GDC 2015 - Game Analytics with AWS Redshift, Kinesis, and the Mobile SDK

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GDC 2015 – Amazon Developer Day

Connecting With Your Players Using AWS Analytics

Nate Wiger Principal, Gaming Solutions

@nateware [email protected]

AWS Gaming Solutions

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


More Screens = More Revenue


Analytics at Supercell

“You cannot predict success. You will only find out after releasing it.”

- Ilkka Paananen, CEO, Supercell


Analyze What?

Emotions •  Enjoying game •  Engaged •  Like/dislike new content •  Stuck on a level •  Bored •  Giving up

Behaviors •  Hours played day/week •  Number of sessions/day •  Level progression •  Friend invites/referrals •  Response to mobile push •  Money spent/week


Winning at Free to Play

•  Phase 1: Collect Data •  Phase 2: Analyze •  Phase 3: Profit


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


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


Wooga – Player Attrition


Wooga – Level Analysis


Solution – Level of the Week


Solution – Level of the Week


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)

•  Context = Always Include –  User –  Action –  Session (context-dependent) –  Timestamp in ISO8601

2014-‐03-‐16T16:28:26


Off The Shelf Analytics

•  Easy To Integrate •  Pre-Baked Reports •  Limited Flexibility •  Retention Windows •  Data Ownership


Ok, A Real Business Plan

Ingest Store Process Analyze


Ok, A Real Business Plan

Ingest • S3 PUT • Analytics SDK • Kinesis

Store • S3 • DynamoDB • HDFS

Process • Redshift • EMR (Hadoop) • Spark

Analyze • Tableau • Pentaho •  Jaspersoft


•  Collect Events on Device •  Periodically Store in S3 •  Process Data into Redshift •  Analyze with GUI Visualization Tool

Start Simple

2015-‐03-‐03,nateware,e4df,login 2015-‐03-‐03,nateware,e4df,gamestart 2015-‐03-‐03,nateware,e4df,gameend 2015-‐03-‐03,nateware,a88c,login 2015-‐03-‐03,nateware,a88c,friendlist 2015-‐03-‐03,nateware,a88c,gamestart

Profit!


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 cores Compute Node

128GB RAM

16TB disk


128GB RAM

16TB disk


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


Tableau + Redshift


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


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 Search Slideshare for "BDT303" – re:Invent 2014


http://tinyurl.com/k2elyhb


•  Collect Server Logs •  Periodically Send to S3 •  Process into Redshift •  External Analytics Data Too

More Data Sources

EC2

External Analytics


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/ ELB Access Logs: http://docs.aws.amazon.com/ElasticLoadBalancing/latest/DeveloperGuide/access-log-collection.html


•  Different File Formats •  Device vs Apache vs CDN •  Cleanup with EMR Job •  Output to Clean Bucket •  Load into Redshift

Dealing With Messy Data

EC2


Redshift vs Elastic MapReduce

Redshift •  Columnar DB •  Familiar SQL •  Structured Data •  Batch Load •  Faster to Query •  Long-term Storage

Elastic MapReduce •  Hadoop •  Custom Java / Python •  Unstructured Data •  Streaming Loop •  Scales > PB's •  Transient


•  Game Servers Uses DynamoDB •  Directly Export to Redshift •  Or Stage to S3

Integrate With Your Game Database

EC2

DynamoDB


AWS Mobile Analytics SDK


Amazon Mobile Analytics Engagement +

Monetization Active Users Sessions In-app Revenue Lifetime Value (LTV) Retention Post-install Retention Funnel Behavior Custom Events


Integrating Mobile Analytics is Simple Initialize the MobileAnalyticsManager.

For Custom Events activity reports, add events.

Handle OnResume() and OnPause()

private static MobileAnalyticsManager analytics;analytics = MobileAnalyticsManager.getOrCreateInstance( this.getApplicationContext(),"yourCompany.yourAppId",

Regions .US_EAST_1, config, cognitoProvider );

EventClient eventClient = analytics.getEventClient();MobileAnalyticsEvent visitEvent = eventClient.createEvent("level1Complete");eventClient.recordEvent(visitEvent);

analytics.getSessionClient().resumeSession();analytics.getSessionClient().pauseSession();analytics.getEventClient().submitEvents();


Key Business Metrics (with one line of code) 1.  Monthly Active Users

(MAU) 2.  Daily Active Users

(DAU) 3.  New Users, 4.  Daily Sessions, 5.  Sticky Factor, 6.  1-Day Retention, 7.  Avg. Revenue per DAU, 8.  Daily Paying Users, 9.  Avg. Paying DAU


