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Zac Litton

VP of Engineering

Telltale Games

DAT316

How Telltale Games Migrated Its Story Analytics

from Apache CouchDB to Amazon DynamoDB

Greg McConnel

Solutions Architect

AWS

What to Expect from the Session

● Intro to DynamoDB

● Telltale story analytics

● Early infrastructure (SQL and

Apache CouchDB)

● Migration to DynamoDB

● Better data, better stories

Telltale Games

Telltale Games history

Why did AWS Build Amazon DynamoDB?

It’s hard to engineer for the

performance you need.

Traditional NoSQL databases run

into challenges as they scale.

Managing non-relational databases is hard.

Quick Intro on Amazon DynamoDB

Document or Key-Value Scales to Any WorkloadFully Managed NoSQL

Access Control Event Driven

Programming

Fast and Consistent

Fast, Consistent Performance

Single-digit millisecond latency

• At any scale

Data stored on Solid State Drives (SSDs)

Automatic partitioning means no need for

hotspot management

Highly Scalable

Simply specify each table’s read and write

throughput capacity

Increase and decrease capacity as needed

• No upper limit

DynamoDB manages all the scaling behind

the scenes

Flexible

Key-value store model

Each item in a DynamoDB

table is a list of attributes (fields)

and values

No need for every item to have the

same attributes

Add attributes at will

Document store

Place JSON-formatted data

into DynamoDB items for robust,

nested data structures

Amazon DynamoDB is a schemaless database

table items

Attributes (name/value

pairs or JSON

documents)

Each item includes a key

Partition key

(DynamoDB maintains

an unordered index)

Each item includes a key

Partition Key

Sort Key

(DynamoDB maintains a

sorted index)

Integration capabilities

DynamoDB Triggers

❑ Implemented as AWS

Lambda functions

❑ Your code scales

automatically

❑ Java, Node.js, and Python

DynamoDB Streams

❑ Stream of table updates

❑ Asynchronous

❑ Exactly once

❑ Strictly ordered

❑ 24-hr lifetime per item

Telltale Story Analytics

Telltale Games will remember that

Telltale Games

Telltale Games

Telltale Games

Telltale Games

Telltale Games

Telltale Games

Choice data

Choice data

Everything is unified JSON event logs

Choice data

Large amounts of diverse data

● Episodes contain over 2000 nodes plus additional data

● Millions of worldwide users

● 21TB of events stored

● >1 million parsed ‘sessions’ daily

● 10x spikes for release, free episodes, & advertising

How we use the data

Aggregated back to players

How we use the data

Player heat map and evaluations

How we use the data

Personalize the stories

Early Infrastructure

SQL, right?

● Can’t handle size

● No real time scaling

● Non starter

Apache CouchDB (Scaling/Maintenance)

● Manual process of spinning servers

● Frequent time consuming node failures

● Document limits

● Full time maintenance for 2 people

Apache CouchDB (Processing)

● Processing nearly impossible with data size

● Limited to more aggregation than analysis

● Couldn’t scale up easily for ‘speedy’ processing

● New queries impractical

Migration to DynamoDB

Managed & Scale

● Immediately ended our maintenance

● No storage limitations(200 Billion event peak)

● 21TB of events, 10GB/day.

● 1M session uploads per day with 900ms response

● Automation scripts to adjust to spikes

● Start at 50 r/w per second, up to 20K write spikes

● Autoscaling using Dynamic DynamoDB

Amazon

ElastiCache

Amazon

RDS

Amazon

DynamoDBAmazon S3

Amazon

CloudFront

Game Clients

Amazon

Route 53

Load

Balancers

Web Servers

Amazon

EC2

Patches &

DLC

Processing

● Separate tables per game for independent processing

● Reading only the data needed

● Export entire tables to S3 in 24 hours with no loss

● Capable of adjusting to new queries

Costs

● 1 server handles what 12 did before

● Costs roughly equivalent, but load is 10x

● Reading only the data needed

● No longer paying for static usage with pay per usage

Challenges / Improvements

● Too much old data still in

couchDB

● Integrated read/write

provisioning

Better data, better stories

Understanding player behavior

● Internal tools read aggregated data as player head

maps

● Know the characters and lines working

● Episodes personalize to audience in a way no

entertainment medium can

But we wanted to take this a step further. . .

User Playstyle Clustering

● Used K-means clustering was based on 2,200 Walking Dead story choice nodes

○ Algorithm determines number of means and initial seed value

○ 8 clusters that represented 88% of our players.

● Analyzed each cluster on two metrics:

○ Which story choices a single cluster endorsed at high rates

○ Which story choices are effective at splitting apart two or more clusters

● Developed player personas

○ Highlighted general preferences of a cluster (e.g., protecting resources over

helping people)

○ Identified minor characters that were influential to play style

Customer Playstyle ClusteringModel OutputModel Selection Personas

“Amoral and Ambivalent”

The second biggest cluster, representing 22% of our players, seems to value independence.

They offer peace first to Russians but lie about Jane's whereabouts, potentially because they think they are bluffing for survival's sake. They don’t start with force, but likely to follow-up with force if not complied with. Highly favors pointing out they have a baby to the Russians during the showdown, perhaps appealing to their humanity, but while this cluster helps others or offers peace when convenient, they don’t hesitate to react with violence once pushed.

“My Best Self”This cluster is reasonable and logical; they may be even tempered individuals, or Players who feel comfortable being a little distanced from the content of the game they are playing. Their responses in conversation generally seem to pick out the most responsive/reactive threads. This cluster is conventionally compassionate and frequently chose offers of condolences and sympathy when appropriate.

Their most common two endings can perhaps be read as either: disillusionment at Howe’s when Clem turns away the family or loyalty as when Clem keeps on with Kenny to Wellington then leaves with him.

Thank you!

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