leetcode_gym/pages/_system_design/link_shortener.md

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page	System Design - link shortener

educative.io

7. data partitioning and replication

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• range based partitioning:
  • all URL's starting with 'A' goes into this bucket
  • can store less frequently used letters in one bucket/node
  • could become unblanced
• hash-based partitioning
  • use a key hash to map to one of 1-256 servers
  • could still lead to overloaded partitions? can be solved by consistent hashing?

8. caching

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• app level cache

• CDN

• cache invalidation

• cache evictin polices

• to reduce DB load, can check cache before hitting the DB

• can precompute frequently used data and store in cache

• consider cache eviction policies like LRU

• what happens in a cache miss? -> fetch from backend DB -> update all cache replicas

• memcache

  • stores in memory to avoid giong to external disks
  • large distributed hashtable
  • reduces DB load
  • client server app over tcp/udp
  • NOT persistent

• redit

  • get/set operations
  • supports richer data types including string, hash, list

9. load balancers

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• add LB at 3 layers

  • between users and app servers
  • between app servers and DB
  • between servers and cache servers

• round robin approach to distribute incoming requests

• algorithms

  • round robin
  • ip hash
  • least connections/response time/bandwidth

10. purging or DB cleanup (expired urls)

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Approaches

• active search for exipred links could put pressure on DB
• lazy cleanup could delete links as user requests them and delete if expired
• cleanup service to be schduled to run at low usage hours

11 and 12. analytics and security

otehr requirements that needs to be considered