How to benchmark mapping and routing libraries from TypeScript: metrics that matter

Benchmarking mapping and routing SDKs from TypeScript: why this matters now

If you maintain a navigation product or embed maps in a high-scale app, you already feel the pain: different SDKs return different routes, latency spikes kill UX, and battery-draining map updates lead to angry users. In 2026, vendors ship smarter on-device models and better offline support — but comparing SDKs reliably is still surprisingly hard. This guide shows how to build a reproducible, open-sourced benchmarking suite in TypeScript that measures the metrics that actually matter: route quality, latency, battery, and offline behavior.

Executive summary (most important takeaways)

• Define objective metrics (route similarity, ETA error, latency percentiles, battery delta, cache hit/miss).
• Use consistent test harness — same devices/emulators, pinned SDKs, synthetic GPS traces and real-world traces.
• Automate device control from TypeScript (adb, simctl, Playwright) and capture low-level telemetry (dumpsys, perfetto traces).
• Store raw telemetry and artifacts (route geometries, logs, screenshots) in a versioned CI artifact store for reproducibility.
• Open-source the suite with Docker images and CI workflows so others can reproduce results.

Context & trends (late 2025 → early 2026)

Recent vendor updates in late 2025 increased focus on offline routing and on-device model inference to reduce latency and power use. At the same time, client-side bundlers and node runtimes in 2025–2026 have made TypeScript-based tooling smaller and faster, enabling more sophisticated local test harnesses. This makes a TypeScript benchmarking suite both practical and future-proof: you can orchestrate device fleets, parse telemetry, and produce visual reports in one typed codebase.

What to measure (metrics that matter)

Route quality

Route quality is multi-dimensional. Measure:

• Geometric similarity between routes (Frechet distance or Hausdorff) to quantify deviation.
• ETA error — predicted ETA vs. actual travel time.
• Distance delta (route length compared with baseline/ground-truth).
• Constraint adherence — avoidance of tolls, highways, ferries when requested.
• Reroute frequency during simulated traffic changes.

Latency & reliability

• Time-to-first-byte / time-to-route (TTFB and end-to-end route response time).
• Percentiles (P50, P95, P99) — averages hide spikes.
• Error rates and fallback behaviors (retry, cached route use).

Battery & CPU

• Battery delta over a standardized scenario (measured with OS tools).
• CPU utilization and threads used (to quantify background processing cost).
• Network activity and data transferred (affects mobile cost).

Offline behavior

• Offline route availability — success rate when network is disabled.
• Cache hit/miss for tiles and route graphs.
• Storage use for offline packs.

Designing the TypeScript benchmarking suite

Keep the suite modular: a small orchestrator that schedules runs, an instrumentation layer that talks to SDKs and OS tools, and an analysis pipeline that produces datasets and visualizations. Use a monorepo approach (pnpm/workspaces or TurboRepo) with distinct packages for device drivers, metric collectors, and report generators.

Project layout (suggested)

• packages/orchestrator — CLI and experiment scheduler
• packages/device-driver — adb, simctl, and Playwright helpers
• packages/collector — SDK adapters (Mapbox, Google/HERE wrappers)
• packages/analysis — metric aggregation and chart generation
• docker/ — images for reproducible headless runs

Key implementation patterns

1. Typed SDK adapters

Create a thin, typed adapter interface that normalizes requests and responses across SDKs. That keeps benchmarks comparable.

export interface RoutingAdapter {
  init(): Promise;
  requestRoute(origin: [number,number], dest: [number,number], opts?: any): Promise<RouteResponse>;
  offlineAvailable(areaGeoJson: any): Promise<boolean>;
  clearCache?(): Promise<void>;
}

2. Deterministic GPS playback

Use prerecorded GPX/JSON traces for reproducible tests. Feed them to emulators or inject them into web maps using the adapter. For Android, push a trace to the emulator and use the emulator’s location injection. For web, simulate geolocation via Playwright.

