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Linked In Lessons Learned And Growth And Scalability
The document discusses LinkedIn's infrastructure and technical challenges in building a distributed system to support their professional social network. Key points include: - LinkedIn needed a way to efficiently perform graph computations across billions of connections stored in a database - Storing the data in memory provided better performance but they needed a way to keep the in-memory data synchronized with the database - They explored various options for a "databus" system to replicate database changes to multiple graph engines in real-time without missing any events - The solution they developed uses Oracle's row-level change tracking to capture all transaction changes and replay them asynchronously to keep the graph engines synchronized with the database.
