Twitter Notifications: Infrastructure, Challenges, and Personalization

Twitter's notifications infrastructure enables real-time user engagement by delivering personalized updates based on interests and interactions.

### Enhancing Real-Time Engagement: Twitter's Notification Infrastructure
* 🏏 Recently, IPL cricket auctions significantly impacted players like Ben Stokes, who shared his **excitement about being signed by Pune**, illustrating the **real-time engagement Twitter facilitates during live events**.
* 📲 My name is Saul Apothic, and I manage the notifications team at Twitter, where we focus on **keeping users informed about real-time happenings** in their world through various notification channels.
* 🔔 We utilize four main notification channels: the **notification timeline**, **push notifications**, **SMS**, and **email**, each serving unique purposes to **enhance user experience**.
* ⚡ However, we face challenges such as **latency**, **spikes in notification volume**, and the need for **resilience in our infrastructure** to ensure **timely delivery of notifications**, especially during **peak events**.
* 🏗 Our push architecture is designed to handle these challenges by implementing **horizontal scaling**, **short-lived caches**, and **priority queues** to manage the diverse types of notifications efficiently.
* 📈 Triggered notifications, the highest volume category, are generated by user interactions, while personalized notifications help users **stay updated on their interests**, enhancing the overall **Twitter experience**.
* 🤖 We also leverage **personalized fan-out algorithms** to ensure users receive notifications relevant to their interests, while recommendations allow us to suggest content **beyond users' immediate followings**.
* 💡 Ultimately, understanding the **balance between real-time and personalized notifications** is crucial for optimizing user engagement, and we continuously adapt our infrastructure to meet these **evolving needs**.

Real-Time & Personalized Notifications @Twitter

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InfoQ

Introduction and Context: Cricket Auction Anecdote & Twitter's Role

The speaker shares a personal anecdote about the IPL cricket auction and Ben Stokes.

The anecdote's relevance to Twitter's jobs-to-be-done framework is explained.

The anecdote showcases Twitter's use case as a platform for live event following.

The speaker's team's role in enabling real-time user notifications is highlighted.

Introduction of the speakers: Saul Apothic and Gary Lamb.

Overview of Twitter Notifications: Channels and Challenges

A high-level overview of Twitter's notification system is presented.

The four main notification channels are outlined: timeline, push, SMS, and email.

Details on each channel's functionality and usage are provided.

Major challenges faced in scaling the notification system are introduced.

The bimodal distribution of notification recipients is discussed.

Four main challenges are highlighted: latency, spikes, heterogeneity, and resilience.

Infrastructure Solutions for Notification Challenges

A high-level overview of the push architecture for push, SMS, and email notifications is given.

Strategies for handling latency and spikes are explained, emphasizing horizontal scaling and short-lived caches.

Solutions for heterogeneity are discussed, focusing on priority queues and task grouping.

The pull architecture for the notification timeline is described, highlighting the use of long-lived caches.

Key considerations for building a notification infrastructure are mentioned: ease of adding notifications and backend-driven experiments.

Key Infrastructure Takeaways and Design Principles

Three key takeaways are emphasized: asynchronous operations, trade-offs between write and read paths, and multi-DC considerations.

Evolution of Twitter Notifications: Triggered Notifications, Personalization, and Recommendations

Triggered notifications, their high volume, and bimodal distribution are discussed.

The limitations of triggered notifications and the need for personalization are explained.

Personalization methods are introduced: personalized fan-out and recommendations.

Personalized Fan-Out: Algorithm and Implementation Details

The personalized fan-out algorithm is explained, focusing on tracking user interests and top followings.

The solution to scalability challenges through co-location and data pre-processing is described.

Data pre-processing techniques for efficient handling of recent engagements and top followings are detailed.

Key takeaways for personalized fan-out are summarized.

Recommendations: A Scalable Approach to Personalization

Recommendations as a second personalization method are introduced, emphasizing improved scalability.

The three stages of the recommendations process are explained: fatigue, fetch, and rank.

The use of machine learning for ranking and the importance of data collection are highlighted.

Key takeaways for recommendations are summarized.

Conclusion: Choosing the Right Approach for Notification Systems

The three notification systems (real-time, personalized fan-out, recommendations) are compared.

The importance of considering the trade-offs between real-time delivery and personalization is emphasized.