What’s New in AWS Database Services

What’s New in AWS Database Services
AWS數據庫服務的新增功能
Dickson Yue,
Solutions Architect, AWS
31 Jan 2019

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Common data categories and use cases
Relational
Referential
integrity, ACID
transactions,
schema-
on-write
Lift and shift, ERP,
CRM, finance
Key-value
High
throughput, low-
latency reads
and writes,
endless scale
Real-time bidding,
shopping cart,
social, product
catalog, customer
preferences
Document
Store JSON
documents with
quick access,
query on any
attribute
Content
management,
personalization,
mobile
In-memory
Query by key
with
microsecond
latency
Leaderboards,
real-time analytics,
caching
Graph
Quickly and
easily create
and navigate
relationships
between
data
Fraud detection,
social networking,
recommendation
engine
Time-series
Collect, store,
and process
data
sequenced by
time
IoT applications,
event tracking
Ledger
Complete,
immutable, and
verifiable history
of all changes to
application data
Systems
of record, supply
chain, health care,
registrations,
financial

AWS databases services
Purpose-builtfor all your app needs
Relational Key-value Document In-memory Graph Time-series Ledger
NeptuneRDS
Aurora CommercialCommunity
ElastiCacheDynamoDB DocumentDB
Redis Memcached
NEW
GA
Timestream
NEW
Preview
QLDB
NEW
Preview

Relational
RDS

Which database engine version should I use?
MySQL: 5.5, 5.6, 5.7, 8.0
MySQL 8.0: Window Functions, Common Table Expressions, JSON functions, Spatial support, improved
performance, crash-safe Data Definition Language (DDL) operations, security roles, TLS 1.2
MariaDB: 10.0, 10.1, 10.2, 10.3
MariaDB 10.3: Oracle compatibility (PL/SQL parser), sequences, INTERSECT and EXCEPT, new ROW type and TYPE
OF stored functions, invisible columns, temporal versioned tables, user-defined aggregates, instant ADD COLUMN
operations
PostgreSQL: 9.3, 9.4, 9.5, 9.6, 10, 11 (Preview)
PostgreSQL 10: Native table partitioning, improved parallelism in query execution, ICU collation support, column
group statistics, enhanced FDW and PLV8 extensions, huge pages enabled by default
PostgreSQL 11: Embedded transactions within a stored procedure, improvements to partitioning, improvements
to parallelism, adding columns with a non-null column default faster
Oracle: 11.2, 12.1, 12.2
Oracle 12.2: New engine versioning scheme—Release Updates (RUs) and Release Update Revisions (RURs)
SQL Server: 2008 R2, 2012, 2014, 2016, 2017
NEW!
NEW!
NEW!
NEW!

How do I manage database log files?
Database engine logs can be viewed in the Amazon RDS console or
downloaded via Amazon RDS API
Now publish logs in real time to Amazon CloudWatch Logs
Set CloudWatch alarms based on text search patterns
Supported engines are MySQL, MariaDB, and Oracle
Coming soon for PostgreSQL
CloudWatch
alarms
NEW!
Oracle DB
instance
Amazon
CloudWatch
Logs
MySQL DB
instance
alternate
PostgreSQL
instance

How can I track events with my Amazon RDS resources?
• Amazon RDS event notifications let you
know when important things happen
• Built-in notifications for Amazon Simple
Notification Service (Amazon SNS)
• Amazon RDS now publishes events to
Amazon CloudWatch Events
• Lets you create rules to respond to changes in your
resources
• Supports cross-account event delivery
• Six different source types (DB instance, DB
parameter group, DB security group, DB
snapshot, DB cluster, DB cluster snapshot)
• 17 different event categories (availability,
backup, deletion, configuration change,
and more)
NEW!
NEW!

How do I adhere to best practices?
• Amazon RDS
Recommendations provides
individualized best practice
guidance by analyzing your
resources
• Initial set of configuration-
based recommendations
• Future launches will include
usage, parameters, and
performance recommendations
• Results are presented in the
AWS Management Console to
apply immediately or schedule
for next maintenance window
NEW!

Amazon RDS on VMware
VMware vSphere ClusterAWS Region
DMZ
Connector
NEW!

