Building with AWS Databases: Match Your Workload to the Right Database | AWS Summit Tel Aviv 2019

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Building With AWS Databases:
Match Your Workload To The Right Database
Ronnen Slasky
Head of Technology, AWS
D A T 3 0 5

1. Database Considerations
2. Common data models/use cases
3. Demo
4. Ledger Databases
Agenda

Why did you choose this database?
“Because we heard X is the best new thing.”
“Because we have a site license for X.”
“Because X is what we know how to use.”

Why should you choose this database?
“Because this database is purpose built to support
what my application is designed to do.”

A common question that I get is why do we offer so many database products? The answer for me is
simple: Developers want their applications to be well architected and scale effectively. To do this, they
need to be able to use multiple databases and data models within the same application.
https://www.allthingsdistributed.com/2018/06/purpose-built-databases-in-aws.html

Database Workloads
Data Considerations
Shape Size Compute

Shape
Purpose-Built For Optimized for When you need to Example Workload
Row Store Operate on a record or group of records Payroll
Column Store Aggregations, scans and joins Analytics
Key-Value Store Query by key with high throughput & fast ingestion Tracking devices
Document Store Index & store documents for query on any property Patient data
Graph Store Persist and retrieve relationships Recommendations
Time-Series Store Store and process data sequence Process Engine telemetry
Unstructured Store Get and put of objects Store user reviews

Size
Considerations Example Workload
Size at limit – bounded or unbounded
Number of employees – bounded
Number of sensors – unbounded
Working set size & caching
10-years of sales data but only the last 12-months is queried
Session data for users of a streaming service
Retrieval size
Get one row
Get one thousand rows
Partition-able or monolithic
Storage and processing of car location data is partition-able
Company payroll data has no natural partition boundary

Compute
Considerations Example Workload
Compute functions
Sum of sales for the last 12-months
Get & Put data
Rate of ingestion
Location telemetry from cars added to the database every minute
New employees records being added to the database
Change rate
Inventory counts are frequently updated
Sales records are never updated
Throughput
Million users browsing a product catalogue every second
50 doctors looking at 300 patient records per day
Latency
Get the location of a car in 5 milliseconds
Get the min, max & average deal size for the last 12-months in 5 seconds

My [insert your favorite DB] works for everything
General purpose Special purpose
One size fits all Efficiency at scale

Modern apps create new requirements
Users: 1 million+
Data volume: TB–PB–EB
Locality: Global
Performance: Milliseconds–microseconds
Request rate: Millions
Access: Web, mobile, IoT, devices
Scale: Up-down, Out-in
Economics: Pay for what you use
Developer access: No assembly requiredSocial mediaRide hailing Media streaming Dating

CHALLENGE
Wanted to enable anyone to learn a
language for free.
SOLUTION
Purpose-built databases from AWS:
• DynamoDB: 31B items tracking
which language exercises completed
• Aurora: primary transactional
database for user data
• ElastiCache: instant access to
common words and phrases
Result:
More people learning a language on
Duolingo than entire US school system
300M total users
7B exercises per month

Relational Key-value Document In-memory Graph
Referential
integrity, ACID
transactions,
schema-
on-write
Low-latency,
key lookups
with high
throughput and
fast ingestion
of data
Indexing and
storing
documents
with support
for query on
any attribute
Microseconds
latency, key-
based queries,
and specialized
data structures
Creating and
navigating
data relations
easily and
quickly
Lift and shift,
EMR, CRM,
finance
Real-time
bidding,
shopping cart,
social
Content
management,
personalization,
mobile
Leaderboards,
real-time
analytics,
caching
Fraud detection,
social
networking,
recommendation
engine
Search
Indexing and
searching
semistructured
logs and data
Product
catalog, help
and FAQs,
full text
Time-series Ledger
Collect, store,
and process
data sequenced
by time
IoT
applications,
event tracking
Complete,
immutable, and
verifiable
history of all
changes
Systems
of record,
supply chain,
health care,
registrations,
financial
Common data categories and use cases

Relational Key-value Document In-memory Graph Search
Amazon
DynamoDB
Amazon
Neptune
Amazon RDS
Amazon
Aurora
CommercialCommunity
Amazon
ElastiCache
Amazon
Elasticsearch
Service
Amazon
DocumentDB
Time-series Ledger
Amazon
Timestream
Amazon
Quantum
Ledger
DatabaseMemcachedRedis
AWS purpose-built databases

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Relational Key-value Document Graph

Patient
* Patient ID
First Name
Last Name
Gender
DOB
* Doctor ID
Visit
* Visit ID
* Patient ID
* Hospital ID
Date
* Treatment ID
Medical Treatment
* Treatment ID
Procedure
How Performed
Adverse Outcome
Contraindication
Doctor
* Doctor ID
First Name
Last Name
Medical Specialty
* Hospital Affiliation
Hospital
* Hospital ID
Name
Address
Rating
Relational
Referential
integrity, ACID
transactions,
schema-on-write
Lift and shift, ERP, CRM,
finance
Amazon Aurora
Amazon RDS
Amazon Redshift

