AWS re:Invent 2016: Deep Dive on Amazon Relational Database Service (DAT305)
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Transcript of AWS re:Invent 2016: Deep Dive on Amazon Relational Database Service (DAT305)
![Page 1: AWS re:Invent 2016: Deep Dive on Amazon Relational Database Service (DAT305)](https://reader030.fdocuments.us/reader030/viewer/2022021500/586f90551a28ab54768b7935/html5/thumbnails/1.jpg)
© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
November 30, 2016
Deep Dive on Amazon
Relational Database Service
Scott Ward, Solutions Architect – Amazon Web Services
KD Singh, Solutions Architect – Amazon Web Services
DAT305
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No infrastructure
management
Scale up/down
Cost-effective
Instant provisioning
Application
compatibility
Amazon Relational Database Service (Amazon RDS)
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Trade-offs with a managed service
Fully managed host and OS
• No access to the database host operating system
• Limited ability to modify configuration that is managed on the
host operating system
• No functions that rely on configuration from the host OS
Fully managed storage
• Max storage limits
• Microsoft SQL Server—4 TB
• MySQL, MariaDB, PostgreSQL, Oracle—6 TB
• Aurora—64 TB
• Growing your database is a process
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Amazon RDS engines
Commercial Open source Cloud native
MySQL Compatible
PostgreSQL Compatible
Amazon
Aurora
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Security
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Amazon Virtual Private Cloud (Amazon VPC)
Securely control network configuration
Availability Zone
AWS Region
10.1.0.0/16
10.1.1.0/24Manage connectivity
AWS Direct
ConnectVPN
connection
VPC
peering
Internet
gateway
Routing
rules
M
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Security groupsDatabase IP firewall protection
Protocol Port Range Source
TCP 3306 172.31.0.0/16
TCP 3306 “Application
security group”
Corporate address admins
Application tierM
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AWS IAM governed access
You can use AWS Identity and Access Management (IAM)
to control who can perform actions on RDS
Users and DBAApplicationsDBA and Ops
Your database RDS
Controlled with IAMControlled with database grants
M
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Compliance
Singapore MTCS
27001/9001
27017/27018
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Compliance
AuroraSOC 1, 2, 3
ISO 20001/9001
ISO 27107/27018
PCI
HIPAA BAA
MySQLSOC 1, 2, 3
ISO 20001/9001
ISO 27107/27018
PCI
FedRamp
HIPAA BAA
UK Gov. Programs
Singapore MTCS
OracleSOC 1, 2, 3
ISO 20001/9001
ISO 27107/27018
PCI
FedRamp
HIPAA BAA
UK Gov. Programs
Singapore MTCSMariaDBSOC 1, 2, 3
ISO 20001/9001
ISO 27107/27018
PCI
PostgreSQLSOC 1, 2, 3
ISO 20001/9001
ISO 27107/27018
PCI
UK Gov. Programs
Singapore MTCS
HIPAA BAA
SQL ServerSOC 1, 2, 3
ISO 20001/9001
ISO 27107/27018
PCI
UK Gov. Programs
Singapore MTCS
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SSL
Available for all six engines
Database traffic encryption
M
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At Rest Encryption
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Transparent Data Encryption
AWS
CloudHSM
M
Oracle
Wallet
M
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At Rest Encryption for all RDS Engines
AWS Key Management Service (KMS)
Two-tiered key hierarchy using envelope encryption:
• Unique data key encrypts customer data
• AWS KMS master keys encrypt data keys
• Available for ALL RDS engines
Benefits:
• Limits risk of compromised data key
• Better performance for encrypting large data
• Easier to manage small number of master keys
than millions of data keys
• Centralized access and audit of key activity
Data key 1 Data key 2 Data key 3 Data key 4
Custom
application
Customer master
key(s)
Amazon
RDS
instance 3
Amazon
RDS
instance 2
Amazon
RDS
instance 1
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Your RDS instance
+
Data key Encrypted data key
Encrypted
data
Master key(s) in
customer’s account
AWS KMS
1. Launch your RDS instance
2. RDS instance requests encryption key to use to encrypt data, passes reference to master key in account
3. Client request authenticated based on permissions set on both the user and the key
4. A unique data encryption key is created and encrypted under the KMS master key
5. Plaintext and encrypted data key returned to RDS
6. Plaintext data key stored in memory and used to encrypt/decrypt RDS data
How keys are used to protect your data
M
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Enabling encryption
AWS Command Line Interface (AWS CLI)
aws rds create-db-instance --region us-west-2 --db-instance-identifier sg-cli-test \
