2. Scale up
Pedro Machado – Soft. Developer
pedro.machado@imobilemagic.com

3. How To Scale
• Vertical Scaling (Scale Up)
• Upgrade the server hardware
• Throw in more RAM, faster disk(s), CPU, etc
• Horizontal Scaling (Scale Out)
• Spin up another server instance

4. Vertical Scaling
• Advantages
• No code changes required
• Easy and fast to do – AWS
• No need to manage multiple machines
• Disadvantages
• Expensive
• Single point of failure
• Vendor lock-in

5. Horizontal Scaling
• Advantages
• Continuous availability/redundancy
• Continuous upgrades, with no downtime
• Better perfomance
• Disadvantages
• May required code/architectural changes
• Manage multiple machines
• Developing distributed systems can be complex

6. Before Scaling
• Cache, cache and cache!
• AWS – ElastiCache, Memcached, Redis
• Avoid unnecessary queries
• Tune your server / application
• Use indexes, wisely
• EXPLAIN SELECT * FROM Table WHERE name = ‘John’;
• Find and fix your bottlenecks

7. Database Scaling
• Sharding :
• Distribute table rows onto different servers
• Example: Customer table -> US customers are on ServerUS, EU customers on
ServerEU
• Partitioning
• Master-slave
• Inserts and Updates go to the master, reads go to the slave
• The single Master server is a limitation to scalability. Can quickly create a
bottleneck.
• Near real time – if master dies data may be lost.

8. Server Scaling – Microservices

9. Server Scaling – Microservices
• The pros
• A service should only do one thing and do it well
• A service is fully independent - self contained
• Has it’s own database type and tech stack (language agnostic)
• Fast deploys, tiny EAR = super fast deploy
• Loosely coupled - communicate asynchronously via messages/events,
queues or REST (sync)
• Self monitoring, e.g. Circuit Breaker, Bulkhead
• Greater resiliency – fault isolation
• Independent dev teams

10. Server Scaling – Microservices
• The cons
• Service discovery
• Distributed services are complex to deal with
• Fan out of requests
• 1 request in a monolithic application can be equal to multiple requests in a
microservices architecture
• Serialization overhead
• Latency

11. General Tips
• Monitor your server resources
• Set and configure alarms
• Elastic scaling
• Use AWS auto-scaling feature in conjunction with CloudWatch
• When X metric is above the limit-> spin up another instance
• When X metric is back to normal values -> kill an instance to save up costs
• https://www.youtube.com/watch?v=7SfVZqOVcCI