Amazon Aurora is a MySQL- and PostgreSQL-compatible relational database with the speed, reliability, and availability of commercial databases at one-tenth the cost. Join this session, and get started with the MySQL-compatible edition, discuss your existing application running on Aurora, or learn about recently announced features, such as Serverless or Parallel Query.

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Build on Amazon Aurora with
MySQL Compatibility
Sachin Holla
Senior Solution Architect
Amazon Web Services
D A T 3 4 8 - R

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Agenda
RDS Aurora MySQL – An intro
Performance
Availability
Benefits
Cost savings
New features

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Traditional approaches to scale databases
Each architecture is limited by the monolithic mindset
SQL
Transactions
Caching
Logging
SQL
Transactions
Caching
Logging
Application Application
SQL
Transactions
Caching
Logging
SQL
Transactions
Caching
Logging
Storage
Application
Storage Storage
SQL
Transactions
Caching
Logging
Storage
SQL
Transactions
Caching
Logging
Storage

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Reimagining the relational database
What if you were inventing the database today?
You would break apart the stack
You would build something that
 Lets layers scale out independently
 Is self-healing
 Leverages distributed services

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A service-oriented architecture applied to the database
Move the logging and storage layer into a
multitenant, scale-out, database-optimized storage
service
Integrate with other AWS services such as Amazon
Simple Storage Service (Amazon S3), Amazon
Elastic Compute Cloud (Amazon EC2), Amazon
Virtual Private Cloud (Amazon VPC), Amazon
DynamoDB, Amazon Simple Workflow Service
(Amazon SWF), and Amazon Route 53 for control
and monitoring
Make it a managed service using Amazon Relational
Database Service (Amazon RDS). Takes care of
management and administrative functions
Amazon
DynamoDB
Amazon SWF
Amazon Route 53
Logging + Storage
SQL
Transactions
Caching
Amazon S3
1
2
3
Amazon RDS

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Amazon Aurora
Databases reimagined for the cloud
Delivered as a managed service
 Speed and availability of high-end commercial databases
 Simplicity and cost-effectiveness of open source databases
 Drop-in compatibility with MySQL and PostgreSQL
 Simple pay as you go pricing

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Scale-out, distributed, multi-tenant architecture
Availability
Zone 1
Availability
Zone 2
Availability
Zone 3
Shared storage volume
Storage nodes with SSDs
Master
SQL
Transactions
Caching
• Purpose-built, log-structured
distributed storage system
designed for databases
Replica Replica
SQL
Transactions
Caching
SQL
Transactions
Caching
• Storage volume is striped across
hundreds of storage nodes
distributed over three different
availability zones
• Master and replicas all point to the
same storage
• Six copies of data, two copies in
each availability zone to protect
against AZ+1 failures

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Automate administrative tasks
Schema design
Query construction
Query optimization
Automatic fail-over
Backup & recovery
Isolation & security
Industry compliance
Push-button scaling
Automated patching
Advanced monitoring
Routine maintenance
Takes care of your time-consuming database management tasks, freeing you
to focus on your applications and business
You
AWS

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WRITE PERFORMANCE READ PERFORMANCE
MySQL SysBench results
R3.8XL: 32 cores / 244 GB RAM
5X faster than RDS MySQL 5.6 & 5.7
Five times higher throughput than stock MySQL
based on industry standard benchmarks
0
25,000
50,000
75,000
100,000
125,000
150,000
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
Aurora MySQL 5.6 MySQL 5.7

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With user connection With number of tables
With database size - SYSBENCH With database size - TPCC
Connections
Amazon
Aurora
RDS MySQL
w/ 30K IOPS
50 40,000 10,000
500 71,000 21,000
5,000 110,000 13,000
Tables
Amazon
Aurora
MySQL
I2.8XL
local SSD
RDS MySQL
w/ 30K IOPS
(single AZ)
10 60,000 18,000 25,000
100 66,000 19,000 23,000
1,000 64,000 7,000 8,000
10,000 54,000 4,000 5,000
8x
U P T O
F A S T E R
11x
U P T O
F A S T E R
DB Size
Amazon
Aurora
RDS MySQL
w/ 30K IOPS
1GB 107,000 8,400
10GB 107,000 2,400
100GB 101,000 1,500
1TB 26,000 1,200
DB Size Amazon Aurora
RDS MySQL
w/ 30K IOPS
80GB 12,582 585
800GB 9,406 69
21x
U P T O
F A S T E R
136x
U P T O
F A S T E R
Aurora performance scales with heavy workloads

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Do fewer I/Os
Minimize network packets
Cache prior results
Offload the database engine
Do less work
Process asynchronously
Reduce latency path
Use lock-free data structures
Batch operations together
Be more efficient
Databases are all about i/o
Network-attached storage is all about packets/second
High-throughput processing is all about context switches
How does Aurora achieve higher performance?

