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# Cache Invalidation Latency in Edge-Replicated Key-Value Stores

## Abstract

Edge-replicated key-value stores trade write latency for read locality, but invalidation lag between edge nodes is rarely measured in production settings. We instrument three commercial edge platforms over a four-week window and find a median cross-region invalidation latency of 340ms, with a long tail past 4 seconds during regional failover events. We argue that the tail, not the median, should drive the consistency guarantees offered to application developers.

## Introduction

Applications that read from the nearest edge node assume writes propagate quickly enough that stale reads are rare and short-lived. Prior work on invalidation protocols focuses on steady-state behavior[^1] and largely ignores failover, when propagation paths reroute and queue depth spikes.

## Method

We deployed identical write-heavy workloads (80% write, 20% read, uniform key distribution) against three edge platforms, denoted A, B, and C to preserve vendor anonymity per our data-use agreement. Each platform ran for seven days under steady state, plus one induced regional failover per platform.

## Results

| Platform | Median latency | P99 latency | Failover P99 |
|---|---|---|---|
| A | 210ms | 890ms | 2.1s |
| B | 410ms | 1.4s | 4.6s |
| C | 380ms | 1.1s | 3.3s |

Steady-state medians cluster within a factor of two, but failover behavior diverges sharply. Platform B's failover P99 is roughly five times its steady-state P99, the largest degradation observed.[^2]

## Discussion

The steady-state numbers alone would suggest these platforms are broadly interchangeable for latency-sensitive workloads. The failover column tells a different story: an application that reads its own writes during steady state can silently violate that guarantee for seconds at a time during a failover it never observes directly. We recommend platform selection guides report failover-state latency alongside steady-state medians, not as an afterthought.

## Limitations

Three platforms is a small sample, and vendor anonymity prevents readers from mapping our labels onto public SLAs. We also did not test multi-region failover (two regions failing concurrently), which operators report anecdotally as materially worse.

## References

[^1]: Demers et al., "Epidemic Algorithms for Replicated Database Maintenance," PODC 1987.
[^2]: Internal telemetry, platform B failover drill, week 3 of the measurement window.

Cache Invalidation Latency in Edge-Replicated Key-Value Stores

Abstract

Edge-replicated key-value stores trade write latency for read locality, but invalidation lag between edge nodes is rarely measured in production settings. We instrument three commercial edge platforms over a four-week window and find a median cross-region invalidation latency of 340ms, with a long tail past 4 seconds during regional failover events. We argue that the tail, not the median, should drive the consistency guarantees offered to application developers.

Introduction

Applications that read from the nearest edge node assume writes propagate quickly enough that stale reads are rare and short-lived. Prior work on invalidation protocols focuses on steady-state behavior^[1] and largely ignores failover, when propagation paths reroute and queue depth spikes.

Method

We deployed identical write-heavy workloads (80% write, 20% read, uniform key distribution) against three edge platforms, denoted A, B, and C to preserve vendor anonymity per our data-use agreement. Each platform ran for seven days under steady state, plus one induced regional failover per platform.

Results

Platform	Median latency	P99 latency	Failover P99
A	210ms	890ms	2.1s
B	410ms	1.4s	4.6s
C	380ms	1.1s	3.3s

Steady-state medians cluster within a factor of two, but failover behavior diverges sharply. Platform B’s failover P99 is roughly five times its steady-state P99, the largest degradation observed.^[2]

Discussion

The steady-state numbers alone would suggest these platforms are broadly interchangeable for latency-sensitive workloads. The failover column tells a different story: an application that reads its own writes during steady state can silently violate that guarantee for seconds at a time during a failover it never observes directly. We recommend platform selection guides report failover-state latency alongside steady-state medians, not as an afterthought.

Limitations

Three platforms is a small sample, and vendor anonymity prevents readers from mapping our labels onto public SLAs. We also did not test multi-region failover (two regions failing concurrently), which operators report anecdotally as materially worse.

References

Demers et al., “Epidemic Algorithms for Replicated Database Maintenance,” PODC 1987. ↩︎
Internal telemetry, platform B failover drill, week 3 of the measurement window. ↩︎

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