Database su AWS scegliere lo strumento giusto per il giusto obiettivo

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Database su AWS:
scegliere lo strumento giusto
per il giusto obiettivo
Antonio D’Ortenzio
AWS Solutions Architecture

1970 1980 1990 2000
Oracle DB2
SQL Server
MySQL
PostgreSQL
DynamoDB
Redis
MongoDB
Elasticsearch
Neptune
CassandraAccess
Aurora
2010
Timestream
QLDB
DocumentDB

Two fundamental areas of focus
“Lift and shift” existing
apps to the cloud
Quickly build new
apps in the cloud

“Lift and shift” existing apps to the cloud
apps to the cloud
Quickly build new
apps in the cloud

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 throughput of standard
MySQL and 3x 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
Managed by RDS:
No hardware provisioning,
software patching, setup,
configuration, or backups

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

Quickly build new apps in the cloud
apps to the cloud
Quickly build new
apps in the cloud

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

Common data categories and use cases
Relational
Referential
integrity, ACID
transactions,
schema-
on-write
Lift and shift, ERP,
CRM, finance
Key-value
High
throughput, low-
latency reads
and writes,
endless scale
Real-time bidding,
shopping cart,
social, product
catalog, customer
preferences
Document
Store
documents and
quickly access
querying on
any attribute
Content
management,
personalization,
mobile
In-memory
Query by key
with
microsecond
latency
Leaderboards,
real-time analytics,
caching
Graph
Quickly and
easily create
and navigate
relationships
between
data
Fraud detection,
social networking,
recommendation
engine
Time-series
Collect, store,
and process
data sequenced
by time
IoT applications,
event tracking
Ledger
Complete,
immutable, and
verifiable history
of all changes to
application data
Systems
of record, supply
chain, health care,
registrations,
financial

AWS purpose-built databases
Relational Key-value Document In-memory Graph Time-series Ledger
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCacheDocumentDB

DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCache
Redis Memcached
DocumentDB

Relational data
• Divide data among tables
• Highly structured
• Relationships established via
keys enforced by the system
• Data accuracy and consistency
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 use case
// Doctors affiliated with Mercy
hospital
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
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;

DynamoDB NeptuneAmazon RDS Timestream QLDBElastiCache
Redis Memcached
DocumentDB

Key-value data
• Simple key value
pairs
• Partitioned by keys
• Resilient to failure
• High throughput,
low-latency reads
and writes
• Consistent
performance at
scale
Gamers
Primary Key Attributes
GamerTag Level Points High Score Plays
Hammer57 21 4050 483610 1722
FluffyDuffy 5 1123 10863 43
Lol777313 14 3075 380500 1307
Jam22Jam 20 3986 478658 1694
ButterZZ_55 7 1530 12547 66
… … … … …
Gamers
Hammer57
21
4050,
483610,
1722
GET {
TableName:"Gamers",
Key: {
"GamerTag":"Hammer57“,
“ProjectionExpression“:”Points”
} }

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

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

(Client)
(App) (Database)
JSON
!=
Evolution of document databases
Relational
JSON became the
de facto data
interchange
format
Friction when
converting JSON
to the relational
model
Object-relational
mappings (ORMs)
were created to help
with this friction
Document
databases solved
the problem
JSON

Document databases
• Data is stored in JSON-like
documents
• Documents map naturally to
how humans model data
• Flexible schema and indexing
• Expressive query language built
for documents (ad hoc queries
and aggregations)
JSON documents are first-class objects
of the database
{
id: 1,
name: "sue",
age: 26,
email: "sue@example.com",
promotions: ["new user", "5%", "dog lover"],
memberDate: 2018-2-22,
shoppingCart: [
{product:"abc", quantity:2, cost:19.99},
{product:"edf", quantity:3, cost: 2.99}
]
}

Use cases for document data
Mobile
Retail and
marketing
User profilesCatalog
Content
management
Personalization

Use cases for document data
Product Catalog & Inventory
{
"Item": "Spray Paint",
"Colors": ["Black","Red","Green"],
"UnitPrice": 3.99
}
{
"UnitPrice": 3.99
}
{
"Inventory": {
"OnHand": 47,
"MinOnHand": 50,
"OrderQnty": 36
},
"UnitPrice": 3.99
}

