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

1
GDC 2015 – Amazon Developer Day
Connecting With Your Players Using AWS Analytics
Nate Wiger
Principal, Gaming Solutions
@nateware
nateware@amazon.com

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

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 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

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
Mobile Analytics Auto-Export to S3

•  Collect Events w/ Mobile Analytics SDK
•  Auto-Export Directly to Redshift
Start Simple

Setup Analytics Auto-Export to Redshift

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

67
Nate Wiger
Principal, Gaming Solutions
@nateware
nateware@amazon.com

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

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