The document outlines various strategies for choosing the right database service, emphasizing Amazon's managed database options, including DynamoDB, RDS, and Aurora. It highlights case studies from Japanese customers demonstrating performance improvements and cost reductions achieved through database consolidation and migration to Amazon Aurora. The content includes a comprehensive overview of database types, their use cases, access patterns, and best practices for optimizing database performance and management.

1. Choosing The Right Database Service
with Japanese customer cases
Sangpill Kim, Solutions Architect, AWS Korea
Yutaka Hoshino, Solutions Architect, AWS Japan
Time : 13:00 – 13:30

2. AGENDA
Afternoon Sessions
1:00 pm — 1:30 pm Choosing the Right Database Service Sangpil Kim, Yutaka Hoshino
1:30 pm — 2:50 pm Aurora Technical Deep Dive KiWaon Kim
2:50 am — 3:10 am Coffee Break
3:10 pm — 4:30 pm DynamoDB for Developers Andy Kim
4:30 pm — 5:50 pm Redshift Deep Dive Kevin Kim
5:50 pm — 6:00 pm Wrap-­up and Closing
Choosing the Right Database Service

3. Agenda
• Amazon Managed Database Services
• Choosing the Right Database Service
• Useful insights from Japaness Database service customer cases
-­ Aurora

4. If you host your databases on-premises
Power, HVAC, net
Rack and stack
Server maintenance
OS patches
DB s/w patches
Database backups
Scaling
High availability
DB s/w installs
OS installation
you
App optimization

5. If you host your databases on-premises
Power, HVAC, net
Rack and stack
Server maintenance
OS patches
DB s/w patches
Database backups
Scaling
High availability
DB s/w installs
OS installation
you
App optimization

6. If you host your databases in Amazon EC2
Power, HVAC, net
Rack and stack
Server maintenance
OS patches
DB s/w patches
Database backups
Scaling
High availability
DB s/w installs
OS installation
you
App optimization

7. If you host your databases in Amazon EC2
OS patches
DB s/w patches
Database backups
Scaling
High availability
DB s/w installs
you
App optimization
Power, HVAC, net
Rack and stack
Server maintenance
OS installation

8. If you choose a managed DB service
Power, HVAC, net
Rack and stack
Server maintenance
OS patches
DB s/w patches
Database backups
App optimization
High availability
DB s/w installs
OS installation
you
Scaling

9. Quick summary of the options
• Self-Managed - You are responsible for the hardware, OS, security, up
dates, backups, replication etc., but have full control over it.
• EC2 Instances - You only need to focus on the database level updates
, patches, replication, backups etc. and don’t have to worry about the
hardware or the OS installation.
• Fully Managed - You get features such as backup and replication etc.
as a package service and don’t have to bother with patching and upd
ates.

10. AWS Managed Database Options
Amazon DynamoDB
• NoSQL database
• Fully managed
• Single-­digit millisecond
latency
• Massive and seamless
scalability
• Low cost
Amazon RDS
• Relational databases
• Fully managed
• Predictable performance
• Simple and fast to scale
• Low cost, pay for what
you use
Aurora
Amazon Elasticache
• In-­memory key-­value store
• High-­performance
• Memcached and Redis
• Fully managed, zero admin
Amazon Redshift
• Relational data warehouse
• Massively parallel;; petabyt
e scale
• Fully managed
• HDD and SSD platforms
• $1,000/TB/year;; starts at $
0.25/hour

11. Database + Search Tier Anti-pattern
RDBMS
Database + Search Tier
Applications

12. Best Practice - Use the Right Tool for the Job
Data Tier
Search
Amazon Elasticsearch
Amazon CloudSearch
Cache
Redis
Memcached
SQL
Amazon Aurora
MySQL
PostgreSQL
Oracle
SQL Server
NoSQL
Cassandra
Amazon DynamoDB
HBase
MongoDB
Applications
Database + Search Tier

13. Materialized Views

14. What Data Store Should I Use?
• Data structure : Fixed schema / JSON / key-value
• Access patterns : Store data in the format you will access it
• Data / access characteristics : Hot / Warm / Cold
• Cost : Right cost

15. Data Structure and Access Patterns
Access Patterns What to use?
Put/Get (Key, Value) Cache, NoSQL
Simple relationships → 1:N, M:N NoSQL
Cross table joins, transaction, SQL SQL
Faceting, Search Search
Data Structure What to use?
Fixed schema SQL, NoSQL
Schema-free (JSON) NoSQL, Search
Key, Value Cache, NoSQL

16. What Is the Temperature of Your Data / Access ?

17. Hot Warm Cold
Volume MB–GB GB–TB PB
Item size B–KB KB–MB KB–TB
Latency ms ms, sec min, hrs
Durability Low–High High Very High
Request rate Very High High Low
Cost/GB $$-$ $-¢¢ ¢
Hot Data Warm Data Cold Data
Data / Access Characteristics: Hot, Warm, Cold

18. Cache
SQL
Request Rate
High Low
Cost/GB
High Low
Latency
Low High
Data Volume
Low High
Glacier
Structure
NoSQL
Hot Data Warm Data Cold Data
Low
High
Search
What Data Store Should I Use?

