Time

1 ns = 10^-9 seconds
1 us = 10^-6 seconds = 1,000 ns
1 ms = 10^-3 seconds = 1,000 us = 1,000,000 ns
1s   = 10^1 seconds  = 1,000 ms = 1,00,000 us  = 1,000,000,000 ns
1d   = 84,600s 

Powers of Two

Power           Exact Value         Approx Value        Bytes
---------------------------------------------------------------
7                             128
8                             256
10                           1024   1 thousand           1 KB
16                         65,536                       64 KB
20                      1,048,576   1 million            1 MB
30                  1,073,741,824   1 billion            1 GB
32                  4,294,967,296                        4 GB
40              1,099,511,627,776   1 trillion           1 TB

Availability

99.9%

DurationAcceptable downtime
Downtime per year8h 45min 57s
Downtime per month43m 49.7s
Downtime per week10m 4.8s
Downtime per day1m 26.4s

99.99%

DurationAcceptable downtime
Downtime per year52min 35.7s
Downtime per month4m 23s
Downtime per week1m 5s
Downtime per day8.6s

Latency

ScenarioTime
Access L1 cache memory0.5 ns
Branch misprediction5 ns
Access L2 cache memory7 ns
Mutex lock/unlock25 ns
Access (main) memory100 ns
Send 2KB over 1Gbps network20,000 ns
Read 1MB sequentially from memory250,000 ns
Fetch from new disk location (seek)8,000,000 ns
Read 1MB sequentially from disk20,000,000 ns
Round trip request from US to Europe150,000,000 ns

QPS

SizeQPSScenarios
Low1-100- Simple unified backends; express/django
- Relational database; PostgreSQL/MySQL
- Single server instance
Medium100-1,000- Horizontal and decoupling important
- Modular; microservices
- Sharding, replication, caching
- Orchestration software, such as K8s
High1,000 - 100,000- Microservices, message queues, redundancy, and distributed systems
- Event-driven architecture; message queues
- Distributed data stores
- Heavy caching on data stores
- Orchestration and load balancers utilized
Very High100,000+- Globally replicated databases
- Runs in multi-regional data centers
- Serverless to scale on demand
- Multiple cloud providers for redundancy