Developers and DBAs from a traditional relational background are spoilt for choice when looking to integrate caching and NoSQL into an application architecture to solve scaling problems and reduce costs. Even when using relational databases there are 3 managed database services on AWS for the MySQL engine alone. Trying to evaluate all the options often creates analysis paralysis, resulting in a reluctance to try something new or different. This session will guide you through a series of use cases that use different databases to solve business problems that customers face today.

3. Databases on AWS
Relational, NoSQL and Caching
Russell Nash
AWS Solutions Architect

4. LoggingAnalytics
Webscale
Throughput
Rich Search
Hot Reads
Complex Queries
and Transactions
The Database

5. LoggingAnalytics
Webscale
Throughput
Rich Search
Hot Reads
Complex Queries
and Transactions
Amazon
DynamoDB
Amazon
RDS
Amazon
Elasticache
Amazon
S3
Amazon
Redshift
Amazon
Elasticsearch
The Data Tier

6. Analytics Webscale
Throughput
Complex Queries
and Transactions
Amazon
DynamoDB
Amazon
RDS
Amazon
Redshift
The Data Tier
Amazon
Elasticache
Hot Reads

7. Power, HVAC, net
Rack & stack
Server maintenance
OS patches
DB s/w patches
Database backups
Scaling
High availability
DB s/w installs
OS installation
Query Construction
Query Optimisation
Schema Design
Traditional
DC
Why Managed Databases?

8. Power, HVAC, net
Rack & stack
Server maintenance
OS patches
DB s/w patches
Database backups
Scaling
High availability
DB s/w installs
OS installation
Query Construction
Query Optimisation
Schema Design
Power, HVAC, net
Rack & stack
Server maintenance
OS patches
DB s/w patches
Database backups
Scaling
High availability
DB s/w installs
OS installation
Query Construction
Query Optimisation
Schema Design
DB
on EC2
Why Managed Databases?
Traditional
DC

9. Power, HVAC, net
Rack & stack
Server maintenance
OS patches
DB s/w patches
Database backups
Scaling
High availability
DB s/w installs
OS installation
Query Construction
Query Optimisation
Schema Design
Power, HVAC, net
Rack & stack
Server maintenance
OS patches
DB s/w patches
Database backups
Scaling
High availability
DB s/w installs
OS installation
Query Construction
Query Optimisation
Schema Design
Power, HVAC, net
Rack & stack
Server maintenance
OS patches
DB s/w patches
Database backups
Scaling
High availability
DB s/w installs
OS installation
Query Construction
Query Optimisation
Schema Design
Managed
Database
Why Managed Databases?
DB
on EC2
Traditional
DC

10. Traditional Database
Choice of Engines
Low Admin
Amazon
RDS
(Relational
Database
Service)
Amazon Aurora

11. Automated Backups
DB Snapshots
1
2
Backup & Restore

12. Default Metrics and Enhanced Monitoring
Alarms &
notifications
1
2
Monitoring and Alerting

13. AZ1 AZ2
Multi-AZ

14. Physical
Synchronous
Replication
AZ1 AZ2
Multi-AZ

15. Physical
Synchronous
Replication
AZ1 AZ2
Multi-AZ

16. AZ1 AZ2
Multi-AZ

17. AZ1 AZ2
Multi-AZ

18. AZ1 AZ2
DNS
Primary
Update
Multi-AZ

19. AZ1 AZ2
DNS
cname update
Primary
Update
Multi-AZ

20. AZ1 AZ2
DNS
cname update
Primary
Update
Multi-AZ

21. • Compute • Network • Storage
Instance Class
• Memory
Storage Type
Performance

22. Magnetic
(HDD)
Provisioned
IOPS
(SSD)
General
Purpose
(SSD)
RDS Storage Types

23. Sync
Replication
Multi-AZ
Read Replicas

24. Async Replication
Read Replicas

25. Read Replicas
Async Replication

26. “In a physical data center, I would need at least 3
administrators to maintain the infrastructure and ensure
similar levels of availability”
- Richard Glew, CTO

27. Analytics Webscale
Throughput
Complex Queries
and Transactions
Amazon
DynamoDB
Amazon
RDS
Amazon
Redshift
The Data Tier
Amazon
Elasticache
Hot Reads

