AWS offers the broadest range of purpose-built databases for specific application use cases. In this session, we will show you an overview of the database offering from AWS and elaborate more on Amazon Aurora which is a MySQL- and PostgreSQL-compatible relational database with the speed, reliability, and availability of commercial databases at one-tenth the cost. We will also explore recently released Aurora features, such as Serverless, Multi-Master, and Global databases, and helps you get started.

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How to Choose the Right Database to Build Internet
Scale Applications in the Cloud & what's new in
Amazon Aurora
Dickson Yue
AWS Manager of Solutions Architecture

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Internet-scale Apps Need Low Latency and High Concurrency
Users 1M+
Data volume TB-PB-EB
Locality Global
Performance Milliseconds
-Microseconds
Request Rate Millions
Access Mobile, IoT,
Devices
Scale Up-Out-In
Economics Pay as you go
Developer access Instant API access
Gaming Fintech Social
media
Ride
hailing
Media
streaming
Dating Session
stores

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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
Shopping cart,
Real-time bidding,
social, product
catalog, customer
preferences
Document
Store
documents and
quickly access
querying on
any attribute
Product info,
Content
management,
personalization,
mobile
In-memory
Query by key
with
microsecond
latency
Caching,
Leaderboards,
real-time analytics,
Graph
Quickly and
easily create
and navigate
relationships
between
data
Recommendation
engine,
fraud detection,
social networking,
Time-series
Collect, store,
and process
data sequenced
by time
Event tracking,
IoT applications
Ledger
Complete,
immutable, and
verifiable history
of all changes to
application data
Supply chain ,
systems
of record, health
care, registrations,
financial

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Purpose-built databases
Relational Key-value Document In-memory Graph Time-series Ledger
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QuantumElastiCacheDocumentDB
NEW NEW NEW

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Characteristics of modern applications
Internet-scale and transactional
Users: 1M+
Data volume: TB–PB–EB
Locality: Global
Performance: Milliseconds–microseconds
Request Rate: Millions
Access: Mobile, IoT, devices
Scale: Up-out-in
Economics: Pay-as-you-go
Developer access: Instant API accessSocial mediaRide hailing Media streaming Dating

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

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

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Relational
RDS

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Amazon RDS is . . .
Cloud native engine Open source engines Commercial engines
RDS platform
• Automatic fail-over
• Backup & recovery
• X-region replication
• Isolation & security
• Industry compliance
• Automated patching
• Advanced monitoring
• Routine maintenance
• Push-button scaling
Image credit: By Mackphillips - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=55946550

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Performance Insights
Dashboard showing database load
 Easy – e.g. drag and drop
 Powerful – drill down using zoom in
Identifies source of bottlenecks
 Sort by top SQL
 Slice by host, user, wait events
Adjustable time frame
 Hour, day, week , month
 Up to 2 years of data; 7 days free
Max vCPU
CPU bottleneck
SQL w/ high CPU

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Which database engine version should I use?
MySQL: 5.5, 5.6, 5.7, 8.0
MySQL 8.0: Window Functions, Common Table Expressions, JSON functions, Spatial support, improved
performance, crash-safe Data Definition Language (DDL) operations, security roles, TLS 1.2
MariaDB: 10.0, 10.1, 10.2, 10.3
MariaDB 10.3: Oracle compatibility (PL/SQL parser), sequences, INTERSECT and EXCEPT, new ROW type and TYPE
OF stored functions, invisible columns, temporal versioned tables, user-defined aggregates, instant ADD COLUMN
operations
PostgreSQL: 9.3, 9.4, 9.5, 9.6, 10, 11 (Preview)
PostgreSQL 10: Native table partitioning, improved parallelism in query execution, ICU collation support, column
group statistics, enhanced FDW and PLV8 extensions, huge pages enabled by default
PostgreSQL 11: Embedded transactions within a stored procedure, improvements to partitioning, improvements
to parallelism, adding columns with a non-null column default faster
Oracle: 11.2, 12.1, 12.2
Oracle 12.2: New engine versioning scheme—Release Updates (RUs) and Release Update Revisions (RURs)
SQL Server: 2008 R2, 2012, 2014, 2016, 2017
NEW!
NEW!
NEW!
NEW!

