LinkedIn — Notes

Functional

• Profile (work history, education, skills).
• Connection graph (1st/2nd/3rd degree).
• Feed of activity (posts, articles, reactions).
• People search, jobs search.
• Messaging + InMail.
• Notifications.

Non-functional

• 1B users; ~300M MAU.
• p99 profile page < 300 ms.
• Multi-region active for reads.

Capacity

• ~50B notifications/day at peak.
• Feed traffic: 100M+ DAU × 30 feed loads.

Schema highlights

• member(id, headline, location, skills[], experience[], education[])
• connections(a_id, b_id, status, since)
• posts(id, author, ts, body, media[], visibility)
• jobs(id, company_id, title, location, skills[], desc)
• Bi-directional shards on connections (by a and by b) for both-direction queries.

Trade-offs

• Graph queries (2nd-degree) = expensive joins; use precomputed degree counts + traversal limits.
• Recommendation quality = key product differentiator; large investment in ML.
• Recruiter Search has different latency vs free search; separate index.

Refs

• LinkedIn engineering blog (Espresso, Voldemort, Pinot, Helix, Kafka, Samza).
• Galene/Search papers.
• "LinkedIn's Economic Graph" talks.
• ByteByteGo "Design LinkedIn".