Performance Benchmarks

This document describes the performance characteristics of the Native Content Relationships Integrity Engine under large-scale datasets. All benchmarks were performed using deterministic test datasets and SQL-native validation logic.

Test Environment

• Dataset Size: 100,000 – 1,000,000 relationship rows
• Storage Engine: InnoDB
• Indexing: Composite covering index (type_lookup)
• WordPress: 6.x
• PHP: 7.4+
• Object Cache: Disabled (baseline measurement)
• MySQL Buffer Pool: Warmed before final run

Latency Metrics

Operation	100k Rows	1.0M Rows
Point Lookup (Mean)	0.49 ms	1.00 ms
Point Lookup (P95)	0.85 ms	2.73 ms
Covering Index Mean	0.22 ms	0.61 ms
Covering Index P95	1.25 ms	3.42 ms
Full Graph Scan	~7.2 s	~64.2 s

* Variation depends on buffer pool state and cache warm-up.

Resource Efficiency

Memory Management

The Integrity Engine uses chunked processing with bounded iteration to ensure stability regardless of dataset size.

• Peak Memory Delta (1.0M rows): ~2.21 MB
• Maximum Observed Memory Usage: < 5 MB
• Scaling Factor: Independent of dataset size

This ensures compatibility with shared hosting and restricted enterprise environments.

Database Optimization Strategy

The schema utilizes a composite covering index to maximize throughput: KEY type_lookup (type, from_id, to_id)

This enables:

• Index-only lookups for common queries.
• Avoidance of full table scans during integrity audits.
• Stable query time growth ($O(\log n)$).

Under realistic workloads, query latency remains sub-2ms even at 1M rows.

Scaling Characteristics

Observed complexity classes:

• Point Lookups: $O(\log n)$
• Constraint Checks: $O(\log n)$
• Integrity Scan: $O(n)$ (chunked, bounded memory)

Projected performance at 10M rows:

• Point lookups remain index-bound.
• Full graph scan expected in ~10–12 minutes (linear scaling).
• Memory usage remains bounded (< 5MB).

Benchmark Methodology

Benchmarks are executed via the benchmarks/performance-report.php utility. The methodology involves:

1. Deterministic Data Generation: Creating predictable relationship graphs.
2. Buffer Pool Warming: Executing primer queries before final measurement.
3. Mean Latency Calculation: Averaging results over multiple iterations.
4. Memory Delta Tracking: Reporting peak usage via memory_get_peak_usage().

IMPORTANT

For enterprise environments requiring high-availability, we recommend monitoring $P95$ query latency during full integrity scans.