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Moving to open database engines

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Relational
Aurora

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

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Write and read throughput
Aurora MySQL is 5x faster than MySQL
0
50,000
100,000
150,000
200,000
250,000
Series1 Series2 Series3 Series4 Series5
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
Series1 Series2 Series3 Series4 Series5
Write Throughput Read Throughput
Using Sysbench with 250 tables and 200,000 rows per table on R4.16XL
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Write and read throughput
Aurora MySQL is 5x faster than MySQL
0
50,000
100,000
150,000
200,000
250,000
MySQL 5.6 MySQL 5.7 MySQL 8.0
Aurora 5.6 Aurora 5.7
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
MySQL 5.6 MySQL 5.7 MySQL 8.0
Aurora 5.6 Aurora 5.7
Write Throughput Read Throughput
Using Sysbench with 250 tables and 200,000 rows per table on R4.16XL

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Availability zone 1
Region
Availability zone 2 Availability zone 3
Scale-out, distributed, multi-tenant design . . .
Purpose-built log-structured,
distributed storage
Shared storage volume
Storage nodes with SSDs
Master
SQL
Transactions
Caching
Reader
5
Reader
9
SQL
Transactions
Caching
SQL
Transactions
Caching
Reader
1
SQL
Transactions
Caching
Reader
8
SQL
Transactions
Caching
Reader
15
SQL
Transactions
Caching
… … …10GiB stripes
Master and up to 15 Readers all point
to the same storage
6-way replication, 2 per AZ
Quorums survive AZ+1 failure

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Aurora Multi-Master – scaling and availability
0
10000
20000
30000
40000
50000
60000
1
3
5
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9
11
13
15
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25
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125
AggregatedThroughput
Time in minutes
Sysbench workload on 4 R4.XL nodes
Adding a node Adding a node Node going down Node recovering

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Aurora Global Database (GA)
High-performance database for globally-distributed applications
Single Global Database with cross region replication
Replication typically completes in less than a second
No impact on database performance
Write master in one region and read replicas in other regions
Cross-region disaster recovery
Local read latency for applications with global users
Primary Region Secondary Region
Application
Read/Write
Master
Storage Storage
Read Instances
(up to 15)
Read Instances
(up to 16)
Replication <1s

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Database backtrack
t0 t1 t2
t0 t1
t2
t3 t4
t3
t4
Rewind to t1
Rewind to t3
Invisible Invisible
Backtrack brings the database to a point in time without requiring restore from backups
• Backtracking from an unintentional DML or DDL operation
• Backtrack is not destructive. You can backtrack multiple times to find the right point in time
• Also useful for QA (rewind your DB between test runs)

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Aurora Serverless
Responds to your application automatically
• Start up
• Shut down
• Scale capacity

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How does it work . . .
Availability zone 1
Region
App
Shared distributed storage volume
Multi-tenant NLB / database proxy layer
Warm-pool of Aurora instances
Monitoring service

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Amazon RDS Data API for serverless applications
Millions of
IOT/mobile devices Data API fleet
API
End-point
Amazon Aurora
Serverless
Access through simple web interface
• Public endpoint addressable from anywhere
• No client configuration required
• No persistent connections required
Ideal for Serverless applications (Lambda)
Ideal for light-weight applications (IOT)

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

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Modern applications require internet-scale performance

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Key-Value

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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 hardware provisioning,
software patching, or
upgrades; scales up or down
automatically; continuously
backs up your data

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DynamoDB
Advancements over the last 2 years
VPC
endpoints
April 2017
Auto
scaling
June 2017
DynamoDB
Accelerator (DAX)
April 2017
Time To
Live (TTL)
February 2017
Global tables
On-demand
backup
Encryption at rest
November 2017 November 2017 November 2017
Point-in-time
recovery
March 2018
SLA
June 2018
99.999% SLA
August 2018
Adaptive
capacity ACID
November 2018
Transactions
November 2018
On-demand

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DynamoDB transactions
Simplify your code by executing multiple, all-or-nothing actions within
and across tables with a single API call
Single
API
Call

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DynamoDB - read/write capacity on-demand (GA)
No more capacity planning - pay only for what you use
No capacity planning
No need to specify how much
read/write throughput you expect to
use
Ideal for unpredictable workloads
Ramp from zero to tens of thousands of requests
per second on demand
Pay only for what you use
Pay-per-request pricing