Provisioning and management
Cluster Control Network Application Network
VMware vSphere Cluster
Connector
Your
Applications
VMware
Control VMs
RDS Control
VMs
Connector

Database engine support
Preview – Happening now!
PostgreSQL 10.4
MySQL 5.7
SQL Server 2017 coming soon
Sign up here: https://aws.amazon.com/rds/vmware/
GA
PostgreSQL, MariaDB, MySQL, and SQL Server
Soon thereafter
Oracle

Relational
Aurora

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
0
50
100
150
200
250
1 2 3 4
Max write throughput – up
100%
0
200
400
600
800
1 2 3 4
Max read throughput – up
42%
Launched with R3.8xl
32 cores, 256GB memory
Now support R4.16xl
R5.24xl coming soon
Besides many performance optimizations, we are also upgrading HW platform
Performance improvement over time
Aurora MySQL – 2015-2018

Aurora Multi-Master – scaling and availability
0
10000
20000
30000
40000
50000
60000
1
3
5
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125
AggregatedThroughput
Time in minutes
Sysbench workload on 4 R4.XL nodes
Adding a node Adding a node Node going down Node recovering

Aurora Global Database (GA)
High-performance database for globally-distributed applications
Single Global Database with cross region replication
Replication typically completes in less than a second
No impact on database performance
Write master in one region and read replicas in other regions
Cross-region disaster recovery
Local read latency for applications with global users
Primary Region Secondary Region
Application
Read/Write
Master
Storage Storage
Read Instances
(up to 15)
Read Instances
(up to 16)
Replication <1s

What’s new in Aurora Serverless
Regions
US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Asia Pacific (Tokyo).
Seoul, Singapore, Sydney, Mumbai, London, N. California, Paris, Frankfurt, Canada Central
Compliance
FedRAMP, HIPPA, PCI, SOC, ISO & HITRUST
Preview
Support for Aurora PostgreSQL
Preview
Support for REST DATA API

Amazon RDS Data API for serverless applications
Millions of
IOT/mobile devices Data API fleet
API
End-point
Amazon Aurora
Serverless
Access through simple web interface
• Public endpoint addressable from anywhere
• No client configuration required
• No persistent connections required
Ideal for Serverless applications (Lambda)
Ideal for light-weight applications (IOT)

Key-Value

DynamoDB
Advancements over the last 21 months
VPC
endpoints
April 2017
Auto
scaling
June 2017
DynamoDB
Accelerator (DAX)
April 2017
Time To
Live (TTL)
February 2017
Global tables
On-demand
backup
Encryption at rest
November 2017 November 2017 November 2017
Point-in-time
recovery
March 2018
SLA
June 2018
99.999% SLA
August 2018
Adaptive
capacity ACID
November 2018
Transactions
November 2018
On-demand

DynamoDB—read/write capacity on-demand (GA)
No more capacity planning—pay only for what you use
No capacity planning
No need to specify how much
read/write throughput you expect to
use
Ideal for unpredictable workloads
Ramp from zero to tens of thousands of requests
per second on demand
Pay only for what you use
Pay-per-request pricing

Amazon DynamoDB Transactions (GA)
Build internet-scale apps with ACID transactions
Simplify application
code with ACID
guarantees
Run transactions
for large scale
workloads
Accelerate legacy
migrations

Document

MongoDB-compatible
Fast, reliable, fully-managed MongoDB-compatible database
MongoDB 3.6 Same drivers,
tools
Migration with
DMS
Replica sets
Compatible with MongoDB
Community Edition 3.6
Use the same MongoDB
drivers and tools with
DocumentDB; as simple as
changing an application
connection string
Live migrations with
DMS; free for 6-months
Read scaling is easy with
automatic replica set
configurations

MongoDB Architecture
Sharded cluster scaling dramatically increases operational complexity
Availability Zone 1 Availability Zone 2 Availability Zone 3
reads / writes
Shard
Primary
Shard
Primary
Shard
Secondaries SecondariesSecondaries
Primary
Shard Router
replication
Shard Router Shard Router
Config Server Config ServerConfig Server
Shard Balancing

DocumentDB Architecture
Separate compute and storage provide 2x throughput of current MongoDB managed
services
reads
AWS Region
Availability Zone 1 Availability Zone 2 Availability Zone 3
Instance
(primary)
Cluster volume
Instance
(replica)
Instance
(replica)
Data copies Data copies Data copies
writes
reads
reads

In Memory

What’s New: Redis & Memcached
• Redis Streams
• SortedSets now have LIST capabilities (POP and BLOCK)
• HyperLogLogs has an optimized algorithm
• Speed Improvements (Jemalloc additions, etc.)
• Active Defragmentation
• Added In-line HELP command for redis-cli
• Native TLS Integration Redis (ElastiCache)
• M o r e a t h t t p s : / / a w s . a m a z o n . c o m / r e d i s / W h a t s _ N e w _ R e d i s 5
• Automated Slab rebalancing
• LRU crawler to background-reclaim memory
• Faster hash table lookups with murmur3 algorithm
 Redis 5.0
 Memcached 1.5.10

