Amazon Aurora is a MySQL-compatible database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. The service is now in preview. Come to our session for an overview of the service and learn how Aurora delivers up to five times the performance of MySQL yet is priced at a fraction of what you'd pay for a commercial database with similar performance and availability.

1. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved
Amazon Aurora: Amazon’s New
Relational Database Engine
Manish Dalwadi, Senior Product Manager
Anurag Gupta, Vice President

2. Current DB architectures are monolithic
Multiple layers of
functionality all on a
single box
SQL
Transactions
Caching
Logging

3. Current DB architectures are monolithic
Even when you scale
it out, you’re still
replicating the same
stack
SQL
Transactions
Caching
Logging
SQL
Transactions
Caching
Logging
Application

4. Current DB architectures are monolithic
SQL
Transactions
Caching
Logging
SQL
Transactions
Caching
Logging
Application Even when you scale
it out, you’re still
replicating the same
stack

5. Current DB architectures are monolithic
SQL
Transactions
Caching
Logging
SQL
Transactions
Caching
Logging
Storage
Application Even when you scale
it out, you’re still
replicating the same
stack

6. This is a problem.
For cost. For flexibility. And for availability.

7. Reimagining the relational database
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.

8. Relational databases reimagined for the cloud
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

9. A service-oriented architecture applied to the database
• 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

10. • Sign up for preview access at:
https://aws.amazon.com/rds/aurora/preview
• Now available in US West (Oregon) and EU (Ireland), in addition to US
East (N. Virginia)
• Thousands of customers already invited to the limited preview
• Now moving to unlimited preview; accepting all requests in 2–3 weeks
• Full service launch in the coming months
Aurora preview

11. 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

12. Aurora Works with Your Existing Apps

13. Establishing our ecosystem
Business Intelligence Data Integration Query and Monitoring SI and Consulting
“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

14. Amazon Aurora Is Easy to Use

15. Simplify database management
• Create a database in minutes
• Automated patching
• Push-button scale compute
• Continuous backups to S3
• Automatic failure detection and failover
Amazon RDS

16. Simplify storage management
• Read replicas are available as failover targets—no data loss
• Instantly create user snapshots—no performance impact
• Continuous, incremental backups to S3
• Automatic storage scaling up to 64 TB—no performance or availability
impact
• Automatic restriping, mirror repair, hot spot management, encryption

17. Simplify data security
• Encryption to secure data at rest
– AES-256; hardware accelerated
– All blocks on disk and in Amazon S3 are encrypted
– Key management via AWS KMS
• SSL to secure data in transit
• Network isolation via Amazon VPC by default
• No direct access to nodes
• Supports industry standard security and data protection
certifications
Storage
SQL
Transactions
Caching
Amazon S3
Application

18. Amazon Aurora Is Highly Available

19. Aurora storage
• Highly available by default
– 6-way replication across 3 AZs
– 4 of 6 write quorum
• Automatic fallback to
3 of 4 if an 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

20. Consistent, low-latency writes
AZ 1 AZ 2
Primary
Instance
Standby
Instance
Amazon Elastic
Block Store (EBS)
Amazon S3
EBS
mirror
EBS
EBS
mirror
AZ 1 AZ 3
Primary
Instance
Amazon S3
AZ 2
Replica
Instance
Improvements
• Consistency—tolerance to outliers
• Latency— synchronous vs. asynchronous replication
• Significantly more efficient use of network I/O
Log records
Binlog
Data
Double-write buffer
FRM files, metadata
Type of writes
MySQL with standby Amazon Aurora
async
4/6 quorum
PiTR
Sequential
write
Sequential
write Distributed
writes

21. Self-healing, fault-tolerant
• 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 availabilityRead availability

22. 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

23. Survivable caches
• 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

24. Multiple failover targets—no data loss
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

25. Simulate failures using SQL
• 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

26. Amazon Aurora Is Fast

27. Write performance (console screenshot)
• MySQL Sysbench
• R3.8XL with 32 cores
and 244 GB RAM
• 4 client machines with
1,000 threads each

28. Read performance (console screenshot)
• MySQL Sysbench
• R3.8XL with 32 cores
and 244 GB RAM
• Single client with
1,000 threads

29. Read replica lag (console screenshot)
• Aurora Replica with 7.27 ms replica lag at 13.8 K updates/second
• MySQL 5.6 on the same hardware has ~2 s lag at 2 K updates/second

30. Writes scale with table count
-
10
20
30
40
50
60
70
10 100 1,000 10,000
ThousandsofWritesperSecond
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)
Tables
Amazon
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)

31. Better concurrency
-
20
40
60
80
100
120
50 500 5,000
ThousandsofWritesperSecond
Concurrent Connections
Write Performance and Concurrency
Aurora RDS MySQL with 30,000 IOPS (Single AZ)
Connections
Amazon
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)

32. Caching improves performance
-
50
100
150
200
250
300
350
400
100/0 50/50 0/100
ThousandsofOperations/Second
Read/Write Ratio
Performance with query cache on and off
Aurora without Caching
Aurora with Caching
RDS MySQL;30,000 IOPS (Single AZ) - without caching
RDS MySQL;30,000 IOPS (Single AZ) - with caching
R/W Ratio
Amazon
Aurora
Without
Caching
Amazon
Aurora
With
Caching
RDS MySQL
30K IOPS
Without
Caching
RDS MySQL
30K IOPS
With
Caching
100/0 160,000 375,000 35,000 19,000
50/50 130,000 93,000 24,000 20,000
0/100 64,000 64,000 16,000 16,000
OLTP workload
1,000 connections
250 tables
Query cache on/off tested

33. 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
ReadReplicaLaginMilliseconds
Read Replica Lag
Aurora RDS MySQL;30,000 IOPS (Single AZ)
Updates/
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)

34. SAN FRANCISCO