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%
Duration | Acceptable downtime |
---|---|
Downtime per year | 8h 45min 57s |
Downtime per month | 43m 49.7s |
Downtime per week | 10m 4.8s |
Downtime per day | 1m 26.4s |
99.99%
Duration | Acceptable downtime |
---|---|
Downtime per year | 52min 35.7s |
Downtime per month | 4m 23s |
Downtime per week | 1m 5s |
Downtime per day | 8.6s |
Latency
Scenario | Time |
---|---|
Access L1 cache memory | 0.5 ns |
Branch misprediction | 5 ns |
Access L2 cache memory | 7 ns |
Mutex lock/unlock | 25 ns |
Access (main) memory | 100 ns |
Send 2KB over 1Gbps network | 20,000 ns |
Read 1MB sequentially from memory | 250,000 ns |
Fetch from new disk location (seek) | 8,000,000 ns |
Read 1MB sequentially from disk | 20,000,000 ns |
Round trip request from US to Europe | 150,000,000 ns |
QPS
Size | QPS | Scenarios |
---|---|---|
Low | 1-100 | - Simple unified backends; express/django - Relational database; PostgreSQL/MySQL - Single server instance |
Medium | 100-1,000 | - Horizontal and decoupling important - Modular; microservices - Sharding, replication, caching - Orchestration software, such as K8s |
High | 1,000 - 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 High | 100,000+ | - Globally replicated databases - Runs in multi-regional data centers - Serverless to scale on demand - Multiple cloud providers for redundancy |