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Latency numbers to know

The classic table of cache, memory, disk and network latencies, as raw values or scaled to human time, with the orders of magnitude that matter in an estimate.

The table

Approximate latency of one operation.
Latency of common operations, as raw values
OperationLatencyRelative size, log scale
CPU and memory
L1 cache reference1 ns
Branch mispredict3 ns
L2 cache reference4 ns
Uncontended mutex lock/unlock20 ns
Main memory reference100 ns
Compress 1 KB with a fast codec2 µs
Read 1 MB sequentially from memory50 µs
Storage
SSD random read (4 KB)100 µs
Read 1 MB sequentially from SSD300 µs
Read 1 MB sequentially from HDD5 ms
HDD seek10 ms
Network
Send 1 KB over a 10 Gbps link1 µs
Round trip in the same datacenter500 µs
Send 1 MB over a 10 Gbps link1 ms
Round trip between availability zones1 ms
Round trip across a continent60 ms
Round trip across an ocean150 ms

These are orders of magnitude, not benchmarks. Hardware, cloud network disks and distance all shift them; what stays true is the ratio between rows.

How this is calculated

Human scale
human time = real time × 10⁹, because an L1 hit is about 1 ns: one nanosecond becomes one second, one millisecond becomes about 11.6 days.
Ratios worth memorising
Memory is about 1,000× faster than an SSD random read, which is about 100× faster than an HDD seek. A datacenter round trip is about 5,000 memory references.
Throughput from latency
max sequential ops/s ≈ 1 ÷ latency: one client doing cross-ocean round trips one after another manages about 7 a second.
Bandwidth
time = bytes × 8 ÷ link bits/s: 1 MB on a 10 Gbps link is about 0.8 ms before any round trips.

What to say in the interview

  • “A memory read is about 100 nanoseconds and a round trip inside the datacenter is about half a millisecond, so one network hop costs as much as thousands of memory reads. That’s why I want the hot path to be one cache lookup, not a chain of calls.”
  • “Crossing an ocean is around 150 milliseconds per round trip, so a request can afford one of those at most; anything that needs several has to be served from a nearby region.”
  • “Sequential disk reads are far cheaper than random ones, which is why I’d lean on an append-only log and batch writes rather than update rows in place.”
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