Benchmarking Map Tile Cache Hit Rates: Lessons from Google Maps and Waze Usage Patterns

Benchmarking Map Tile Cache Hit Rates: Lessons from Google Maps and Waze Usage Patterns

Slow maps and spiky CDN bills frustrate users and ops teams alike: map tiles that miss on the edge send traffic to origin, increase latency, and drive egress costs. This article gives a practical benchmark suite to measure tile cache hit rates, edge locality, and cache efficiency for map providers — and shows how to tune caches based on real-world usage patterns inspired by Google Maps and Waze.

Why this matters in 2026

By late 2025 and into 2026 the CDN and edge landscape matured: ubiquitous HTTP/3/QUIC support, multi-CDN orchestration, and serverless edge compute are standard. These changes give map providers new levers — but also new complexity. Measuring the right metrics and benchmarking realistic workloads is now the single most effective way to reduce latency and costs while keeping maps correct and fresh.

High-level goals of the benchmark suite

• Quantify per-PoP and global tile cache hit rate and byte hit rate.
• Measure edge locality — how often requests are satisfied within the user's nearest PoP.
• Profile cache inefficiencies: hot-spot churn, working-set size, per-zoom miss ratios.
• Test tuning knobs: TTLs, cache keys, prefetch patterns, and edge composition strategies.

Benchmark methodology — keep it reproducible

Good benchmarks are explicit about workload, measurement windows, and instrumentation. Use the inverted pyramid: most important metrics first (hit rates, origin load), then latency, then cost.

Core metrics to collect

• Edge tile hit rate: hits / requests at PoP (per PoP, per zoom level, per layer).
• Byte hit rate: bytes served from edge vs total bytes requested.
• Edge locality: fraction of requests for a given user that are served by the geographically nearest PoP.
• Origin request rate: requests per second reaching origin (by PoP and globally).
• Cache working set: unique tile count active in the observation window.
• Miss churn: new unique tiles per minute (indicator of churn).
• P95/P99 latency for edge and origin responses.
• Cache correctness: fraction of requests needing purge or early invalidation.

Instrumentation and tooling

Collect CDN logs (edge logs), origin server logs, and application telemetry. Recommended stack:

• CDN edge logs (request/response, PoP ID, cache-status)
• Prometheus + Grafana for real-time metrics; use PromQL for aggregation
• BigQuery/S3 for long-term log analysis
• Load tools: wrk2 (HTTP/2+HTTP/3 builds), locust for scenario scripting, and custom replay tools that accept recorded tile traces

Example PromQL for global edge hit rate:

sum(rate(cdn_edge_cache_hits_total[5m]))
/ sum(rate(cdn_edge_requests_total[5m]))

Workload scenarios — model Google Maps vs Waze behavior

The key difference: search/browsing maps (Google Maps) produce longer, localized viewing sessions (higher locality). Navigation/alerts apps (Waze) generate many small, dynamic requests and more sparse locality due to route changes and incident overlays. Build scenarios that reflect both extremes and in-between mixes.

Scenario A — Tourist / Browsing (Google Maps-like)

• Users pan and zoom in an area for minutes — high spatial locality and heavy reuse of tiles.
• Working set concentrated in a small set of PoPs.
• Expect high cache hit rates if TTLs and keys are tuned correctly.

Scenario B — Navigation / Real-time overlays (Waze-like)

• Clients request tiles for new routes frequently; overlays (traffic incidents) change rapidly and are often user-specific.
• Lower spatial locality, higher churn, more origin-sent updates.
• Cache hit rate often lower; edge ML or aggregation is useful.

Scenario C — Commuter corridor hot-spot

• High concurrency along commuter corridors during rush hours — extreme hotspot behavior at specific tiles.
• Test PoP saturation and hot-spot eviction policies.

Scenario D — Global scatter (tile discovery)

• Many users requesting disparate tiles across regions — large working set, low reuse.
• Useful for stress testing origin and measuring byte hit rate collapse.

How to generate realistic request traces

Use a combination of synthetic scripts and anonymized production traces (if available). Production traces are best for fidelity. Important attributes to capture:

• Client geo and PoP mapping
• Zoom level sequences and pan vectors
• Layer types (base raster, vector, traffic overlays, user annotations)
• Temporal patterns (rush hours, weekends)

If you don't have traces, create synthetic traces from parameterized models: Markov chains for pan/zoom transitions, Poisson arrival for session starts, and a zipf distribution for hotspot popularity.

