Scaling

Web server Scaling

• vertical scaling(scale up) : adding more power(CPU, RAM)
  • 💡 When traffic is low, vertical scaling is a great option
  • ✅ simplicity
  • ⛔ has a hard limit, it is impossible to add unlimited CPU and memory
  • ⛔ does not have failover and redundancy
• horizontal scaling(scale out): adding more services into your poll of resources
  • 💡 for large scale applications due to the limitations of vertical scaling
  • ✅ failover and redundancy
  • ⛔ difficult

Database Scaling

• vertical scaling(scale up) : adding more power(CPU, RAM)
  • 💡 When traffic is low, vertical scaling is a great option
  • Amazon RDS 24 TB of RAM . For example, stackoverflow.com in 2013 had over 10 million monthly unique visitors, but it only had 1 master database
  • ✅ simplicity
  • ⛔ has a hard limit, it is impossible to add unlimited CPU and memory
  • ⛔ does not have failover and redundancy
  • ⛔ The overall cost of vertical scaling is high. Powerful servers are much more expensive
• horizontal scaling(sharding): adding more services into your poll of resources
  • 💡 for large data due to the limitations of vertical scaling
  • 💡sharding key examples- user_id % 4
    • When choosing a sharding key, one of the most important criteria is to choose a key that can evenly distributed data.
  • ✅ failover and redundancy
  • ⛔ complexity and new challenges
    • Resharding data
    • Celebrity problem.To solve this problem, we may need to allocate a shard for each celebrity. Each shard might even require further partition.
    • Join and de-normalization. A common workaround is to de- normalize the database so that queries can be performed in a single table.