지난 2월 26일 있었던 AWS 정윤진 솔루션스 아키텍트의 "성공적인 게임 개발을 위한 비밀의 레시피" 네 번째 강연에서 쓰인 발표자료입니다. Amazon Redshift, Kinesis, DynamoDB 등을 활용해 게임 내 사용자 행동패턴 등을 실시간으로 분석하는 방법에 대한 내용입니다.

1. © 2014 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.
GAM301
Real-Time Game Analytics with Amazon Kinesis,
Amazon Redshift, and Amazon DynamoDB
Amazon Web Services

2. Talk Outline
• Mobile Game Analytics – use case
• Decisions, Mistakes & Challenges
• Deep dive – Analytics Platform using AWS Tech
• Lessons we learned

3. GREE Headquarters
Tokyo, Japan
GREE International,
Inc.
San Francisco, CA
GREE Canada
Vancouver, BC
QUICK FACTS
6
Continents playing GREE games
1,882
Employees Worldwide
13
Games made in North America
2004
2011
2013
MILESTONES GAME STATS - 4 titles in top 100 grossing*
Crime City (Studios)
Reached Top 10 Grossing in 140 countries
Top 100 Grossing in 19 countries, over 3 years
since launch
*As of Sep. 2014 – Source: App Annie
A Global Gaming Powerhouse
Knights & Dragons (Publishing)
Reached Top 10 Grossing in 41 countries
Top 100 Grossing in 22 countries

4. Success Factors in Mobile Gaming
• Great gameplay & mechanics
• Great content
• Effective engagement & retention
• Generate in-app purchases
….. keep adding new content, features
?
?
?
I know its a
great game.
Why is my
game not
successful
anymore?
It had good
KPIs initially.

5. As a game developer ...
Why?
• Game not performing well
• Players not spending?
• Retention so poor?
How?
• Optimize game design
• Improve ARPDAU, %Spenders
• Improve Retention
You need – Game Analytics & Insights

6. Analytics @ GREE
Ad Clicks
Downloads
Perf Data
Attribution
Campaign Performance
SC Balance
HC Balance
IAP
Player Targeting

7. Data Collection
• Mobile Devices
• Game Servers
• Ad Networks
• Size of event ~ 1 KB
• 500M+ events/day
• 500G+/day & growing
• JSON format
Source of Data Data Size & Growth

8. Database Schema
• Every game – database schema
• Each game event = table (e.g., battle_fight, iap)
• 40-50 tables per DB schema
• All game titles ~ 1000 tables in DW

9. Key Requirements
• Data collection & streaming to database
• Zero data loss
• Zero data corruption
• Guaranteed data delivery

10. History – Analytics Architecture

11. Gen 1 – Analytics Platform
Analytics DB
Game DB
LAMP
Built on a LAMP Stack
Sharded DBs
Not scalable
Game
Servers

12. Gen2 – Flume/MPP Data
Warehouse
Game DB
MPP Data WarehouseFlume
Collectors
Flume
Master
Consumer
Game
Servers

13. In-house: Relay Engine
Game DB
Replicator
Amazon S3
DW
Relay Pipeline
Senders
Copiers
Game
Servers
Listeners
Cost of maintenance - HIGH

14. Challenges
• Hard to maintain and scale
• Spike in Live Ops events can clog other events
• Difficult to add new sink
• Writes to DW impacted query performance for BI
users
• Poor data latency

15. Key Requirements – the list grew
• Data collection &
streaming to database
• Zero data loss
• Zero data corruption
• Guaranteed data
delivery
• Near real-time data latency
• Real-time ad-hoc analysis
• Ease of adding consumers
• Managed Service

