includes rate-limiting third-party API calls, controlling parallel file operations in build scripts, managing browser fetch concurrency, or any scenario where you're orchestrating promises in a single process without durability needs. If adding Redis to your stack feels like overkill, it probably is—p-queue solves the immediate problem without infrastructure overhead. Don't use p-queue if jobs must survive restarts or run across multiple processes; don't use BullMQ if you're just limiting concurrent HTTP requests in a one-off script.

When to Use Each

If you are building a microservice that processes webhook payloads asynchronously with retry logic → use BullMQ because webhook delivery failures require persistent retry queues and your service likely runs multiple instances behind a load balancer, necessitating distributed job coordination through Redis.

If you are writing a CLI tool that fetches data from 50 APIs but want to limit concurrent requests to 5 to avoid rate limits → use p-queue because this is ephemeral work in a single process where losing progress on Ctrl+C is acceptable, and installing Redis for a developer tool is absurd overhead.

If you are implementing scheduled email campaigns that must send at specific times, track delivery status, and handle failures → use BullMQ because delayed job execution, persistence across deployments, and delivery guarantees are core requirements that p-queue fundamentally cannot provide.

If you are building a Next.js API route that aggregates data from multiple sources in parallel but want to limit backend load → use p-queue because this is request-scoped work within a single server process where simplicity and zero dependencies matter more than cross-instance coordination.

Learn More

bullmq →p-queue →

Head-to-Head

Persistence & Reliability→ bullmq wins

bullmq

p-queue

Distribution & Scalability→ bullmq wins

bullmq

p-queue

Setup Complexity→ p-queue wins

bullmq

p-queue

Memory & Resource Footprint→ p-queue wins

bullmq

p-queue

Feature Richness→ bullmq wins

bullmq

p-queue

Developer Experience for Simple Tasks→ p-queue wins

bullmq

p-queue

Verdict

When to Use Each