VenmoPlus.com is a platform developed by Qingpeng Zhang for exploring the Venmo network, focusing on real-time data processing of transactions and social connections. The system handles historical and real-time transactions through a partitioned graph database, addressing challenges such as calculating graph distances and utilizing caching for second-degree friends. The architecture employs a combination of technologies including Redis for fast graph updates and Elasticsearch for data storage and search.

1. Introducing VenmoPlus.com
Explore your Venmo network
Qingpeng “Q.P.” Zhang
Insight Data Engineering Fellow

2. Venmo ~= Facebook + Paypal

3. Demo VenmoPlus.com
http://venmoplus.com:8999/#/

4. Pipeline
Historical
transactions

5. Pipeline

6. Historical
transactions
Real time
transactions
Pipeline

7. 2013
Biggest Challenge:
● Calculate/Query graph distance in real time

8. ● 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)

9. ● 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?

10. Historical
transactions
Real time
transactions
Two Databases

11. 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)

12. Two Databases

13. This, or that? - to build graph

14. This, or that? - for fast searching

15. Historical
transactions
Real time
transactions
Two Databases

16. Lesson learned

20. VenmoPlus.com
m4.xlarge
m4.large
m4.xlarge
m4.large
t2.micro
$29.11/day

21. About Me
● PhD in Computer Science
● BS in Physics
Volunteers:
● Software Carpentry
● Data Carpentry
● American Red Cross
Christmas Eve 2014, ice storm, Michigan

22. 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 == []?

23. 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.

24. 1 Spark m4.large 0.12 2.88
2 Spark m4.large 0.12 2.88
3 redis m4.xlarge 0.24 5.76
4 Elasticsearc
h
m4.xlarge 0.24 5.76
5 Elasticsearc
h
m4.xlarge 0.24 5.76
6 Kafka,
producer
m4.large 0.12 2.88
7 kafka m4.large 0.12 2.88
8 webserver t2.micro 0.013 0.312
https://github.com/qingpeng/VenmoPlus for more details!
$29.11/24hours

25. 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 == []?

26. Producer [10]
[7,8]
[1-6]
[1-6]
[4,5,6]
[1]
Backend/API
[9]
Frontend [9]
[2,3]

27. 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