Amazon Mobile Analytics Post-install Retention Funnel


# of Likes /Shares

Player Abort Rates per Level

# of Plays per Hour

In-app Item Popularity

Track Custom Events


Custom Events public void onLevelComplete(String levelName, String difficulty, double timeToComplete, int playerState) { // Create a Level Complete event with some attributes and metrics AnalyticsEvent levelCompleteEvent = analytics.getEventClient().createEvent("LevelComplete") .withAttribute("LevelName", levelName) .withAttribute("Difficulty", difficulty) .withAttribute("EndState", playerState) .withMetric("TimeToComplete", timeToComplete); //Record the Level Complete event analytics.getEventClient().recordEvent(levelCompleteEvent); }


Amazon Mobile Analytics

Fast Event data is processed in < 60 minutes Affordable Free 100 MM Events per Month $1 per MM Events beyond the free tier Private You own your data We do not use your data, or share with 3rd parties


Exporting to S3 or Redshift

S3

Redshift


•  Collect Events w/ Mobile Analytics SDK •  Auto-Export to S3 •  Process Data into Redshift •  Analyze with GUI Visualization Tool

Mobile Analytics Auto-Export to S3


•  Collect Events w/ Mobile Analytics SDK •  Auto-Export Directly to Redshift •  Analyze with GUI Visualization Tool

Start Simple


Setup Analytics Auto-Export to Redshift





http://amzn.to/mobile-redshift-export


Exporting to Amazon Redshift

172.16.0.0/20

Public Subnet 172.16.0.0/22

172.16.0.0/20 Local

0.0.0.0/0 IGW

Amazon Mobile Analytics

EC2


SQL with Redshift – Mobile Analytics Data


SQL – User Segmentation


DAU and MAU in SQL

select date, count(*) over (partition by date_trunc('day', date) order by date) as dau, count(*) over (partition by date_trunc('month', date) order by date) as mau from user_sessions order by date;


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;


First-Pass Retention – Individual Plays

0 5

10 15 20 25 30 35 40

# Play Sessions / Month

nateware Lazyd0g AK187 3strikes


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


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


Retention by Cohort – Join Events

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


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


5-9 notifications retain 1.5x better than 10-14 notifications

Forza: Push Notifications vs Retention



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


Redshift Schema

•  Every game has its own database

•  Each game event = table (e.g., battle_fight, iap)

•  40-50 tables per DB schema

•  All game titles ~ 1000 tables in DW


{"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 •  JSON data format

Data Stream


Analytics Store Architecture

S3

S3

Consumer

Game DB

Game Servers

Kinesis

Amazon Redshift

Elastic MapReduce

DSV

JSON


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" }


Amazon S3

Spark SQL JDBC Server

SQL

Dashboard

Redshift

Consumer

EMR

Consumer

Game DB

Game Servers

Kinesis

DynamoDB

DSV

JSON


Saving Money… EC2 Spot Instances You bid your own price for instances

c3.large

$0.105/hour

$0.0161/hour*

m3.2xlarge

$0.560/hour

$0.0641/hour*

On-Demand

Spot

On-Demand

Spot

* Price in US-East @ Feb 26, 2014 – 9:40AM PST

85% off

88% off


Example Price History


Auto Scaling: Let AWS Optimize For You

as-create-launch-config spotlc-5cents --image-id ami-e565ba8c --instance-type m1.small --spot-price “0.05” . . . as-create-auto-scaling-group spotasg --launch-configuration spotlc-5cents --availability-zones “us-east-1a,us-east-1b” --max-size 16 --min-size 1 --desiredcapacity 3


EMR Cluster With Spot

aws emr create-cluster --name "Multiple task instance group cluster" --ami-version 3.2.3 \--ec2-attributes AvailabilityZone=eu-west-1b \--instance-groups InstanceGroupType=MASTER,Name="Master",InstanceCount=1,InstanceType=m3.xlarge \InstanceGroupType=CORE,Name="Core",InstanceCount=4,InstanceType=m3.xlarge \InstanceGroupType=TASK,Name="Task1",InstanceCount=25,InstanceType=m3.xlarge,BidPrice=0.28 \InstanceGroupType=TASK,Name="Task2",InstanceCount=10,InstanceType=r3.xlarge,BidPrice=0.28


•  S3 as Data Lake •  Redshift for Analysis •  Collect Everything •  Iterate on Reports

Bring It All Back

EC2

External Analytics


Amazon S3

Spark SQL JDBC Server

SQL

Dashboard

Redshift

Consumer

EMR

Consumer

Game DB

Game Servers

Kinesis

DynamoDB

DSV

JSON

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Nate Wiger Principal, Gaming Solutions

@nateware [email protected]

Technology

GDC 2015 - Game Analytics with AWS Redshift, Kinesis, and the Mobile SDK