System Design Calculator

Users & Traffic

Turn DAU into requests per second.

QPS_avg = DAU × req/user/day ÷ 86,400 QPS_peak = QPS_avg × peak_ratio

Inputs

Daily Active Users users

Type "10m" or "1.5b"

Requests per user per day req

Peak-to-average ratio ×

Web: 2–3×. Spiky: 5–10×.

Read : Write ratio : 1

Social 100:1. Logging 1:100.

Results

Total requests / day

—

Average QPS

—

Peak QPS

—

Peak read QPS

—

Peak write QPS

—

Storage

Disk needed for records over a retention window.

total = records/day × size × 365 × years × (1 + index) × replicas

Inputs

Records per day rec

Size per record

Tweet ≈ 300 B. Image ≈ 1 MB.

Replication factor ×

Retention yr

Index / metadata overhead frac

0.30 = +30%

Results

Per day

—

Per month

—

Per year

—

Raw over retention

—

Total provisioned

—with replication + overhead

Bandwidth

Network traffic in and out of your service.

bandwidth = QPS × payload_size (per direction)

Inputs

Peak QPS req/s

Avg request size

JSON API ~1 KB. Photo ~1 MB.

Avg response size

Webpage ~100 KB. Video chunk ~256 KB.

Results

Ingress (in)

——

Egress (out)

——

Total (in + out)

—

Per month

—

Per year

—

Throughput & Concurrency

How many in-flight requests does this load create?

Little's Law: L = λ × W (concurrency = QPS × latency)

Inputs

Target QPS (λ) req/s

Avg request latency (W) ms

Server count srv

Results

Total in-flight requests

—across the whole fleet

Per-server QPS

—

Per-server concurrency

—threads/connections per box

CPU cores hint

—rough: concurrency/2 (I/O-bound)

Cache Sizing

Size a Redis / Memcached tier for the hot working set.

cache = items × hot_fraction × bytes_per_item × (1 + overhead)

Inputs

Total items in dataset items

Hot fraction frac

0.20 = 80/20 rule

Size per item

Cache overhead frac

0.20 = +20% for keys / metadata

Results

Hot items

——

Working set

—

Recommended cache size

—with overhead

RAM per Server

Memory for in-memory session / connection state.

per_server = users × bytes/user ÷ servers × (1 + headroom)

Inputs

Concurrent users / sessions users

Size per session

Server count srv

Headroom frac

0.30 = +30% safety

Results

Total session memory

—

Per server (steady)

—

Per server (with headroom)

—size boxes to at least this

QPS_avg = DAU × req/user/day ÷ 86,400 QPS_peak = QPS_avg × peak_ratio

Inputs

Daily Active Users users

e.g. 10,000,000 = 10M DAU. Type "10m".

Requests per user per day req

Avg API calls each active user makes daily

Peak-to-average ratio ×

Typical web: 2–3×. Spiky workloads (events): 5–10×.

Read : Write ratio : 1

Typical social: 100:1. Logging: 1:100.

Results

Total requests / day

—

Average QPS

—

Peak QPS

—(avg × peak ratio)

Peak read QPS

—

Peak write QPS

—

total = records/day × size × 365 × years × (1 + index) × replicas

Inputs

Records per day rec

New rows/events per day

Size per record

Tweet ≈ 300 B. Image metadata ≈ 1 KB. Image ≈ 1 MB.

Replication factor ×

Cassandra/HDFS: 3. S3: 3+ (internally).

Retention yr

How long you keep the data

Index / metadata overhead frac

0.30 = +30%. B-tree indexes typically add 10–40%.

Results

Per day

—

Per month

—

Per year

—

Raw over retention

—before replication & overhead

Total provisioned

—with replication + overhead

bandwidth = QPS × payload_size (per direction)

Inputs

Peak QPS req/s

Use the peak QPS from the Users tab

Avg request size

JSON API: ~1 KB. Photo upload: ~1 MB.

Avg response size

Webpage: ~100 KB. Video chunk: ~256 KB.

