Document performance characteristics and scalability limits

## Problem

The ogc-client-CSAPI implementation has **comprehensive test coverage (94%+) and excellent architecture** but **lacks performance and scalability documentation**. After completing work items #32-37 (benchmark infrastructure and component-specific performance measurements), this documentation gap means:

- **No documented performance characteristics**: Users cannot estimate latency, throughput, or resource usage
- **No documented scalability limits**: Users don't know maximum collection sizes, nesting depths, or concurrent request limits
- **No optimization guidance**: Users cannot make informed decisions about caching, validation, or format selection
- **No performance baselines**: Cannot track performance regressions or improvements
- **No capacity planning**: Server operators cannot estimate resource requirements

**Real-World Impact:**
- **Mobile/embedded**: Cannot determine if library is suitable for resource-constrained devices
- **Server-side**: Cannot plan server capacity or estimate concurrent user limits
- **Large datasets**: Unknown if library can handle 10,000+ features or deeply nested structures
- **Real-time**: Cannot determine if suitable for time-sensitive applications
- **Production deployment**: Risk of performance issues without documented limits

## Context

This issue was identified during the comprehensive validation conducted January 27-28, 2026.

**Related Validation Issues:** [#19](https://github.com/Sam-Bolling/CSAPI-Live-Testing/issues/19) (Overall Test Coverage & Quality Metrics), [#23](https://github.com/Sam-Bolling/CSAPI-Live-Testing/issues/23) (Architecture Assessment)

**Work Item ID:** 38 from [Remaining Work Items](https://github.com/Sam-Bolling/CSAPI-Live-Testing/blob/main/docs/remaining-work-items.md)

**Repository:** https://github.com/OS4CSAPI/ogc-client-CSAPI

**Validated Commit:** `a71706b9592cad7a5ad06e6cf8ddc41fa5387732`

## Detailed Findings

### 1. Architecture Validated but Performance Not Documented

**From Issue #23 (Architecture Assessment):**
> **Overall Verdict:** ✅ **WELL-ARCHITECTED CODE** - Strengths significantly outweigh weaknesses
> 
> **7 Confirmed Architectural Strengths:**
> 1. ✅ Layered Architecture (5 distinct layers with clear boundaries)
> 2. ✅ Multi-Format Support (GeoJSON, SensorML, SWE Common with automatic detection)
> 3. ✅ Type Safety (Generic interfaces, type guards, discriminated unions)
> 4. ✅ Extensibility (Template method pattern, composition, pluggable validators)
> 5. ✅ Production Ready (94%+ test coverage, extensive error handling, comprehensive JSDoc)
> 6. ✅ **Performance Considerations** (Navigator caching, lazy parser instantiation, optional validation, efficient format detection)
> 7. ✅ Developer Experience (High-level API, request builders, clear errors)

**Performance Features Identified:**
- **Navigator caching per collection** (Map-based caching)
- **Optional validation** (default off for performance)
- **Efficient format detection** (O(1) with short-circuit)
- **Lazy parser instantiation** (mentioned but needs verification)

**However, NO PERFORMANCE DOCUMENTATION exists:**
- ❌ No documented latency (how long does parsing take?)
- ❌ No documented throughput (features/sec, requests/sec)
- ❌ No documented memory usage (KB per feature, per collection)
- ❌ No documented scalability limits (max features, max nesting depth)
- ❌ No performance comparison (GeoJSON vs SensorML vs SWE)

