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  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Background Processing
  5. Hangfire vs Que

Hangfire vs Que

OverviewComparisonAlternatives

Overview

Que
Que
Stacks16
Followers20
Votes0
Hangfire
Hangfire
Stacks333
Followers249
Votes17
GitHub Stars9.9K
Forks1.7K

Hangfire vs Que: What are the differences?

Developers describe Hangfire as "Perform background processing in .NET and .NET Core applications". It is an open-source framework that helps you to create, process and manage your background jobs, i.e. operations you don't want to put in your request processing pipeline. It supports all kind of background tasks – short-running and long-running, CPU intensive and I/O intensive, one shot and recurrent. On the other hand, Que is detailed as "A Ruby job queue that uses PostgreSQL's advisory locks for speed and reliability". Que is a high-performance alternative to DelayedJob or QueueClassic that improves the reliability of your application by protecting your jobs with the same ACID guarantees as the rest of your data.

Hangfire and Que can be primarily classified as "Background Processing" tools.

Hangfire and Que are both open source tools. It seems that Hangfire with 4.93K GitHub stars and 1.12K forks on GitHub has more adoption than Que with 1.49K GitHub stars and 124 GitHub forks.

According to the StackShare community, Hangfire has a broader approval, being mentioned in 9 company stacks & 9 developers stacks; compared to Que, which is listed in 4 company stacks and 6 developer stacks.

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Detailed Comparison

Que
Que
Hangfire
Hangfire

Que is a high-performance alternative to DelayedJob or QueueClassic that improves the reliability of your application by protecting your jobs with the same ACID guarantees as the rest of your data.

It is an open-source framework that helps you to create, process and manage your background jobs, i.e. operations you don't want to put in your request processing pipeline. It supports all kind of background tasks – short-running and long-running, CPU intensive and I/O intensive, one shot and recurrent.

Concurrency; Efficiency; Safety;Transactional Control;Atomic Backups;Fewer Dependencies; Security
-
Statistics
GitHub Stars
-
GitHub Stars
9.9K
GitHub Forks
-
GitHub Forks
1.7K
Stacks
16
Stacks
333
Followers
20
Followers
249
Votes
0
Votes
17
Pros & Cons
No community feedback yet
Pros
  • 7
    Integrated UI dashboard
  • 5
    Simple
  • 3
    Robust
  • 2
    In Memory
  • 0
    Simole

What are some alternatives to Que, Hangfire?

Sidekiq

Sidekiq

Sidekiq uses threads to handle many jobs at the same time in the same process. It does not require Rails but will integrate tightly with Rails 3/4 to make background processing dead simple.

Beanstalkd

Beanstalkd

Beanstalks's interface is generic, but was originally designed for reducing the latency of page views in high-volume web applications by running time-consuming tasks asynchronously.

Resque

Resque

Background jobs can be any Ruby class or module that responds to perform. Your existing classes can easily be converted to background jobs or you can create new classes specifically to do work. Or, you can do both.

delayed_job

delayed_job

Delayed_job (or DJ) encapsulates the common pattern of asynchronously executing longer tasks in the background. It is a direct extraction from Shopify where the job table is responsible for a multitude of core tasks.

Faktory

Faktory

Redis -> Sidekiq == Faktory -> Faktory. Faktory is a server daemon which provides a simple API to produce and consume background jobs. Jobs are a small JSON hash with a few mandatory keys.

Kue

Kue

Kue is a feature rich priority job queue for node.js backed by redis. A key feature of Kue is its clean user-interface for viewing and managing queued, active, failed, and completed jobs.

Bull

Bull

The fastest, most reliable, Redis-based queue for Node. Carefully written for rock solid stability and atomicity.

Flow-Like

Flow-Like

Mission-critical automation you can audit, control and run on-prem. No black boxes. No silent failures. No data leaks. Built for teams that cannot afford uncertainty.

ETLR

ETLR

Production-grade workflow automation. No drag-and-drop required. Build, version, and deploy your workflows with YAML.

Maestro

Maestro

Run AI coding agents autonomously for days. Maestro is a cross-platform desktop app for orchestrating your fleet of AI agents and projects. It's a high-velocity solution for hackers who are juggling multiple projects in parallel. Designed for power users who live on the keyboard and rarely touch the mouse. Collaborate with AI to create detailed specification documents, then let Auto Run execute them automatically, each task in a fresh session with clean context. Allowing for long-running unattended sessions, my current record is nearly 24 hours of continuous runtime. Run multiple agents in parallel with a Linear/Superhuman-level responsive interface. Currently supporting Claude Code, OpenAI Codex, and OpenCode with plans for additional agentic coding tools (Aider, Gemini CLI, Qwen3 Coder) based on user demand.

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