16. New Analytics Architecture

17. Start Simple
Ingest
• Kafka
• HTTP PUT
• Amazon Kinesis
Store
• HDFS
• Amazon S3
• Amazon Redshift
Analyze

18. Producer & Data
Data
{"player_id":"323726381807586881","player_level":169,"device":"iPhone
5","version":"iOS 7.1.2”,"platfrom":"ios","client_build":"440”,
"db":”mw_dw_ios","table":"player_login",
"uuid":"1414566719-rsl3hvhu7o","time_created":"2014-10-29 00:11:59”}
{"fight_time":"2014-10-29 00:11:59","attacker_id":"413142642356365377”,
"attacker_clan_size":500,"attacker_level":270,
"db":"mw_dw_ios","table":"battle_fight",
"uuid":"1414566719-p0oogk0bep","time_created":"2014-10-29 00:11:59"}
• PHP/Python Web Services
• Client side analytics SDK
Produce Send Consume Load

19. Amazon Kinesis Sender
Produce Send Consume Load

20. Sender
Sender
Worker
Kinesis
Stream
Shard 1
Shard 2
Shard 3
Shard n
Describe
Stream
Sync
Shards
Analytics
Files
Send
PutRecord
Compress
Read Buffer
50KB

21. Design Choices for Sender
• Single stream VS stream per game
• Batch VS Single Event
• Compressed VS Uncompressed
• PartitionKey VS ExplicitHashKey

22. Sender Deployment
Elastic Load
Balancing
AMI
Amazon
EC2
Auto Scaling Group
Amazon
EBS Data
Volume
Pending: Wait
EC2 EBS Data
Volume
Amazon
S3
Sender
Scale
Out
Pending: Proceed
EC2 EBS Data
Volume
EC2
Auto Scaling Group
EBS Data
Volume
InService
Update
Terminating: Wait
EC2 EBS Data
Volume
Scale
InChecks
Terminating: Proceed
EC2 EBS Data
Volume
Terminated

23. Consumer – S3 Store
Produce Send Consume Load

24. Consumer – Amazon S3 Store
Kinesis
Stream
Shard 1
Shard 2
Shard n
S3File Metadata DB
Decompress De-Dupe
BufferTransformation
Validation Target Table
Compress
Size/
Timeout
Record
Consumer
Kinesis Client Library
Record Processor
Record Processor
Consumer
Kinesis Client Library
Record Processor
Auto Scaling Group

25. Loading data into Amazon
Redshift
Produce Send Consume Load

26. Loading data into Amazon Redshift
Amazon S3
File Metadata DB Amazon
Redshift
Update Status
Transaction
Create Manifest Execute COPY
Create Manifest Execute COPY
Create Manifest Execute COPY
Status

27. Architecture – Simple Store
Produce Send Consume Load

28. Architecture – Simple Store
S3
Amazon S3
Consumer
Game DB
Game
Servers
Kinesis
Amazon
Redshift
Amazon
Elastic
MapReduce
DSV
JSON

29. Real-time Aggregates

30. DynamoDB – Real-time Aggregates
Amazon
DynamoDB
DashboardConsumerKinesis

31. Spark

32. Spark – Real-time Analysis
Spark SQLSpark Streaming
SQLKinesis

33. Final Architecture

34. Amazon S3
Amazon
S3
Spark SQL
JDBC Server
SQL
Dashboard
Amazon
Redshift
Consumer
Amazon
EMR
Consumer
Game DB
Game
Servers
Kinesis
DynamoDB
DSV
JSON

35. Lessons Learned

36. Lessons Learned
Sender
• Decouple data generation from sending
• Batch and compress
• PutRecord HTTP:5XX can result in duplicates
• Monitor ProvisionedThroughputExceeded exception

37. Lessons Learned (Cont.)
Consumer
• Use KCL
• Auto-scale and monitor load
Overall
• Provision enough shards
• Handle shutdown gracefully
• Follow AWS best practices for error retries and
exponential back-off

38. Takeaway

39. Takeaway
Kinesis
• Data available for processing within seconds
• Robust API, KCL, and Connector libraries
AWS
• Managed
• Scalable
• Cost effective
• Quick to get up and running

40. Please give us your feedback on this session.
Complete session evaluations and earn re:Invent swag.
http://bit.ly/awsevals

41. © 2014 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.
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