Patient
* Patient ID
First Name
Last Name
Gender
DOB
* Doctor ID
Visit
* Visit ID
* Patient ID
* Hospital ID
Date
* Treatment ID
Medical Treatment
* Treatment ID
Procedure
How Performed
Adverse Outcome
Contraindication
Doctor
* Doctor ID
First Name
Last Name
Medical Specialty
* Hospital Affiliation
Hospital
* Hospital ID
Name
Address
Rating
// Number of patient visits each doctor completed last
week
SELECT
d.first_name, d.last_name, count(*)
FROM
visit as v,
hospital as h,
doctor as d
WHERE
v.hospital_id = h.hospital_id
AND h.hospital_id = d.hospital
AND v.t_date > date_trunc('week’,
CURRENT_TIMESTAMP - interval '1 week')
GROUP BY
d.first_name, d.last_name;
Relational
Referential
integrity, ACID
transactions,
schema-on-write

Table 1 Table 2 Table N
…
…
…
… … …
Partitions Partitions Partitions
… …
…
Highly partitionable data
Low-latency,
key look-ups
with high
throughput and
fast ingestion
of data
Real-time bidding,
shopping cart, IoT
device tracking
Key-value
Amazon
DynamoDB

Gamers
Primary Key
Attributes
Gamer Tag Type
Hammer57
Rank
Level Points Tier
87 4050 Elite
Status
Health Progress
90 30
Weapon
Class Damage Range
Taser 87% 50
FluffyDuffy
Rank
Level Points Tier
5 1072 Trainee
Status
Health Progress
37 8
// Status of Hammer57
GET {
TableName:"Gamers",
Key: {
"GamerTag":"Hammer57",
"Type":"Status” } }
// Return all Hammer57
Gamers
GamerTag = :a
:a Hammer57
Key-value

Document
(client) (app) (database)
JSON !JSON
!=
Indexing and
storing
documents with
support
for query on
any attribute
Content management,
personalization,
mobile
Amazon
DocumentDB

Indexing and
storing
documents with
support
for query on
any attribute
Self Contained
Flexible Schema
Document
Amazon
DocumentDB
User profiles
{
id: 181276,
username: "sue1942",
name: {first: "Susan",
last: "Benoit"}
}
{
id: 181276,
username: "sue1942",
name: {first: "Susan",
last: "Benoit"},
ExplodingAcorns: {
hi_score: 3185400,
global_rank: 5139,
bonus_levels: true
},
promotions: ["new user","5%",”acorn"]
}

Graph
Creating and
navigating
relations
between data
easily
and quickly
Fraud detection, social
networking,
recommendation
engine
Amazon
Netptune

PURCHASED PURCHASED
FOLLOWS
PURCHASED
KNOWS
PRODUCT
SPORT
FOLLOWS
Graph
Creating and
navigating
relations
between data
easily
and quickly
Fraud detection, social
networking,
recommendation
engine
Amazon
Netptune

// Product recommendation to a user
gremlin> V().has(‘name’,’sara’).as(‘customer’).out(‘follows’).in(‘follows’).out(‘purchased’)
( (‘customer’)).dedup() (‘name’) ('name')
KNO
W
S
PURCHASED PURCHASED
FOLLOWS
PURCHASED
KNOWS
PRODUCT
SPORT
FOLLOWS
FOLLOWS
// Identify a friend in common and
make a recommendation
gremlin> g.V().has('name','mary').as(‘start’).
both('knows').both('knows’).
where(neq(‘start’)).
dedup().by('name').properties('name')
Graph

Retail demo application
Demo application:
1. Available today
2. On GitHub:
/aws-samples/aws-
bookstore-demo-app
3. One click
CloudFormation
deployment
Search
Indexing and
searching
semistructured
logs and data
Product
search
Amazon
Neptune
Amazon Elasticsearch
Service
Key-value
High
throughput,
Low-latency
reads
and writes,
endless scale
Shopping cart, user
profile
Graph
Quickly and
easily create
and navigate
relationships
between
data
Product
recommendation
In-memory
Query by key
with
microsecond
latency
Product
leaderboard
DynamoDB ElastiCache

Retail demo application

Amazon Quantum Ledger Database (QLDB) (Preview)
Fully managed ledger database
Track and verify history of all changes made to your application’s data
Immutable
Maintains a sequenced record of
all changes to your data, which
cannot be deleted or modified;
you have the ability to query and
analyze the full history
Cryptographically
verifiable
Uses cryptography to
generate a secure output
file of your data’s history
Easy to use
Easy to use, letting you
use familiar database
capabilities like SQL APIs
for querying the data
Highly scalable
Executes 2–3X as many
transactions than ledgers
in common blockchain
frameworks

What have we learned?
Choose the right tool for the job
Start from the application and work backwards
Focus on shape, size and compute Demo Application On GitHub:
/aws-samples/aws-bookstore-demo-app
Keep on Building !

Thank you!
Ronnen Slasky
Head of Technology
Amazon Web Services
http://bit.ly/2SGYHp4

Building with AWS Databases: Match Your Workload to the Right Database | AWS Summit Tel Aviv 2019

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