The document discusses Amazon Aurora, a database service from Amazon Web Services (AWS). It provides the following key points: - Aurora is a relational database that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. - It offers features like auto-scaling, continuous backups, automated patching and replication across Availability Zones. - Performance tests showed that Aurora can be 5 times faster for reads and writes compared to MySQL, with more consistent performance under load. Parallel query processing allows further performance gains.

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What’s new in Amazon Aurora
A D B 2 0 3
Kathy Gibbs
Sr. Database Specialist SA
AWS

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Amazon Relational Database Service (Amazon RDS)
Choice Value Innovation

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Amazon RDS
Choice of open-source and commercial databases
RDS platform
Open-source engines Commercial engines
Advanced monitoring
Routine maintenance
Push-button scaling
Automatic fail-over
Backup & recovery
X-region replication
Isolation & security
Industry compliance
Automated patching
Cloud-native engine

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Amazon Aurora
Amazon Aurora
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
Enterprise database at open-source price,
delivered as a managed service

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Aurora innovations
Reimagining databases for the cloud
Manageability and security
Performance and scalability
Availability and reliability

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5x faster than MySQL, 3x faster than PostgreSQL

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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
Write and read throughput
Aurora MySQL is 5x faster than MySQL

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Bulk data load performance
Aurora PostgreSQL loads data 2x faster than PostgreSQL
Copy In
Copy In
Vacuum
Vacuum
Index Build
Index Build
0 500 1000 1500 2000 2500 3000 3500 4000
PostgreSQL
Amazon Aurora
Runtime (seconds)
pgbench initialization, scale 10000 (150 GiB)
86% reduction in vacuum time

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Performance variability under load
Aurora PostgreSQL is ~10x more consistent than PostgreSQL
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400.00
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700.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Responsetime,ms
sysbench response time (p95), 30 GiB, 1024 clients
PostgreSQL (Single AZ, No Backup) Amazon Aurora (Three AZs, Continuous Backup)

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Performance variability under load
Aurora MySQL is ~25x more consistent than MySQL
0
500
1,000
1,500
2,000
2,500
0 100 200 300 400 500 600
Time from start of run (sec.)
Write response time (ms) Amazon Aurora
MySQL
Sysbench write-only workload with 250 tables and 200,000 initial rows per table

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Performance improvement over time
Aurora MySQL – 2015 to 2018
Launched with R3.8xl
32 cores, 256 GB memory
Now support R4.16xl
64 cores, 512 GB memory
R5.24xl coming soon
96 cores, 768 GB memory
Besides many performance optimizations, we are also upgrading HW platform
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150
200
250
2015 2016 2017 2018
Max write throughput – up 100%
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2015 2016 2017 2018
Max read throughput – up 42%

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

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Aurora I/O profile
MySQL with Replica Aurora
EBS mirrorEBS mirror
AZ 1 AZ 2
EBS
Amazon Elastic Block
Store (EBS)
Primary
instance
Replica
instance
1
2
3
4
5
Amazon
S3
MySQL I/O profile for 30 min. Sysbench run
780K transactions
7,388K I/Os per million txns (excludes mirroring, standby)
Average 7.4 I/Os per transaction
AZ 1 AZ 3
Primary
instance
AZ 2
Replica
instance
ASYNC 4/6 QUORUM
Distributed writes
Replica
instance
Amazon
S3
Aurora IO profile for 30 min Sysbench run
27,378K transactions – 35x more
0.95 I/Os per transaction (6x amplification) – 7.7x less
Binlog Data Double-writeLog .frm files

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Aurora parallel query processing
Aurora storage has thousands of CPUs
Opportunity to push down and parallelize query processing
Moving processing close to data reduces network traffic
and latency
Challenges
Data is not range partitioned – require full scans
Data may be in-flight
Read views may not allow viewing most recent data
Not all functions can be pushed down
Database node
Storage nodes
Push down
predicates
Aggregate
results

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Performance results with parallel query
Well-known decision support benchmark
0x
20x
40x
60x
80x
100x
120x
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22
Query response time reduction
▪ Peak speed up ~120x
▪ > 10x speed-up: 8 of 22 queries
We were able to test Aurora’s parallel query feature and the performance gains were very good. To be
specific, we were able to reduce the instance type from r3.8xlarge to r3.2xlarge. For this use case,
parallel query was a great win for us. —Jyoti Shandil, Cloud Data Architect

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Designed to offer > 99.99% availability, RPO = 1 second, RTO < 1 minute

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Six-way replicated storage
Survives catastrophic failures
Six copies across three Availability Zones
Four out of 6 write quorum; 3 out of 6 read
quorum
Peer-to-peer replication for repairs
Volume striped across hundreds of storage
nodes
SQL
Transaction
AZ 1 AZ 2 AZ 3
Caching
SQL
Transaction
AZ 1 AZ 2 AZ 3
Caching

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Read scaling with custom endpoints
Master
Read
replica
Read
replica
Read
replica
Shared distributed storage volume
Reader endpoint #1 Reader endpoint #2
• Up to 15 promotable read replicas across multiple Availability Zones
• Redo log-based replication leads to low replica lag – typically < 10 ms
• Custom reader endpoint with configurable failover order

