AWS Game Analytics - GDC 2014

1. AWS Gaming Solutions | GDC 2014 Game Analytics with AWS Or, How to learn what your players love so they will love your game Nate Wiger @nateware | Principal Gaming Solutions Architect

2. AWS Gaming Solutions | GDC 2014 Mobile Game Landscape •  Free To Play •  In-App Purchases •  Long-Tail •  Cross-Platform •  Go Global •  User Retention = Revenue

3. AWS Gaming Solutions | GDC 2014 Projected Mobile App Revenue 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 2011 2012 2013 2014 2015 2016 2017 Ads IAP Paid Source: Gartner

4. AWS Gaming Solutions | GDC 2014 Winning at Free to Play •  Phase 1: Collect Data •  Phase 2: Analyze •  Phase 3: Profit

5. AWS Gaming Solutions | GDC 2014 Analyze What? Emotions •  Enjoying game •  Engaged •  Like/dislike new content •  Stuck on a level •  Bored •  Abandonment Behaviors •  Hours played day/week •  Number of sessions/day •  Level progression •  Friend invites/referrals •  Response to mobile push •  Money spent/week

6. AWS Gaming Solutions | GDC 2014 Example: Level Progression (One Metric) 0 2 4 6 8 10 L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 Tries / Level # of Tries

7. AWS Gaming Solutions | GDC 2014 Example: Level Progression (Two Metrics) 0 10 20 30 40 50 60 0 2 4 6 8 10 L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 Tries / Level % Highest Level # of Tries

8. AWS Gaming Solutions | GDC 2014 Key Takeaways •  Multiple data sources •  Correlate variables •  Deltas vs absolutes •  Settle on terminology (game vs level) •  Time matters

9. AWS Gaming Solutions | GDC 2014

10. AWS Gaming Solutions | GDC 2014 Events & Metrics •  Event = Moment in Time –  Login/quit –  Game start/end –  Level up –  In-app purchase •  Metrics = What to Measure –  KISS –  Numbers –  Booleans –  Strings (Enums) •  Always Include (ALWAYS) –  User –  Action –  Session (context-dependent) –  Timestamp in ISO8601 2014-‐03-‐16T16:28:26

11. AWS Gaming Solutions | GDC 2014 Off The Shelf Analytics •  Easy To Integrate •  Pre-Baked Reports •  Rate Limits •  Retention Windows •  Data Lock-In

12. AWS Gaming Solutions | GDC 2014 Ok, A Real Business Plan Ingest Store Process Analyze

13. AWS Gaming Solutions | GDC 2014 Ok, A Real Business Plan Ingest •  HTTP PUT •  Kafka •  Kinesis •  Scribe Store •  S3 •  DynamoDB •  HDFS •  Redshift Process •  EMR (Hadoop) •  Spark •  Storm Analyze •  Tableau •  Pentaho •  Jaspersoft

14. AWS Gaming Solutions | GDC 2014 •  Write Events File on Device •  Periodically Upload to S3 •  Process into Redshift •  Point GUI Tool to Redshift Start Simple 2014-‐01-‐24,nateware,e4df,login 2014-‐01-‐24,nateware,e4df,gamestart 2014-‐01-‐24,nateware,e4df,gameend 2014-‐01-‐25,nateware,a88c,login 2014-‐01-‐25,nateware,a88c,friendlist 2014-‐01-‐25,nateware,a88c,gamestart Profit!

15. AWS Gaming Solutions | GDC 2014 Redshift at a Glance 10 GigE (HPC) Ingestion Backup Restore SQL Clients/BI Tools 128GB RAM 16TB disk 16 cores Amazon S3/DynamoDB JDBC/ODBC 128GB RAM 16TB disk 16 coresCompute Node 128GB RAM 16TB disk 16 coresCompute Node 128GB RAM 16TB disk 16 coresCompute Node Leader Node •  Leader Node –  SQL endpoint –  Stores metadata –  Coordinates query execution •  Compute Nodes –  Columnar table storage –  Load, backup, restore via Amazon S3 –  Parallel load from Amazon DynamoDB •  Single node version available

