System Design

Interviews

During the interview, the answer might not be objectively correct
- Because of this, it's important to defend your answer

Throughput (QPS for read and write queries)
Latency expected from the system (for read and write queries)
Read/Write ratio
Traffic estimates
- Write (QPS, Volume of data)
- Read (QPS, Volume of data)
Storage estimates
Memory estimates
- If we are using a cache, what is the kind of data we want to store in cache
- How much RAM and how many machines do we need for us to achieve this?
- Amount of data you want to store in disk/ssd

Latency and Throughput requirements
Consistency vs Availability (Weak/strong/eventual => consistency | Failover/replication => availability)

Scaling the algorithm
Scaling individual components
- Availability, Consistency and Scale story for each component
- Consistency and availability patterns
Think about the following components, how they would fit in and how it would help
- DNS
- CDN (Push vs Pull)
- Load Balancers (Active-Passive, Active-Active, Layer 4, Layer 7)
- Reverse Proxy
- Application layer scaling (Microservices, Service Discovery)
- DB (RDBMS, NoSQL)
  - RDBMS
    - Master-slave, Master-master, Federation, Sharding, Denormalization, SQL Tuning
  - NoSQL
    - Key-Value, Wide-Column, Graph, Document
      - Fast-lookups
        
        RAM (Bounded size) => Redis, Memcached
        
        AP (Unbounded size) => Cassandra, RIAK, Voldemort
        
        CP (Unbounded size) => HBase, MongoDB, Couchbase, DynamoDB
- Caches
  - Client caching, CDN caching, Webserver caching, Database caching, Application caching, Cache @Query level, Cache @Object level
  - Eviction policies
    - Cache aside
    - Write through
    - Write behind
    - Refresh ahead
- Asynchronism
  - Message queues
  - Task queues
  - Back pressure
- Communication
  - TCP
  - UDP
  - REST
  - RPC