--allocated-storage 20 --storage-encrypted \
--db-instance-class db.m4.large --engine mysql \
--master-username myawsuser --master-user-password myawsuser
aws rds create-db-instance --region us-west-2 --db-instance-identifier sg-cli-test1 \ --
allocated-storage 20 --storage-encrypted --kms-key-id xxxxxxxxxxxxxxxxxx \ --db-
instance-class db.m4.large --engine mysql \ --master-username myawsuser --master-user-
password myawsuser
Console
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Amazon RDS + AWS KMS useful tips
• You can only encrypt on new database creation
• Encryption cannot be removed
• Master and read replica must be encrypted
• Unencrypted snapshots can be turned into encrypted
snapshots
• Cannot restore MySQL to Aurora or Aurora to MySQL
• Cannot copy encrypted snapshots or replicate encrypted DB
across regions
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Metrics and monitoring
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Standard monitoringAmazon CloudWatch
metrics for Amazon RDS
CPU utilization
Storage
Memory
Swap usage
DB connections
I/O (read and write)
Latency (read and write)
Throughput (read and write)
Replica lag
Many more
Amazon CloudWatch Alarms
Similar to on-premises custom
monitoring tools
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Enhanced Monitoring
Access to over 50 new CPU, memory, file system, and disk I/O metrics as
low as 1 second intervals
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Simplify monitoring with AWS Management Console
Amazon Performance Insights
for RDS
Database Load : Identifies
database bottlenecks
Easy
Powerful
Identifies source of bottlenecks
Top SQL
Adjustable Time frame
Hour, day, week and longer
Max CPU
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High availability
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Availability Zone A
AWS Region
10.1.0.0/16
10.1.1.0/24
Availability Zone B
10.1.2.0/24
Synchronous Replication
M S
Single
Availability Zone
Configuration
HA Multi Availability Zone Configuration
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High availability—Multi-AZ to DNS
Availability Zone A
AWS Region
Availability Zone B
M S
DNS
mydatabase.us-west-2.rds.amazonaws.com:3306
M
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Read Replicas
Bring data close to your customer’s
applications in different regions
Relieve pressure on your master
node for supporting reads and
writes
Promote a Read Replica to a
master for faster recovery in the
event of disaster
Within or cross-region
• MySQL
• MariaDB
• PostgreSQL
• Aurora
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High availability—Amazon Aurora storage• 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 Amazon 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
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High availability—Amazon Aurora nodes• Aurora cluster contains primary
node and up to 15 secondary
nodes
• Failing database nodes are
automatically detected and
replaced
• Failing database processes are
automatically detected and recycled
• Secondary nodes automatically
promoted on persistent outage, no
single point of failure
• Customer application can scale out
read traffic across secondary nodes
AZ 1 AZ 3AZ 2
Primary
NodePrimary
NodePrimary
Node
Primary
NodePrimary
NodeSecondary
Node
Primary
NodePrimary
NodeSecondary
Node
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Scaling on RDS
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Why Scale?
• Handle higher load or lower usage
• Naturally grow over time
• Control costs
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What can I scale?
Database Instance
Read Replicas
Storage
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Amazon Aurora - Balanced Read Replica
Access
AZ 1 AZ 2 AZ 3
Aurora Primary
instanceAurora Replica Aurora Replica
Read Replica Endpoint
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Scaling your instance up/downAWS Management Console
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Scaling—single Availability Zone
Availability Zone A
AWS Region
M S
DNS
mydatabase.us-west-2.rds.amazonaws.com:3306
MM
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Scaling—Multi-AZ
Availability Zone A
AWS Region
Availability Zone B
M S
DNS
mydatabase.us-west-2.rds.amazonaws.com:3306
SS M
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Scaling—automation
AWS CLI
Scheduled CLI—cron
aws rds modify-db-instance --db-instance-identifier sg-cli-test --db-instance-class
db.m4.large --apply-immediately
#Scale down at 8:00 PM on Friday
0 20 * * 5 /home/ec2-user/scripts/scale_down_rds.sh
#Scale up at 4:00 AM on Monday
0 4 * * 1 /home/ec2-user/scripts/scale_up_rds.sh
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Scaling—automation
Scheduled—AWS Lambda
No server but still runs on a schedule!