19. Amazon
ElastiCache
Amazon
DynamoDB
Amazon
Aurora
Amazon
Elasticsearch
Amazon
EMR (HDFS)
Amazon
S3
Amazon
Glacier
Average
latency
ms ms ms, sec ms,sec sec,min,hrs ms,sec,min
(~ size)
hrs
Data
volume
GB GB–TBs
(no limit)
GB–TB
(64 TB
Max)
GB–TB GB–PB
(~nodes)
MB–PB
(no limit)
GB–PB
(no limit)
Item size B-KB KB
(400 KB max)
KB
(64 KB)
KB
(1 MB max)
MB-GB KB-GB
(5 TB max)
GB
(40 TB max)
Request rate High -
Very High
Very High
(no limit)
High High Low – Very High Low –
Very High
(no limit)
Very Low
Storage cost
GB/month
$$ ¢¢ ¢¢ ¢¢ ¢ ¢ ¢/10
Durability Low - Moderate Very High Very High High High Very High Very High
Hot Data Warm Data Cold Data
What Data Store Should I Use?

20. Cost Conscious Design – Example : DynamoDB vs. S3?
“I’m currently scoping out a project that will greatly increase my team’s u
se of Amazon S3. Hoping you could answer some questions. The current
iteration of the design calls for many small files, perhaps up to a billion
during peak. The total size would be on the order of 1.5 TB per month
…” Request rate
(Writes/sec)
Object size
(Bytes)
Total size
(GB/month)
Objects per month
300 2,048 1,483 777,600,000
http://calculator.s3.amazonaws.com/index.html
SIMPLE MONTHLY CALCULATOR

21. Cost Conscious Design – Example : DynamoDB vs. S3?
Request rate
(Writes/sec)
Object size
(Bytes)
Total size
(GB/month)
Objects per month
300 2,048 1,483 777,600,000

22. Request rate
(Writes/sec)
Object size
(Bytes)
Total size
(GB/month)
Objects per month
Scenario 1 300 2,048 1,483 777,600,000
Scenario 2 300 32,768 23,730 777,600,000
Amazon S3
Amazon DynamoDB
use
use
Cost Conscious Design – Example : DynamoDB vs. S3?

23. Amazon Aurora among Japanese Customers
～Lessons from Case Studies～
Yutaka Hoshino (Solutions Architect)
Amazon Web Services Japan K.K.
Time : 13:00 – 13:30

24. http://grani.jp/
Getting performance from Amazon Aurora

25. Before
RDS for MySQL
MultiAZ + Read Replica
r3.4xlarge
After
Amazon Aurora
2 node (1 writer / 1 reader)
r3.4xlarge
Getting performance from Amazon Aurora

26. Aurora 3x faster than MySQL (Total)
Getting performance from Amazon Aurora

27. Update : 5x+ faster
Getting performance from Amazon Aurora

28. Insert : 2x+ faster
Getting performance from Amazon Aurora

29. We could reduce # of nodes by DB consolidation and improved ov
erall performance.
In Grani’s case, they achieve $10.6K cost saving in a year by DB
consolidation.
RDS (db.r3.4xlarge / gp2 /
OnDemand)
Hourly Daily Yearly
RDS for MySQL
(MultiAZ + 1 ReadReplica)
$4.54 + $2.27 = $6.81 $163.44 $59,655.60
Aurora (+ 1 Replica) $2.8 * 2 = $5.6 $134.40 $49,056.00
Reduction ▲$1.21 ▲$29.04 ▲$10,599.6
Getting performance from Amazon Aurora

30. http://reader.livedwango.com/
Consolidating complex databases to simple

31. •Live dwango reader(LDR) / LDR Pocket
• Online RSS Reader for PC and Smartphone
• 9 years old service
• Very old source code…
•A bunch of data in MySQL servers
•Difficulties in touching the old source codes
Consolidating complex databases to simple

32. DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
DB on
Instance
• 5 shards to store article data
• Aurora will achieve 5x throughput
• Consolidate to Single Aurora cluster !!
Aurora
Consolidating complex databases to simple

33. •Before
• Data Size: 5TB
• EBSs: 500GiB * 5, 800GiB * 10
• DBs: master * 7 instances, slave * 8 instances (All r3.large)
•After
• Aurora(r3.xlarge) * 2 instances
• No code change!!
•Cost reduction
• $24,000/year (estimated)
Consolidating complex databases to simple

34. The Mainichi Newspapers Co.,Ltd
http://mainichi.jp/english/
Wire compatibility with MySQL5.6

35. • Full migration from on-­prem to AWS
• All contents data are stored into Aurora
• At first, they planed to use RDS MySQL
• Change RDS MySQL to Aurora two week
s before launch without code and configur
ation change
• Fast failover
• Stability
• Fault tolerance
• Cost reduction
Wire compatibility with MySQL5.6

36. All contents data are stored into Aurora
Wire compatibility with MySQL5.6

37. • Good cost performance
• Reduce cost compared to MySQL
• RDS MySQL: # of instance * storage (provisioned IOPS and
storage size in advance)
• Aurora: 1 Aurora cluster needs single storage price (pay as
you go)
• Wire compatibility with MySQL5.6
Wire compatibility with MySQL5.6

38. Thank you!