15. BINLOG DATA DOUBLE-WRITELOG FRM FILES
TYPE OF WRITE
MySQL with replica
EBS mirrorEBS mirror
AZ 1 AZ 2
Amazon S3
EBS
Amazon Elastic
Block Store
(Amazon EBS)
Primary
Instance
Replica
Instance
1
2
3
4
5
AZ 1 AZ 3
Primary
Instance
Amazon S3
AZ 2
Replica
Instance
ASYNC
4/6 QUORUM
DISTRIBUTED
WRITES
Replica
Instance
Amazon Aurora
780K transactions
7,388K I/Os per million txns (excludes mirroring, standby)
Average 7.4 I/Os per transaction
MySQL I/O profile for 30 min Sysbench run
27,378K transactions 35X MORE
0.95 I/Os per transaction (6X amplification) 7.7X LESS
Aurora I/O profile for 30 min Sysbench run
Comparison of Aurora I/O profile

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17. Scale-out, distributed, log structured storage
Master Replica Replica Replica
Availability Zone 1
Shared Storage Volume – Transaction Aware
Primary
Database
Node
Read
Replica /
Secondary
Node
Read
Replica /
Secondary
Node
Read
Replica /
Secondary
Node
Availability Zone 2 Availability Zone 3
AWS Region
Storage
monitoring
Database and
instance
monitoring

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Amazon Aurora storage engine overview
Data is replicated six times across three availability
zones
Continuous backup to Amazon S3
(built for 11 9s durability)
Continuous monitoring of nodes and disks for repair
10 GB segments as unit of repair or hotspot
rebalance
Quorum system for read/write; latency tolerant
Quorum membership changes do not stall writes
Storage volume automatically grows up to 64 TB
AZ 1 AZ 2 AZ 3
Amazon S3
Database
Node
Storage
Node
Storage
Node
Storage
Node
Storage
Node
Storage
Node
Storage
Node
Storage
Monitoring

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What can fail?
Segment failures (disks)
Node failures (machines)
AZ failures (network or
datacenter)
Optimizations
4 out of 6 write quorum
3 out of 6 read quorum
Peer-to-peer replication for repairs
SQL
Transaction
AZ 1 AZ 2 AZ 3
Caching
Amazon Aurora storage engine fault-tolerance
SQL
Transaction
AZ 1 AZ 2 AZ 3
Caching

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Amazon Aurora replicas
Availability
Failing database nodes are automatically
detected and replaced
Failing database processes are
automatically detected and recycled
Replicas are automatically promoted to
primary if needed (failover)
Customer specifiable fail-over order
AZ 1 AZ 3AZ 2
Primary
Node
Primary
Node
Primary
Database
Node
Primary
Node
Primary
Node
Read
Replica
Primary
Node
Primary
Node
Read
Replica
Database and
Instance
Monitoring
Performance
Customer applications can scale out read
traffic across read replicas
Read balancing across read replicas

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Faster, more predictable failover with Amazon Aurora
App
RunningFailure Detection DNS Propagation
Recovery
Database
Failure
Amazon RDS for PostgreSQL is good: failover times of ~60 seconds
Replica-Aware App Running
Failure Detection DNS Propagation
Recovery
Database
Failure
Amazon Aurora is better: failover times < 30 seconds
15-20 sec 3-10 sec
App
Running
15-20 sec 30-40 sec

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Amazon Aurora continuous backup
Segment snapshot Log records
Recovery point
Segment 1
Segment 2
Segment 3
Time
• Take periodic snapshot of each segment in parallel; stream the logs to Amazon S3
• Backup happens continuously without performance or availability impact
• At restore, retrieve the appropriate segment snapshots and log streams from S3 to
storage nodes
• Apply log streams to segment snapshots in parallel and asynchronously

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Traditional databases
Have to replay logs since the last
checkpoint
Typically five minutes between
checkpoints
Single-threaded in MySQL and
PostgreSQL; requires a large number of
disk accesses
Amazon Aurora
No replay at startup because storage system
is transaction-aware
Underlying storage replays log records
continuously, whether in recovery or not
Coalescing is parallel, distributed, and
asynchronous
Checkpointed Data Log
Crash at T0 requires
a re-application of the
SQL in the log since
last checkpoint
T0 T0
Crash at T0 will result in logs being applied to
each segment on demand, in parallel,
asynchronously
Amazon Aurora instant crash recovery

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Availability: Aurora has a 6-way replicated storage for HA
Six copies across three availability zones
• 4 out of 6 write quorum; 3 out of 6 read quorum
• Peer-to-peer replication for repairs
SQL
Transaction
AZ 1 AZ 2 AZ 3
Caching
SQL
Transaction
AZ 1 AZ 2 AZ 3
Caching
Read availabilityRead and write availability

