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Back of the Envelope Calculations Cheat Sheet

1. Powers of 10 (for large counts)

  • 1 million = 10^6
  • 10 million = 10^7
  • 100 million = 10^8
  • 1 billion = 10^9
  • 1 trillion = 10^12

2. Storage Units in Bytes

  • 1 Byte (B) = 10^0 bytes (base unit)
  • 1 Kilobyte (KB) = 10^3 bytes = 1,000 bytes
  • 1 Megabyte (MB) = 10^6 bytes = 1,000,000 bytes
  • 1 Gigabyte (GB) = 10^9 bytes = 1 billion bytes
  • 1 Terabyte (TB) = 10^12 bytes = 1 trillion bytes
  • 1 Petabyte (PB) = 10^15 bytes = 1 quadrillion bytes

3. Common Calculations for Certain Sizes

  • 1 KB × 1 million:
    10^3 × 10^6 = 10^9 bytes = 1 GB

  • 1 KB × 10 million:
    10^3 × 10^7 = 10^10 bytes = 10 GB

  • 1 KB × 100 million:
    10^3 × 10^8 = 10^11 bytes = 100 GB

4. Time Units

  • Seconds in a minute: 60
  • Seconds in an hour:
    60 × 60 = 3,600 seconds
  • Seconds in a day:
    3,600 × 24 = 86,400 seconds
  • Seconds in a 30-day month:
    86,400 × 30 = 2,592,000 seconds
  • Seconds in a year (approximate):
    365 × 86,400 = 31,536,000 seconds

5. Requests Per Second (RPS) for Common EC2 Instances

  • t3.medium: ~200–500 RPS (depending on workload)
  • m5.large: ~1,000–2,000 RPS
  • c5.large: ~2,000–5,000 RPS (for optimized applications)

6. Average Database Query Latency (in milliseconds)

  • DynamoDB: 10–20 ms (single read/write)
  • PostgreSQL: 1–10 ms for simple queries, up to 100 ms for complex queries
  • Redis (in-memory): < 1 ms (often sub-millisecond)

7. Average Network Request Latency (Round-Trip)

  • Within the same data center (in-region): 1–2 ms
  • Cross-region (e.g., US-East to US-West): 50–100 ms
  • From client to server (average public internet): 50–200 ms

Additional Notes

  • Bandwidth Formula:
    Bandwidth (bytes/second) = File size (bytes) ÷ Transfer time (seconds)

  • 1 Mbps Bandwidth:
    1 Mbps = 10^6 bits/second ≈ 125,000 bytes/second

  • Disk IOPS (Input/Output Operations Per Second):

    • SSD: ~10,000–100,000 IOPS
    • HDD: ~100–200 IOPS

Key Takeaways

  • Latency adds up: Disk reads, network hops, and DB lookups can slow things down—using cache layers like Redis improves performance.
  • Throughput considerations: Higher throughput can require sharding databases and using load balancers.
  • Data transfer optimization: Data compression and batching improve transfer speed for large files.

Squarespace Markdown Tips

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