We actively monitor our application bundle size and API latency to prevent performance regressions. Our target is sub-1ms database persistence and sub-250ms cold starts.

📊 Enterprise Performance Matrix

The Enterprise Benchmark Matrix (Audit) has transitioned to a High-Frequency, Self-Healing Infrastructure. Each benchmark test is autonomous—managing its own server lifecycle via dynamic port assignment and exporting high-fidelity telemetry directly to structured JSON payloads. This eliminates cross-test pollution and ensures deterministic scaling across SQLite, MongoDB, PostgreSQL, and MariaDB.

It measures:

• REST API Latency: High-frequency entry retrieval (Target: 0.35ms).

• Migration & Ingestion: Bulk I/O throughput (Verified: 6,625 entries/s).

• Revision Stability: Performance impact of deep document history (Verified: 0% degradation at 100+ versions).

• Chaos Resilience: System stability during 500ms database “brownouts.”

• Index Pressure: Complex filtering and sorting on 100,000+ entry collections.

• 5-Layer Truth Model: Measures performance from Pure Logic (CPU) → Full SDK (DB/IO) → Dispatcher (Middleware) → HTTP Warm (Keep-Alive) → HTTP Cold (Handshake).

• Memory Stability: RSS and Heap growth detection across sustained decentralized loads.

• World Life Data: High-fidelity diagnostics including Success/Failure Latency Splits, the per-row lane/cache/hook-count context table, and the rejection-path cost split — rejected requests land at 0.35–0.39 ms, i.e. parity with the authenticated rows (0.33–0.36 ms turbo-cache served, 0.29–0.33 ms full chain; tests/benchmarks/auth-performance.test.ts, SQLite, measured 2026-09-22, five runs each side). Both sides are labelled by lane, because they are different work: Auth Validation @ 1c is served from the turbo cache (4 hooks) while a rejection runs the full chain (7). Resolved as Rejection-path cost parity in the 2026 achievements.

• Client Journey Simulation: Cumulative latency of multi-step E2E workflows (Auth → List → View → Save).

🚀 Running the Orchestrator

The orchestrator code is modularized inside scripts/benchmark-matrix/ for ultimate maintainability, but is easily executed via the main barrel file:

# Run the full matrix across all 4 databases (add USE_REDIS=true for the 4 Redis variants)
bun run scripts/benchmark-matrix/index.ts

The cross-adapter comparison table in docs/project/benchmarks/index.mdx is patched by the matrix itself after each adapter (the SUMMARY_MATRIX zone in reporting.ts) — there is no separate leaderboard command.

Retention & Trend Analysis (SRE)

The matrix maintains a specialized retention policy to balance historical context with actionable trends:

• Time-Series History: All runs (standalone or matrix) log to history.jsonl, allowing for per-module regression analysis.
• Trend Indicators: Every report includes a Traffic Light Trend (🔴/🟢/⚪) and inline terminal delta percentages (e.g., +5.2%).
• Master Leaderboard: Cross-engine comparison providing a side-by-side “Database Showdown.”
• Percentile Tracking: Standardized on p50 (Median), p95 (User Experience), and p99 (Tail Risk). Percentiles and min/max are computed on raw samples — GC spikes and OS jitter stay visible in the tail instead of being trimmed away. The mean is IQR-trimmed (robust central tendency), and small-sample confidence intervals use the t-distribution (z = 1.96 only for n ≥ 30).

🔬 SRE Truth Audit: The Gold Standard

Our “Truth Audit” suite (truth-latency.test.ts) is designed to provide SRE-grade credibility by isolating where latency actually comes from:

1. Logic Layer: Theoretical minimum (No I/O).
2. SDK Layer: Real-world logic with DB/Cache.
3. Dispatcher Layer: Framework/Middleware overhead.
4. HTTP Warm: Production network performance (Keep-Alive).
5. HTTP Cold: Worst-case network performance (TLS/TCP Handshakes).

🔗 Relational Performance Testing

With the 2026 update, we’ve introduced specialized relational benchmarks to ensure that complex data structures remain performant:

• GQL Nested: Tests Depth 2-3 relationship resolution.
• REST Relational Search: Validates filtering by related fields via the aggregation engine.

File: tests/benchmarks/relational-performance.test.ts

Unified benchmark runtime flag

GraphQL, scanners, and compile.ts all use isBenchmarkRuntime() from preset-collections.server.ts (checks the single BENCHMARK=true flag). Standalone bun test tests/benchmarks/* and the matrix server set the same flag so behavior is consistent.

Running Analysis Locally

To check the bundle size and basic performance locally:

bun run build:stats

This generates detailed reports including:

• Size of each chunk (raw, gzipped, brotli).
• Comparison with the previous build size.
• Warnings reported if predefined performance budgets are exceeded.
• An exported bundle-report.json designed for automated CI consumption.

🧩 Widget Performance Auditing (Core & Custom)

As part of the Enterprise Matrix, SveltyCMS performs a specialized overhead pass for widgets. This ensures that custom widgets do not introduce “Silent Regressions” into the core API.

The Audit Process:

1. Definition Pillar Profiling: Measures the latency added by a widget’s modifyRequest and transform logic in the server-side pipeline.
2. Aggregation Stress Test: Verifies that custom listSchemas and search aggregations remain efficient as database sizes grow.
3. Core Baseline: All custom widgets are measured against the Input (Core) baseline (~0.01ms overhead).

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📚 Related Documentation

• Benchmark Collection Isolation
• Performance Benchmarks (Progress Reports)
• Enterprise Monitor Dashboard
• Contributing Documentation

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Related

• Test Documentation
• Security Testing
• Architecture Overview