28. MPP SQL Database
Optimised for analytics
Gigabytes to petabytes
Fully relational
Amazon
Redshift

29. JDBC/ODBC
Massively Parallel

30. ID Name
1 John Smith
2 Jane Jones
3 Peter Black
4 Pat Partridge
5 Sarah Cyan
6 Brian Snail
1 John Smith
4 Pat Partridge
2 Jane Jones
5 Sarah Cyan
3 Peter Black
6 Brian Snail
Massively Parallel
JDBC/ODBC

31. • Column storage
• Data compression
• Zone maps
ID Age State Amount
123 20 QLD 500
345 25 WA 250
678 40 NSW 125
957 37 WA 375
Reduces I/O
Row storage
Have to read the
entire row

32. Column storage
Only read the
data you need
• Column storage
• Data compression
• Zone maps
Reduces I/O
ID Age State Amount
123 20 QLD 500
345 25 WA 250
678 40 NSW 125
957 37 WA 375

33. • Column storage
• Data compression
• Zone maps
2-4 x
Reduces I/O

34. • Column storage
• Data compression
• Zone maps
Reduces I/O
02-JAN-2016
04-JAN-2016
07-JAN-2016
08-JAN-2016
09-JAN-2016
10-JAN-2016
15-JAN-2016
21-JAN-2016
22-JAN-2016
29-JAN-2016
MIN: 02-JAN-2016
MAX: 09-JAN-2016
MIN: 10-JAN-2016
MAX: 29-JAN-2016
SELECT AVG(AMOUNT) WHERE SALES_DATE = 22-JAN-2016

35. “When our analysts first started to do queries on
Amazon Redshift they thought it was broken
because it was working so fast”
- FT CTO John O’Donovan

36. “We intend to grow 100x in terms of
data size in the next few years”
- Kaushik Paranjape, CTO

37. Analytics Webscale
Throughput
Complex Queries
and Transactions
Amazon
DynamoDB
Amazon
RDS
Amazon
Redshift
The Data Tier
Amazon
Elasticache
Hot Reads

38. Scalability
Data complexity
RDBMS
key/value
document
graph
NoSQL

39. ID Age State
123 20 CA
345 25 WA
678 40 FL
Relational Table
ID Attributes
123 Age:20, State:CA
345 Age:25, Country: Australia, Gender: F, Smoker: No
678 Age:40
Non-Relational Table
Relational vs non-relational

40. NoSQL database
Key/Value + document
Very low latency
Fully managed
Amazon
DynamoDB

41. Low Latency

42. WRITES
Continuously replicated to 3 AZ’s
Persisted to disk (custom SSD)
READS
Strongly or eventually
consistent
Durability

43. Throughput
Reads
Writes

44. DynamoDB Local
44
Disconnected development with full API support
• No network
• No usage costs
• No SLA

45. “I was extremely comfortable running
our business-critical systems in an
AWS architecture.”
- Harry Teng CIO

46. Analytics Webscale
Throughput
Complex Queries
and Transactions
Amazon
DynamoDB
Amazon
RDS
Amazon
Redshift
The Data Tier
Amazon
Elasticache
Hot Reads

47. In-Memory Store
High-performance
Memcached and RedisAmazon
ElastiCache

48. Redis
Powerful
~200 commands + Lua scripting
In-memory database
Utility data structures
strings, lists, hashes, sets, sorted sets,
bitmaps & HyperLogLogs
Simple
Atomic operations
supports transactions
has ACID properties
Ridiculously fast!
<500microsecond latency for
most commands
Highly Available
replication
Persistent
snapshots or append-only log
Open Source
48

49. Memcached
Slab allocator
In-memory key-value datastore
Supports strings, objects
Multi-threaded
Insanely fast!
Very established
No persistence
Patterns for sharding

50. • Caching layer for performance or cost optimization
• Storage of ephemeral key-value data
• leader-boards (for gaming users),
• session management,
• event counters,
• in-memory lists.
Popular Use Cases

51. Elastic Load
Balancing
EC2 App
Instances
RDS MySQL
DB Instance
ElastiCache
Database Writes
App
Reads
Clients
Cache
Updates
Database Reads
Caching Architecture

52. “The latency of a cab call must be low, and remain low even
in times of peak traffic of hundreds of thousands of cab
requests per minute.“
Ryan Ooi
Sr. Devops Engineer, Grab

53. Analytics Webscale
Throughput
Complex Queries
and Transactions
Amazon
DynamoDB
Amazon
RDS
Amazon
Redshift
The Data Tier
Amazon
Elasticache
Hot Reads

54. Visit bit.ly/aws-tech-chat
or download via SoundCloud or iTunes