This document provides an overview of Amazon Aurora and discusses its performance advantages over traditional databases. Aurora delivers the performance and availability of commercial databases at 1/10th the cost by leveraging simple open source architecture. The document describes how Aurora achieves high performance through its distributed, asynchronous architecture and integration with other AWS services. It also discusses how Aurora provides high availability through its quorum-based storage system and ability to handle failures without stopping writes or restarting the database. Finally, the document shares benchmark results and customer use cases that demonstrate Aurora's ability to scale to large workloads and datasets at significantly lower costs than alternative solutions.

1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Abdul Sathar Sait, Principal Product Manager, RDS
Lynn Ferrante, Business Development Manager, RDS
Amazon Aurora Deep Dive

2. MySQL-compatible relational database
Performance and availability of
commercial databases
Simplicity and cost-effectiveness of
open source databases
What is Amazon Aurora?

3. Fastest growing service in AWS history
Business Applications
Web and mobile
Content management
E-commerce, retail
Internet of Things
Search, advertising
BI and analytics
Games, media
Common customer use cases

4. Expedia: On-line travel marketplace
 Real-time business intelligence and analytics on a
growing corpus of on-line travel market place
data.
 Current SQL server based architecture is too
expensive. Performance degrades as data
volume grows.
 Cassandra with Solr index requires large memory
footprint and hundreds of nodes, adding cost.
Aurora benefits:
 Aurora meets scale and performance
requirements with much lower cost.
 25,000 inserts/sec with peak up to 70,000. 30ms
average response time for write and 17ms for
read, with 1 month of data.
World’s leading online travel
company, with a portfolio that
includes 150+ travel sites in 70
countries.

5. PG&E: Large public utility
 Servicing high traffic surge during power events
had always been a problem.
 Availability is critical when databases are down, it
adversely affects service to gas and electrical
customers.
Aurora benefits:
 Being able to create multiple database replicas
with millisecond latency allows them handle large
surges in traffic and still give customers timely, up-
to-date information during a power event .
 Amazon Aurora, with 6-way replication, self
healing storage and automatic instance repair,
provides the availability and reliability needed for
mission critical applications.
One of the largest combination natural gas
and electric utilities in the United States
with approximately 16 million customers in
70,000-square-mile service area in northern
and central California.

6. ISCS: Insurance claims processing
 Have been using Oracle and SQL server for
operational and warehouse data
 Cost and maintenance of traditional commercial
database has become the biggest expenditure and
maintenance headache.
Aurora benefits:
 The cost of a “more capable” deployment on Aurora
has proven to be about 70% less than ISCS’s SQL
Server deployments.
 Eliminated backup window with Aurora’s continuous
backup; exploiting linear scaling with number of
connections; continuous upload to Redshift using
Aurora read replicas.
Provides policy management,
claim, billing solutions for casualty
and property and insurance
organizations

7. Alfresco: Enterprise content management
 Scaling Alfresco document repositories to billions
of documents
 Support user applications that require sub-
second response times
Aurora benefits:
 Scaled to 1 billion documents with a throughput
of 3 million per hour, which is 10 times faster than
their current environment.
 Moving from large data centers to cost-effective
management with AWS and Aurora.
Leading the convergence of Enterprise
Content Management and Business
Process Management. More than 1,800
organizations in 195 countries rely on
Alfresco, including leaders in financial
services, healthcare, and the public sector.

8. 4 client machines with 1,000 connections each
WRITE PERFORMANCE READ PERFORMANCE
Single client machine with 1,600 connections
Using MySQL SysBench with Amazon Aurora R3.8XL with 32 cores and 244 GB RAM
SQL Benchmark Results

9. Reproducing these results
https ://d0.a wsstat ic . com /product -m ark eting/Aurora /R DS_ Auro ra_Perf orm ance_Assessm ent_Benchm ark ing_v 1-2 .pdf
AMAZON
AURORA
R3.8XLARG
E
R3.8XLARGE
R3.8XLARGE
R3.8XLARGE
R3.8XLARGE
• Create an Amazon VPC (or use an existing one).
• Create four EC2 R3.8XL client instances to run the
SysBench client. All four should be in the same AZ.
• Enable enhanced networking on your clients
• Tune your Linux settings (see whitepaper)
• Install Sysbench version 0.5
• Launch a r3.8xlarge Amazon Aurora DB Instance in
the same VPC and AZ as your clients
• Start your benchmark!
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2
3
4
5
6
7

