How to choose the right Database for your workload - AWS DevDay Online

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How to choose the right database
for your workload
Boaz Ziniman
Principal Developer Advocate, Amazon Web Service
@ziniman
boaz.ziniman.aws
bziniman
boaz.work

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
semistructure
d 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
Amazon
DynamoDB
Amazon
Neptune
Aurora CommercialCommunity
Amazon
ElastiCache
Amazon
Elasticsearch
Service
Amazon
DocumentDB
Amazon
Timestream
Amazon
Quantum
Ledger
Database
Relational Key-value Document In-memory Graph Search Time-series Ledger
Amazon
RDS
PostgreSQL PostgreSQL

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
RESULTS
More people learning a language on
Duolingo than entire US school system
300M total users
7B exercises per month

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
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
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;

You
You
Fully managed services on AWS
Spend time innovating and building new applications, not managing infrastructure
AWS
Self-managed Fully managed
Schema design
Query construction
Query optimization
Automatic failover
Backup and recovery
Isolation and security
Industry compliance
Push-button scaling
Automated patching
Advanced monitoring
Routine maintenance
Built-in best practices

Move to managed relational databases
Reduce database administrative burden
No need to re-architect existing applications
Get better performance, availability, scalability, and security
Migrate on-premises or cloud-hosted relational databases to managed services
Amazon Aurora
MySQL, PostgreSQL
Amazon RDS
MySQL, PostgreSQL, MariaDB,
Oracle, SQL Server
PostgreSQL

Amazon Aurora
MySQL- and PostgreSQL-compatible relational database built for the cloud
Performance and availability of commercial-grade databases at 1/10th the cost
Performance
and scalability
Availability
and durability
Highly secure Fully managed
5x the throughput of standard
MySQL and 3x that of standard
PostgreSQL; scale out up to
15 read replicas
Fault-tolerant, self-healing
storage; six copies of data
across three Availability Zones;
continuous backup to Amazon S3
Network isolation,
encryption at
rest/transit, compliance
and assurance programs
Managed by Amazon RDS:
No server provisioning,
software patching, setup,
configuration, or backups

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
QUERY {
TableName:“Gamers”,
KeyConditionExpression:”GamerTag = :a”,
ExpressionAttributeValues: {
“: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 (with MongoDB compatibility)
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
Fast, scalable, and fully managed MongoDB-compatible database service

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
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
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
Fully managed graph database

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)
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

Learn databases with AWS Training and Certification
25+ free digital training courses cover topics and services related
to relational and nonrelational databases
Resources created by the experts at AWS to help you build and validate database skills
Validate expertise with the AWS Certified Database – Specialty exam
The classroom offering, Planning and Designing Databases on
AWS, features AWS expert instructors and hands-on activities
Visit the databases learning path at aws.amazon.com/training/path-databases

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
Principal Developer Advocate, Amazon Web Service
@ziniman
boaz.ziniman.aws
bziniman
boaz.work

How to choose the right Database for your workload - AWS DevDay Online

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How to choose the right Database for your workload - AWS DevDay Online