There are two main types of scaling: vertical scaling involves upgrading hardware on a single server, while horizontal scaling involves adding more servers. Before scaling, strategies like caching and optimizing queries can improve performance. When scaling databases, options include sharding data across multiple servers and using master-slave replication. Server scaling with microservices breaks applications into independent, communicating components. General tips for scaling include monitoring resources, auto-scaling with metrics, and killing instances to reduce costs when possible.

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