16. AWS Gaming Solutions | GDC 2014 Tableau + Redshift

17. AWS Gaming Solutions | GDC 2014 Plumbing ①  Create S3 bucket ("mygame-analytics-events") ②  Request a security token for your mobile app: http://docs.aws.amazon.com/STS/latest/UsingSTS/Welcome.html ③  Upload data from your users' devices ④  Run a scheduled copy to Redshift ⑤  Setup Tableau to access Redshift ⑥  Go to the Beach

18. AWS Gaming Solutions | GDC 2014 Loading Redshift from S3 copy events from 's3://mygame-‐analytics-‐events' credentials 'aws_access_key_id=<access-‐key-‐id>; aws_secret_access_key=<secret-‐access-‐key>' delimiter=','; Scheduled Redshift Load using Data Pipeline: http://aws.amazon.com/articles/1143507459230804

19. AWS Gaming Solutions | GDC 2014 •  Also Collect Server Logs •  Periodically Upload to S3 •  Stuff into Redshift •  External Analytics Data Too More Data Sources EC2 External Analytics

20. AWS Gaming Solutions | GDC 2014 Logrotate to S3 /var/log/apache2/*.log { sharedscripts postrotate sudo /usr/sbin/apache2ctl graceful s3cmd sync /var/log/*.gz s3://mygame-‐logs/ endscript } Blog Entry on Log Rotation: http://www.dowdandassociates.com/blog/content/howto-rotate-logs-to-s3/ And/or, Use ELB Access Logs: http://docs.aws.amazon.com/ElasticLoadBalancing/latest/DeveloperGuide/ access-log-collection.html

21. AWS Gaming Solutions | GDC 2014 •  Different File Formats •  Device vs Apache vs CDN •  Cleanup with EMR Job •  Output to Clean Bucket •  Load into Redshift Dealing With Messy Data EC2

22. AWS Gaming Solutions | GDC 2014 Redshift vs Elastic MapReduce Redshift •  Columnar DB •  Familiar SQL •  Structured Data •  Batch Load •  Faster to Query •  Long-term Storage Elastic MapReduce •  Hadoop •  Hive/Pig are SQL-like •  Unstructured Data •  Streaming Loop •  Scales > PB's •  Transient

23. AWS Gaming Solutions | GDC 2014 •  Integrate Game DB •  Load Directly into Redshift •  Redshift does Intelligent Merge •  Tracks Hash Keys, Columns Direct From DynamoDB EC2

24. AWS Gaming Solutions | GDC 2014 •  Integrate Game DB •  Load Directly into Redshift •  Redshift does Intelligent Merge •  Tracks Hash Keys, Columns •  Or Stream into EMR Direct From DynamoDB EC2

25. AWS Gaming Solutions | GDC 2014 Loading Redshift from DynamoDB copy games from 'dynamodb://games' credentials 'aws_access_key_id=<access-‐key-‐id>; aws_secret_access_key=<secret-‐access-‐key>'; copy events from 's3://mygame-‐analytics-‐events' credentials 'aws_access_key_id=<access-‐key-‐id>; aws_secret_access_key=<secret-‐access-‐key>' delimiter=',';

27. AWS Gaming Solutions | GDC 2014 Funnel Cake

28. AWS Gaming Solutions | GDC 2014 Back To Basics 2014-‐01-‐24,nateware,e4df,login 2014-‐01-‐24,nateware,e4df,gamestart 2014-‐01-‐24,nateware,e4df,gameend 2014-‐01-‐25,nateware,a88c,login 2014-‐01-‐25,nateware,a88c,friendlist 2014-‐01-‐25,nateware,a88c,gamestart

29. AWS Gaming Solutions | GDC 2014 Measure Retention: Repeated Plays create view events_by_user_by_month as select user_id, date_trunc('month', event_date) as month_active, count(*) as total_events from events group by user_id, month_active;

30. AWS Gaming Solutions | GDC 2014 First-Pass Retention – Too Noisy 0 5 10 15 20 25 30 35 40 # Play Sessions / Month nateware Lazyd0g AK187 3strikes

31. AWS Gaming Solutions | GDC 2014 Cohorts & Cambria •  Enables calculating relative metrics •  Group users by a common attribute –  Month game installed –  Demographics •  Run analysis by cohort –  Join with metrics •  Use Redshift as it's SQL –  Example of where SQL is a good fit

32. AWS Gaming Solutions | GDC 2014 Creating Cohorts with Redshift create view cohort_by_first_event_date as select user_id, date_trunc('month', min(event_date)) as first_month from events group by user_id; http://snowplowanalytics.com/analytics/customer- analytics/cohort-analysis.html

