A Go framework for asynchronous job processing and scheduling with live monitoring, built into your web app.
JobRunner is a Go framework for performing asynchronous work outside of the HTTP request flow, reducing response latency by offloading non-critical tasks. It allows developers to schedule and queue job functions using cron-like syntax, with built-in live monitoring of job schedules and states.
Go developers building web apps or API service backends who need a built-in job processing framework to handle background tasks without creating separate microservices.
Developers choose JobRunner for its framework-agnostic design that integrates directly into existing Go applications, avoiding premature microservice complexity while providing live monitoring and flexible scheduling options like immediate, delayed, and recurring jobs.
Framework for performing work asynchronously, outside of the request flow
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Supports immediate, delayed, and recurring jobs with cron-like syntax, enabling precise timing for tasks like sending midnight emails or cleaning S3 every 16 minutes.
Provides JSON and HTML endpoints to view current job schedules and states directly within the app, eliminating the need for external monitoring setups initially.
Works with pure Go and popular frameworks like Gin and Echo, as shown in the README examples, allowing easy integration without vendor lock-in.
Processes jobs asynchronously outside the HTTP request flow, which the project claims can reduce response latency by over 200% by offloading non-critical tasks.
Jobs are stored in memory, so application restarts can lead to lost scheduled jobs, making it unreliable for critical or long-running tasks without manual handling.
Monitoring is limited to simple endpoints without features like alerting, historical analytics, or integration with common DevOps tools, which may suffice only for basic oversight.
The README admits 'examples & recipes are coming soon,' indicating gaps in detailed guides, which can hinder adoption or troubleshooting for complex use cases.
Since it runs within the main application, it lacks native support for distributed execution, making scaling challenging as job volume increases without decoupling.