10. Beyond benchmarks
If only real world applications saw benchmark performance
POSSIBLE DISTORTIONS
Real world requests contend with each other
Real world metadata rarely fits in data dictionary cache
Real world data rarely fits in buffer cache
Real world production databases need to run HA

11. Scaling User Connections
SysBench OLTP Workload
250 tables
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
8x
UP TO
FA STER

12. Scaling Table Count
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
SysBench write-only workload
1,000 connections
Default settings
11x
UP TO
FA STER
Number of writes per second

13. Scaling Data Set Size
DB Size Amazon Aurora
RDS MySQL
30K IOPS (single AZ)
1GB 107,000 8,400
10GB 107,000 2,400
100GB 101,000 1,500
1TB 26,000 1,200
67x
U P TO
FA STER
SYSBENCH WRITE-ONLY
DB Size Amazon Aurora
RDS MySQL
30K IOPS (single AZ)
80GB 12,582 585
800GB 9,406 69
CLOUDHARMONY TPC-C
136x
U P TO
FA STER

14. Running with Read Replicas
Updates per
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
SysBench Write-only Workload
250 tables
500x
UP TO
LOW ER LA G

15. Do fewer IOs
Minimize network packets
Cache prior results
Offload the database engine
DO LESS WORK
Process asynchronously
Reduce latency path
Use lock-free data structures
Batch operations together
BE MORE EFFICIENT
How Do We Achieve These Results?
DATABASES ARE ALL ABOUT I/O
NETWORK-ATTACHED STORAGE IS ALL ABOUT PACKETS/SECOND
HIGH-THROUGHPUT PROCESSING DOES NOT ALLOW CONTEXT SWITCHES

16. 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
1
2
3

17. Aurora Cluster
Amazon S3
AZ 1 AZ 2 AZ 3
Aurora Primary
instance
Cluster volume spans 3 AZs

18. Aurora Cluster with Replicas
Amazon S3
AZ 1 AZ 2 AZ 3
Aurora Primary
instance
Cluster volume spans 3 AZs
Aurora Replica Aurora Replica

19. IO Traffic in RDS MySQL
BINLOG DATA DOUBLE-WRITELOG FRM FILES
T Y P E O F W R IT E
MYSQL WITH STANDBY
EBS mirrorEBS mirror
AZ 1 AZ 2
Amazon S3
EBS
Amazon Elastic
Block Store (EBS)
Primary
Instance
Standby
Instance
1
2
3
4
5
Issue write to EBS – EBS issues to mirror, ack when both done
Stage write to standby instance
Issue write to EBS on standby instance
IO FLOW
Steps 1, 3, 5 are sequential and synchronous
This amplifies both latency and jitter
Many types of writes for each user operation
Have to write data blocks twice to avoid torn writes
OBSERVATIONS
780K transactions
7,388K I/Os per million txns (excludes mirroring, standby)
Average 7.4 I/Os per transaction
PERFORMANCE
30 minute SysBench write-only workload, 100GB dataset, RDS Single AZ, 30K
PIOPS

20. IO Traffic in Aurora (Database)
AZ 1 AZ 3
Primary
Instance
Amazon S3
AZ 2
Replica
Instance
AMAZON AURORA
ASYNC
4/6 QUORUM
DISTRIBUTED
WRITES
BINLOG DATA DOUBLE-WRITELOG FRM FILES
T Y P E O F W R IT E S
30 minute SysBench writeonly workload, 100GB dataset
IO FLOW
Only write redo log records; all steps asynchronous
No data block writes (checkpoint, cache replacement)
6X more log writes, but 9X less network traffic
Tolerant of network and storage outlier latency
OBSERVATIONS
27,378K transactions 35X MORE
950K I/Os per 1M txns (6X amplification) 7.7X LESS
PERFORMANCE
Boxcar redo log records – fully ordered by LSN
Shuffle to appropriate segments – partially ordered
Boxcar to storage nodes and issue writes

21. IO Traffic in Aurora (Storage Node)
LOG RECORDS
Primary
Instance
INCOMING QUEUE
STORAGE NODE
S3 BACKUP
1
2
3
4
5
6
7
8
UPDATE
QUEUE
ACK
HOT
LOG
DATA
BLOCKS
POINT IN TIME
SNAPSHOT
GC
SCRUB
COALESCE
SORT
GROUP
PEER TO PEER GOSSIPPeer
Storage
Nodes
All steps are asynchronous
Only steps 1 and 2 are in foreground latency path
Input queue is 46X less than MySQL (unamplified, per node)
Favor latency-sensitive operations
Use disk space to buffer against spikes in activity
OBSERVATIONS
IO FLOW
① Receive record and add to in-memory queue
② Persist record and ACK
③ Organize records and identify gaps in log
④ Gossip with peers to fill in holes
⑤ Coalesce log records into new data block versions
⑥ Periodically stage log and new block versions to S3
⑦ Periodically garbage collect old versions
⑧ Periodically validate CRC codes on blocks

