🍔🧠 1M Events/Sec: Inside Airbnb's Personalization System

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📚 Software Engineering Articles

• 1M+ engineers read this to learn AI. Shouldn’t you?

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🗞️ Tech and AI Trends

• OpenAI considers Chrome acquisition to build AI-first browser

• Driverless trucks hit Texas highways soon

• Perplexity's new browser will track everything for personalized ads

👨🏻‍💻 Coding Tip

• Use Postgres LATERAL JOIN for efficient top-N queries and dynamic filtering

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How Airbnb Powers Real-time Personalization at Scale 🧑‍🔬 

Airbnb built a User Signals Platform (USP) that processes millions of user interactions in real-time to deliver personalized experiences. This system turns raw events into actionable insights within seconds, enabling teams to build features without deep streaming expertise.

The challenge: Build a system that processes 1 M+ events/second while maintaining sub-second latency and making it accessible to product teams who aren't streaming experts.

Implementation highlights:

1. Event-driven architecture: Choose Flink over Spark for true event-by-event processing instead of micro-batching

2. Append-only data model: Used versioned timestamps for writes to ensure idempotency and simplify debugging

3. Config-first interface: Created YAML-based signal definitions to abstract away streaming complexity

4. Real-time joins: Implemented stateful joins between streams using RocksDB for richer context

5. Hot standby recovery: Deployed pre-warmed Flink Task Managers for instant failover

Results and learnings:

• Massive scale: Processing 1M+ events/second across 100+ Flink jobs

• Lightning fast: Achieved sub-second end-to-end latency for personalization

• Developer-friendly: Enabled dozens of teams to build features without streaming expertise

The USP shows that real-time systems don't have to be complex to be powerful. By focusing on developer experience and operational resilience, Airbnb created a platform that's both sophisticated and accessible.

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🤖 Anthropic Unveils Claude Code Best Practices for Agentic Coding (5 min)

Brief: Anthropic shares proven workflows and optimization tips for its Claude Code CLI tool, including context management, safety controls, and multi-agent collaboration to enhance developer productivity.

💻 Anthropic Shares Claude Code Best Practices for Agentic Coding (5 min)

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🤖 OpenAI Considers Buying Chrome to Create AI-First Browser (3 min)

Brief: OpenAI's exec reveals the company is interested in acquiring Chrome to transform it into an AI-first experience, potentially integrating ChatGPT across the browser. The move could give OpenAI access to 4 billion users and valuable training data.

🔍 Perplexity CEO Reveals Controversial Browser Tracking Plan for Hyper-Personalized Ads (2 min)

Brief: Perplexity's CEO discloses plans for comprehensive user tracking in their browser to enable hyper-personalized advertising, raising immediate privacy concerns.

🚛 Driverless Semi-Trucks Hit Texas Highways in Groundbreaking Pilot (2 min)

Brief: Texas approves a large-scale pilot program allowing driverless semi-trucks to operate on public highways, marking a major step toward autonomous freight transportation.

This week’s coding challenge:

This week’s tip:

Use Postgres' LATERAL JOIN to reference previous FROM items in subqueries, enabling powerful correlated queries and dynamic filtering across related tables. This advanced JOIN type allows subqueries in the FROM clause to access columns from preceding tables, making complex data transformations more efficient.

Wen?

• Top-N per group queries: Fetch the most recent orders, highest-value transactions, or latest comments for each user/category efficiently.

• Dynamic filtering: Apply row-dependent calculations where subsequent joins need to reference values from previous tables.

• Performance optimization: Replace multiple correlated subqueries with a single LATERAL join, potentially improving query execution time for complex aggregations.

“In order to control myself, I must first accept myself by going with and not against my nature.”
Bruce Lee

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