How to choose the right database for your workload

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
How to choose the right database
for your workload
Boaz Ziniman
Technical Evangelist
Amazon Web Services
D A T 0 0 1
Robert Jan Erkelens
Program Manager
Cloud 2020
Portbase
Melvyn de Kort
Cloud Architect
Portbase
@ziniman
ziniman

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.”

Modern apps have modern requirements
Users: 1 million+
Data volume: TB–PB–EB
Locality: Global
Performance: Milliseconds–microseconds
Request rate: Millions per second
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

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
Indexing and
searching
semistructured
logs and data
Product
catalog, help
and FAQs,
full text
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 Time-series Ledger

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

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

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Relational Key-value Document Graph
Let’s take a closer look at…

Relational

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

Relational
// Doctors affiliated with Mercy hospital
SELECT
d.first_name, d.last_name
FROM
doctor as d,
hospital as h
WHERE
d.hospital = h.hospital_id
AND h.name = ‘Mercy';
// 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;
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

Amazon Relational Database Service (RDS)
Managed relational database service with a choice of six popular database engines
Easy to administer Available and durable Highly scalable Fast and secure
No need for infrastructure
provisioning, installing, and
maintaining DB software
Automatic Multi-AZ data
replication; automated backup,
snapshots, failover
Scale database compute
and storage with a few
clicks with no app
downtime
SSD storage and guaranteed
provisioned I/O; data
encryption at rest and in
transit

The right database for Portbase
Melvyn de Kort
Cloud Architect @ Portbase
Robert Jan Erkelens
Program Manager Cloud 2020 @ Portbase

4.000
parties
14.500
users
43
services
90.000.000
messages/yr

DevOps and Public Cloud Strategy

Our Database
is a
Mission Critical
Component
!!!

Database requirements – migration
Oracle Relational Database
Existing Oracle license
Applications use Oracle specific database features
Low risk migration
Low latency communication with database

Database requirements – production
High availability
Managed maintenance and upgrades
In depth monitoring
Tech support on demand

Database requirements – strategy
Being inside AWS ecosystem
Flexibility to migrate to application specific database solutions, e.g.
DynamoDB
ElasticSearch

Amazon RDS for Oracle
Amazon RDS Multi-AZ deployment
Amazon RDS CloudWatch Metrics
Fully managed database service
Encryption at rest & in transit

Amazon RDS for Oracle – extras
Amazon RDS Performance Insights
AWS Glue for Business Intelligence
Amazon RDS Read Replica for Oracle
Amazon S3 integration for data ingress and egress
Amazon DMS (Data Migration Services)

What has AWS RDS brought us?
Results Business Value
Monitoring and alerting are top notch Higher availability
DevOps team is in full control Faster incident resolution
Vertical scaling within minutes Prepared for changing demand
Encryption (at rest & in transit) Enhanced security

Next steps
Read replica for
Reporting queries
and copying data to
Data Lake
Data archival to S3
triggered by AWS
Lambda
Split the single big
database into
multiple smaller
ones
Move from Oracle
to Aurora
PostgreSQL
compatible
Move to Aurora
Serverless

Key-value & Document

Amazon DynamoDB
Fast and flexible key value database service for any scale
Comprehensive
security
Encrypts all data by
default and fully integrates
with AWS Identity and
Access Management for
robust security
Performance at scale
Consistent, single-digit
millisecond response times at
any scale; build applications with
virtually unlimited throughput
Global database for
global users and apps
Build global applications with
fast access to local data by easily
replicating tables across multiple
AWS Regions
Serverless
No server provisioning,
software patching, or
upgrades; scales up or down
automatically; continuously
backs up your data

Amazon DynamoDB Data Structure
Table
Items
Attributes
Partition
Key
Sort
Key
Mandatory
Key-value access pattern
Determines data distribution
Optional
Model 1:N relationships
Enables rich query capabilities
All items for key
==, <, >, >=, <=
“begins with”
“between”
“contains”
“in”
sorted results
counts
top/bottom N values

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
Key-value
// Status of Hammer57
GET {
TableName:"Gamers",
Key: {
"GamerTag":"Hammer57",
"Type":"Status” } }
// Return all Hammer57
Gamers
GamerTag = :a
:a Hammer57

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

Document
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"]
}
Indexing and storing
documents with
support
for query on
any attribute
Content management,
personalization, mobile

Amazon DocumentDB
Fast, scalable, and fully managed MongoDB-compatible database service
Fast Scalable Fully managed MongoDB
compatible
Millions of requests per second
with millisecond latency; twice
the throughput of MongoDB
Separation of compute and
storage enables both layers
to scale independently;
scale out to 15 read replicas
in minutes
Managed by AWS:
no hardware provisioning;
auto patching, quick setup,
secure, and automatic
backups
Compatible with MongoDB 3.6;
use the same SDKs, tools, and
applications with Amazon
DocumentDB

Graph

Relationships enable new applications
Retail fraud detectionRestaurant recommendationsSocial networks

Graph
// 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')

Different approaches for highly connected data
Purpose-built for a business process
Purpose-built to answer questions about
relationships

Amazon Neptune
Fully managed graph database
Easy
Build powerful queries
easily with Gremlin and
SPARQL
Fast
Query billions of relationships
with millisecond latency
Open
Supports Apache TinkerPop &
W3C RDF graph models
Reliable
Six replicas of your data across
three AZs with full backup and
restore

https://github.com/aws-samples/aws-bookstore-demo-app

Bookstore Demo

Amazon Timestream (sign up for the preview)
Fast, scalable, fully managed time-series database
1,000x faster and 1/10th the
cost of relational databases
Collect data at the rate of
millions of inserts per
second (10M/second)
Trillions of
daily events
Adaptive query processing
engine maintains steady,
predictable performance
Time-series analytics
Built-in functions for
interpolation, smoothing,
and approximation
Serverless
Automated setup,
configuration, server
provisioning, software patching

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
Modern applications
considerations
Demo Application On
GitHub: /aws-samples/aws-
bookstore-demo-app
Keep on Building !
AWS: Purpose-built databases
Relational Key-value Document In-memory Graph Search
Amazon
DynamoDB
Amazon
Neptune
Amazon RDS
Aurora CommercialCommunity
Amazon
ElastiCache
Amazon
Elasticsearch
Service
Amazon
DocumentDB
Time-series Ledger
Amazon
Timestream
Amazon
Quantum
Ledger
DatabaseMemcachedRedis
Social mediaRide hailing Media streaming Dating

Thank you!
Boaz Ziniman
Technical Evangelist - Amazon Web Service
@ziniman ziniman

How to choose the right database for your workload

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