2015/08/19 - aws - introducing amazon aurora

© 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

August 19, 2015

Introducing Amazon Aurora

Dave Lang, Senior Product Manager, Amazon Aurora

Lynn Ferrante, Senior Business Development Manager, AWS

Why we built Amazon Aurora

Current DB architectures are monolithic

Multiple layers of

functionality all on a

single box

SQL

Transactions

Caching

Logging


Even when you scale

it out, you’re still

replicating the same

stack

SQL

Transactions

Caching

Logging

SQL

Transactions

Caching

Logging

Application

SQL

Transactions

Caching

Logging

SQL

Transactions

Caching

Logging

Application Even when you scale



stack


SQL

Transactions

Caching

Logging

SQL

Transactions

Caching

Logging

Storage

Application Even when you scale



stack


This is a problem.

For cost. For flexibility.

And for availability.

What if you were inventing the database today?

You wouldn’t design it the way we did in 1970. At least not entirely.

You’d build something that can scale out, that is self-healing, and that

leverages existing AWS services.

Reimagining the relational database

Speed and availability of high-end commercial databases

Simplicity and cost-effectiveness of open source databases

Drop-in compatibility with MySQL

Simple pay as you go pricing

Delivered as a managed service

Relational databases reimagined for the cloud

Moved the logging and storage layer into

a multi-tenant, scale-out database-

optimized storage service

Integrated with other AWS services like

Amazon EC2, Amazon VPC, Amazon

DynamoDB, Amazon SWF, and Amazon

Route 53 for control plane operations

Integrated with Amazon S3 for continuous

backup with 99.999999999% durability

Control PlaneData Plane

Amazon

DynamoDB

Amazon SWF

Amazon

Route 53

Logging + Storage

SQL

Transactions

Caching

Amazon S3

A service-oriented architecture applied to the

database

Simple pricing

No licenses

No lock-in

Pay only for what you use

Discounts

44% with a 1-year RI

63% with a 3-year RI

vCPU Mem Hourly Price

db.r3.large 2 15.25 $0.29

db.r3.xlarge 4 30.5 $0.58

db.r3.2xlarge 8 61 $1.16

db.r3.4xlarge 16 122 $2.32

db.r3.8xlarge 32 244 $4.64

• Storage consumed, up to 64 TB, is $0.10/GB-month

• IOs consumed are billed at $0.20 per million I/O

• Prices are for Virginia

Enterprise grade, open source pricing

Aurora Works with

Your Existing Apps

Query and Monitoring SI and ConsultingData Integration

“It is great to see Amazon Aurora remains MySQL compatible; MariaDB connectors

work with Aurora seamlessly. Today, customers can take MariaDB Enterprise with

MariaDB MaxScale drivers and connect to Aurora, MariaDB, or MySQL without worrying about

compatibility. We look forward to working with the Aurora team in the future to further

accelerate innovation within the MySQL ecosystem.” - Roger Levy, VP Products, MariaDB

