The Math Behind the Numbers
How We Get to 93% Latency Reduced
Every nanosecond traced, every hop measured. Here is where trading cache latency actually goes -- and what Cachee removes.
Where does 93% come from?
A typical order lifecycle touches the cache layer 14 times -- market data lookups, risk checks, routing decisions, position updates, and post-trade reporting. With Redis, each lookup costs ~1ms (network hop + serialization). With Cachee, each lookup costs 17 nanoseconds (in-process memory, zero serialization). The 93% measures the reduction in total cache-layer latency across the full order lifecycle.
Today's trading cache latency -- where each millisecond goes:
0ns~1,000,250ns per cache call
Application logic (250ns)
Cache layer -- Cachee eliminates this (~1,000,000ns)
Per-call comparison: Redis vs Cachee
Redis (Traditional)
1.0ms
Per cache GET
PathNetwork hop
SerializationRequired
P992.5ms
Ops/sec~250K
BottleneckTCP + kernel
Cachee L1
17ns
Per cache GET
PathIn-process
SerializationZero-copy
P9924ns
Ops/sec59M
BottleneckNone (L1 cache)
1,000us
Redis per-call latency
(network + serialize + process)
−
0.017us
Cachee L1 per-call
(in-process memory)
=
999.98us
Latency eliminated
per cache call
→
93%
end-to-end order
latency reduced
Full order lifecycle -- 14 cache lookups per order:
| Order Lifecycle Step |
Cache Lookups |
Redis Cost |
Cachee Cost |
Saved |
| Market data check |
2 |
2.0 ms |
34 ns |
1.999 ms |
| Pre-trade risk validation |
4 |
4.0 ms |
68 ns |
3.999 ms |
| Order routing decision |
3 |
3.0 ms |
51 ns |
2.999 ms |
| Position update |
2 |
2.0 ms |
34 ns |
1.999 ms |
| Post-trade reporting |
3 |
3.0 ms |
51 ns |
2.999 ms |
| Total per order |
14 |
14.0 ms |
238 ns |
13.999 ms |
End-to-end order latency reduction:
14.0ms
Redis total
(14 lookups x 1ms)
→
238ns
Cachee total
(14 lookups x 17ns)
=
58,824x
faster cache
layer overall
→
93%
of end-to-end order
latency eliminated
How 93% is calculated: A complete order lifecycle totals ~15ms end-to-end with Redis (14ms cache + ~1ms application logic and computation). With Cachee, that drops to ~1.000238ms (238ns cache + ~1ms application logic). The cache layer itself drops from 14ms to 238ns -- a 99.998% reduction in cache latency alone. The end-to-end order latency reduction is 93% because the ~1ms of non-cache computation (risk math, routing logic) remains constant.
Compounding at scale -- daily latency savings:
| Daily Orders |
Redis Cache Time |
Cachee Cache Time |
Time Recovered |
| 10,000 |
140 seconds |
2.38 ms |
~140 seconds |
| 100,000 |
23.3 minutes |
23.8 ms |
~23.3 minutes |
| 1,000,000 |
3.9 hours |
238 ms |
~3.9 hours |
| 10,000,000 |
38.9 hours |
2.38 seconds |
~38.9 hours |
Every nanosecond compounds. At 1M orders/day, Redis spends 3.9 hours of cumulative time waiting on cache lookups. Cachee reduces that to 238 milliseconds. That is not just faster -- it is a fundamentally different operational profile. Queues do not build. Tail latency stays flat. Throughput scales linearly because the cache layer is never the bottleneck.
P99 tail latency -- where it matters most:
Redis P99
2.5ms
99th percentile GET
CauseGC pauses + TCP retransmit
Worst case10-50ms spikes
JitterHigh (2-3x median)
Cachee P99
24ns
99th percentile GET
CauseCPU cache miss (rare)
Worst case~40ns
JitterNear-zero (1.4x median)
Tail latency defines trading performance. The median does not matter when a single P99 spike causes a missed fill. Redis P99 is 104,000x worse than Cachee P99. In HFT, the tail is the only number that counts -- it determines your worst-case execution, your queue depth under load, and ultimately whether your strategy is viable at scale.
Your matching engine runs in nanoseconds.
Your cache should too.
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