Challenges of existing document databases
Hard
to manage
Hard to
scale
Hard to
set up
Hard to
secure
Hard to
back up

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

DynamoDB NeptuneAmazon RDS Timestream QLDBElastiCacheDocumentDB

Amazon ElastiCache
Redis and Memcached compatible, in-memory data store and cache
Secure and
reliable
Network isolation,
encryption at rest/transit,
HIPAA, PCI, FedRAMP,
multi AZ, and automatic
failover
Redis & Memcached
compatible
Fully compatible with open
source Redis and Memcached
Easily scalable
Scale writes and reads with
sharding and replicas
Extreme
performance
In-memory data store and
cache for microsecond
response times

Graph data
• Relationships are first-class
objects
• Vertices connected by Edges
PURCHASED PURCHASED
FOLLOWS
PURCHASED
KNOWS
PRODUCT
SPORT
FOLLOWS

Graph use case
// Product recommendation to a user
gremlin> V().has(‘name’,’sara’).as(‘customer’).out(‘follows’).in(‘follows’).out(‘purchased’)
( (‘customer’)).dedup() (‘name’) ('name')
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')

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

Time-series use case
Application events
IoT Sensor
Readings
Vehicle Telematics
Humidity
% WATER VAPOR
91.094.086.093.0

Existing time-series databasesRelational databases
Difficult to
maintain high
availability
Difficult to scale Limited data
lifecycle
management
Inefficient
time-series data
processing
Unnatural for
time-series
data
Rigid schema
inflexible for fast
moving time-series
data
Building with time-series data is challenging

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

Airbnb uses different databases based
on the purpose
User search history: Amazon DynamoDB
• Massive data volume
• Need quick lookups for personalized search
Session state: Amazon ElastiCache
• In-memory store for submillisecond site rendering
Relational data: Amazon RDS
• Referential integrity
• Primary transactional database

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

HELLO!
I am Paolo Perrucci
Director of IT Infrastructures & Operations
Sara Assicurazioni
You can find me at p.perrucci@sara.it

Sara Assicurazioni
70+
years in non-life
insurance market
30+
years in life
insurance market
650+ agencies
600+
employees
86%
Property &
casualty
14%
Life
583M
non-life insurance
premiums
96M
life insurance
premiums
1M
customers
Total
premiums

The Project
» Migration of all core business applications from on-premises DC to AWS
» Applications running on Windows, Unix and Mainframe systems
» Main insurance applications (property & casualty, life) re-engineered
» Lift and Shift migration of other applications

The Challenge
» Source data stored and organized using heterogeneous methods: flat files,
sequential data sets, RDBMS
» Objectives:
⋄ phase 1: data representation consolidation using RDBMS
⋄ phase 2 (in progress): data representation leveraging additional modern
database technologies
⋄ focus on applications and business reducing time-consuming database
administration tasks

Our choice: Amazon RDS
» Amazon RDS (Relational Database Service) benefits:
⋄ Immediate infrastructure provisioning
⋄ Ready-to-use database software installation
⋄ Automated common administrative tasks (backups, software
patching, …)
⋄ Multi-AZ deployments: Amazon RDS also manages synchronous
data replication across Availability Zones with automatic failover
⋄ Powerful monitoring

» Project timing
How we did it
Feb 2018
Start
Sept 2018
Data migration
dry-run
Jan 2019
Migration

The day after (today)
» Workloads separation
⋄ core workloads on primary read/write DB instance
⋄ move non-core workloads to secondary read/only DB instances
⋄ first use case: reporting (leveraging DMS)

Tomorrow - Exploiting AWS ecosystem
» Amazon Neptune
⋄ Fully managed graph database service that makes it easy to build and run
applications that work with highly connected datasets
⋄ Use case: fraud detection

» Amazon Timestream
⋄ fully managed time series database service that makes it easy to store
and analyze trillions of events per day at 1/10th the cost of relational
databases
⋄ Use case: IoT and operational applications

» Amazon QuickSight
⋄ fast, cloud-powered business intelligence service that makes it easy for
you to deliver insights to everyone in your organization
⋄ Use case: from batch to self service & real-time reporting and analytics

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