What is Capacity Planning?

Capacity Planning is the process of determining the resources needed to meet current and future demands. It involves estimating storage, compute, and network requirements.

Why It Matters

Avoid over-provisioning: Save costs
Avoid under-provisioning: Prevent outages
Plan for growth: Scale proactively
Interview requirement: Often asked in system design

Key Metrics to Estimate

Metric	Question
DAU/MAU	How many users?
QPS	How many requests per second?
Storage	How much data to store?
Bandwidth	How much data transferred?
Memory	How much RAM needed?

Estimation Framework

1. Define Requirements

- 100M daily active users (DAU)
- Average user makes 10 requests/day
- Average request size: 1 KB
- Data retention: 5 years

2. Calculate Traffic

Total requests/day = DAU × requests/user
                   = 100M × 10
                   = 1 billion/day

QPS = 1B / 86,400 seconds
    ≈ 12,000 QPS (average)

Peak QPS = 2-3x average
         ≈ 30,000 QPS

3. Calculate Storage

Daily data = requests × size
           = 1B × 1 KB
           = 1 TB/day

5-year storage = 1 TB × 365 × 5
               = 1.8 PB

4. Calculate Bandwidth

Incoming = 1B requests × 1 KB
         = 1 TB/day
         = ~12 MB/s average

Outgoing (if 10 KB response) = 10 TB/day
                              = ~120 MB/s average

Useful Numbers to Know

Metric	Value
Seconds in a day	86,400 (~100K)
Seconds in a month	~2.5M
1 million requests/day	~12 QPS
1 billion requests/day	~12,000 QPS

Server Capacity

Typical Server Specs

Resource	Typical Value
CPU cores	8-64 cores
RAM	32-256 GB
Storage	1-10 TB SSD
Network	1-10 Gbps

Capacity per Server

Web server: 1,000-10,000 QPS
Database:   1,000-5,000 QPS (reads)
Cache:      100,000+ QPS (Redis)

Server Count Estimation

Required QPS = 30,000 (peak)
Server capacity = 5,000 QPS

Servers needed = 30,000 / 5,000
               = 6 servers

Add redundancy (2x) = 12 servers

Storage Estimation

Data Types

Type	Size (typical)
User record	1 KB
Tweet/Post	250 bytes
Image (compressed)	200 KB
Video (1 min, compressed)	50 MB
Log entry	100 bytes

Replication Factor

Actual storage = Raw storage × Replication factor

If replication factor = 3:
1 PB raw → 3 PB actual

Cache Sizing

80/20 Rule

20% of data serves 80% of requests:

Total data = 1 PB
Hot data = 1 PB × 20% = 200 TB

If cache hit ratio target = 90%:
Cache size ≈ 200 TB

Example: URL Shortener

Requirements:
- 100M URLs created/month
- 10:1 read/write ratio
- URL stored for 5 years

Writes:
- 100M / month = 40 URLs/second

Reads:
- 400 URLs/second

Storage:
- URL + short code ≈ 500 bytes
- 100M × 12 months × 5 years = 6B URLs
- 6B × 500 bytes = 3 TB

Cache:
- 20% hot URLs = 1.2B × 500 bytes = 600 GB

Interview Tips

Round numbers for easy math (use powers of 10)
State assumptions clearly
Consider peak vs average traffic
Account for replication and redundancy
Mention growth projections
Know common capacity numbers

Finished reading?

Mark it done to track your progress in Concepts.

Capacity Planning

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