Build faster, more scalable database applications with Amazon Aurora, a MySQL- and PostgreSQL-compatible relational database built for the cloud. We cover Aurora Serverless, which automatically scales your database up and down to meet demand; Fast Database Cloning, which makes data instantly available for application development; Backtrack, which rolls back your database between test runs; and Performance Insights, which helps assess the load on your database and optimize your SQL queries.

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Accelerate Database Development
and Testing with Amazon Aurora
Steve Abraham
Principal DB Specialist SA
AWS
D A T 3 1 3
Vlad Vlasceanu
Principal DB Specialist SA
AWS

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Database development and testing perspective
Responsibilities of today’s developers, DevOps and DBAs:
• DB architecture
• deployment & automation
• improve dev/test outcomes
• performance and availability focused development
• cost effective development
Amazon Aurora provides native capabilities to meet these goals

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Amazon Aurora: re-imaging the relational database
Scale-out, distributed architecture using purpose built
storage system
Service oriented architecture leveraging AWS services
Fully managed service, automating administrative tasks
1
2
3
Cloud-native capabilities simplify use4
Amazon Aurora is fast…
5✕ more throughput than MySQL; 3✕ more throughput than PostgreSQL

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Amazon Aurora – key characteristics
1 Log-structured storage volume shared
between all cluster nodes
2
Optimized for high throughput3
Readers strictly read-only

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Core use cases: read scaling architecture
Auto Scaling optional
All readers treated the same
Any reader can be a failover
target
Application should use cluster
and reader endpoints
Dev Tip: Monitor cluster
topology for faster failover

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Core use cases: separated workloads architecture
Each reader used for a
specific purpose
Ordered failover targeting
Application uses cluster,
custom or even DB instance
endpoints
Caveat: Readers share the
same storage and undo log

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Deployment automation with Amazon Aurora
Why?
• Reduce risk of human errors
• Repeatable deployments
• Accountability and management of change
How?
• Declarative automation
AWS CloudFormation, Terraform
• Procedural automation
AWS Command Line Interface (AWS CLI) tools, AWS SDKs

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Automate responsibly!
But... Aurora DB clusters are stateful resources
Declarative automation
It’s about the outcome, less about the journey
• How does the tool define the resource state?
• How do you perform changes with minimal downtime?
Procedural automation
It’s about the journey, less about the outcome
• How do you ensure predictable outcomes?
• How do you define and track the resource state?

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Deployment automation using AWS CloudFormation
Check feature gaps between AWS CloudFormation (or third-
party tool) and Amazon Relational Database Service (Amazon
RDS) APIs (AWS Management Console, AWS CLIs, SDKs)
Check feasibility of workarounds using AWS CloudFormation
Custom Resources (or similar)
Ensure efficient ordering during resource provisioning
(dependencies + custom resources)
1
2
3
Create alternative automation for minimal downtime
production workflows
4

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Risk free dev/test, data extracts and analytics
Bad practices we’ve seen:
• App development and testing on tiny subsets of non-production data
• Bulk data exports from live production instances
• Running long, unoptimized queries on cluster readers
• Lack of outcome focus
The right way:
• Know your features: Point-in-Time Restore, Database Cloning, Backtrack
• Proper workload isolation
• Test, don’t hope for the best

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Database Cloning
Get faster access to copies of your data so you can:
Test changes in pre-production on
relevant data sets
Reorganize a database with minimal
production impact
Save a point in time snapshot for data
analysis without impacting
production systems
1
2
3

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Fast database cloning with Amazon Aurora
Available for both MySQL and PostgreSQL compatible
versions of Amazon Aurora
Creation of clone takes a few minutes
Data copy happens only on write, when the original and
cloned volume start to differ
Operations on clone do not affect the source cluster
Up to 15 clones from the same source
Pay only for the data storage difference on the clone PRODUCTION DATABASE
CLONE CLONE
CLONE
DEV/TEST
APPLICATIONS
BENCHMARKS
PRODUCTION
APPLICATIONS
PRODUCTION
APPLICATIONS

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Backtracking databases
Easily undo changes to your data:
Reduce risk of database changes at
scale
Undo unintentional DML and DDL
changes
Mitigate risk of malicious changes to
your data
1
2
3
Avoid time consuming data restore
from backups
4

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How does Backtrack work?
• We keep periodic snapshots of each segment; we also preserve the logs
• For Backtrack, we identify the appropriate segment snapshots
• Apply log streams to segment snapshots in parallel and asynchronously
SEGMENT SNAPSHOT
CHANGE
RECORDS
RECOVERY POINT
SEGMENT 1
SEGMENT 2
SEGMENT 3
TIME

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Going back in time with Backtrack
t0 t1 t2
t0 t1
t2
t3 t4
t3
t4
Rewind to t1
Rewind to t3
Invisible Invisible
• Backtrack is not destructive
• You can backtrack multiple times to find the right point in time
• Pay for the volume of change records retained for the desired duration (up to 72 hours)
• Available for Aurora MySQL 5.6 compatible