3. Measuring latency precisely

Measure client-side timestamps around network calls and capture SDK-provided timing fields. For web SDKs, use the Performance API; for native SDKs, capture timestamps inside the adapter (or via instrumentation hooks if the SDK exposes them).

const start = performance.now();
const route = await adapter.requestRoute(from, to);
const end = performance.now();
const latencyMs = end - start;

4. Battery measurement from TypeScript

For Android, use adb and dumpsys. For iOS, use simctl (limited) or Instruments on macOS. The simplest reproducible approach for CI is to use Android emulators and adb battery stats. Steps:

1. Reset battery stats: adb shell dumpsys batterystats --reset
2. Run scenario
3. Dump stats: adb shell dumpsys batterystats --charged
4. Parse battery usage for package

import { execSync } from 'child_process';

function resetBattery() {
  execSync('adb shell dumpsys batterystats --reset');
}

function readBatteryFor(pkg: string) {
  const out = execSync(`adb shell dumpsys batterystats --charged | grep -A 20 "Estimated" || true`).toString();
  // Parse package-specific entries (implementation will depend on Android version)
  return out;
}

In field tests on real devices you can combine Android’s Battery Historian / Perfetto traces and the battery API to get higher fidelity. Capture CPU and network with perfetto and parse with the analysis pipeline.

5. Offline pack validation

Pre-download offline packs via the SDK adapter, verify filesystem storage, then turn off the network and request a route. Record success/failure and cache hit statistics. Also measure storage size and download time.

Ground truth & route quality algorithms

Choose a baseline: an open-source routing engine (OSRM/GraphHopper) or a labeled set of human-verified traces. Use geometric distances and statistical measures to quantify differences.

Frechet distance (discrete) in TypeScript

Frechet distance is a good measure for path similarity. Here’s a small discrete implementation to compute an upper bound for two coordinate arrays.

function euclidean(a: [number,number], b: [number,number]) {
  const dx = a[0]-b[0];
  const dy = a[1]-b[1];
  return Math.sqrt(dx*dx + dy*dy);
}

// Discrete Frechet (simple, O(nm))
function discreteFrechet(P: [number,number][], Q: [number,number][]) {
  const n = P.length, m = Q.length;
  const ca: number[][] = Array.from({length:n},()=>Array(m).fill(-1));
  function c(i:number,j:number): number {
    if(ca[i][j] > -1) return ca[i][j];
    let val: number;
    if(i===0 && j===0) val = euclidean(P[0],Q[0]);
    else if(i>0 && j===0) val = Math.max(c(i-1,0), euclidean(P[i],Q[0]));
    else if(i===0 && j>0) val = Math.max(c(0,j-1), euclidean(P[0],Q[j]));
    else val = Math.max(Math.min(Math.min(c(i-1,j), c(i-1,j-1)), c(i,j-1)), euclidean(P[i],Q[j]));
    ca[i][j]=val; return val;
  }
  return c(n-1,m-1);
}

Experiment orchestration & reproducibility

Reproducibility is the difference between an interesting one-off and an actionable benchmark. Do the following:

• Pin SDK versions and commit adapter implementations.
• Use Docker images for the orchestrator and analysis tools.
• Describe device state in configuration (OS build, emulator image, sensor injection settings).
• Record random seeds for any stochastic elements (traffic simulation, route snapping).
• Persist raw artifacts (routes as GeoJSON, perfetto traces, screenshots) and metadata to CI artifacts or an S3 bucket.

CI integration (example with GitHub Actions)

Run headless web SDK tests in GitHub Actions with a pinned Node and Docker image. For device runs, wire Actions to a remote device farm or self-hosted runner connected to physical devices. Always attach artifacts and CSV outputs for transparency.

Analysis and visualization

Emit canonical CSV/JSON results. Use TypeScript or Python for analysis. Key steps:

1. Aggregate runs per scenario and SDK.
2. Compute percentiles for latency and ETA error.
3. Run bootstrap sampling to produce 95% confidence intervals.
4. Visualize with Vega-Lite or D3: latency CDFs, ETA error boxplots, battery delta bars, offline success rates.