33. AWS Gaming Solutions | GDC 2014 Retention by Cohort – Join Events with Cohort 0 5 10 15 20 25 Week 1 Week 2 Week 3 Week 5 Week 6 Week 7 # Sessions / Week 2013-11 2013-12 2014-01 2014-02 2014-03 2014-04

34. AWS Gaming Solutions | GDC 2014 Moar Cohorts •  Define multiple cohorts –  By activity, time, demographics –  As many as you like •  Change cohort depending on analysis •  Join same metrics with different cohorts –  Retention by date –  Retention by demographic –  Retention by average plays/month quartile

35. AWS Gaming Solutions | GDC 2014 Example Event Stream 2014-‐03-‐17T09:52:08-‐07:00,nateware,e4b5,login 2014-‐03-‐17T09:52:54-‐07:00,nateware,e4b5,gamestart 2014-‐03-‐17T09:53:15-‐07:00,nateware,e4b5,levelup 2014-‐03-‐17T09:54:06-‐07:00,nateware,e4b5,gameend 2014-‐03-‐17T09:54:23-‐07:00,nateware,30a4,gamestart 2014-‐03-‐17T09:55:14-‐07:00,nateware,30a4,gameend 2014-‐03-‐17T09:55:41-‐07:00,nateware,30a4,gamestart 2014-‐03-‐17T09:57:12-‐07:00,nateware,6ebd,levelup 2014-‐03-‐17T09:58:50-‐07:00,nateware,6ebd,levelup 2014-‐03-‐17T09:59:52-‐07:00,nateware,6ebd,gameend

36. AWS Gaming Solutions | GDC 2014 Example Event Stream 2014-‐03-‐17T09:52:08-‐07:00,nateware,e4b5,login 2014-‐03-‐17T09:52:54-‐07:00,nateware,e4b5,gamestart 2014-‐03-‐17T09:53:15-‐07:00,nateware,e4b5,levelup 2014-‐03-‐17T09:54:06-‐07:00,nateware,e4b5,gameend 2014-‐03-‐17T09:54:23-‐07:00,nateware,30a4,gamestart 2014-‐03-‐17T09:55:14-‐07:00,nateware,30a4,gameend 2014-‐03-‐17T09:55:41-‐07:00,nateware,30a4,gamestart 2014-‐03-‐17T09:57:12-‐07:00,nateware,6ebd,levelup 2014-‐03-‐17T09:58:50-‐07:00,nateware,6ebd,levelup 2014-‐03-‐17T09:59:52-‐07:00,nateware,6ebd,gameend

37. AWS Gaming Solutions | GDC 2014 Cohorts by Type of Activity create view cohort_by_first_play_date as select user_id, date_trunc('month', min(event_date)) as first_month from events where action = 'gamestart' group by user_id;

39. AWS Gaming Solutions | GDC 2014 Post-Match Heatmaps

40. AWS Gaming Solutions | GDC 2014 Real-Time Analytics Batch •  What game modes do people like best? •  How many people have downloaded DLC pack 2? •  Where do most people die on map 4? •  How many daily players are there on average? Real-Time •  What game modes are people playing now? •  Are more or less people downloading DLC today? •  Are people dying in the same places? Different? •  How many people are playing today? Variance?

41. AWS Gaming Solutions | GDC 2014 Why Real-Time Analytics? 30x in 24 hours What if you ran a promo?

42. AWS Gaming Solutions | GDC 2014 Real-Time Tools Spark •  High-Performance Hadoop Alternative •  Berkeley.edu •  Compatible with HiveQL •  100x faster than Hadoop •  Runs on EMR Kinesis •  Amazon fully-managed streaming data layer •  Similar to Kafka •  Streams contain Shards •  Each Shard ingests data up to 1MB/sec, 1000 TPS •  Data stored for 24 hours

43. AWS Gaming Solutions | GDC 2014 •  Always Batch Due to S3 Back To Basics [Dubstep Remix] EC2

44. AWS Gaming Solutions | GDC 2014 •  Stream Data With Kinesis •  Multiple Writers and Readers •  Still Output to Redshift Need Data Faster! EC2

