Que vs Sidekiq: What are the differences?

Introduction

In this Markdown code, we will outline the key differences between Que and Sidekiq, two popular background job processing libraries for Ruby.

1. Concurrency Model: Que uses a threaded concurrency model, where each job is processed within its own thread. This allows for parallel execution of jobs, minimizing overall processing time. On the other hand, Sidekiq uses a multi-process concurrency model, where multiple worker processes can be spun up to handle jobs concurrently. This approach allows for scalability by utilizing multiple CPU cores effectively.

2. Job Prioritization: Que has built-in support for job prioritization, allowing developers to specify the urgency of each job. Que assigns a priority value to each job, and the worker then processes jobs in priority order. In contrast, Sidekiq does not have native support for job prioritization. However, developers can use various plugins or custom implementation to achieve prioritization functionality with Sidekiq.

3. Dependency Requirements: Que has minimal dependencies and is designed to work with PostgreSQL as its primary data store. It leverages PostgreSQL's advanced features like advisory locks and MVCC to provide robust job queueing and execution. Sidekiq, on the other hand, requires Redis as a dependency to store job data and use it as a messaging backend. This dependency on Redis provides high-speed job processing and distributed work across multiple processes or machines.

4. Concurrency Limit: In Que, the number of concurrent threads that can execute jobs is determined by the database connection pool. By adjusting the connection pool size, developers can control the concurrency limit. Sidekiq, on the contrary, allows setting a fixed number of worker processes to achieve concurrency control. This approach provides flexibility in managing resource utilization and balancing the load.

5. Retry Mechanism: Sidekiq has a comprehensive retry mechanism built-in, where failed jobs can be automatically retried based on configurable rules, such as exponential backoff. Developers can specify the maximum number of retries or even implement custom retry logic. In comparison, Que does not have native support for automatic retries. However, developers can implement their own retry mechanism by monitoring the job status and re-enqueuing failed jobs manually.

6. Monitoring and Dashboard: Sidekiq provides a web-based dashboard to monitor jobs, queues, and worker processes in real-time. This dashboard offers insights into job status, failures, and performance metrics. Que, on the other hand, does not provide a built-in dashboard. However, developers can use various third-party libraries or build custom monitoring solutions based on Que's provided APIs.

In summary, Que and Sidekiq differ in their concurrency model, job prioritization capabilities, dependency requirements, concurrency limit control, retry mechanism availability, and built-in monitoring/dashboard features.