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
Vlad Vlasceanu
Principal Database Specialist SA
AWS
A D B 2 0 8

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Database development and testing perspective
Responsibilities of today’s developers, DevOps, and database admins:
• Database architecture
• Deployment & automation
• Improve dev/test outcomes
• Performance and availability focused development
• Cost effective development
Aurora provides native capabilities to meet these goals

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Aurora: Reimaging 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
Aurora is fast…
5x more throughput than MySQL; 3x 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
Automatic 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 database instance
endpoints
Caveat: Readers share the same
storage and undo log

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Automating deployments for Aurora
Why?
• Reduce the risk of human errors
• Repeatable deployments
• Accountability and management of change

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Automating deployments for Aurora
Why?
• Reduce the risk of human errors
• Repeatable deployments
• Accountability and management of change
How?
+

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Deployment automation using AWS CloudFormation
AWS CloudFormation:
Outcome-driven deployment automation, templates define desired state
… but how do you define the state of an Aurora database cluster?
Supplement capabilities to match desired processes:
Check capability gaps in AWS CloudFormation versus API1
Use AWS CloudFormation custom resources or a master snapshot to bootstrap
database (initial users, permissions, schemas)
2
Create alternative automation for workflows that need to be completed with
minimal disruption
3

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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
datasets
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 Aurora
Available for both MySQL and PostgreSQL compatible versions
of 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 the 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 aware
of the cluster or topology
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 or
preferred 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
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 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)
Seven days of performance data history free. Perfect for
developers; up to two 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 Development and test activities are rarely continuous
2
Development and test environment isolation3
Scale of development and test activities vary

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Aurora features helping with cost optimization

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Aurora features helping with cost optimization

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Aurora Serverless use cases
Development and test databases:
• Easily provisioned
• Cost savings when databases are not in use
• Simplify development and 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 database if there’s no activity
INSTANCE
DATABASE
STORAGE
APPLICATION
WARM POOL
DATABASE TRAFFIC ROUTERS

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Aurora Serverless pricing
• You pay for the capacity your database consumes while it
is active
• Database capacity is measured in Aurora Capacity Units
(ACUs): 1 ACU ~ 2 Gb 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 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

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