45. AWS Gaming Solutions | GDC 2014 •  Stream Data With Kinesis •  Multiple Writers and Readers •  Still Output to Redshift •  Stream to Spark on EMR •  Storm via Kinesis Spout •  Custom EC2 Workers Lots of Ins and Outs EC2 EC2

46. AWS Gaming Solutions | GDC 2014 Data Sources App.4 [Machine Learning] AWS Endpoint App.1 [Aggregate & De-‐Duplicate] Data Sources Data Sources Data Sources App.2 [Metric Extrac=on] S3 DynamoDB Redshift App.3 [Sliding Window Analysis] Data Sources Availability Zone Shard 1 Shard 2 Shard N Availability Zone Availability Zone Introducing Amazon Kinesis Service for Real-Time Big Data Ingestion

47. AWS Gaming Solutions | GDC 2014 Putting Data into Kinesis •  Producers use PUT to send data to a Stream •  PutRecord {Data, PartitionKey, StreamName} •  Partition Key distributes PUTs across Shards •  Unique Sequence # returned on PUT call •  Documentation: http://docs.aws.amazon.com/kinesis/latest/dev/ introduction.html Producer Shard 1 Shard 2 Shard 3 Shard n Shard 4 Producer Producer Producer Producer Producer Producer Producer Producer Kinesis

48. AWS Gaming Solutions | GDC 2014 Writing to a Kinesis Stream POST / HTTP/1.1 Host: kinesis.<region>.<domain> x-‐amz-‐Date: <Date> Authorization: AWS4-‐HMAC-‐SHA256 Credential=<Credential>, SignedHeaders=content-‐ type;date;host;user-‐agent;x-‐amz-‐date;x-‐amz-‐target;x-‐amzn-‐requestid, Signature=<Signature> User-‐Agent: <UserAgentString> Content-‐Type: application/x-‐amz-‐json-‐1.1 Content-‐Length: <PayloadSizeBytes> Connection: Keep-‐Alive X-‐Amz-‐Target: Kinesis_20131202.PutRecord { "StreamName": "exampleStreamName", "Data": "XzxkYXRhPl8x", "PartitionKey": "partitionKey" }

49. AWS Gaming Solutions | GDC 2014 Kinesis + Spark http://aws.amazon.com/articles/4926593393724923

50. AWS Gaming Solutions | GDC 2014 Death in Real-Time PUT "kills" {"game_id":"e4b5","map":"Boston","killer":38,"victim":39,"coord":"274,591,48"} PUT "kills" {"game_id":"e4b5","map":"Boston","killer":13,"victim":27,"coord":"101,206,35"} PUT "kills" {"game_id":"e4b5","map":"Boston","killer":38,"victim":39,"coord":"165,609,17"} PUT "kills" {"game_id":"e4b5","map":"Boston","killer":6,"victim":29,"coord":"120,422,26"} PUT "kills" {"game_id":"30a4","map":"Los Angeles","killer":34,"victim":18,"coord":"163,677,18"} PUT "kills" {"game_id":"30a4","map":"Los Angeles","killer":20,"victim":37,"coord":"71,473,20"} PUT "kills" {"game_id":"30a4","map":"Los Angeles","killer":21,"victim":19,"coord":"332,381,17"} PUT "kills" {"game_id":"30a4","map":"Los Angeles","killer":0,"victim":10,"coord":"14,108,25"} PUT "kills" {"game_id":"6ebd","map":"Seattle","killer":32,"victim":18,"coord":"13,685,32"} PUT "kills" {"game_id":"6ebd","map":"Seattle","killer":7,"victim":14,"coord":"16,233,16"} PUT "kills" {"game_id":"6ebd","map":"Seattle","killer":27,"victim":19,"coord":"16,498,29"} PUT "kills" {"game_id":"6ebd","map":"Seattle","killer":1,"victim":38,"coord":"138,732,21"}

51. AWS Gaming Solutions | GDC 2014 Real-Time Heatmaps

52. AWS Gaming Solutions | GDC 2014 But A Bow On It •  Collect data from the start •  Store it even if you can't process it (yet) •  Start simple – S3 + Redshift •  Add data sources – process with EMR •  Real-time – Kinesis + Spark •  Tons of untapped potential for gaming

53. AWS Gaming Solutions | GDC 2014 Fallback Plan Cheers – Nate Wiger @nateware