22. Asynchronous group commits
Read
Write
Commit
Read
Read
T1
Commit (T1)
Commit (T2)
Commit (T3)
LSN 10
LSN 12
LSN 22
LSN 50
LSN 30
LSN 34
LSN 41
LSN 47
LSN 20
LSN 49
Commit (T4)
Commit (T5)
Commit (T6)
Commit (T7)
Commit (T8)
LSN GROWTH
Durable LSN at head-node
COMMIT QUEUE
Pending commits in LSN order
TIME
GROUP
COMMIT
TRANSACTIONS
Read
Write
Commit
Read
Read
T1
Read
Write
Commit
Read
Read
Tn
TRADITIONAL APPROACH AMAZON AURORA
Maintain a buffer of log records to write out to disk
Issue write when buffer full or time out waiting for writes
First writer has latency penalty when write rate is low
Request I/O with first write, fill buffer till write picked up
Individual write durable when 4 of 6 storage nodes ACK
Advance DB Durable point up to earliest pending ACK

23. Re-entrant connections multiplexed to active threads
Kernel-space epoll() inserts into latch-free event queue
Dynamically size threads pool
Gracefully handles 5000+ concurrent client sessions on r3.8xl
Standard MySQL – one thread per connection
Doesn’t scale with connection count
MySQL EE – connections assigned to thread group
Requires careful stall threshold tuning
CLIENTCONNECTION
CLIENTCONNECTION
LATCH FREE
TASK QUEUE
epoll()
MYSQL THREAD MODEL AURORA THREAD MODEL
Adaptive thread pool

24. IO Traffic in Aurora (Read Replica)
PAGE CACHE
UPDATE
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
Logical: Ship SQL statements to Replica
Write workload similar on both instances
Independent storage
Can result in data drift between Master and
Replica
Physical: Ship redo from Master to Replica
Replica shares storage. No writes performed
Cached pages have redo applied
Advance read view when all commits seen
MYSQL READ SCALING AMAZON AURORA READ SCALING

25. Continuing the Improvements
Write batch size tuning
Asynchronous send for read/write I/Os
Purge thread performance
Bulk insert performance
BATCH OPERATIONS
Failover time reductions
Malloc reduction
System call reductions
Undo slot caching patterns
Cooperative log apply
OTHER
Binlog and distributed transactions
Lock compression
Read-ahead
CUSTOMER FEEDBACK
Hot row contention
Dictionary statistics
Mini-transaction commit code path
Query cache read/write conflicts
Dictionary system mutex
LOCK CONTENTION

26. Availability
“Performance only matters if your database is up”

27. Storage node availability
Quorum system for read/write; latency tolerant
Peer to peer gossip replication to fill in holes
Continuous backup to S3 (designed for 11 9s durability)
Continuous scrubbing of data blocks
Continuous monitoring of nodes and disks for repair
10GB segments as unit of repair or hotspot rebalance to
quickly rebalance load
Quorum membership changes do not stall writes
AZ 1 AZ 2 AZ 3
Amazon S3

28. Traditional databases
Have to replay logs since the last
checkpoint
Typically 5 minutes between checkpoints
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
No replay for startup
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

29. 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

30. Faster, more predictable failover
App
RunningFailure Detection DNS Propagation
Recovery Recovery
DB
Failure
MYSQL
App
Running
Failure Detection DNS Propagation
Recovery
DB
Failure
AURORA WITH MARIADB DRIVER
1 5 - 2 0 s e c
3 - 2 0 s e c

31. ALTER SYSTEM CRASH [{INSTANCE | DISPATCHER | NODE}]
ALTER SYSTEM SIMULATE percent_failure DISK failure_type IN
[DISK index | NODE index] FOR INTERVAL interval
ALTER SYSTEM SIMULATE percent_failure NETWORK failure_type
[TO {ALL | read_replica | availability_zone}] FOR INTERVAL interval
Simulate failures using SQL
To cause the failure of a component at the database node:
To simulate the failure of disks:
To simulate the failure of networking:

32. http://aws.amazon.com/rds/aurora