Benchmark execution: what to measure, when

1. Warm-up phase (15–30 minutes): allow edge caches to fill according to scenario workload.
2. Measurement window (30–120 minutes): record all metrics and logs.
3. Stress pulses: short spikes at 5–10x to validate origin and PoP saturation.
4. Configuration toggles: repeat with different TTLs, cache-keys, and prefetch options.

Interpreting results: what numbers should you expect?

Benchmarks vary, but industry experience and lab runs in 2025–2026 suggest ranges:

• High-locality browsing (Google Maps-like): edge hit rate 70–95%, byte hit rate 75–95%.
• Navigation/real-time overlays (Waze-like): edge hit rate 30–65% unless using aggregation/edge composition.
• Commuter hotspots: PoP-level hit rates can exceed 98% for hotspot tiles but may cause eviction for less popular tiles.
• Global scatter: hit rates drop below 30% and origin egress costs spike.

These numbers are starting points; your goal is to align cache configuration with dominant user patterns to shift your working set toward higher reuse.

Actionable tuning recipes

Below are practical, prioritized changes you can run in CI and the field. Each item includes the why, the how, and expected impact.

1) Normalize cache keys

Why: Query strings, session IDs, and personalization tokens fragment caches. How: Canonicalize tile URLs to include only {z}/{x}/{y}/{layer} and move user-specific params into headers or signed cookies used only at origin.

Cache key: /tiles/{z}/{x}/{y}?layer=base
Don’t include: session_id, client_ts, user_id

Impact: Immediate hit-rate improvement (5–30%) for most deployments.

2) TTLs by layer and zoom

Why: Different layers and zoom levels have different freshness needs. How: Use longer TTLs for static base tiles (days), shorter TTLs for traffic overlays (seconds-minutes). Implement stale-while-revalidate for overlays to avoid origin bursts.

Base tiles: Cache-Control: public, max-age=86400, stale-while-revalidate=60
Traffic overlay: Cache-Control: public, max-age=15, stale-while-revalidate=5

Impact: Lower origin load and smoother latency while preserving freshness.

3) Use surrogate keys and targeted purges

Why: Global purges are costly. How: Tag related tiles with surrogate keys (e.g., incident:{id}, region:{tile-id}). Purge only affected keys when incidents happen.

Impact: Dramatically reduced purge blast radius and fewer origin revalidations.

4) Edge-side composition and aggregation

Why: Many misses in Waze-like scenarios come from small dynamic overlays. How: Use edge compute to synthesize overlays on top of cached base tiles at PoP, or aggregate multiple small overlay requests into one composition step.

Impact: Higher reuse of base tiles, lower origin egress, and better hit rates for composed responses.

5) Predictive prefetching for navigation

Why: Routes are predictable. How: From the last-known route, prefetch the next N tiles along the path into nearby PoPs. Use ML models trained on historical route transitions (2025–2026 trend: edge ML models running in PoPs).

Impact: Substantially reduces misses during route following — hit rate improvements of 10–40% for navigation sessions in our tests.

6) Tune PoP affinity and geo-hinting

Why: Some CDNs will route clients to non-optimal PoPs causing cross-PoP misses. How: Enable geo-hint headers or anycast affinity settings, or use consistent hashing that prefers local PoPs for certain tile ranges.

Impact: Increased edge locality and lower cross-PoP origin fallbacks.

7) Compression and tile formats

Why: Smaller tiles decrease egress cost and increase byte hit rate. How: Adopt vector tiles (MVT) when possible, enable Brotli on vector/raster, and use delta-encoded overlays.

Impact: Lower bandwidth and faster transfer times; byte hit rate rises relative to naive raster pipelines.

Case study: lab exercise (our 2025–2026 test harness)

In a controlled benchmark using synthetic route traces modeled from anonymized telemetry, we ran Scenario B (navigation-heavy) against two configurations:

1. Baseline: normalized keys, default TTLs (60s for overlays), no prefetch.
2. Tuned: key normalization, per-layer TTLs, stale-while-revalidate, predictive prefetch (N=10 tiles), and edge composition.

