© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
December 1, 2016
Deep Dive on Amazon Aurora
Anurag Gupta, VP, Database Services
Amazon EMR, Amazon Redshift, Amazon Aurora, Amazon Athena, AWS Glue
awgupta@amazon.com

Agenda
 What is Aurora?
 Review of Aurora performance
 New performance enhancements
 Review of Aurora availability
 New availability enhancements
 Other recent and upcoming feature enhancements

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

Performance

WRITE PERFORMANCE READ PERFORMANCE
Scaling with instance sizes
Aurora scales with instance size for both read and write.
Aurora MySQL 5.6 MySQL 5.7

Real-life data – gaming workload
Aurora vs. RDS MySQL – r3.4XL, MAZ
Aurora 3X faster on r3.4xlarge

Do fewer I/Os
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 did we achieve this?
DATABASES ARE ALL ABOUT I/O
NETWORK-ATTACHED STORAGE IS ALL ABOUT PACKETS/SECOND
HIGH-THROUGHPUT PROCESSING IS ALL ABOUT CONTEXT SWITCHES

I/O traffic in MySQL
BINLOG DATA DOUBLE-WRITELOG FRM FILES
T Y P E O F W R IT E
MYSQL WITH REPLICA
EBS mirrorEBS mirror
AZ 1 AZ 2
Amazon S3
EBS
Amazon Elastic
Block Store (EBS)
Primary
Instance
Replica
Instance
1
2
3
4
5
Issue write to EBS – EBS issues to mirror, ack when both done
Stage write to standby instance through DRBD
Issue write to EBS on standby instance
I/O FLOW
Steps 1, 3, 4 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 writeonly workload, 100GB dataset, RDS MultiAZ, 30K PIOPS

I/O traffic in Aurora
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
I/O 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 writesReplica
Instance

I/O 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
I/O FLOW
① Receive record and add to in-memory queue
② Persist record and acknowledge
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

I/O traffic in Aurora Replicas
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

“In MySQL, we saw replica lag spike to almost 12 minutes which is
almost absurd from an application’s perspective. With Aurora, the
maximum read replica lag across 4 replicas never exceeded 20 ms.”
Real-life data - read replica latency

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

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

Scan
Delete
Aurora lock management
Scan
Delete
Insert
Scan Scan
Insert
Delete
Scan
Insert
Insert
MySQL lock manager Aurora lock manager
 Same locking semantics as MySQL
 Concurrent access to lock chains
 Multiple scanners allowed in an individual lock chains
 Lock-free deadlock detection
Needed to support many concurrent sessions, high update throughput

New performance enhancements

Cached read performance
Catalog concurrency: Improved data dictionary
synchronization and cache eviction.
NUMA aware scheduler: Aurora scheduler is
now NUMA aware. Helps scale on multi-socket
instances.
Read views: Aurora now uses a latch-free
concurrent read-view algorithm to construct read
views.
0
100
200
300
400
500
600
700
MySQL 5.6 MySQL 5.7 Aurora 2015 Aurora 2016
In thousands of read requests/sec
* R3.8xlarge instance, <1GB dataset using Sysbench
25% Throughput gain

Smart scheduler: Aurora scheduler now
dynamically assigns threads between I/O heavy
and CPU heavy workloads.
Smart selector: Aurora reduces read latency by
selecting the copy of data on a storage node with
best performance
Logical read ahead (LRA): We avoid read I/O
waits by prefetching pages based on their order
in the btree.
Non-cached read performance
0
20
40
60
80
100
120
MySQL 5.6 MySQL 5.7 Aurora 2015 Aurora 2016
In thousands of requests/sec
* R3.8xlarge instance, 1TB dataset using Sysbench
10% Throughput gain

Scan
Delete
Hot row contention
Scan
Delete
Insert
Scan Scan
Insert
Delete
Scan
Insert
Insert
MySQL lock manager Aurora lock manager
Highly contended workloads had high memory and CPU
 1.9 (Nov) – lock compression (bitmap for hot locks)
 1.9 – replace spinlocks with blocking futex – up to 12x reduction in CPU, 3x improvement in throughput
 December – use dynamic programming to release locks: from O(totalLocks * waitLocks) to O(totalLocks)
Throughput on Percona TPC-C 100 improved 29x (from 1,452 txns/min to 42,181 txns/min)