import boto3
client=boto3.client('rds')
def lambda_handler(event, context):
response=client.modify_db_instance(DBInstanceIdentifier='sg-cli-test',
DBInstanceClass='db.m4.xlarge',
ApplyImmediately=True)
print response
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Scaling—automation
Metrics-based scaling
• Amazon CloudWatch and AWS Lambda!
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Scaling—automationimport boto3
import json
client=boto3.client('rds')
def lambda_handler(event, context):
message = event['Records'][0]['Sns']['Message']
parsed_message=json.loads(message)
db_instance=parsed_message['Trigger']['Dimensions'][0]['value']
print 'DB Instance: ' + db_instance
response=client.modify_db_instance(DBInstanceIdentifier=db_instance,
DBInstanceClass='db.m4.large',
ApplyImmediately=True)
print response
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Backups and snapshots
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Automated BackupsMySQL, PostgreSQL, MariaDB, Oracle, SQL Server• Scheduled daily volume backup of entire instance
• Archive database change logs
• 35-day retention
• Multiple copies in each AZ when running multi-AZ
• Taken from standby when running multi-AZ
Aurora• Automatic, continuous, incremental backups
• No impact on database performance
• 35-day retention
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How do automated RDS backups work?
If database is Multi-AZ, the snapshot is taken
from the standby
= LatestRestorableTime
Every day during your backup window,
the RDS service creates a storage
volume snapshot of your database
Every five minutes, RDS backs up the
transaction logs of your database
Single AZ deployment = multiple backup copies in one AZ
Multi AZ deployment = multiple backup copies in multiple AZs
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Restoring
• Creates an entire new database instance
• You define all the instance configuration, just like
creating a new instance
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Snapshots
• Full copies of your RDS database
• Independent of scheduled backups
• Used to create a new RDS instance
• Taken from the standby when running multi-AZ
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Migrating onto RDS
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MySQL Backup to Aurora via S3
Source MySQL
DatabaseTarget Aurora
Database
Amazon S3
Use multi-part upload or S3
Transfer Acceleration for faster
uploads
AWS Import/Export Snowball
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MySQL Backup to Aurora via S3
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SQL Server Backup to RDS SQL Server via S3
Source SQL Server
DatabaseTarget RDS
Database
Amazon S3
Use multi-part upload or S3
Transfer Acceleration for faster
uploads
AWS Import/Export Snowball
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SQL Server Backup to RDS SQL Server via S3
Importing to RDS
No Encryption
Encryption
exec msdb.dbo.rds_restore_database
@restore_db_name=‘database_name’,
@s3_arn_to_restore_from=‘arn:aws:s3:::bucket_name/file_name_and_extension’;
exec msdb.dbo.rds_restore_database
@restore_db_name=‘database_name’,
@s3_arn_to_restore_from=‘arn:aws:s3:::bucket_name/file_name_and_extension’,
@kms_master_key_arn=‘arn:aws:kms:region:account-id:key/key-id’;
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Move data to the same or different database engine
Keep your apps running during the migration
Start your first migration in 10 minutes or less
Replicate within, to, or from Amazon EC2 or RDS
AWS Database
Migration Service
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Migrate from Oracle and SQL Server
Move your tables, views, stored procedures,
and data manipulation language (DML) to
MySQL, MariaDB, and Aurora
Highlight where manual edits are neededAWS Schema
Conversion Tool
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Related Sessions
DAT 303 – Deep Dive on Amazon Aurora
DAT 320 – AWS Database State of the Union
DAT 301 – Amazon Aurora Best Practices
DAT 302 - Best Practices for Migrating from Commercial
Database Engines to Amazon Aurora or PostgreSQL
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Thank you!
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Remember to complete
your evaluations!