// Minimal aggregator
interface RunResult { sdk: string; scenario: string; latencyMs: number; etaErrorSec: number; batteryDelta: number; }

function aggregate(results: RunResult[]) {
  const bySdk: Record = {};
  for (const r of results) {
    (bySdk[r.sdk] ??= []).push(r);
  }
  return Object.entries(bySdk).map(([sdk, arr]) => ({
    sdk,
    p50Latency: percentile(arr.map(x=>x.latencyMs), 50),
    p95Latency: percentile(arr.map(x=>x.latencyMs), 95),
  }));
}

Practical example: Comparing two SDKs on a commute scenario

Here's a compact sequence you can reproduce: pick a 15-minute urban commute GPX, run both SDKs with identical constraints (avoid tolls), and capture route geometry, ETA, latency, and battery on an Android emulator. Repeat 10 times for each SDK, resetting battery stats and emulator state between runs.

• Record raw GeoJSON for each route and compute the Frechet distance to a baseline engine (OSRM).
• Calculate ETA error (predicted ETA - actual playback duration).
• Collect latency percentiles and battery delta per run.

Handling caveats and edge cases

Beware vendor SDKs that perform internal caching or neural warm-up. Control for this by clearing caches or running warm-up iterations and excluding them from the main dataset. Note too that simulator behavior can differ from real devices (GPS noise, radio stacks). Include at least some real-device runs for credibility.

Strong recommendation: never publish a single-run benchmark. Always include standard deviations, sample sizes, and raw artifacts so others can audit your conclusions.

Open-sourcing your suite

A high-quality open-source benchmark should include:

• Clear README with reproducible instructions.
• Docker images to reproduce the orchestrator environment.
• Seeded GPX/geo datasets under permissive licenses or instructions to create them.
• CI workflows that produce human-readable reports and attach raw data.
• Contributor guidelines and code of conduct for device donations (if you run a device lab).

Legal, privacy, and ethical points

Routing data and real user traces can contain private information. Use synthetic traces or obtain explicit consent for real traces. Respect vendor SDK terms of service when benchmarking, especially around automated requests and rate limits.

Case study sketch (how a team used this in production)

In a recent internal evaluation, a mobility team used a comparable TypeScript harness to compare three SDKs across 50 urban routes. They automated emulator runs and 20 real-device runs, collecting perfetto traces for CPU/battery. The multi-run approach exposed a P95 latency difference that the P50 masked, and offline pack size became a decisive factor for low-end devices. The open-sourced suite allowed cross-team validation and sped decision-making from weeks to days.

Future-proofing your benchmark (2026+)

• Monitor vendor announcements around on-device ML routing — re-run tests when those updates land.
• Extend adapters for new runtimes (WebAssembly, on-device NN accelerators).
• Support federated device labs and privacy-preserving aggregation for field telemetry.

Actionable checklist to get started (in 30–90 minutes)

1. Initialize a TypeScript monorepo (pnpm or npm workspaces).
2. Implement the RoutingAdapter interface for one SDK.
3. Load a reproducible GPX trace and implement a deterministic GPS injector for your test target (emulator or Playwright).
4. Measure simple latency and route geometry and persist GeoJSON output.
5. Publish results and add CI to re-run nightly; iterate with battery and offline tests next.

Final notes

Building a robust benchmarking suite takes discipline, but the payoff is clarity: you move from noisy opinions to repeatable measurements that inform architecture and product trade-offs. In 2026, with vendors optimizing for on-device inference and offline features, a reproducible TypeScript harness is the best way to keep decisions evidence-based.

Call to action

Ready to benchmark your routing SDKs? Start a repo with the structure above, seed it with one adapter and one trace, and open-source it. Share your results and artifacts with the community so others can reproduce and extend your tests — if you publish a repo URL, I’ll review it and suggest improvements you can apply to your CI and analysis pipeline. See also notes on open-sourcing pipelines and governance when publishing reproducible tooling.

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