gistfile1.md

Headline

Checklist

1. Understand the problem and establish design scope

1. User/Customer

• who will use this system
• how this system will be used
• features they will use

2. Scale (read / write) [SCALE == PARTITIONING]

• how many read query per second?
• how much data is stored in a day

3. Perfomance

• [RELIABLE == REPLICATION AND CHECKPOINTING]
• [SPEED == IN-MEMORY]
• What is the expected p99 latency for read/write
• Can we do things asyncly (delay between write and read)

4. Cost

• cheap Load Balancer
• open source tech to reduce the cost
• data retention and DB clearing

5. Non-functional Requirement

• Use CAP + Scalable (CAPS)
  • Consistency (everyone sees the same data)
  • Availablity (service/hardware failure recovering - no single point of failure)
  • Performant(handle lots of traffic)
  • Scailability

2. api / data model / HL design

1. basic APIs

• input/output/generic

2. HL Design

• [Add abstraction layer to make it simpler] All problems in computer science can be solved by another level of indirection
• single responsblity (seperation of concerns)

3. Data Model (what we are storing)

3. detailed design and deep-

generic

• Load Balancing

  • 3 Types (Layer 4 vs Layer 7 vs Elastic LB)
  • SSL Termination
  • LB Algorithm:
    • Static (Round-robin - simple hashing)
    • Dynamic (Least Response Time, Least Connection, Agent-based)

• API design

  • pagination algorithms
    • offset pagination
    • cursor pagination

• Session Management

  • traditional vs jwt (json web token)

• Data Model

  • DB Types:
    • SQL
    • NoSql
      • key-value
      • document
      • wide-column
      • graph

• Cache

  • Cache Architecutre (cache for everything)

• Sharding

  • Horizontal Sharding (range based sharding)
    • Dedicated Server + Cluster Proxy similar to MongoDB
    • Co-located Cluster Proxy similar to Cassandra
  • Vertical Sharding
  • Hash Sharding (Simple and consistant hashing)
  • Cost optimization for sharding:
    • pre-node TPS estimation and leaking bucket optimization

• Fault Tolerance

  • Replication
    • Primary with backupt replication
    • Primary with a coordinator with seperate read/write
  • find leader algorithm?
    • RAFT
  • Checkpointing
  • WAL (Write Ahead Log)
  • task breaking using StepFunciton
  • Circuite Breaking in API Gateway

• Strong Consistency:

  • RAFT strong replication
  • Distributed Transactions:
    • Two-phase Commit (2PC)
    • Liniear Order Execution (Saga)

• End talks

  • What metrics to emit:
    • Latency
    • Cache misses
    • CPU and Memory Utilization
    • Network I/O
    • cloudwatch (cheaper) / DataDog
  • Logging (small but useful):
    • Details of every request to the Cache
    • Who and When access the cache
    • Return status code
  • Auditing:
    • simple canary flow (canary to make sure all is good)
    • Immutable DB like Quantum Ledger

specific

• Deduplication

  • db lookup
  • in-memory hash
  • bloom filter

• Rate Limiter Algo:

  • Fixed Window
  • Sliding Window
  • Leacking Bucket

• Queue

  • Distributed Queue
  • Dedup
  • Sharding

4. bottlenecks

scale, redundancy, SPOFs, metrics, logs, alerts, dashboards, pagerduty, deployment, failure scenarios

Timeline

1. [0 - 15 min] initial E2E Diagram