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Also has up to 15 promotable read replicas
Master
Read
Replica
Read
Replica
Read
Replica
Shared distributed storage volume
Reader end-point
► Up to 15 promotable read replicas across multiple availability zones
► Re-do log based replication leads to low replica lag – typically < 10ms
► Reader end-point with load balancing and auto-scaling *NEW*

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When database fails, recovery is fast: <30 seconds
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435
0 - 5s – 30% of fail-overs
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
5 - 10s – 40% of fail-overs
0%
10%
20%
30%
40%
50%
60%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
10 - 20s – 25% of fail-overs
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
20 - 30s – 5% of fail-overs

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Everything you get from Amazon RDS
Power, HVAC, net
Rack and stack
Server maintenance
OS patches
DB software patches
Database backups
Scaling
High availability
DB software installs
OS installation
App optimization
Power, HVAC, net
Rack and stack
Server maintenance
OS patches
DB software patches
Database backups
Scaling
High availability
DB software installs
OS installation
App optimization
Power, HVAC, net
Rack and stack
Server maintenance
OS patches
DB software patches
Database backups
Scaling
High availability
DB software installs
OS installation
App optimization
Database on-premises Database on EC2 Amazon RDS
Managed
by you
Managed
by AWS

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…and more
 Automatic storage scaling up to 64 TB—no performance impact
Up to 64 TB of storage – auto-incremented in 10 GB units
up to 64 TB
 Continuous, incremental backups to Amazon S3
 Instantly create user snapshots—no performance impact
 Automatic restriping, mirror repair, hot spot management, encryption

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Fast database cloning
Create a copy of a database without
duplicate storage costs
• Creation of a clone is nearly instantaneous
– we don’t copy data
• Data copy happens only on write – when
original and cloned volume data differ
Typical use cases
• Clone a production DB to run tests
• Reorganize a database
• Save a point in time snapshot for analysis
without impacting production system.
Production database
Clone Clone
Clone
Dev/test
applications
Benchmarks
Production
applications
Production
applications

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Security and compliance
 Network isolation with Amazon Virtual
Private Cloud (Amazon VPC)
 AWS Identify and Access Management (IAM)
based resource-level permission controls
 Encryption to secure data at rest using
customer managed keys
• AES-256; hardware accelerated
• All blocks on disk and in Amazon S3 are encrypted
• Key management via AWS Key Management Service
(AWS KMS)
 Encrypted cross-region replication, snapshot
copy - SSL to secure data in transit
 Advanced auditing and logging without any
performance impact
Data Key 1 Data Key 2 Data Key 3 Data Key 4
Customer Master
Key(s)
Storage
Node
Storage
Node
Storage
Node
Storage
Node
Database
Engine

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Database activity monitoring and insights
Search: Look for specific events across log files
Metrics: Measure activity in your Aurora DB cluster
 Continuously monitor activity in your DB clusters by sending these audit logs to CloudWatch logs
 Export to S3 for long term archival; analyze logs using Athena; visualize logs with Amazon
QuickSight
Visualizations: Create activity dashboards
Alarms: Get notified or take actions
Amazon Aurora Amazon CloudWatch
Amazon Athena
Amazon QuickSight
Amazon S3

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Industry certifications
 Amazon Aurora gives each database
instance IP firewall protection
 Aurora offers transparent encryption at
rest and SSL protection for data in transit
 Amazon VPC lets you isolate and control
network configuration and connect
securely to your IT infrastructure
 AWS Identity and Access Management
provides resource-level permission
controls

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36. Cost of ownership: Aurora vs. MySQL
MySQL configuration hourly cost
Primary
r3.8XL
Standby
r3.8XL
Replica
r3.8XL
Replica
R3.8XL
Storage
6 TB / 10 K PIOP
Storage
6 TB / 10 K PIOP
Storage
6 TB / 5 K PIOP
Storage
6 TB / 5 K PIOP
$1.33/hr
$1.33/hr
$1.33/hr $1.33/hr
$2,42/hr
$2,42/hr $2,42/hr
Instance cost: $5.32 / hr
Storage cost: $8.30 / hr
Total cost: $13.62 / hr
$2,42/hr

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Cost of ownership: Aurora vs. MySQL
Aurora configuration hourly cost
Instance cost: $4.86 / hr
Storage cost: $4.43 / hr
Total cost: $9.29 / hr
Primary
r3.8XL
Replica
r3.8XL
Replica
R3.8XL
Storage / 6 TB
$1.62 / hr $1.62 / hr $1.62 / hr
$4.43 / hr
*At a macro level, Aurora saves over 50% in
storage costs compared to RDS MySQL
31.8%
Savings
 No idle standby instance
 Single shared storage volume
 No PIOPs – pay for use I/O
 Reduction in overall IOP