Business Intelligence

Establishing our ecosystem

1 Establish baseline

a) MySQL dump/import

b) RDS MySQL to Aurora DB

snapshot migration

2 Catch-up changes

a) Binlog replication

b) Tungsten Replicator

AuroraMySQL

2 - Replication

1 - Baseline

Achieving near zero downtime migration to

Aurora

Migrate your RDS Instance using the AWS Console

Amazon Aurora Is Easy to Use

Create a database in minutes

Automated patching

Push-button scale compute

Continuous backups to Amazon S3

Automatic failure detection and failover

Amazon RDS

Simplify database management

Read replicas are available as failover targets—no data loss

Instantly create user snapshots—no performance impact

Continuous, incremental backups to Amazon S3

Automatic storage scaling up to 64 TB—no performance or availability

impact

Automatic restriping, mirror repair, hot spot management

Simplify storage management

Amazon Aurora Is

Highly Available

Highly available by default• 6-way replication across 3 AZs

• 4 of 6 write quorum

• Automatic fallback to 3 of 4 if an

Availability Zone (AZ) is unavailable

• 3 of 6 read quorum

SSD, scale-out, multi-tenant storage• Seamless storage scalability

• Up to 64 TB database size

• Only pay for what you use

Log-structured storage• Many small segments, each with their own redo logs

• Log pages used to generate data pages

• Eliminates chatter between database and storage

SQL

Transactions

AZ 1 AZ 2 AZ 3

Caching

Amazon S3

Aurora storage

Lose two copies or an AZ failure without read or write availability impact

Lose three copies without read availability impact

Automatic detection, replication, and repair

SQL

Transaction

AZ 1 AZ 2 AZ 3

Caching

SQL

Transaction

AZ 1 AZ 2 AZ 3

Caching

Read and write availability Read availability

Self-healing, fault-tolerant

Traditional databases

Have to replay logs since the last

checkpoint

Single-threaded in MySQL; requires a

large number of disk accesses

Amazon Aurora

Underlying storage replays redo

records on demand as part of a disk

read

Parallel, distributed, asynchronous

Checkpointed Data Redo Log

Crash at T0 requires

a re-application of the

SQL in the redo log since

last checkpoint

T0 T0

Crash at T0 will result in redo

logs being applied to each segment

on demand, in parallel, asynchronously

Instant crash recovery

We moved the cache out of the

database process

Cache remains warm in the event

of a database restart

Lets you resume fully loaded

operations much faster

Instant crash recovery +

survivable cache = quick and

easy recovery from DB failures

SQL

Transactions

Caching

SQL

Transactions

Caching

SQL

Transactions

Caching

Caching process is outside the DB process

and remains warm across a database restart

Survivable caches

Aurora replicas can be promoted instantly

Page cache

invalidation

Aurora Master

30% Read

70% Write

Aurora Replica

100% New

Reads

Shared Multi-AZ Storage

MySQL Master

30% Read

70% Write

MySQL Replica

30% New Reads

70% Write

Single-threaded

binlog apply

Data Volume Data Volume

MySQL read scaling

Replicas must replay logs

Replicas place additional load on master

Replica lag can grow indefinitely

Failover results in data loss

To cause the failure of a component at the database node:

ALTER SYSTEM CRASH [{INSTANCE | DISPATCHER | NODE}]

To simulate the failure of disks:

ALTER SYSTEM SIMULATE percent_failure DISK failure_type IN

[DISK index | NODE index] FOR INTERVAL interval

To simulate the failure of networking:

ALTER SYSTEM SIMULATE percent_failure NETWORK failure_type

[TO {ALL | read_replica | availability_zone}] FOR INTERVAL interval

To simulate the failure of an Aurora Replica:

ALTER SYSTEM SIMULATE percentage_of_failure PERCENT

READ REPLICA FAILURE [TO ALL | TO "replica name"] FOR INTERVAL interval

Simulate failures using SQL

Amazon Aurora Is Fast

Write performance

MySQL Sysbench

R3.8XL with 32 cores

and 244 GB RAM

4 client machines with

1,000 threads each

Read performance

MySQL Sysbench

R3.8XL with 32 cores

and 244 GB RAM

Single client with

1,000 threads

Read replica lag

Aurora Replica with 7.27 ms replica lag at 13.8 K updates per second

MySQL 5.6 on the same hardware has ~2,000 ms lag at 2K updates per second

Writes scale with table count

-

10

20

30

40

50

60

70

10 100 1,000 10,000

Th

ou

san

ds o

f w

rite

s p

er

seco

nd

Number of tables

Write performance and table count

Aurora

MySQL on I2.8XL

MySQL on I2.8XL with RAM Disk

RDS MySQL with 30,000 IOPS (Single AZ)

TablesAmazon

Aurora

MySQL

I2.8XL

local SSD

MySQL

I2.8XL

RAM disk

RDS MySQL

30K IOPS

(single AZ)

10 60,000 18,000 22,000 25,000

100 66,000 19,000 24,000 23,000

1,000 64,000 7,000 18,000 8,000

10,000 54,000 4,000 8,000 5,000

Write-only workload

1,000 connections

Query cache (default on for Amazon Aurora, off for MySQL)

Better concurrency

-

20

40

60

80

100

120

50 500 5,000

Th

ou

san

ds o

f w

rite

s p

er

seco

nd

Concurrent connections

Write performance and concurrency

Aurora

RDS MySQL with 30,000 IOPS (Single AZ)

ConnectionsAmazon

Aurora

RDS MySQL

30K IOPS

(single AZ)

50 40,000 10,000

500 71,000 21,000

5,000 110,000 13,000

OLTP Workload

Variable connection count

250 tables

Query cache (default on for Amazon Aurora, off for MySQL)

Replicas have up to 400 times less lag

2.6 3.4 3.9 5.4

1,000 2,000 5,000 10,000

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Updates per second

Read

rep

lica l

ag

in

milliseco

nd

s

Read replica lag

Aurora

RDS MySQL;30,000 IOPS (Single AZ)

Updates per

second

Amazon

Aurora

RDS MySQL

30K IOPS

(single AZ)

1,000 2.62 ms 0 s

2,000 3.42 ms 1 s

5,000 3.94 ms 60 s

10,000 5.38 ms 300 s

Write workload

250 tables

Query cache on for Amazon Aurora, off for MySQL (best

settings)

Getting started

aws.amazon.com/rds/aurora

aws.amazon.com/rds/aurora/getting-started


aws.amazon.com/rds/aurora/getting-started

Questions?



Thank you!



2015/08/19 - aws - introducing amazon aurora

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