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Development focused on production scale
Bad practices we’ve seen:
• App development and testing on tiny subsets of non-production data
• My database is always there for me and never fails me
• Running long, unoptimized queries on cluster readers
• Lack of outcome focus
The right way:
• Be aware of architectural differences between Aurora and equivalent engines
• Know Aurora capabilities and operational procedures: automated failovers, upgrades
• Extend Aurora features with application side cluster awareness
• Test, don’t hope for the best: failure injection

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Connection management and availability
Minimize failure downtime, and connection management overhead
Common MySQL and PostgreSQL drivers are not
cluster/topology aware
Use a client side connection pool; recycle
connections periodically; avoid connection storms
Use a smart driver, or build topology awareness in
your client data layer, validate connections
1
2
3
Understand the behavior of the Aurora DNS
endpoints, manage DNS caching
4

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Monitor the performance of your queries
Establish a baseline of
acceptable/desired query performance
Assess performance impact of
workload changes
Troubleshoot poor performance,
identify bottlenecks
1
2
3
Effective capacity planning4

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Database health at a glance
Amazon Aurora comes with comprehensive monitoring built-in:

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Database health at a glance
Amazon Aurora comes with comprehensive monitoring built-in:

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Performance Insights
Available for both MySQL and PostgreSQL compatible
versions of Amazon Aurora
Easy and powerful dashboard showing load on your
database
Helps you identify source of bottlenecks: top SQL
queries, wait statistics
Adjustable time frame (hour, day week, month)
7 days of performance data history free – perfect for
developers; up to 2 years of long term retention for
production use cases
Max CPU

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Cost optimized development and testing
Use database resources at appropriate scale only when they are needed
1 Dev and test activities are rarely
continuous
2
Dev and test environment isolation3
Scale of dev and test activities varies

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Aurora features helping with cost optimization

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Aurora features helping with cost optimization

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Amazon Aurora Serverless use cases
Development and test databases:
• Easily provisioned
• Cost savings when DBs are not in use
• Simplify dev/test pipelines
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)

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Aurora Serverless architecture
• Application talks to MySQL compatible endpoint
• Fleet of routers manage queue, client connections
and route DB traffic
• Instance handles database operations
• Data kept durable and highly available on Aurora
storage volume
• When scaling thresholds are reached we scale the
instance substituting with capacity from warm pool
• Scaling operations are transparent to applications
• You configure min. and max. capacity, whether and
when to pause DB if there’s no activity
INSTANCE
DATABASE
STORAGE
APPLICATION
WARM POOL
DATABASE TRAFFIC ROUTERS

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Aurora Serverless pricing
• You pay for capacity your database consumes while
it is active
• Database capacity is measured in Aurora Capacity
Units (ACUs): 1 ACU ~ 2Gb of memory with
corresponding CPU and network capacity
• Flat rate per second of ACU usage, with a minimum
of 5 minutes of usage each time the database is
activated
• Storage and I/O are billed the same as with Aurora
instances
Duration ACUs ACU-Hours Rate Charges
1 hour, 10 minutes, and 15
seconds (1.020 hours)
4 4.08 $0.06 $0.25
40 minutes and 34 seconds
(0.676 hours)
8 5.41 $0.06 $0.32
Usage total for 24 hours 9.49 $0.06 $0.57
Usage Rate Charges
9.49 ACU-hours $0.06 per ACU-hour $0.57
80 GB of storage for 24
hours
$0.10 per GB-month $0.26
90,000 I/O requests $0.20 per 1 million requests $0.02
Total charges for 24 hours $0.85

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Introducing the RDS Data API
• Public HTTP API endpoint, integrated with AWS
Authentication
• Automated server side connection pooling
• Ideal for AWS Lambda or serverless apps reusing
connections across multiple invocations
• Fully Integrated with AWS Secrets Manager and
AWS AppSync
• Optional session construct for efficient prepared
statement executions
• Interactive Query Editor in the console for
ad-hoc queries
• Available for Aurora Serverless (MySQL)

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Parting Thoughts
Use automation wisely and map out
your operational processes in detail
Know Aurora’s features and how they
can improve on your development
and testing process
Develop and test with performance in
mind
1
2
3
Quantify the expected baseline
performance of your workload
4

35. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Steve Abraham
Vlad Vlasceanu

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Companion workshops
Tuesday, November 27th
Accelerating Application Development with Amazon Aurora
11:30am – 2:00pm | Bellagio, Level 1, Grand Ballroom 6
Wednesday, November 28th
Accelerating Application Development with Amazon Aurora
2:30pm – 5:00pm | Aria West, Level 3, Ironwood 1
Thursday, November 29th
Accelerating Application Development with Amazon Aurora
11:30am – 2:00pm | Mirage, Mirage Event Center C3

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Related breakouts
DAT204 | What’s New in Amazon Aurora
DAT304 | Deep Dive on Amazon Aurora with MySQL Compatibility
DAT305 | Deep Dive on Amazon Aurora with PostgreSQL Compatibility

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