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Instant crash recovery
Traditional database:
Have to replay logs since the last checkpoint
Typically 5 minutes between checkpoints
Single-threaded in MySQL; requires a large number
of disk accesses
Aurora:
Underlying storage replays redo records on demand
as part of a disk read
Parallel, distributed, asynchronous
No replay for startup
Checkpointed data Redo log
Crash at T0 requires
a reapplication 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

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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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Fast database cloning
Production database
Clone Clone
Clone
Dev/test
applications
Benchmarks
Production
applications
Production
applications
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
Use cases
• Clone a production DB to run tests
• Reorganize a database
• Save a point-in-time snapshot for analysis without
impacting production system

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Continuous availability with multi-master
Master Master Master Master
Shared distributed storage volume
Application #1 Application #2

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Scaling write workloads
There are many “oases” of consistency in Aurora
Database nodes know transaction orders from
that node
Storage nodes know transactions orders applied
at that node
Only have conflicts when data changed at both
multiple database nodes and multiple storage
nodes
Much less coordination required
Near linear throughput scaling for workloads
with no or low conflict
Lower commit latency for workloads with low
conflict
Optimistic conflict management

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Transactions with no conflicts
Transactions T1 and T2 from blue
and orange masters update
different tables (pages) Table 1 and
Table 2
No logical or physical conflicts—no
coordination required
Near linear scaling with number of
masters for this type of sharded or
partitioned workloads
1 1 1 1 11 2 2 2 2 22
Page1 Page2
Master Master
Begin Trx (T1)
Update (Table1)
Commit (T1)
Begin Trx (T2)
Update (Table2)
Commit (T2)
Table 1 Table 2

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Conflict resolution
Transactions T1 and T2 from blue and
orange masters update the same
table (page) Table 1
Transaction T1 from blue master
achieves quorum; transaction T1 is
committed
Transaction T2 from orange master
fails to achieves quorum; transaction
T2 is rolled back
Storage nodes act as arbitrator for
conflict resolution
Logical conflicts are handled through
MVCC
1 1 11
Page1 Page2
Master
Master
Begin Trx (T1)
Update (Table1)
Commit (T1)
Begin Trx (T2)
Update (Table1)
Rollback (T2)
Table 1 Table 2
X
2 2

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Global replication
Faster disaster recovery and enhanced data locality
• Promote read-replica to a master for
faster recovery in the event of disaster,
RPO = 1 second, RTO < 1 minute
• Bring data close to your customer’s
applications in different regions
• Promote to a master for easy migration

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Aurora global database
MR R
Region 1
AZ 1 AZ 2 AZ 3
Shared storage
R
Region 2
AZ 1 AZ 2 AZ 3
Shared storage
Replication
fleet
Replication
fleet
High throughput: Up to 150K writes/sec. – negligible performance impact
Low replica lag: < 1 sec cross-country replica lag under heavy load
Fast recovery: < 1 min to accept full read-write workloads after region failure

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Performance: logical vs. physical replication
Logical replication Physical replication
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seconds
QPS
QPS
Lag
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5.00
0
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150,000
200,000
250,000
seconds
QPS
QPS
Lag

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Serverless, Data API, and advanced monitoring capabilities

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Aurora serverless use cases
Infrequently used applications (e.g., low-
volume blog site)
Applications with variable load—peaks of
activity that are hard to predict (e.g., news
site)
Development or test databases not needed
on nights or weekends
Consolidated fleets of multi-tenant SaaS
applications

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Aurora serverless architecture
Starts up on demand, shuts
down when not in use
Scales up/down automatically
No application impact when
scaling
Pay per second, 1 minute
minimum
Warm pool
of instances
Application
Database storage
Scalable DB capacity
Request routers

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Amazon RDS data API
Millions of IoT/mobile
devices Data API fleet
API
endpoint
Aurora
Serverless
Access through simple web interface
• Public endpoint addressable from anywhere
• No client configuration required
• No persistent connections required
Ideal for serverless applications (AWS Lambda)
Ideal for lightweight applications (IoT)

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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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Database activity monitoring
Amazon
CloudWatch
consumer
Aurora
Amazon
CloudWatch
Third-party
consumer
Amazon
Kinesis
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 Amazon S3 for long-term archival; analyze logs using Amazon Athena; visualize logs with Amazon QuickSight
Visualizations: Create activity dashboards
Alarms: Get notified or take actions

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Security management
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
Database activity monitoring
Database
engine
*NEW*
Customer master key(s)
Data key 1
Storage
node
Storage
node
Storage
node
Storage
node
Data key 1 Data key 1 Data key 1

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

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Aurora customer adoption
Fastest-growing service in Amazon Web Services (AWS) history
Aurora is used by ¾ of the top 100 AWS customers

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Get started!
Amazon Aurora
aws.amazon.com/aurora
AWS Database Migration Service (AWS DMS)
aws.amazon.com/dms
Amazon RDS
aws.amazon.com/rds/
10-minute tutorials
aws.amazon.com/getting-started/tutorials/?awsf.getting-started-content=use-
case-tmt%23databases

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Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Kathy Gibbs
katgibbs@amazon.com