This document describes Epiphany, Rocket Fuel's real-time attribution platform. It connects millions of events to 50 billion data points to attribute conversions across devices and algorithms. It uses HBase to lookup impressions in milliseconds. Data flows from actions keyed by user/impression/conversion days to HBase and Hive tables. It enables idempotent attribution across advertisers and algorithms at scale in real-time.

1. Epiphany:
Connecting Millions Of Events To 50 Billion
Data Points In Real-time
Anirban Banerjee
abanerjee@rocketfuel.com
Shahansad Kp
skp@rocketfuel.com

2. 01
ONLINE ADVERTISING ECOSYSTEM
In a nutshell

3. Advertisers
Publishers
Users
ZZZX
Exchanges
Page Request
1
Ad Request
2
Bid Request
3
Bid Response
4
Bid Win
5
Ad served
6
Ad served
7
0
Set preferences
Click & Visit
Serve Impression
Convert (e.g. buy a product)
Observers

4. 02 Attribution
Mapping effects to causes

5. How Was This "Conversion" Achieved?
- Identify the effect of every single impression across
every medium during the customer’s journey
- Needed by modeling, reporting, analysts, customers.

6. Last Touch Attribution
Whole credit to a single event

7. Multi Touch Attribution Across Multiple Devices
Partial credits across impressions.

8. Multiple Algorithms
Algorithm 1.
Algorithm 2.
Algorithm 3.

9. Attribution using Advertiser Data

10. 03 Epiphany
Requirements

11. Action by
Impression day
Action by
Conversion day
Rocket Fuel
Attribution
Previous Day
Advertiser Data
Current Day
Rocket Fuel Conversion
Data
Reattribution
Data Flow & Data Democracy
Analysts
Downstream
ETL
Rocket Fuel Impression
History Data

12. Batch and Realtime

13. (|Conversions| * |Impressions| *
|Algorithms|)
Impressions
Tens of Billions
Advertiser reports
Thousands
Conversions
Hundreds
of millions
Algorithms
Hundreds
O

14. 04 Epiphany
Rocket Fuel attribution platform

15. Rocket Fuel Attribution

16. HBase backed object lookup of impression table
Powered by Blackbird Collections
Lookup in milliseconds

17. Rocket Fuel Attribution

18. Advertiser Attribution

19. Skipping filter on column qualifiers

20. Point updates to hive
Point updates to a intermediate HBase table
Periodically pulled to Hive

21. Epiphany Tables
Action keyed by
Impression day
Action keyed by
Conversion day
Action keyed by
User Id
HBase Table
Hive Table
INDEXAction by
Conversion day
Action by
Impression day

22. Intermediate Table Data Flow
Action keyed by
User Id
Records with deltaTimestamp
based scan
Action keyed by
Impression day
Action keyed by
Conversion day
Old state of records
with delta
Point reads
Computed “changes”

23. Idempotency
Reducer_1_attempt_1
Reducer_2_attempt_1
Reducer_1_attempt_2
Job
Hbase
Idempotency at record level is necessary for correctness

24. Hive Table Data Flow
Hbase intermediate table
Hive table
Snapshot
Scan with
prefix filter
Snapshot using
HBase admin

25. Epiphany Architecture

27. Test releases with HBase snapshots
Monitor health of HBase instance
Use WAL (Write ahead log)

28. Generic solution at scale
One ring to rule them all
- Multiple attribution algorithms
- Cross-device scenario
- Advertiser attribution data
Faster availability, faster experiments
More accessible data
- e.g. point-readable actions

29. Anirban Banerjee
abanerjee@rocketfuel.com
Shahansad Kp
skp@rocketfuel.com
[Major Contributors]
Abhijit Pol
Savin Goyal
Zhan Yuan
WE ARE HIRING!!!