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©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved
Amazon Aurora: Amazon’s New
Relational Database En...
Current DB architectures are monolithic
Multiple layers of
functionality all on a
single box
SQL
Transactions
Caching
Logg...
Current DB architectures are monolithic
Even when you scale
it out, you’re still
replicating the same
stack
SQL
Transactio...
Current DB architectures are monolithic
SQL
Transactions
Caching
Logging
SQL
Transactions
Caching
Logging
Application Even...
Current DB architectures are monolithic
SQL
Transactions
Caching
Logging
SQL
Transactions
Caching
Logging
Storage
Applicat...
This is a problem.
For cost. For flexibility. And for availability.
Reimagining the relational database
What if you were inventing the database today?
You wouldn’t design it the way we did i...
Relational databases reimagined for the cloud
Speed and availability of high-end commercial databases
Simplicity and cost-...
A service-oriented architecture applied to the database
• Moved the logging and storage layer
into a multi-tenant, scale-o...
• Sign up for preview access at:
https://aws.amazon.com/rds/aurora/preview
• Now available in US West (Oregon) and EU (Ire...
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...
Aurora Works with Your Existing Apps
Establishing our ecosystem
Business Intelligence Data Integration Query and Monitoring SI and Consulting
“It is great to s...
Amazon Aurora Is Easy to Use
Simplify database management
• Create a database in minutes
• Automated patching
• Push-button scale compute
• Continuous ...
Simplify storage management
• Read replicas are available as failover targets—no data loss
• Instantly create user snapsho...
Simplify data security
• Encryption to secure data at rest
– AES-256; hardware accelerated
– All blocks on disk and in Ama...
Amazon Aurora Is Highly Available
Aurora storage
• Highly available by default
– 6-way replication across 3 AZs
– 4 of 6 write quorum
• Automatic fallback t...
Consistent, low-latency writes
AZ 1 AZ 2
Primary
Instance
Standby
Instance
Amazon Elastic
Block Store (EBS)
Amazon S3
EBS
...
Self-healing, fault-tolerant
• Lose two copies or an AZ failure without read or write availability impact
• Lose three cop...
Traditional databases
• Have to replay logs since the last
checkpoint
• Single-threaded in MySQL;
requires a large number ...
Survivable caches
• We moved the cache out of
the database process
• Cache remains warm in the
event of a database restart...
Multiple failover targets—no data loss
Page cache
invalidation
Aurora Master
30% Read
70% Write
Aurora Replica
100% New
Re...
Simulate failures using SQL
• To cause the failure of a component at the database node:
ALTER SYSTEM CRASH [{INSTANCE | DI...
Amazon Aurora Is Fast
Write performance (console screenshot)
• MySQL Sysbench
• R3.8XL with 32 cores
and 244 GB RAM
• 4 client machines with
1,0...
Read performance (console screenshot)
• MySQL Sysbench
• R3.8XL with 32 cores
and 244 GB RAM
• Single client with
1,000 th...
Read replica lag (console screenshot)
• Aurora Replica with 7.27 ms replica lag at 13.8 K updates/second
• MySQL 5.6 on th...
Writes scale with table count
-
10
20
30
40
50
60
70
10 100 1,000 10,000
ThousandsofWritesperSecond
Number of Tables
Write...
Better concurrency
-
20
40
60
80
100
120
50 500 5,000
ThousandsofWritesperSecond
Concurrent Connections
Write Performance ...
Caching improves performance
-
50
100
150
200
250
300
350
400
100/0 50/50 0/100
ThousandsofOperations/Second
Read/Write Ra...
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
3...
SAN FRANCISCO
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Amazon Aurora: Amazon’s New Relational Database Engine

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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.

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Amazon Aurora: Amazon’s New Relational Database Engine

  1. 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. 2. Current DB architectures are monolithic Multiple layers of functionality all on a single box SQL Transactions Caching Logging
  3. 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. 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. 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. 6. This is a problem. For cost. For flexibility. And for availability.
  7. 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. 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. 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. 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. 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. 12. Aurora Works with Your Existing Apps
  13. 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. 14. Amazon Aurora Is Easy to Use
  15. 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. 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. 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. 18. Amazon Aurora Is Highly Available
  19. 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. 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. 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. 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. 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. 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. 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. 26. Amazon Aurora Is Fast
  27. 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. 28. Read performance (console screenshot) • MySQL Sysbench • R3.8XL with 32 cores and 244 GB RAM • Single client with 1,000 threads
  29. 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. 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. 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. 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. 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. 34. SAN FRANCISCO

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