benchmarkmobiletelemetry

Cache Telemetry for Mobile: Building Benchmarks to Compare Google Maps, Waze, and Your App

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2026-02-10

11 min read

A practical framework to measure cache hit rate, cold start latency, and perceived responsiveness across Google Maps, Waze, and your app.

Hook: Your users blame the map, but caches are the real culprit

Slow map loads, reroute pauses, and spiky bandwidth bills are symptoms — not the root cause. For navigation apps, the real levers are caching policies, on‑device storage behaviour, and how OEM Android skins handle background processes. This article gives a practical, reproducible framework to measure cache hit rate, cold start latency, and user‑perceived responsiveness across Google Maps, Waze, and your own navigation app on real devices and OEM skins.

Executive summary — what you’ll get

Clear definitions and metrics: what exactly to measure and why.
Reproducible test bed: hardware, OS, network shaping, and instrumentation.
Telemetry pipeline: capturing hits/misses, cold start traces, and UX timing — design the pipeline using principles from ethical data pipelines.
Analysis recipes: computing hit rates, bandwidth savings, and cost impact.
Hands‑on snippets: adb, tc, mitmproxy, UIAutomator, and trace processor commands.

The 2026 context — why now

In late 2025 and early 2026 we saw two important shifts: navigation apps have moved more aggressively to on‑device predictive prefetching and OEMs delivered stricter memory/thermal policies across Android skins. That combination increases variance in perceived performance between devices. Benchmarks that ignore device and network realism will mislead product and SRE teams. You need reproducible telemetry to pinpoint whether latency comes from network, cache misses, or OEM memory management.

Important concepts and precise definitions

Cache hit rate (asset level)

Definition: For a given asset class (map tiles, navigation icons, routing segments), cache hit rate = hits / (hits + misses). Count a hit when the client does not fetch the asset over the network because it exists and is valid locally or an intermediate cache returns a fresh response.

Cold start latency

Definition: Time from launching the app (user tap) until the first meaningful map frame or turn instruction is rendered. Record both process cold start (app process not in memory) and disk cold start (tile DB empty). Differentiate them — they have different mitigation strategies.

User‑perceived responsiveness

Definition: Composite metrics that correlate with what users notice: initial map draw time, pan/zoom input latency, first voice instruction delay, and 95th percentile frame drop rate during navigation. These are measured from UI traces and frame timings.

Designing a reproducible test bed

Reproducibility requires controlling hardware, OS, network, and app state. Follow these rules:

Devices: Use at least three representative models per OEM skin. For pure OEM comparison, use the same hardware model flashed with different vendor images where possible; otherwise, select device pairs with similar SoC and storage (UFS/eMMC). For guidance on durable phones and reliability in the field, consult How to Choose a Phone That Survives.
OS and builds: Record Android version, security patch, and app build. Prefer stable retail builds to emulate real users.
Network shaping: Use a controlled Wi‑Fi AP with a Linux host to shape bandwidth and latency via tc/qdisc (or use a hardware WAN emulator).
Isolation: Airplane mode off (for real radio), disable aggressive background task killers and auto‑updates, but document changes. Run battery at 100% and disable adaptive battery during tests to avoid variability.
Start/stop automation: Use UIAutomator or adb input to ensure consistent user flows. If you’re building a repeatable automation pipeline, the ideas in Composable UX Pipelines translate well to device automation.

Example hardware matrix

Pixel 8a — stock Android 15
Samsung S24 FE — One UI (2025 build)
Xiaomi 14 — MIUI (late 2025 build)
OnePlus 12 — OxygenOS

Network shaping and reproducible connectivity

Realistic navigation tests require latency and packet‑loss scenarios: dense urban LTE, 5G with intermittent handoffs, and congested Wi‑Fi. The Linux hotspot approach is robust and reproducible.

Sample tc commands (Linux host used as AP)

# 50ms base latency, 5% packet loss, 5Mbps down
sudo tc qdisc replace dev wlan0 root netem delay 50ms loss 5% rate 5mbit

Adjust for uplink and downlink shaping according to your topology (if the host is NAT gateway for devices, shaping on wlan0 suffices).

Capturing HTTP cache behavior: practical constraints

Navigation apps communicate over HTTPS and often use certificate pinning. There are three practical approaches to capture cache metrics:

Rooted device or emulator: Install a CA or intercept TLS. This gives full visibility but requires non‑retail builds.
Network gateway with TLS termination: Use a corporate TLS forward proxy if you control the certificate chain.
Application telemetry and OS traces: When TLS interception is impossible, instrument the app (if you control it) or use platform-level telemetry (socket stats, bytes sent) and UI timing to infer hits vs misses.

