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LinkedIn HLD: Degrees of Separation on a Billion-Edge Graph

A LinkedIn system design: the connection graph, degrees of separation via BFS, People You May Know ranked by mutual connections, why deep traversal is precomputed, and scaling the graph.

By fiveyearsdevJune 14, 20268 min read

"Design LinkedIn." Profiles and a feed are the visible surface, but the thing that makes it a network is the connection graph — and two queries on it define the product. "How are we connected?" (the 1st / 2nd / 3rd-degree badge) is a shortest-path search through the graph. "People You May Know" is the friends-of-friends you share the most connections with. Both are graph traversals, and both run into the same wall: with a billion members each holding hundreds of connections, naïvely fanning out three hops touches millions of nodes per query — so the design is as much about when to precompute a…

What’s inside

Let's start nowhere near a computer

Where this exact shape shows up

Step 1 — Functional requirements (sentences first)

Step 2 — Non-functional requirements

Step 3 — The graph and degrees of separation

Step 4 — People You May Know

Step 5 — Which datastore

Step 6 — Trade-offs (each one keeping an NFR)

The complete implementation

Step 7 — Only now, the boxes

Step 8 — Scaling the design, one bottleneck at a time

Step 9 — When a piece fails: designing for failure

The interview corner

Where to go from here

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