---

### 2. Test Coverage Excellent but Performance Tests Missing

**From Issue #19 (Overall Test Coverage):**
> **Test Suite Quality:** ⭐⭐⭐⭐⭐ (5/5)
> 
> **Statistics:**
> - **Total Tests:** ~832+ tests (vs. claimed 549 - EXCEEDS claims!)
> - **CSAPI Tests:** ~479+ tests (vs. claimed 196)
> - **Pass Rate:** 100% (549/549 or higher)
> - **CSAPI Coverage:** 94.03%
> 
> **Component Coverage:**
> - Navigator: 92.7% (186 tests)
> - Typed Navigator: 96.66% (26 tests)
> - GeoJSON Validation: 97.4% (61 tests)
> - SWE Validation: 100% (50 tests)
> - Parsers (resources): 97.63% (79 tests)
> - Parsers (base): 96.62% (29 tests)
> - Request Builders: 97.5% (30 tests)
> - Formats: 100% (8 tests)
> - Endpoint Integration: 100% (10 tests)

**Test Categories:**
- ✅ Unit Tests (186 tests) - Navigator URL building, query parameters
- ✅ Integration Tests (10 tests) - Endpoint integration, conformance checking
- ✅ Validation Tests (111 tests) - GeoJSON, SWE Common, SensorML validation
- ✅ Parser Tests (108 tests) - Format detection, conversion, error handling
- ✅ Builder Tests (30 tests) - Request body construction

**Missing Test Categories:**
- ❌ **Performance Tests** (0 tests) - Latency, throughput, scalability
- ❌ **Load Tests** (0 tests) - Concurrent requests, stress testing
- ❌ **Memory Tests** (0 tests) - Heap usage, GC pressure, leak detection
- ❌ **Benchmark Tests** (0 tests) - Comparative performance measurements

---

### 3. Known Performance Features Undocumented

**From Issue #23, Performance Features Confirmed:**

**Navigator Caching:**
```typescript
// navigator.ts - Map-based caching per collection
private cachedNavigators = new Map<string, CSAPINavigator>();

getCollectionNavigator(collectionId: string): CSAPINavigator {
  if (this.cachedNavigators.has(collectionId)) {
    return this.cachedNavigators.get(collectionId)!;
  }
  const navigator = new CSAPINavigator(collectionId, this.httpClient);
  this.cachedNavigators.set(collectionId, navigator);
  return navigator;
}
```

**Optional Validation:**
```typescript
// parsers/base.ts - Validation opt-in for performance
parse(data: unknown, options: ParserOptions = {}): ParseResult<T> {
  // ... parsing logic ...
  
  // Validate if requested (default: false)
  if (options.validate) {
    const validationResult = this.validate(parsed, format.format);
    // ... validation logic ...
  }
  
  return { data: parsed, format, errors, warnings };
}
```

**Efficient Format Detection:**
```typescript
// formats.ts - O(1) detection with short-circuit
export function detectFormat(contentType: string | null, body: unknown): FormatDetectionResult {
  const headerResult = detectFormatFromContentType(contentType);
  
  // SHORT-CIRCUIT if high confidence from header
  if (headerResult && headerResult.confidence === 'high') {
    return headerResult;
  }
  
  // Otherwise inspect body (fallback)
  const bodyResult = detectFormatFromBody(body);
  
  if (bodyResult.confidence === 'high') {
    return bodyResult;
  }
  
  return headerResult || bodyResult;
}
```

**Performance Impact Questions (Undocumented):**
1. **Caching:** How much faster is cached vs uncached navigator? 10%? 50%? 100%?
2. **Validation:** What's the overhead? 5%? 20%? 50%?
3. **Format detection:** How long does detection take? <1ms? <10ms?
4. **SensorML conversion:** How much overhead vs GeoJSON direct? 10%? 100%?

---

### 4. Code Size but No Performance Impact Documentation

**From Issue #23, File Sizes:**
- navigator.ts: **79,521 bytes** (79 KB)
- typed-navigator.ts: 11,366 bytes (11 KB)
- parsers/base.ts: 13,334 bytes (13 KB)
- parsers/resources.ts: 15,069 bytes (15 KB)
- parsers/swe-common-parser.ts: **16,218 bytes** (16 KB)
- request-builders.ts: 11,263 bytes (11 KB)
- formats.ts: 4,021 bytes (4 KB)

**Total estimated bundle:** 250-300 KB before minification

**Performance Questions:**
- What's the **initialization time** for the library? <10ms? <100ms?
- What's the **memory footprint** at startup? <1 MB? <10 MB?
- What's the **impact of lazy loading**? How much can be deferred?
- What's the **tree-shaking effectiveness**? Can unused resources be eliminated?