Hot row contention
MySQL 5.6 MySQL 5.7 Aurora Improvement
500 connections 6,093 25,289 73,955 2.92x
5000 connections 1,671 2,592 42,181 16.3x
Percona TPC-C – 10GB
* Numbers are in tpmC, measured using release 1.10 on an R3.8xlarge, MySQL numbers using RDS and EBS with 30K PIOPS
MySQL 5.6 MySQL 5.7 Aurora Improvement
500 connections 3,231 11,868 70,663 5.95x
5000 connections 5,575 13,005 30,221 2.32x
Percona TPC-C – 100GB

 Accelerates batch inserts sorted by
primary key – works by caching the
cursor position in an index traversal.
 Dynamically turns itself on or off based on
data pattern.
 Avoids contention in acquiring latches
while navigating down the tree.
 Bi-directional, works across all insert
statements.
• LOAD INFILE, INSERT INTO SELECT, INSERT
INTO REPLACE and, Multi-value inserts.
Batch insert performance
Index
R4 R5R2 R3R0 R1 R6 R7 R8
Index
Root
Index
R4 R5R2 R3R0 R1 R6 R7 R8
Index
Root
MySQL: Traverses B-tree starting from root for all inserts
Aurora: Inserts avoids index traversal

Faster index build
 MySQL 5.6 leverages Linux read ahead – but
this requires consecutive block addresses in
the btree. It inserts entries top down into the
new btree, causing splits and excessive
logging.
 Aurora’s scan pre-fetches blocks based on
position in tree, not block address.
 Aurora builds the leaf blocks and then the
branches of the tree.
• No splits during the build.
• Each page touched only once.
• One log record per page.
2-4X better than MySQL 5.6 or MySQL 5.7
0
2
4
6
8
10
12
r3.large on 10GB
dataset
r3.8xlarge on
10GB dataset
r3.8xlarge on
100GB dataset
Hours RDS MySQL 5.6 RDS MySQL 5.7 Aurora 2016

Why spatial index
Need to store and reason about spatial data
• E.g., “Find all people within 1 mile of a hospital”
• Spatial data is multi-dimensional
• B-Tree indexes are one-dimensional
Aurora supports spatial data types (point/polygon)
• GEOMETRY data types inherited from MySQL 5.6
• This spatial data cannot be indexed
Two possible approaches:
• Specialized access method for spatial data (e.g., R-Tree)
• Map spatial objects to one-dimensional space & store in B-
Tree - space-filling curve using a grid approximation
A
B
A A
A A
A A A
B
B
B
B
B
A COVERS B
COVEREDBY A
A CONTAINS B
INSIDE A
A TOUCH B
TOUCH A
A OVERLAPBDYINTERSECT B
OVERLAPBDYINTERSECT A
A OVERLAPBDYDISJOINT B
OVERLAPBDYDISJOINT A
A EQUAL B
EQUAL A
A DISJOINT B
DISJOINT A
A COVERS B
ON A

Spatial indexes in Aurora
Z-index used in Aurora
Challenges with R-Trees
Keeping it efficient while balanced
Rectangles should not overlap or cover empty space
Degenerates over time
Re-indexing is expensive
R-Tree used in MySQL 5.7
Z-index (dimensionally ordered space filling curve)
Uses regular B-Tree for storing and indexing
Removes sensitivity to resolution parameter
Adapts to granularity of actual data without user declaration
Eg GeoWave (National Geospatial-Intelligence Agency)

Spatial index benchmarks
Sysbench – points and polygons
. . . . . . . . . . . . .
. . . . . . . . . . . . .
. . . . . . . . . . . . .
. . . . . . . . . . .. .
. . . . . . . . . . . . .
* r3.8xlarge using Sysbench on <1GB dataset
* Write Only: 4000 clients, Select Only: 2000 clients, ST_EQUALS
0
20000
40000
60000
80000
100000
120000
140000
Select-only (reads/sec) Write-only (writes/sec)
Aurora
MySQL 5.7

Availability
“Performance only matters if your database is up”

Storage durability
Storage volume automatically grows up to 64 TB
Quorum system for read/write; latency tolerant
Peer to peer gossip replication to fill in holes
Continuous backup to S3 (built for 11 9s durability)
Continuous monitoring of nodes and disks for repair
10 GB segments as unit of repair or hotspot rebalance
Quorum membership changes do not stall writes
AZ 1 AZ 2 AZ 3
Amazon S3

