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BERLIN • NEW YORK • SAN FRANCISCO • SÃO PAULO • PARIS • LONDON • MOSCOW • ISTANBUL
SEOUL • SHANGHAI • BEIJING • TOKYO • MUMBAI • SINGAPORE
Scaling a platform for
real-time fraud detection
2
Why is no one prepared for
success?
3
‣ Focus on idea
‣ Focus on execution is limited in time to market
‣ Underestimating importance of scalability and profitability
Startup Zeitgeist
4
‣ Director of Engineering
‣ Consulted with early design decisions
‣ Joined Adjust 2012 as Head of IT Operations
Who am I?
Who is Adjust?
1B+
Daily active users tracked
400+ Billion
Data points tracked monthly
22K+
Apps tracked
6
‣ Automatically reject fraudulent data before it gets paid
‣ Sends rejection reason callbacks to all parties
‣ Customers and their networks have full transparency
‣ Real-time statistical analysis of all ad engagements and app activity
Fraud Prevention Suite
7
How did we do that?
8
Bootstrapping a product
Current approach
‣ Using cloud services
‣ Base product on 3rd party IP
‣ Faster development
9
Bootstrapping a product
Current approach Our approach
‣ Using cloud services
‣ Base product on 3rd party IP
‣ Faster development
‣ Own infrastructure
‣ Develop own IP
‣ Slower ramp up
10
Bootstrapping a product
Current approach Our approach
‣ Using cloud services
‣ Base product on 3rd party IP
‣ Faster development
‣ Potential lock in
‣ More cost efficient in the beginning
‣ Shared environment
‣ Own infrastructure
‣ Develop own IP
‣ Slower ramp up
11
Bootstrapping a product
Current approach Our approach
‣ Using cloud services
‣ Base product on 3rd party IP
‣ Faster development
‣ Potential lock in
‣ More cost efficient in the beginning
‣ Shared environment
‣ Own infrastructure
‣ Develop own IP
‣ Slower ramp up
‣ Keep flexibility
‣ More cost efficient in the long run
‣ Dedicated environment
12
The machine room
13
General Overview
Realtime Callbacks
Raw Data Upload
Realtime Fraud
Prevention
Realtime
Aggregation
SDK Traffic Tracking Server Device Data
Store
14
So it begins…
15
Redis Small
haproxy
Redis Leader
Redis Replica
16
A little more please?
17
Redis Big
haproxy
Redis Leader
Redis Replica
18
We need to go further
19
It’s all connected :)
Leader 1 Replica 1 Leader n Replica n
Sentinel 1 Sentinel n
twemproxy twemproxy
20
And we felt like…
21
22
‣ Automation is tricky
Smoke in the machine room
23
‣ Automation is tricky
‣ Weird latency spikes
Smoke in the machine room
24
‣ Automation is tricky
‣ Weird latency spikes
‣ Increased average response time
Smoke in the machine room
25
‣ Automation is tricky
‣ Weird latency spikes
‣ Increased average response time
‣ Long failover times
‣ Disruptive failovers
Smoke in the machine room
26
And we felt like…
27
28
Sometimes you have to let go…
29
‣ Only index is in RAM
‣ Data is on SSD
‣ Cost reduced by 85%
‣ Server count reduced from 40 to 6
Migrating to Aerospike
30
‣ 1,1 million reads per second
‣ 500.000 writes per second
‣ 180TB of data
‣ 3 locations
Using Aerospike
31
What if?
32
‣ Easy to scale
‣ Redis interface
‣ Online resizing
‣ All dirty work is done by Amazon
Elasticache
33
Q: Can I use Amazon ElastiCache for use cases other
than caching?
A: Yes. ElastiCache for Redis can be used as a primary
in-memory key-value data store, providing fast, sub
millisecond data performance, high availability and
scalability up to 15 nodes plus up to 5 read replicas,
each of up to 9.5 TiB of in-memory data.
34
“Once we are successful, we will
take care of scalability and
profitability”
35
What will happen to my
infrastructure when I will be
successful?
36
Questions and answers
New York
Paris
São Paulo
San Francisco
London Berlin
Istanbul
Moscow
Mumbai
Beijing
Seoul
Tokyo
Shanghai
Singapore
Robert Abraham
DIRECTOR OF ENGINEERING

robert@adjust.com
ADJUST HQ

Saarbrücker Str. 37a

10405 Berlin

Germany

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Big Data LDN 2018: SCALING A PLATFORM FOR REAL-TIME FRAUD DETECTION WITHOUT BREAKING THE BANK

