The document discusses scaling relational and NoSQL databases on AWS. It provides an overview of AWS RDS features for scaling databases, including read replicas, multi-AZ deployments, and maintenance windows. It also discusses database replication strategies like synchronous and asynchronous replication. For scaling write-intensive databases, it recommends database sharding and introduces AWS DynamoDB as a NoSQL option that scales for both reads and writes. Streams in DynamoDB enable triggers on row updates.

1. AWSome Day 2018
Scale-out your Database
AWSome Day - Torino 2018

2. Walter Dal Mut
Solution Architect - AWS Certified
SysOps Administrator - AWS Certified
@walterdalmut
walter.dalmut@corley.it
corley.it

3. Scalability and cost optimization with Container Services
RDS
Replication
DynamoDB
Summary

4. AWS RDS
Relational Database Service
(Oracle/Mysql/Postgres/etc…)

5. Interesting features
➢ Read-replicas (1 click)
➢ Multi-AZ deployments (1 click)
➢ Configuration versioning
➢ Scale-up & reconfiguration (1 click)
➢ Maintenance Windows
➢ Continuous Data Protection

6. Why we are interested in Scale-Out databases?

8. We need more resources
➢ Single hard drives has limits
➢ Networks too
➢ and so on...

9. We want to ensure
SERVICE availability
NOT SERVER
availability

10. Relational Databases works
(typically) with replication,
which kind of replications?

12. How many kinds of
replications?

13. Synchronous Replication
➢ a process that writes data to the
primary and secondaries at the SAME
time
○ expensive process
○ ensure data consistency

14. Asynchronous Replication
➢ a process that writes data to the
primary and on all secondaries at
different times
○ LAG (measure data discordance)

15. Thanks to RDS we can achieve
both Sync and Async
replication strategies with one
click
➢ Different Availability Zones
➢ Different Regions (read-replicas)
➢ API First
➢ So many scenarios for your processes

16. Scale with read-replicas

17. Read-Replicas

18. Increase Read Capacity

19. Here a real example

20. Here a real example

21. And write intensive?

22. And write intensive?

23. And write intensive?

24. It works?

25. Yes?

28. How can i scale writes?

29. We need to look into database
shardings
➢ Primary key selection
➢ Scale-out writes
➢ Scale-out reads
➢ many other things...

30. AWS DynamoDB
NoSQL databases

31. NoSQL
var documentClient = new AWS.DynamoDB.DocumentClient();
var params = {
Item: {
hashkey: 'hashkey'
numbers: documentClient.createSet([1, 2, 3]);
}
};
documentClient.put(params, (err, data) => { /** .. */ });

32. Database shards

33. DynamoDB - Scaling for Reads
and Writes
➢ Select index read/write speed
➢ Pay only for index read/write speed
➢ Pay only for your database size
➢ Autoscaling on your index speed to
support cost-saving

34. Streams to support data more
operations!
➢ On row updates we can engage
triggers
➢ A trigger can be anything like:
Lambda, SNS, and so many other
things

35. Stream notification enable so many new scenarios

36. Thanks for listening.
AWSome Day - Torino 2018 - walter.dalmut@corley.it