Results

Ingress (in)

——

Egress (out)

——

Total (in + out)

—

Per month

—

Per year

—

Little's Law: L = λ × W (concurrency = QPS × latency)

Inputs

Target QPS (λ) req/s

Peak load you want to serve

Avg request latency (W) ms

Wall-clock time per request, including I/O waits

Server count srv

Horizontal fleet behind the load balancer

Results

Total in-flight requests

—across the whole fleet

Per-server QPS

—

Per-server concurrency

—threads/connections needed per box

CPU cores hint

—rough: concurrency/2 (I/O-bound, async)

cache = items × hot_fraction × bytes_per_item × (1 + overhead)

Inputs

Total items in dataset items

All cacheable keys (users, posts, sessions…)

Hot fraction frac

0.20 = top 20% serves most traffic (80/20 rule)

Size per item

Avg serialized value size (incl. JSON overhead)

Cache overhead frac

0.20 = +20% for keys, pointers, eviction metadata

Results

Hot items

——

Working set

—hot items × bytes/item

Recommended cache size

—with overhead

per_server = users × bytes/user ÷ servers × (1 + headroom)

Inputs

Concurrent users / sessions users

Active sessions held in memory at peak

Size per session

Session token + user context + connection buffer

Server count srv

Sessions spread evenly across the fleet

Headroom frac

0.30 = +30% safety. Aim for ~60–70% steady-state util.

Results

Total session memory

—

Per server (steady)

—

Per server (with headroom)

—size your boxes to at least this

Latency Numbers (Jeff Dean, updated for modern hardware)

Operation	Time	Notes
L1 cache reference	1 ns
Branch mispredict	3 ns
L2 cache reference	4 ns	≈ 4× L1
Mutex lock/unlock	17 ns
Main memory reference	100 ns	≈ 20× L2, 200× L1
Compress 1 KB w/ Snappy	2 µs
Read 1 MB sequentially from memory	3 µs
Send 1 KB over 1 Gbps network	10 µs
Read 4 KB random from SSD	16 µs
Read 1 MB sequentially from SSD	49 µs
Round trip within same datacenter	500 µs	0.5 ms
Read 1 MB sequentially from disk	825 µs
Disk seek	2 ms
Packet CA → Netherlands → CA	150 ms

Powers of 2 — quick lookup

Power	Value	Meaning
2¹⁰	1,024	≈ 1 thousand (Kilo)
2¹⁶	65,536	max value of UInt16
2²⁰	1,048,576	≈ 1 million (Mega)
2³⁰	1,073,741,824	≈ 1 billion (Giga)
2³²	4,294,967,296	max UInt32, ~4.3 B
2⁴⁰	1,099,511,627,776	≈ 1 trillion (Tera)
2⁵⁰	1.13 × 10¹⁵	≈ 1 quadrillion (Peta)
2⁶³	9.22 × 10¹⁸	max Int64 (signed)

Common Assumptions

Day length86,400 sec ≈ 10⁵ — useful for QPS math

Month≈ 30 days for napkin math

Year≈ 3.15 × 10⁷ sec ≈ π × 10⁷

Replication factor3 (Cassandra, HDFS, Kafka default)

Read:Write ratioSocial 100:1, Read-heavy CMS 10:1, Logging 1:100

Peak-to-average2–3× typical; 5–10× for spiky / event-driven

Cache hit rate target80–95% (80/20 rule on hot keys)

Disk speed (SSD)500 MB/s sequential, 100k IOPS random

Network1 Gbps ≈ 125 MB/s; 10 Gbps ≈ 1.25 GB/s

CompressionText: 5–10× (gzip); binary: 1–3×

Throughput & Bandwidth Ballparks

Single MySQL/Postgres node~1k–10k QPS reads, ~1k QPS writes

Redis (single node)~100k QPS

Kafka broker~1 GB/s ingest, ~100k msg/s

Nginx (single box)~50k req/s static content

App server (single box)~1k–10k QPS depending on workload

S3 / object storeEffectively unlimited; ~100 ms p50 GET

DNS lookup~20–120 ms cold; ~1 ms cached

TCP handshake (RTT)~1 ms LAN, ~30–100 ms WAN

TLS handshake1–2 extra RTTs

Mobile 4G RTT~50–100 ms