I've helped huge companies scale logs analysis. Here’s how.

Log analysis at scale, especially in multi-tenant architectures, presents unique challenges that go beyond just storing and searching logs. This discussion emphasizes the importance of effective log ingestion, storage, and retrieval in user-facing systems. Different approaches to log ingestion include direct logging, utilizing a sidecar container, and hybrid methods that accommodate evolving needs. For storage, managing multi-tenancy effectively requires strategies like separating storage and compute, or identifying logs with unique client IDs to avoid inefficiencies. Efficient log retrieval involves pre-calculating aggregates to provide quick insights and leveraging pagination to reduce latency, ensuring users can explore logs swiftly without overwhelming systems. While free text search over logs remains complex, tools like Tinybird offer scalable solutions for log analytics, as demonstrated by companies like Vercel. The overall emphasis is on choosing methods that balance simplicity, cost-efficiency, and scalability to enhance the user experience in log exploration and analysis.