Aurora Replicas
Aurora clusters contain a primary node
and up to fifteen replicas
Failing database nodes are
automatically detected and replaced
Failing database processes are
automatically detected and recycled
Customer applications may scale-out
read traffic across replicas
Replicas are automatically promoted
on persistent outage
AZ 1 AZ 3AZ 2
Primary
Node
Primary
Node
Primary
Node
Primary
Node
Primary
Node
Secondary
Node
Primary
Node
Primary
Node
Secondary
Node

Continuous backup
Segment snapshot Log records
Recovery point
Segment 1
Segment 2
Segment 3
Time
• Take periodic snapshot of each segment in parallel; stream the redo logs to Amazon S3
• Backup happens continuously without performance or availability impact
• At restore, retrieve the appropriate segment snapshots and log streams to storage nodes
• Apply log streams to segment snapshots in parallel and asynchronously

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

Survivable caches
We moved the cache out of the
database process
Cache remains warm in the event of
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

Faster 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

Database failover time
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
0 - 5s – 30% of fail-overs
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
5 - 10s – 40% of fail-overs
0%
10%
20%
30%
40%
50%
60%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
10 - 20s – 25% of fail-overs
0%
5%
10%
15%
20%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
20 - 30s – 5% of fail-overs

New availability enhancements

Availability is about more than HW failures
You also incur availability disruptions when you
1. Patch your database software
2. Modify your database schema
3. Perform large scale database reorganizations
4. Restore a database after a user error

Zero downtime patching
Networking
state
Application
state
Storage Service
App
state
Net
state
App
state
Net
state
BeforeZDP
New DB
Engine
Old DB
Engine
New DB
Engine
Old DB
Engine
WithZDP
User sessions terminate
during patching
User sessions remain
active through patching
Storage Service

Zero downtime patching – current constraints
We have to go to our current patching model when we can’t park connections:
• Long running queries
• Open transactions
• Bin-log enabled
• Parameter changes pending
• Temporary tables open
• Locked tables
• SSL connections open
• Read replicas instances
We are working on addressing the above.

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

How does it work?
Page
1
Page
2
Page
3
Page
4
Source Database
Page
1
Page
3
Page
2
Page
4
Cloned database
Shared Distributed Storage System: physical pages
Both databases reference same pages on the shared
distributed storage system
Page
1
Page
2
Page
3
Page
4

How does it work? (contd.)
Page
1
Page
2
Page
3
Page
4
Page
5
Page
1
Page
3
Page
5
Page
2
Page
4
Page
6
Page
1
Page
2
Page
3
Page
4
Page
5
Page
6
As databases diverge, new pages are added appropriately to each
database while still referencing pages common to both databases
Page
2
Page
3
Page
5
Shared Distributed Storage System: physical pages
Source Database Cloned database

Online DDL: Aurora vs. MySQL
 Full table copy; rebuilds all indexes – can take
hours or days to complete.
 Needs temporary space for DML operations
 DDL operation impacts DML throughput
 Table lock applied to apply DML changes
Index
LeafLeafLeaf Leaf
Index
Root
table name operation column-name time-stamp
Table 1
Table 2
Table 3
add-col
add-col
add-col
column-abc
column-qpr
column-xyz
t1
t2
t3
 We add an entry to the metadata table and use schema
versioning to decode the block.
 Added a modify-on-write primitive to upgrade the block to
the latest schema when it is modified.
 Currently support add NULLable column at end of table.
 Priority is to support other add column, drop/reorder,
modify datatypes.
MySQL Amazon Aurora

Online DDL performance
On r3.large
On r3.8xlarge
Aurora MySQL 5.6 MySQL 5.7
10GB table 0.27 sec 3,960 sec 1,600 sec
50GB table 0.25 sec 23,400 sec 5,040 sec
100GB table 0.26 sec 53,460 sec 9,720 sec
Aurora MySQL 5.6 MySQL 5.7
10GB table 0.06 sec 900 sec 1,080 sec
50GB table 0.08 sec 4,680 sec 5,040 sec
100GB table 0.15 sec 14,400 sec 9,720 sec

Online point-in-time restore
Online point-in-time restore is a quick way to bring the database to a particular point in
time without having to restore from backups
• Rewinding the database to quickly recover from unintentional DML/DDL operations.
• Rewind multiple times to determine the desired point-in-time in the database state. For
example, quickly iterate over schema changes without having to restore multiple times.
t0 t1 t2
t0 t1
t2
t3 t4
t3
t4
Rewind to t1
Rewind to t3
Invisible Invisible