For comparisons between Google Maps, Waze, and your app, a mix of rooted devices and instrumented app builds gives the best fidelity. Document which method you used and exclude pinned flows from HTTP header analysis if you cannot decrypt them. Also consider the ethics and governance of trace collection — see guidance on ethical data pipelines.

Mitmproxy setup (rooted or instrumented device)

# start mitmproxy to capture traffic on port 8080
mitmproxy -p 8080 -w maps_waze_capture.mitm

# on device, point proxy to host:8080 (or use global HTTP proxy)
adb shell settings put global http_proxy 192.168.1.1:8080

Collect headers like Cache‑Control, Age, ETag, and any X‑Cache or CDN headers. For each response record timestamp, URL path, asset type, size, and headers.

Clearing caches and defining cold/warm states

To create reproducible cold and warm starts:

Cold app start (no process): Kill process and clear process memory: adb shell am force-stop com.example.maps
Cold disk cache: Remove tile DBs and cache directories. Example:

adb shell pm clear com.example.maps
# or remove tile DBs — run only when you know paths
adb shell rm -rf /sdcard/Android/data/com.example.maps/files/tiles*

Document the exact directories and commands; different OEMs store files in different places (external vs scoped storage). For Google Maps and Waze, some tile caches are stored in app‑controlled databases; clearing via pm clear is the most reliable approach but also clears preferences — reconfigure automation accordingly.

Driving scenarios to measure

Create realistic scenarios and script them. Cover common user journeys:

Launch app, search a destination 15km away, start navigation (process cold start vs warm).
Pan and zoom across a 5km route to simulate rapid tile requests.
Trigger reroute with simulated traffic incident.
Resume after app backgrounded for 10 minutes (test background killing by OEM skin).
Offline mode: remove network and measure fallback behaviour.

Automation example: UIAutomator pseudo

from uiautomator import Device
d = Device()
# launch
d.screen.on()
d.press.home()
d.app_start('com.example.maps')
# search and start nav
# measure t0 at tap, t1 when first map tile drawn (see trace points)

Record timestamps at each meaningful milestone using adb shell date +%s%3N for millisecond resolution.

Collecting traces for UI and rendering metrics

Use Perfetto (Android) or simple frame counters to capture app start to first frame. Perfetto traces give GPU composition, frame timestamps, and binder interactions. Use adb to start the tracing session around each scenario.

# start perfetto (simple config file perfetto_config.pbtxt)
adb shell perfetto -c /data/misc/perfetto-traces/perfetto_config.pbtxt -o /data/misc/perfetto-traces/trace.pb
adb pull /data/misc/perfetto-traces/trace.pb
# process with trace_processor_shell
trace_processor_shell trace.pb -o trace.sqlite

Look for SurfaceFlinger, Choreographer and app frame events to compute input latency and frame drops. If you’re staffing up to analyze traces at scale, hiring and training specialists is covered in Hiring Data Engineers in a ClickHouse World.

Calculating cache hit rate and cold start latency

Aggregate the captured network events and app traces. A simple pipeline:

Parse mitmproxy HAR or mitm logs to extract response records with headers.
Tag asset class (tile, icon, routing segment, voice resource) by URL path regex.
Count responses as hits if the client returned 304 Not Modified or if a proxy header indicates HIT; otherwise count as miss.

Hit rate formula

Hit rate = SUM(hits for asset class) / SUM(hits + misses for asset class). Compute per run and then report median and p95 across runs.

Cold start latency statistics

Measure TTFM (time to first meaningful map frame) per run and report median, p75, p95. Also separate cold process vs cold disk cases to see the impact of tile caching.

Example analysis pipeline (Python sketch)

import pandas as pd
# load mitm CSV with columns: run, timestamp, url, status, size, header_XCache
r = pd.read_csv('mitm.csv')
# detect hits
r['hit'] = r['header_XCache'].str.contains('HIT') | (r['status']==304)
# compute per run and asset type
summary = r.groupby(['run','asset_type'])['hit'].agg(['sum','count'])
summary['hit_rate'] = summary['sum'] / summary['count']
print(summary.reset_index())

Translating results to actionable engineering tasks

Once you have hit rates and latency numbers, map them to fixes:

Low tile hit rate: increase tile retention windows, review Cache‑Control max‑age, or implement better on‑device LRU with eviction tuned for UFS — planning for storage cost changes and hardware supply risk is covered in Preparing for Hardware Price Shocks.
High cold start TTFM: defer heavy initialization using lazy loading and show a low‑cost baseline map layer immediately (vector base layer + progressive tile replacement).
OEM skin variance: if One UI devices show aggressive background killing causing low warm start hit rates, implement resilient prefetch checkpoints and persist key DBs to external storage paths that survive aggressive cleanup.
Bandwidth spikes: add differential compression for tile payloads, or reduce tile zoom prefetch windows when on metered networks.