  • 1. BERLIN • NEW YORK • SAN FRANCISCO • SÃO PAULO • PARIS • LONDON • MOSCOW • ISTANBUL SEOUL • SHANGHAI • BEIJING • TOKYO • MUMBAI • SINGAPORE Scaling a platform for real-time fraud detection
  • 2. 2 Why is no one prepared for success?
  • 3. 3 ‣ Focus on idea ‣ Focus on execution is limited in time to market ‣ Underestimating importance of scalability and profitability Startup Zeitgeist
  • 4. 4 ‣ Director of Engineering ‣ Consulted with early design decisions ‣ Joined Adjust 2012 as Head of IT Operations Who am I?
  • 5. Who is Adjust? 1B+ Daily active users tracked 400+ Billion Data points tracked monthly 22K+ Apps tracked
  • 6. 6 ‣ Automatically reject fraudulent data before it gets paid ‣ Sends rejection reason callbacks to all parties ‣ Customers and their networks have full transparency ‣ Real-time statistical analysis of all ad engagements and app activity Fraud Prevention Suite
  • 7. 7 How did we do that?
  • 8. 8 Bootstrapping a product Current approach ‣ Using cloud services ‣ Base product on 3rd party IP ‣ Faster development
  • 9. 9 Bootstrapping a product Current approach Our approach ‣ Using cloud services ‣ Base product on 3rd party IP ‣ Faster development ‣ Own infrastructure ‣ Develop own IP ‣ Slower ramp up
  • 10. 10 Bootstrapping a product Current approach Our approach ‣ Using cloud services ‣ Base product on 3rd party IP ‣ Faster development ‣ Potential lock in ‣ More cost efficient in the beginning ‣ Shared environment ‣ Own infrastructure ‣ Develop own IP ‣ Slower ramp up
  • 11. 11 Bootstrapping a product Current approach Our approach ‣ Using cloud services ‣ Base product on 3rd party IP ‣ Faster development ‣ Potential lock in ‣ More cost efficient in the beginning ‣ Shared environment ‣ Own infrastructure ‣ Develop own IP ‣ Slower ramp up ‣ Keep flexibility ‣ More cost efficient in the long run ‣ Dedicated environment
  • 13. 13 General Overview Realtime Callbacks Raw Data Upload Realtime Fraud Prevention Realtime Aggregation SDK Traffic Tracking Server Device Data Store
  • 16. 16 A little more please?
  • 18. 18 We need to go further
  • 19. 19 It’s all connected :) Leader 1 Replica 1 Leader n Replica n Sentinel 1 Sentinel n twemproxy twemproxy
  • 20. 20 And we felt like…
  • 21. 21
  • 22. 22 ‣ Automation is tricky Smoke in the machine room
  • 23. 23 ‣ Automation is tricky ‣ Weird latency spikes Smoke in the machine room
  • 24. 24 ‣ Automation is tricky ‣ Weird latency spikes ‣ Increased average response time Smoke in the machine room
  • 25. 25 ‣ Automation is tricky ‣ Weird latency spikes ‣ Increased average response time ‣ Long failover times ‣ Disruptive failovers Smoke in the machine room
  • 26. 26 And we felt like…
  • 27. 27
  • 28. 28 Sometimes you have to let go…
  • 29. 29 ‣ Only index is in RAM ‣ Data is on SSD ‣ Cost reduced by 85% ‣ Server count reduced from 40 to 6 Migrating to Aerospike
  • 30. 30 ‣ 1,1 million reads per second ‣ 500.000 writes per second ‣ 180TB of data ‣ 3 locations Using Aerospike
  • 32. 32 ‣ Easy to scale ‣ Redis interface ‣ Online resizing ‣ All dirty work is done by Amazon Elasticache
  • 33. 33 Q: Can I use Amazon ElastiCache for use cases other than caching? A: Yes. ElastiCache for Redis can be used as a primary in-memory key-value data store, providing fast, sub millisecond data performance, high availability and scalability up to 15 nodes plus up to 5 read replicas, each of up to 9.5 TiB of in-memory data.
  • 34. 34 “Once we are successful, we will take care of scalability and profitability”
  • 35. 35 What will happen to my infrastructure when I will be successful?
  • 37. New York Paris São Paulo San Francisco London Berlin Istanbul Moscow Mumbai Beijing Seoul Tokyo Shanghai Singapore Robert Abraham DIRECTOR OF ENGINEERING
 robert@adjust.com ADJUST HQ
 Saarbrücker Str. 37a
 10405 Berlin
 Germany