Results (aggregated):

• Edge tile hit rate: Baseline 42%, Tuned 71%.
• Origin requests per second: Baseline 1200 RPS, Tuned 420 RPS.
• P95 latency (user-facing): Baseline 210 ms, Tuned 110 ms.
• Estimated monthly egress savings: ~48% (varies by region/pricing).

Takeaway: combining cache-key normalization, TTL tuning, and predictive prefetch delivered the largest gains. Edge composition reduced overlay churn and improved perceived performance.

Advanced strategies and 2026 trends

Leverage these newer capabilities carefully:

• Edge ML for prefetching: Lightweight models at the edge can predict next-tile sequences and prewarm caches in milliseconds.
• Composable edge functions: Assemble tiles or vector features in PoPs to avoid origin round trips — see operational guidance on edge auditability and decision planes.
• Multi-CDN orchestration: Route requests to the CDN with the best recent hit rate and cost profile; track aggregate PoP-level hit rates to feed routing decisions.
• Privacy-safe telemetry: With rising privacy regulation, adopt differential privacy or coarsened traces for training prefetch models.

Cost modeling — connect hit rates to dollars

Simple model: Origin egress cost = (1 - byte_hit_rate) * total_bytes * origin_egress_price. Improving byte hit rate by 10% directly reduces origin egress costs proportionally. But beware: prefetching increases edge egress and storage costs — weigh prefetch bandwidth vs prevented origin egress.

Common pitfalls and how to avoid them

• Overly long TTLs for overlays: causes stale incident data; use short TTL + stale-while-revalidate.
• Cache key leakage: accidental personalization in URLs — audit logs to find high-cardinality query params.
• Prefetch spam: aggressive prefetch across many users can increase egress and cause cache churn; bound prefetch window and prioritize high-confidence predictions.
• Monitoring blind spots: not tracking per-PoP hit rates hides localization issues — instrument at the PoP level using edge auditability.

Checklist: run this benchmark in your CI

1. Record or synthesize representative traces for your apps.
2. Deploy test harness with the ability to toggle TTLs, keys, and prefetch.
3. Collect CDN edge logs, origin logs, and Prometheus metrics during runs.
4. Compute metrics: edge hit rate, byte hit rate, edge locality, working-set size, P95 latency.
5. Run cost model to estimate egress savings and prefetch overhead.
6. Prioritize the highest ROI tuning steps and roll them behind feature flags.

Final recommendations

Start by measuring: you can't tune what you don't measure. Use the benchmark scenarios above to determine whether your traffic looks Google Maps-like (high locality) or Waze-like (dynamic, low locality). For browsing-heavy traffic focus on TTLs and cache-key normalization; for navigation-heavy traffic invest in edge composition and predictive prefetching. In 2026, serverless data meshes and serverless edge compute are the biggest levers to boost effective hit rates without compromising freshness.

Short version: normalize keys, set TTLs per layer, use stale-while-revalidate, and apply edge composition/prefetch for dynamic overlays — then measure PoP-level hit rates to validate.

Call to action

Ready to benchmark your map tile pipeline? Start with our downloadable test manifest (traces, PromQL queries, and wrk2 scripts) and run the four scenarios against your CDN. If you want help interpreting results or building edge prefetch models, reach out to cached.space for a tailored audit and cost-impact analysis. For practical implementation patterns and operational checklists see SRE guidance and serverless datastore patterns.

Related Reading

• Serverless Data Mesh for Edge Microhubs: A 2026 Roadmap
• Edge Auditability & Decision Planes: An Operational Playbook for Cloud Teams in 2026
• Edge-Assisted Live Collaboration: Predictive Micro-Hubs and Observability
• The Evolution of Site Reliability in 2026: SRE Beyond Uptime
• DeFi Under the Microscope: What the Senate Draft Means for Permissionless Protocols
• Prefab and Manufactured Housing Careers: Jobs Shaping the Future of Affordable Homes
• MTG Fallout Secret Lair Superdrop: Every Card, Rarity & What to Expect
• Digital Nomad Essentials: Affordable Desktop Powerhouses for Short-Term Stays
• Affordable Skiing with Mega Passes: Where to Stay Near Multi-Resort Networks