AWS customers have been asking us for Amazon RDS for PostgreSQL, and we’re excited to announce its immediate availability. Learn how you can offload the management of your PostgreSQL database instances to Amazon RDS using automated backups and point-in-time recovery, Multi-AZ deployments for high availability, and provisioned IOPS for fast and predictable performance. Also learn how to take advantage of familiar PostgreSQL features such as PostGIS with Amazon RDS for PostgreSQL.

1. DAT210 – Introducing Amazon RDS for PostgreSQL
Srikanth Deshpande - Senior Product Manager, AWS
Nick Hertl – Software Development Manager, AWS
Gabe Arnett – Senior Director, Moody’s Analytics
November 14, 2013
© 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.

2. Amazon Relational Database Service
RDS is a managed relational database service that is simple to deploy,
easy to scale, reliable, and cost-effective
Choice of Database Engines
Managed Service
Easy to Scale and Operate
High Performance
High Availability
Amazon Relational Database Service (RDS)

3. Backups and Disaster Recovery

4. Push-Button Scaling

5. Multi-AZ Deployments

6. Security
Internet
VPC
IAM

7. DB Parameter Groups
Filter=“connection”
{DBInstanceClassMemory/12582880}

8. Amazon RDS for PostgreSQL
• Database version: PostgreSQL 9.3.1
• Includes valuable Amazon RDS functionality
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Fast deployment
Backups and point-in-time recovery
Snapshots and restore
Compute and storage scaling
Multi-AZ
Provisioned IOPS
8

9. Launching a Postgres DB Instance

10. Select Production Use (or not)

11. Instance Details

12. Additional Configuration

13. Management Options

14. Running Instance

15. Connecting

16. Permissions
superuser role (Postgres)
rds_superuser role (RDS provided)
Load and use extensions
View and kill sessions
Create tablespace
Assign replication role
…

17. Extensions
• PostGIS available
• rds.extensions parameter:
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btree_gin
btree_gist
chkpass
citext
cube
dblink
dict_int
dict_xsyn
earthdistance
fuzzystrmatch
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hstore
intagg
intarray
isn
ltree
pgcrypto
pgrowlocks
pg_trgm
plperl
plpgsql
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pltcl
postgis
postgis_tiger_geocoder
postgis_topology
sslinfo
tablefunc
tsearch2
unaccent
uuid-ossp

18. High Performance
16,500+ Read and 8,500+ Write = 25,000+ IOPS

19. Getting Started
• Launch an instance from AWS Management
Console
• Configure network
• Load extensions
• Export from existing database using pg_dump
• Import to RDS using pg_restore

20. RDS PostgreSQL
Gabe Arnett, Senior Director, Moody’s Analytics
November 14, 2013
© 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.

21. •
Moody’s Analytics offers unique tools and best practices for measuring and
managing risk through expertise and experience in credit analysis,
economic research, and financial risk management.
•
Product offerings include leading-edge software, advisory services, and
credit and economic research.
•
A subsidiary of Moody's Corporation (NYSE: MCO), which reported revenue
of $2.7 billion in 2012, employs approximately 7,200 people worldwide and
maintains a presence in 29 countries.

22. Legacy Platform
Calculation Engine

23. Job 1
(4x daily)
1x monthly
1x daily
Source 1
4x daily
App Server 1
Source 2
4x daily
Sybase 1
1x daily
Routine 1
EDF
Front End Db’s
(11 servers)
excl EJV
1x daily
Source 3
Routine 2
Sybase 2
Pasta,
anyone?
Data
Transfer
App
Routine 3
Source 4
1x daily
Legacy App Db
Routine 4
Oracle
Source 5
Source 6
Job 2
(4x daily)
Job 3
(1x daily)
Source 7
Sybase 3
App Server 2
Ftp server
MS SQL Server
Source 8
Job 4
(1x daily)
App Server 3
Vendor
Data
Engine /
Calculator
Standalone
C++ App
DataPortal

24. Overhaul

25. Summer Fun
External Data Sources
Write Master (AZ 1)
ETL Cluster
WAL
Read Replica 1
(Warm Standby AZ 2)
Cascading Replication
Calculation Engine
(reads from RR2 and writes results to WM)
Read Replica 2 (AZ 1)
Amazon Simple Storage
Service

26. Pros
• We learned a tremendous amount and could probably write a solid
blog post or whitepaper
• No cost other than infrastructure
• Support/maintenance tasks now very reasonable and can be done
with existing resources without incurring additional costs

27. Cons
• Lots of time spent finding the write configurations, trial and error,
testing and more testing
• I have to convince really talented Java, Python, and .NET
developers that they have to be PostgreSQL system admins
• We are an enterprise, and as such I have to have an enterprise level
of support

28. RDS PostgreSQL
External Data Sources
Write Master (Multi-AZ)
ETL Cluster
Snapshot Copy
future
Warm Standby
Region 2
Calculation Engine
(Reads from RR2 and writes results to WM)
Read Replica
Amazon Simple Storage

29. RDS PostgreSQL
External Data Sources
Write Master (Multi-AZ)
ETL Cluster
Snapshot Copy
future
Warm Standby
Region 2
Calculation Engine
(Reads from RR and writes results to WM)
Read Replica
Amazon Simple Storage

30. Why Amazon RDS PostgreSQL?
• Achieve the same performance as existing setup on Amazon EC2, if
not better, in a matter of minutes
• We get built-in backup/recovery/replication/fault tolerance/multi-AZ
• More robust operational support built in, and my developers can get
back to the business of development

31. Up and Running
Self-Managed(hours)
RDS PostgreSQL(minutes)
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Launch Amazon EC2 w/EBS
Mount and Raid0 Amazon EBS
Install PostgreSQL
Move data and logs
Edit .conf files
Create users
Load/Use DB
Create snapshot, and then…
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Add/Edit CIDR/IP block to security
group (pg_hba.conf)
Edit DB parameter group to apply
configuration settings
(postgresql.conf)
Launch RDS instance
Load/Use DB
Sit back and monitor or let
Amazon CloudWatch do it for us…

32. Backup/Retention
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Single-click backup policy upon creation
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No schedule to implement or forget
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Snapshots are easy to find
– All easily found in the AWS Management Console and searchable
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One-click restore to point in time = AWESOME!!!

33. Monitoring
•
Amazon CloudWatch metrics alongside instance details
– A challenge to find and consolidate all the EBS volumes + EC2 instances
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Logs are in the console
– Not fun to dig through the logs, assuming we actually had that kind of time
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Event subscriptions for faults
– Extra pro-active protection

34. Scale and Redundancy
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At launch, RDS PostgreSQL is multi-AZ enabled with a click
– We had to spin up a second instance and then configure WAL and hope and
pray
– Bit of configuration and tuning to get the correct performance for this without
impacting write performance and ensuring near real-time reads
– Lossless factor is a risk if the write master fails

35. Next
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Additional legacy data platforms
•
Extending PostgreSQL
– Developing key/value store for near real-time data ingestion
– Integrating with Solr
– Front end datamart
•
Redshift for BI use cases

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presentation
DAT210
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