4. ● Cache of 2nd degree friends list
● Partitioned GraphDB
● Good for Linkedin (hundreds of million
users, with higher degree)
● 5 million vertices (users)
● 32 million distinct edges (transactions)
● 88 million total edges (transactions)
5. ● Cache of 2nd degree friends list
● Partitioned GraphDB
● Good for Linkedin (hundreds of million
users, with higher degree)
● 5 million vertices (users)
● 32 million distinct edges (transactions)
● 88 million total edges (transactions)
No cache (precalculation)?
No GraphDB?
7. Two Databases
420890 Graham Hadley
1630476 Leon Tang
810029 Harminder Toor
1371353 Ephraim Park
562884 Paul Min
420890 set(14935158, 562884)
1630476 set(1371353)
810029 set(190230,14935158)
1371353 set(810029,971156)
562884 set(196371,1371353)
11. About Me
● Postdoc in Lawrence Berkeley National Lab
● PhD in Computer Science, Michigan State
● BS in Physics, Nanjing U.
Certified Volunteers:
● Software Carpentry
● Data Carpentry
● American Red Cross
Christmas Eve 2014, ice storm, Michigan
12. Algorithm Optimization
Shortest distance -> intersection of sets (friend lists)
● 1st degree friends of A ∩ 1st degree friends of B == [] ?
● 2nd degree friends of A ∩ 1st degree friends of B == []?
13. Algorithms Design -2
Query distance between vertices in a historic moment in a constantly changing graph (because we
don’t pre-calculate the distance….)
● A recent transaction for a user is history and has changed the graph
● Query distance of the two users at that moment.
○ not considering that specific transaction)
○ Remove the influence of that specific transaction temporarily and restore
■ Test if that transaction is the first between the pair of users.
15. Algorithms
Distance detection between vertices in graph (1st, 2nd, 3rd friends?)
● 1st degree friends of A ∩ 1st degree friends of B == [] ?
● 2nd degree friends of A ∩ 1st degree friends of B == []?
17. Redis:
● Graph Edges: userID -> userID
● Graph Vertices: userID -> userName
In memory DB -> Fast graph updating, graph traversal, in real time
ElasticSearch:
● Everything about the transactions
Distributed -> Data storage and full text search, in real time
Big Challenge:
● Graph distance + Common connections in real time