New: Amazon ElastiCache: In-Place Version Upgrades
• Upgrade a Redis Cluster to newer engine version in-place
• No Manual Steps or Application Changes
• Available for ElastiCache for Redis Version 3.2 and Higher

New: Amazon ElastiCache Optimized Instances M5 | R5
• Scale up to 9.5 TiB of in-
memory capacity
• AWS Nitro System,
dedicated hardware &
lightweight hypervisor,
delivers performance
indistinguishable from
bare metal
• Custom Intel Xeon
Scalable processors up to
3.1 GHz & AVX-512

When 9.5 TiB is not enough!
Example 1:
• Assume 125 shards made of 1 Primary + 1 Replica = 250 nodes
• Assume R5.24xlarge ( 635.61 GiB )
• Cluster memory 635.61 GiB X 125 = ~80 TiB = ~88 TB
Example 2:
• Assume 250 shards made of 1 Primary + 0 Replica = 250 nodes
• Assume R5.24xlarge ( 635.61 GiB )
• Cluster memory 635.61 GiB X 250 = ~159 TiB = ~170 TB
New: Amazon ElastiCache 250 node support

Coming soon
PERFORMANCE BOOST FOR MULTI-CORE NODES
Further optimizations providing significant throughput boost
RENAME COMMAND SUPPORT
Ability to rename a command
SELF-SERVICE PATCHING
Provides increased flexibility to control when updates occur

Ledger

Common customer use cases
Ledgers with centralized control
Healthcare
Verify and track providers
and insurance
Manufacturers
Track distribution of a
recalled product
HR & Payroll
Track changes to an
individual’s profile
Government
Track vehicle title
history

Challenges with building ledgers
Adds unnecessary
complexity
BlockchainRDBMS - audit tables
Difficult to
maintain
Hard to use
and slow
Hard to build
Custom audit functionality using
triggers or stored procedures
Impossible to verify
No way to verify changes
made to data by sys admins

Amazon Quantum Ledger Database (QLDB) (Preview)
Fully managed ledger database
Track and verify history of all changes made to your application’s data
Immutable
Maintains a sequenced record of
all changes to your data, which
cannot be deleted or modified;
you have the ability to query
and analyze the full history
Cryptographically
verifiable
Uses cryptography to
generate a secure output
file of your data’s history
Easy to use
Easy to use, letting you
use familiar database
capabilities like SQL APIs
for querying the data
Highly scalable
Executes 2–3x as many
transactions than ledgers
in common blockchain
frameworks

ID Manufacturer Model Year VIN Owner
ID Version Start End Manufacturer Model Year VIN Owner
How it works
1 Tesla Model S 201
2
12345678
9
Traci Russell
INSERT INTO cars <<
{ 'Manufacturer': 'Tesla',
'Model': 'Model S',
'Year': '2012',
'VIN': '123456789',
'Owner': 'Traci Russel' }
>>
FROM cars WHERE VIN = '123456789' UPDATE owner = 'Ronnie Nash'FROM cars WHERE VIN = '123456789' UPDATE owner = 'Elmer Hubbard'
J
1 1 07/16/201
2
NULL Tesla Model S 201
2
12345678
9
Traci Russell
current.cars
C
history.cars
H ID Version Start End Manufacturer Model Year VIN Owner
1 1 07/16/201
2
08/03/201
3
Tesla Model S 201
2
12345678
9
Traci Russell
1 2 08/03/201
3
2
12345678
9
Ronnie Nash
1 1 07/16/201
2
08/03/201
3
Tesla Model S 201
2
12345678
9
Traci Russell
1 2 08/03/201
3
09/02/201
6
Tesla Model S 201
2
12345678
9
Ronnie Nash
1 3 09/02/201
6
2
12345678
9
Elmer Hubbard
1 Tesla Model S 201
2
12345678
9
Ronnie Nash
1 Tesla Model S 201
2
12345678
9
Elmer Hubbard
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
H (x) UPDATE cars
ID:1
Owner: Ronnie Nash
Metadata: {
Date:08/03/2013
}
H (x) UPDATE cars
ID:1
Owner: Elmer Hubbard
Metadata: {
Date: 09/02/2016
}
H (x)

Time series

Amazon Timestream (sign up for the preview)
Fast, scalable, fully managed time series database
1,000x faster and 1/10th
the cost of relational
databases
Collect data at the rate of
millions of inserts per
second (10M/second)
Trillions of
daily events
Adaptive query processing
engine maintains steady,
predictable performance
Analytics optimized
for time series data
Built-in functions for
interpolation, smoothing,
and approximation
Serverless
Automated setup,
configuration, server
provisioning, software patching

What’s New in AWS Database Services

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