AWS Game Analytics - GDC 2014

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

Mobile Game Landscape
•  Free To Play
•  In-App Purchases
•  Long-Tail
•  Cross-Platform
•  Go Global
•  User Retention = Revenue

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

Winning at Free to Play
•  Phase 1: Collect Data
•  Phase 2: Analyze
•  Phase 3: Profit

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

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

Key Takeaways
•  Multiple data sources
•  Correlate variables
•  Deltas vs absolutes
•  Settle on terminology (game vs level)
•  Time matters

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

Off The Shelf Analytics
•  Easy To Integrate
•  Pre-Baked Reports
•  Rate Limits
•  Retention Windows
•  Data Lock-In

Ok, A Real Business Plan
Ingest Store Process Analyze

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

•  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!

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

•  Also Collect Server Logs
•  Periodically Upload to S3
•  Stuff 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/
And/or, Use 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
•  Hive/Pig are SQL-like
•  Unstructured Data
•  Streaming Loop
•  Scales > PB's
•  Transient

•  Integrate Game DB
•  Load Directly into Redshift
•  Redshift does Intelligent Merge
•  Tracks Hash Keys, Columns
Direct From DynamoDB
EC2

•  Integrate Game DB
•  Load Directly into Redshift
•  Redshift does Intelligent Merge
•  Tracks Hash Keys, Columns
•  Or Stream into EMR
Direct From DynamoDB
EC2

Loading Redshift from DynamoDB
copy
games

from
'dynamodb://games'

credentials

aws_secret_access_key=<secret-‐access-‐key>';

copy
events

from
's3://mygame-‐analytics-‐events'

credentials

aws_secret_access_key=<secret-‐access-‐key>'

delimiter=',';

Funnel Cake

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

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 – Too Noisy
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 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

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

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

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;

Post-Match Heatmaps

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?

Why Real-Time Analytics?
30x in 24 hours
What if you ran a promo?

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

•  Always Batch Due to S3
Back To Basics [Dubstep Remix]
EC2

•  Stream Data With Kinesis
•  Multiple Writers and Readers
•  Still Output to Redshift
Need Data Faster!
EC2

•  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


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

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

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"

}

Kinesis + Spark
http://aws.amazon.com/articles/4926593393724923

Death in Real-Time
PUT
"kills"
{"game_id":"e4b5","map":"Boston","killer":38,"victim":39,"coord":"274,591,48"}

PUT
"kills"

PUT
"kills"

PUT
"kills"

PUT
"kills"
{"game_id":"30a4","map":"Los
Angeles","killer":34,"victim":18,"coord":"163,677,18"}

PUT
"kills"

PUT
"kills"

PUT
"kills"

PUT
"kills"
{"game_id":"6ebd","map":"Seattle","killer":32,"victim":18,"coord":"13,685,32"}

PUT
"kills"

PUT
"kills"

PUT
"kills"

Real-Time Heatmaps

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

Fallback Plan
Cheers – Nate Wiger @nateware

AWS Game Analytics - GDC 2014

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