Case study (illustrative)

We ran 50 scripted runs across three devices (Pixel stock, Samsung One UI, Xiaomi MIUI) under a 50ms/5Mbps network. Key findings (illustrative numbers):

Google Maps tile hit rate median: 78% (p95 89%)
Waze tile hit rate median: 65% (p95 78%) — due to more aggressive short‑lived cache entries but better reroute freshness
YourApp (baseline): 54% — indicates opportunity in both server cache headers and on‑device DB retention
Cold start TTFM medians: Pixel 1.2s, Samsung 2.4s, Xiaomi 1.9s — OEM memory and startup throttling explain differences

From this you can prioritize: increase your tile retention, investigate startup CPU blocking, and add OS‑aware background prefetch to survive vendor cleaners.

Metrics to report to stakeholders

Tile hit rate (median, p75, p95) by device and network condition
Bandwidth saved per active user per month (estimate from hits * average tile size)
Cold start TTFM (median/p95) and % of launches under target SLA (e.g., 1.5s)
Frame drop rate and input latency during navigation (95th %ile)
Regression deltas when changing cache headers or client eviction policies

CI and continuous benchmarking

Integrate these benchmarks into a nightly lab harness. Key practices:

Run deterministic scenarios on the same device pool and preserve baselines.
Automate environment setup: clear caches, set network profile, restore app configuration.
Store raw traces and use trace processor to detect regressions in TTFM and hit rates; surface key indicators on resilient operational dashboards (Operational Dashboards).
Gate merges that increase median cold start or reduce hit rate significantly.

Limitations and gotchas

Certificate pinning will block network inspection on retail devices — use rooted devices or instrumented builds to see headers; for device-level testbeds, mobile studio guidance helps set up reliable rooted test devices (Mobile Studio Essentials).
OEM skins change policies frequently. Re‑benchmark after major OEM updates (Android skins were updated heavily in late 2025).
Edge CDNs and server TTLs affect metrics; isolate client behaviour by running flows against a staging backend with known cache headers when possible. For deep dives on edge caching strategy, see Edge Caching Strategies for Cloud‑Quantum Workloads.

Measure what matters: cache hit rate tells you about wasted network trips; cold start latency tells you what users notice. Both are needed to prioritize engineering work.

Future trends to track (2026 and beyond)

Look for three developments that will change how you benchmark:

On‑device ML prefetching: Apps will increasingly predict routes and prefetch tiles; benchmarking must measure prediction precision and its cache efficiency — these patterns tie back to composable UX and predictive pipelines (Composable UX Pipelines).
Edge caching and near‑user CDNs: Closer edge caching reduces cold start cost for first network hit; record CDN X‑Cache headers to attribute savings and compare against edge playbooks (Edge Caching Strategies).
Stronger OEM resource controls: Vendors continue to tighten background app budgets, so resilience to aggressive pruning will be critical; coordinate with low-latency capture and edge encoding strategies from hybrid studio playbooks (Hybrid Studio Ops).

Checklist: Reproducible benchmark run

Record device model, Android build, app build, and configuration.
Set network shape and verify with iperf.
Clear app state (pm clear) or prepare cold disk state.
Start capture (mitmproxy/perfetto) and drive scenario via UIAutomator.
Pull traces and HARs, label assets, and compute hit rates and latencies.
Compare medians and p95 across devices and runs; visualize deltas.

Final recommendations — immediate next steps

If you don’t have device‑level visibility, add nightly runs using a small pool of rooted devices to get header‑level metrics.
Prioritize reducing cold process TTFM under 1.5s for top 20% of your user base — a common user expectation in 2026.
Tune on‑device eviction to favor recent route tiles and persist routing segments for faster reroutes on OEMs with aggressive pruning.

Call to action

Start building this benchmark today: instrument one device, automate your most common route, and run ten cold vs warm iterations. If you want a jumpstart, download our open benchmark scaffold (includes UIAutomator scripts, tc profiles, and parsing notebooks) or contact our team to run a custom OEM skin analysis for your navigation stack. For infrastructure reliability in a lab, consider micro‑DC orchestration tips (Micro‑DC PDU & UPS Orchestration).

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