Pinterest Engineering

Overview

Pinterest is the largest visual discovery platform, where every pin is a multimodal object and the central engineering problem is recommending images that a user will save now from a corpus of hundreds of billions of pins. The blog is strongest in three areas: real-time recommendations that incorporate within-session user actions to update the homefeed in seconds, large-scale memcached and distributed caching tuned for the read-heavy fan-out pattern that recommendations create, and the 60x scale-out of the ads corpus that the retrieval and ranking stack now has to support. For interviews, 'How Pinterest Leverages Realtime User Actions for Homefeed' is a strong reference for session-aware recommendations, and 'Improving Distributed Caching Performance at Pinterest' anchors any caching-architecture design.

Interview questions

• What can I learn from Pinterest's engineering blog?
• Which Pinterest posts are most useful for recommendation-system interviews?
• Why is Pinterest's blog valuable for caching-architecture discussions?

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