Boost Notifications: Auto-Mark & Resilient Caching

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Boost Notifications: Auto-Mark & Resilient Caching

Hey guys! Let's dive into a cool project: enhancing the notification system with auto-mark-on-view and resilient caching. This setup will make sure those notifications are super reliable, work smoothly offline, and give you a better overall experience. We're talking about making it effortless to keep up with what's happening, even when you're on the go or have a flaky internet connection. I'll break down the key parts, from how notifications get marked as read to how we're making them available offline. Ready? Let's get started!

Auto-Mark-on-View Implementation

Auto-mark-on-view is a fantastic feature. Think of it as a smart way to manage your notifications without you having to manually click on each one. The system automatically marks notifications as read when they're visible on your screen. We're using the IntersectionObserver API for this, which is a powerful tool for detecting when an element enters or leaves the viewport (the visible area of your browser window). To make things extra efficient, we're implementing a queue and throttling the requests. This means we avoid bombarding the server with a ton of requests all at once. We'll be retrying any failed requests to ensure everything gets marked as read eventually. And because we value data, we're persisting any pending IDs locally. This prevents any data loss and guarantees that everything is synced up, even in shaky network conditions. We don't want to accidentally mark notifications as read on ubq.fi by default, so we're keeping that aspect separate.

Now, how does this work under the hood? First, we need to understand how the IntersectionObserver helps us. This API passively monitors the visibility of our notification threads. When a thread comes into view, the observer signals that it's visible. With a clever combination of a queue and throttling, we can ensure that we handle these 'mark as read' requests efficiently. When a notification becomes visible, its ID gets added to a queue. A separate process then pulls IDs from this queue and sends them to the server to be marked as read. Throttling comes into play here, which means we limit the number of requests sent within a specific timeframe. This prevents overloading the server and keeps everything running smoothly. What if a request fails? No problem! The system is set up to retry any failed attempts. This retry mechanism ensures that notifications are eventually marked as read, even if there are temporary network issues. We also need to think about what happens when the user is offline or experiences network instability. In these cases, we'll store the IDs of the notifications that need to be marked as read locally. This local storage acts as a backup, ensuring that even if the network fails mid-operation, we don't lose track of what needs to be done. Upon reconnection, the system can sync the local data with the server.

This process is designed to be highly reliable. We're focusing on minimizing any disruptions and providing a seamless experience. Imagine you're browsing through a long list of notifications, and as you scroll, the items automatically get marked as read. You don't have to lift a finger—the system takes care of it. This automation saves time, reduces cognitive load, and keeps you informed with minimal effort. The auto-mark-on-view system is a game changer, improving both efficiency and usability. Our primary goal is to provide a notification system that's intuitive and always on. This approach gives you peace of mind, knowing your notifications are managed effectively without your constant involvement.

Resilient Notification Caching

Alright, let's talk about resilient notification caching. We need to make sure your notifications are readily available, even when you're offline or dealing with a slow internet connection. We are using IndexedDB to store both raw and aggregated notifications. We'll set a Time To Live (TTL) of around 5 minutes, which will help us keep the data fresh while also ensuring we're not storing outdated information. In the case of aggregated data, this will be rebuilt from the cached raw data if needed. This means that even if you're offline, you'll still be able to access your notifications. The system will leverage the cached data. This is achieved through a multi-layered caching strategy. The raw notifications are stored in IndexedDB, which is a local, persistent storage system. These raw notifications serve as the foundation of your notification data. From this foundation, we will build aggregated data. This might include counting unread notifications or grouping notifications. So, if the aggregated data is missing or becomes outdated, the system rebuilds it from the raw data. This way, your view of the notifications remains consistent.

Next, the system will use a pull-to-refresh mechanism. This will not only clear the in-memory cache and the IndexedDB cache but also signal the service worker to drop the API cache before refetching the data. This means that when you refresh, the system fetches the newest data from the server. The service worker is essential for providing the offline experience. The service worker will use a network-first strategy for the GitHub API, with a TTL of roughly 10 minutes. This strategy ensures the service worker first attempts to fetch data from the network, falling back to the cached version if the network is unavailable. This is crucial for a smooth offline experience. A TTL of 10 minutes ensures that the cache is frequently updated. We will be using a CLEAR_API_CACHE message to handle these scenarios. This message is sent to the service worker to clear the API cache. This keeps the data up-to-date.

By leveraging the combined power of IndexedDB, the network-first strategy, and a service worker, we're building a highly resilient notification system. The offline capabilities make sure you're always in the loop. The TTL settings make sure the data stays fresh. This is a robust approach to notification management, giving you the best of both online and offline worlds. This strategy not only enhances the user experience but also makes the notification system significantly more reliable. It is designed to work whether you have a strong, weak, or non-existent internet connection. This is the cornerstone of a smooth, uninterrupted notification experience.

Empty-State Rendering

Last but not least, let's look at the empty-state rendering. It is important to cover all the bases to make sure that the user has a great experience, no matter the scenario. This rendering ensures that the user is never left with a blank screen. It works when there are no notifications at all or when filters remove all items, such as those from a bot or slash command. When there are no notifications, a friendly message will appear, letting the user know there's nothing new to see. When filters are applied, and all the items are hidden, the empty-state rendering will inform the user about the absence of notifications based on the filters. For instance, if the user has filtered out all bot notifications, the system will explain why no notifications are shown. This feature improves usability, providing clear feedback in all circumstances. It avoids any confusion about what's going on with the notifications. By handling empty states correctly, we are creating a more user-friendly and intuitive experience. The empty-state rendering is a crucial component of a user-friendly design. It prevents confusion and makes sure users always understand the current status of their notifications. This is another important detail we cannot ignore.

Files in Focus

Let's break down the files we will be working with.

  • src/home/rendering/render-github-notifications.ts: This file is responsible for the visual representation of the notifications. It takes the data from the cache and renders it in a user-friendly format. The key is to display the right information at the right time. This includes handling different notification types, formatting timestamps, and displaying relevant details.

  • src/home/home.ts: This file will be the central hub for the notification system. It manages the flow of data, handles user interactions, and coordinates the actions of different parts of the system. This file will orchestrate the fetching, caching, and displaying of the notifications.

  • src/home/getters/get-indexed-db.ts: This is where we handle the IndexedDB interaction. This file will manage the storage and retrieval of cached notifications. We will also include strategies for updating and deleting data within IndexedDB.

  • static/progressive-web-app.js: This is where we will enable the Progressive Web App (PWA) features. This file is critical for offline capabilities and background synchronization. This file's implementation enables features such as caching and offline support. It's essentially the backbone for making our web application function seamlessly, even with no network connection.

  • src/home/fetch-github/fetch-data.ts: This file handles fetching data from GitHub's API. It includes all the necessary code for making API requests, handling responses, and managing errors. This file also addresses how we handle network requests, including error handling and the caching strategies described above.

Conclusion

So there you have it, guys. We are setting up a super-reliable and user-friendly notification system by implementing auto-mark-on-view and resilient caching. We're prioritizing performance, reliability, and an intuitive user experience. We're building a system that keeps you informed, whether you're online or offline. This project is all about making the notification experience seamless and accessible. We are creating a system that adapts to your needs and keeps you in the loop without you having to lift a finger. Keep an eye out for updates as we continue to refine and improve this system. Thanks for joining me on this journey, and I hope this gives you a good understanding of what we're aiming for.