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DynamoDB Accelerator (DAX)
• Fully managed, highly available: Handles all software
management, fault tolerant, replication across multi-AZs within a
Region
• DynamoDB API compatible: Seamlessly caches DynamoDB API calls,
no application rewrites required
• Write-through: DAX handles caching for writes
• Flexible: Configure DAX for one table or many
• Scalable: Scales-out to any workload with up to 10 read replicas
• Manageability: Fully integrated AWS service: Amazon CloudWatch,
Tagging for DynamoDB, AWS Console
• Security: Amazon VPC, AWS IAM, AWS CloudTrail, AWS
Organizations
Features
DynamoDB
Your Applications
DynamoDB Accelerator
Table #1
Table #2

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CHALLENGE
Needed a solution that scales and
manage up to 8x more riders during
peak times.
SOLUTION
DynamoDB stores GPS coordinates of
all rides.
With AWS, Lyft saves on infrastructure
costs and enables massive growth of
ridesharing platform. There are now
23M people who use Lyft worldwide.
Lyft >1M rides/day,
8x traffic in peak hours

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Use cases
Gaming
leaderboards
Chat apps
Caching
Session
store
Machine
learning
Real-time
analytics
Media
streaming
Message
queues
Geospatial

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In memory
Real-time applications

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Amazon ElastiCache
Fully-managed,Redis or Memcached compatible, low-latency, in-memorydata store
Fully
Managed
Extreme
Performance
Easily
Scalable
AWS manages all
hardware and software
setup, configuration,
monitoring
In-memory data store and
cache for sub-millisecond
response times
Read scaling with
replicas. Write and memory
scaling with sharding.
Non disruptive scaling

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About Redis
Fast
<1ms latency for
most commands
Open source
Simple
Highly available
Replication
Atomic operations
Supports transactions
In-memory
key-value store
Powerful
~200 commands, Lua scripting,
Geospatial, Pub/Sub
Various data structures
Strings, lists, hashes, sets, sorted sets,
streams, bitmaps, streams
and HyperLogLogs
Backup/Restore
Enables snapshotting

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What’s New: Redis & Memcached
• Redis Streams
• SortedSets now have LIST capabilities (POP and BLOCK) )
• HyperLogLogs has an optimized algorithm
• Speed Improvements (Jemalloc additions, etc.)
• Active Defragmentation
• Added In-line HELP command for redis-cli
• Native TLS Integration Redis (ElastiCache)
• More at https://aws .a ma z o n. co m/r e d is / Wh a ts _Ne w _R e di s5
• Automated Slab rebalancing
• LRU crawler to background-reclaim memory
• Faster hash table lookups with murmur3 algorithm
 Redis 5.0
 Memcached 1.5.10

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When 9.5 TiB is not enough!
Example 1:
• Assume 125 shards made of 1 Primary + 1 Replica = 250 nodes
• Assume R5.24xlarge ( 635.61 GiB )
• Cluster memory 635.61 GiB X 125 = ~80 TiB = ~88 TB
Example 2:
• Assume 250 shards made of 1 Primary + 0 Replica = 250 nodes nodes
• Assume R5.24xlarge ( 635.61 GiB )
• Cluster memory 635.61 GiB X 250 = ~159 TiB = ~170 TB
New: Amazon ElastiCache 250 node support

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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

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Architect services ground-up for the cloud
and for the explosion of data
Offer a portfolio of purpose-built
services, optimized for your workloads
Help you innovate faster through
managed services
Our approach
Provide services that help you migrate
existing apps and databases to the cloud

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When to Use Which Services
Situation Solution
Existing application Use your existing engine on RDS
• MySQL Amazon Aurora, RDS for MySQL
• PostgreSQL Amazon Aurora, RDS for PostgreSQL
• MariaDB Amazon Aurora, RDS for MariaDB
• Oracle Use SCT to determine complexity Amazon Aurora, RDS for Oracle
• SQL Server Use SCT to determine complexity Amazon Aurora, RDS for SQL Server
New application • If you can avoid relational features DynamoDB
• If you need relational features Amazon Aurora
In-memory store/cache • Amazon ElastiCache
Time series data • Amazon Timestream
Track every application change, crypto verifiable.
Have a central trust authority
• Amazon Quantum Ledger Database (QLDB)
Don’t have a trusted central authority • Amazon Managed Blockchain
Data Warehouse & BI • Amazon Redshift, Amazon Redshift Spectrum, and Amazon QuickSight
Adhoc analysis of data in S3 • Amazon Athena and Amazon QuickSight
Apache Spark, Hadoop, HBase (needle in a
haystack type queries)
• Amazon EMR
Log analytics, operational monitoring, & search • Amazon Elasticsearch Service and Amazon Kinesis

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Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Dickson Yue

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