Optimize Event Tracking: Hybrid Failure System

Hey guys! Ever feel like you're drowning in event processing errors? You know, those pesky issues that pop up when your scrapers are trying to snag data, and suddenly, poof, data is missing, malformed, or just plain wrong? It's a common headache in the world of data aggregation, and we've been feeling it too. That's why we've cooked up something pretty neat: a Hybrid Event Failure Tracking System. This isn't just another bug tracker; it's a smart, multi-layered approach designed to give us crystal-clear visibility into why and how our event processing fails, helping us fix things faster and keep our data pipeline humming.

This system is all about combining the best of both worlds: real-time insights for immediate problem detection and historical analysis for spotting trends and identifying deeper issues. We're talking about leveraging existing tools like Oban job metadata for quick checks and building a dedicated table for more in-depth historical tracking. Plus, we're beefing up our dashboard so everyone can see what's going on at a glance. The main goal here is to make our data sources more reliable, figure out which scrapers are top-notch and which need a little TLC, and generally make our lives easier. We want to move from reactive firefighting to proactive improvement, and this hybrid system is our ticket to get there. So, let's dive into how this works and why it's gonna be a game-changer!

Why We Need This: The Pain Points of Event Processing

Alright, let's get real for a sec. When you're dealing with multiple data sources, each with its own quirks and API changes, things will break. It’s not a matter of if, but when. Before this hybrid system, tracking these failures was kind of like searching for a needle in a haystack, blindfolded. We'd get alerts about missing data, dive into logs, and spend ages trying to piece together what went wrong. Was it a temporary network glitch? A change in the source's data format? A bug in our own processing logic? It was tough to say without a lot of manual digging.

Here are some of the big headaches we were facing:

• Lack of Visibility: It was hard to tell in real-time if a specific scraper was having a bad day. We often found out about widespread issues only after significant data loss had occurred.
• Inconsistent Error Reporting: Different scrapers would throw different kinds of errors, making it impossible to compare their reliability or identify common failure patterns across the board.
• Difficulty Prioritizing: Without clear data on which failures were happening most often or impacting data quality the most, it was hard to know where to focus our improvement efforts. Should we fix the rare but critical geocoding error, or the frequent but less severe validation error?
• Debugging Nightmares: When an error did pop up, identifying the exact cause often meant sifting through massive log files, cross-referencing timestamps, and trying to reproduce the issue – a serious time sink.
• No Historical Context: We couldn't easily look back and see if a specific source's error rate was increasing or decreasing over time, making it hard to track the effectiveness of our fixes.

This is where the Hybrid Event Failure Tracking System comes in. It’s designed to tackle these exact problems head-on. By combining real-time metrics from Oban jobs with historical data stored in a dedicated table, we get a comprehensive view. This allows us to not only catch failures as they happen but also to analyze them deeply, understand patterns, and, most importantly, make data-driven decisions to improve the quality and reliability of our entire event discovery system. It’s about transforming our response from ‘panic and fix’ to ‘monitor, analyze, and optimize’.

Architecture Overview: The Three Pillars of Failure Tracking

So, how does this magical hybrid system actually work? We've broken it down into three core components that work together seamlessly to give us the best of both worlds: immediate insights and deep historical analysis. Think of it as a three-legged stool – each part is essential for stability and effectiveness.

Component 1: Oban Job Metadata – The Real-Time Pulse

This is our first line of defense, the quick-and-dirty way to know what's happening right now. We’re leveraging the existing metadata capabilities within Oban, our trusty job-queueing system. When an event processing job runs – whether it succeeds or fails – we attach a small piece of information to that job's record. This metadata includes a status (success or failed), the external_id of the event being processed, and a processed_at timestamp. Crucially, if it fails, we also log the error_category and a brief error_message. This is super lightweight; we’re not creating new tables or complex processes here. It means we can query these jobs directly and see, almost instantly, which ones are failing and why, right from the Oban interface or through simple database queries. This gives us immediate visibility without a heavy performance hit.

Component 2: Failure Aggregation Table – The Historical Deep Dive

While Oban metadata is great for real-time checks, it's not ideal for long-term historical analysis. That's where our dedicated discovery_event_failures table comes in. This table is designed to aggregate recurring failures. Instead of logging every single instance of the same error, we group them. Each entry in this table represents a unique combination of source_id, error_category, and error_message. We store how many times this specific error has occurred (occurrence_count), when it was first seen (first_seen_at), and when it was last seen (last_seen_at). To help with debugging, we also keep a small sample of external_ids that encountered this error. This table has a 90-day retention policy, meaning old records are automatically pruned to keep storage manageable. This component allows us to spot trends, identify the most persistent issues, and analyze the historical reliability of each data source.

Component 3: Enhanced Dashboard – The Unified View

All this data is useless if you can't easily access and understand it. That's why we’re enhancing our admin dashboard. This dashboard will query both the Oban job metadata and the discovery_event_failures table. It will present key metrics like the success rate percentage per scraper, a breakdown of errors by category, and the ability to drill down for more details. We'll be able to see, at a glance, which sources are performing well, which are struggling, and what types of errors are most common. This provides a single source of truth for understanding the health of our event discovery system and guides our efforts for improvement. Together, these three components create a robust system that’s both responsive to current issues and insightful for long-term optimization.

Component 1: Oban Job Metadata Tracking – The Immediate Feedback Loop

Let's get down to the nitty-gritty of how we're implementing the first component: tracking failures directly within Oban job metadata. This is all about getting immediate feedback without adding a ton of complexity. The core idea is simple: whenever an event processing job finishes, we update its metadata with a status and relevant details. This is super efficient because Oban already stores this metadata, so we're not introducing new database tables or complex background jobs just for this basic tracking.

Metadata Structure: What We Store

When a job completes, whether it's a success or a failure, we add or update a JSON blob in the meta field of the oban_jobs table. This metadata is structured to be easily readable and queryable.

• On Success: We store `{