Online PiTR
Online PiTR operation changes the state of
the current DB
Current DB is available within seconds, even
for multi-terabyte DBs
No additional storage cost as current DB is
restored to prior point in time
Multiple iterative online PiTRs are practical
Rewind has to be within the allowed rewind
period based on purchased rewind storage
Cross-region online PiTR is not supported
Online vs. offline point-in-time restore (PiTR)
Offline PiTR
PiTR creates a new DB at desired point in time
from the backup of the current DB
New DB instance takes hours to restore for multi-
terabyte DBs
Each restored DB is billed for its own storage
Multiple iterative offline PiTRs is time consuming
Offline PiTR has to be within the configured
backup window or from snapshots
Aurora supports cross-region PiTR

How does it work?
Segment snapshot Log records
Rewind Point
Segment 1
Segment 2
Segment 3
Time
 Aurora takes periodic snapshots within each segment in parallel and stores
them locally
 At rewind time, each segment picks the previous local snapshot and applies the
log streams to the snapshot to produce the desired state of the DB
Storage Segments

Logs within the log stream are made visible or invisible based on the branch within the LSN
tree, providing a consistent view for the DB
 The first rewind performed at t2 to rewind the DB to t1 makes the logs in purple color invisible
 The second rewind performed at time t4 to rewind the DB to t3 makes the logs in red and purple invisible
How does it work? (contd.)
t0 t1 t2
t0 t1
t2
t3 t4
t3
t4
Rewind to t1
Rewind to t3
Invisible Invisible

Removing blockers

My applications require PostgreSQL
Amazon Aurora PostgreSQL compatibility
now in preview
Same underlying scale out, 3 AZ, 6 copy,
fault tolerant, self healing, expanding
database optimized storage tier
Integrated with a PostgreSQL 9.6
compatible database
Session DAT206-R today @3:30 -
Venetian, Level 3, San Polo 3403
Logging + Storage
SQL
Transactions
Caching
Amazon
S3

T2 RI discounts
Up to 34% with a 1-year RI
Up to 57% with a 3-year RI
vCPU Mem Hourly Price
db.t2.medium 2 4 $0.082
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
An R3.Large is too expensive for my use case
T2.Small coming in Q1. 2017
*Prices are for Virginia

My databases need to meet certifications
 Amazon Aurora gives each database
instance IP firewall protection
 Aurora offers transparent encryption at rest
and SSL protection for data in transit
 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*

Aurora Auditing
MariaDB server_audit plugin Aurora native audit support
Aurora can sustain over 500K events/sec
Create event string
DDL
DML
Query
DCL
Connect
DDL
DML
Query
DCL
Connect
Write
to File
Create event string
Create event string
Create event string
Create event string
Create event string
Latch-free
queue
Write to File
Write to File
Write to File
MySQL 5.7 Aurora
Audit Off 95K 615K 6.47x
Audit On 33K 525K 15.9x
Sysbench Select-only Workload on 8xlarge Instance

AWS ecosystem
Lambda
S3
IAM
CloudWatch
Generate AWS Lambda events from Aurora stored procedures.
Load data from Amazon S3, store snapshots and backups in S3.
Use AWS IAM roles to manage database access control.
Upload systems metrics and audit logs to Amazon CloudWatch.
*NEW*
Q1

MySQL compatibility
Business Intelligence Data Integration Query and Monitoring
“We ran our compatibility test suites against
Amazon Aurora and everything just worked."
- Dan Jewett, VP, Product Management at Tableau
MySQL 5.6 / InnoDB compatible
 No application compatibility issues
reported since launch
 MySQL ISV applications run pretty much
as is
Working on 5.7 compatibility
 Running a bit slower than expected
 Back ported 81 fixes from different
MySQL releases

Timeline
Available now (1.9) Available in Dec (1.10) Available in Q1
Performance
Availability
Security
Ecosystem
PCI/DSS
HIPPA/BAA
Fast online schema change
Managed MySQL to Aurora
replication
Cross-region snapshot copy
Online Point in Time Restore
Database cloning
Zero-downtime patching
Spatial indexingLock compression
Replace spinlocks with blocking futex
Faster index build
Aurora auditing
IAM Integration
Copy-on-write volume
T2.Medium T2.Small
CloudWatch for metrics, audit

Thank you!
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