Tj_lavin: Configuring Job Rescheduling At Worker Level

Hey everyone! Today, we're diving deep into an essential feature for robust job processing in tj_lavin: rescheduling jobs. Specifically, we'll explore how to make job rescheduling a configurable option at the worker level, which gives you much finer control over your background tasks. Currently, the way tj_lavin is set up, any job that fails is essentially dead in the water – it won't be retried. This can be a major headache, especially in systems where transient errors are common.

The Problem: Jobs That Never Retry

Right now, in the tj_lavin codebase, there are a couple of lines that prevent failed jobs from being rescheduled. Let's take a look at these lines:

- https://github.com/russ/tj_lavin/blob/9e8c0d341a62dfc65c7ed5dfc4ef44ff1016b5a2/src/tj_lavin/runner.cr?plain=1#L33
- https://github.com/russ/tj_lavin/blob/9e8c0d341a62dfc65c7ed5dfc4ef44ff1016b5a2/src/tj_lavin/runner.cr?plain=1#L41

These lines effectively ensure that if a job fails for any reason, it won't be automatically requeued or retried. This is a pretty rigid approach and doesn't account for situations where a job might fail due to temporary issues like network hiccups, database connection problems, or external service outages. In these cases, retrying the job makes perfect sense, and we definitely don't want to lose that capability.

Why is Rescheduling Important?

Before we move on, let's emphasize why rescheduling is so important in background job processing.

• Reliability: Rescheduling helps make our systems more reliable. If a job fails due to a transient error, retrying it increases the chances of it eventually succeeding.
• Data Consistency: In many cases, jobs perform critical tasks that affect data consistency. If a job fails and isn't retried, it can lead to inconsistencies in our data, which can be a nightmare to debug and fix.
• User Experience: For jobs that directly impact user experience (e.g., sending emails, processing payments), rescheduling can prevent disruptions and ensure a smoother experience for our users.
• Resource Utilization: In distributed systems, resources can sometimes be temporarily unavailable. Rescheduling allows jobs to be processed when resources become available again, optimizing resource utilization.

To make tj_lavin more robust, we need a more flexible way to handle job failures. This is where worker-level configuration comes in.

The Solution: Worker-Level Rescheduling Configuration

The core idea here is to give each worker the ability to define its own rescheduling policy. Instead of a global setting that applies to all jobs, we want to be able to say,