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Cost of ownership: Aurora vs. MySQL
Further opportunity for savings
Instance cost: $2.43 / hr
Storage cost: $4.43 / hr
Total cost: $6.86 / hr
Storage IOPs assumptions
1. Average IOPs is 50% of Max IOPs
2. 50% savings from shipping logs vs. full pages
49.6%
Savings
Primary
r3.8XL
Replica
r3.8XL
Replica
r3.8XL
Storage / 6TB
$0.81 / hr $0.81 / hr $0.81 / hr
$4.43 / hr
r3.4XL r3.4XL r3.4XL
 Use smaller instance size
 Pay-as-you-go storage

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Aurora MySQL 5.7 compatibility: Available now
Aurora MySQL 1.x = MySQL 5.6 compatible
Aurora MySQL 2.x = MySQL 5.7 compatible
• Available now
o JSON support
o Generated columns
o Spatial indexes (already supported in Aurora 5.6)
o …
o General performance improvements
• Coming later
o Performance schema
o GTID
o Multi-source replication
o Selective replication
o …

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“Backtrack” provides near-instantaneous restores: Available now
Backtrack quickly brings the database to a desired point in time
No restore from backup. No copying of data. Not destructive – can backtrack many times
Quickly recover from unintentional DML/DDL operations
T0 T1 T2
T0 T1
T2
T3 T4
T3
T4
REWIND TO T1
REWIND TO T3
INVISIBLE INVISIBLE

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Aurora Serverless: Available now
When you provision a database,
Aurora Serverless
Creates an Aurora storage volume
Provisions proxy endpoint in your VPC
for application connection
Configures network load balancing
behind proxy
Initializes request routers to route
database traffic
Provisions initial capacity
Use cases include: Infrequently used
applications (e.g. low-volume blog
site); spiky workload; test &
development databases DATABASE STORAGE
APPLICATION
CUSTOMER VPC
VPC PROXY
ENDPOINTS
VPC
ENDPOINTS
NETWORK LOAD BALANCER
REQUEST
ROUTERS
INITIAL
CAPACITY

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Performance insights: Available now
Dashboard showing
Load on database
• Easy
• Powerful
Identifies source of bottlenecks
• Top SQL
Adjustable time frame
• Hour, day, week, month
• Up to 35 days of data
Max CPU

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First step: Enhanced monitoring
Released 2016
O/S metrics
Process & thread list
Up to 1-second granularity

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Next step: Performance insights
Why database tuning?
RDS is all about managed databases
Customers also want performance managed
 Want easy tool for optimizing cloud database
workloads
 May not have deep tuning expertise
 Want a single pane of glass to achieve this

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Beyond database load: Other performance insights features
• Lock detection
• Execution plans
• API access
• Up to 2 years data retention
• Free tier available
• Support for all RDS database
engines in 2018

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Additional resources
• Demo video
• https://www.youtube.com/watch?v=xzVyu1prBvY&feature=youtu.be
• Documentation
• https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_PerfInsights.html
• Blog
• https://aws.amazon.com/blogs/database/analyzing-amazon-rds-database-workload-with-
performance-insights
• Marketing page
• https://aws.amazon.com/rds/performance-insights/

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Parallel query: Available now
Aurora storage has thousands of CPUs
 Presents opportunity to push down and parallelize
query processing using the storage fleet
 v1: single-table predicates (selections,
projections, 200+ SQL functions, case
statements, filters) and hash joins
 v2: group by, order by, aggregation
 Moving processing close to data reduces network
traffic and latency
DATABASE NODE
STORAGE NODES
PUSH DOWN
PREDICATES
AGGREGATE
RESULTS

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Parallel query – Expect to bring orders of magnitude faster OLAP
queries
Latency (seconds)
Decision support benchmark, R3.8xlarge, cold buffer cache
Improvement factor
with parallel query
24.6x
18.3x
5.0x
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Aggregate + 2-table join
Aggregate query
Point query on non-indexed column
With Parallel Query Without Parallel Query

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But wait, there’s more about RDS Aurora
• Database blog with Amazon Aurora posts
• Analyzing Amazon RDS Database Workloads with Performance Insights
• Amazon Aurora as an Alternative to Oracle RAC
• How Autodesk Increased Database Scalability and Reduced Replication
Lag with Amazon Aurora
• Best practices for migrating RDS for MySQL databases to Amazon
Aurora
• How to encrypt Amazon Aurora using AWS KMS and your own CMK

55. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Sachin Holla
sacholla@amazon.com

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