---

### 5. Architectural Weaknesses with Performance Implications

**From Issue #23, Confirmed Weaknesses:**

**1. Browser Bundle Size:**
> **⚠️ CONFIRMED**: Navigator.ts alone is 79 KB. Full CSAPI with types ~250-300 KB before minification. May impact mobile users on slow connections.

**Performance Impact:**
- Slow initial page load (especially on 3G/4G)
- Increased memory usage (entire bundle loaded)
- Potential for tree-shaking optimizations not explored

**2. Limited Encoding Support:**
> **⚠️ CONFIRMED**: JSON-only (`application/geo+json`, `application/sml+json`, `application/swe+json`). No binary/text encodings for efficient observation data.

**Performance Impact:**
- JSON is verbose (larger payloads than binary)
- Slower parsing (text vs binary)
- Higher bandwidth usage (gzip helps but not as efficient as binary)

**3. No WebSocket Streaming:**
> **⚠️ CONFIRMED**: No WebSocket implementation found. HTTP-only client. Cannot receive real-time streaming data.

**Performance Impact:**
- Polling required for real-time data (inefficient)
- Higher latency (HTTP request/response overhead)
- Higher server load (many polling requests)

---

### 6. Dependencies for Performance Documentation

**Work Items #32-37** must be completed first to gather performance data:

**#32 (Issue #55):** Comprehensive performance benchmarking infrastructure
- Establishes Tinybench framework, npm scripts, CI/CD integration
- **Status:** Must be completed first

**#33 (Issue #56):** Measure and optimize URL construction performance
- Navigator performance: URL building, query serialization, caching overhead
- **Provides:** Navigator performance characteristics

**#34 (Issue #57):** Measure and optimize parsing performance
- Parser performance: format detection, conversion, position extraction, validation overhead
- **Provides:** Parser performance characteristics

**#35 (Issue #58):** Measure and optimize validation performance
- Validator performance: GeoJSON vs SWE vs SensorML, constraint validation overhead, collection scaling
- **Provides:** Validation performance characteristics

**#36 (Issue #59):** Measure and optimize format detection performance
- Format detection: header vs body inspection, confidence levels, detection precedence
- **Provides:** Format detection performance characteristics

**#37 (Issue #60):** Measure and optimize memory usage
- Memory: per feature, per collection, nesting depth, GC pressure, leak detection
- **Provides:** Memory usage characteristics and scalability limits

**AFTER all benchmarks complete:**
- Aggregate all performance data
- Document performance characteristics
- Document scalability limits
- Provide optimization guidance

---

### 7. Performance Considerations vs. Reality

**From Issue #23, Claimed Performance Features:**
> ✅ **Performance Considerations** (Navigator caching, lazy parser instantiation, optional validation, efficient format detection)

**Reality:**
- ✅ Features exist and work correctly
- ⚠️ **Actual performance characteristics UNKNOWN**
- ⚠️ No baseline measurements
- ⚠️ No optimization guidance
- ⚠️ No scalability limits documented

**Example:**
- **Claim**: "Optional validation (opt-in for performance)"
- **Question**: How much performance? 5%? 50%? 100% faster?
- **Answer**: **UNKNOWN** - needs benchmarking (work item #35)

---

## Proposed Solution

### 1. Establish Prerequisites (DEPENDS ON #32-37)

**PREREQUISITES:** This work item **REQUIRES** all benchmark work items (#32-37 / Issues #55-60) to be completed first.

**Dependency Chain:**
```
#55 (Infrastructure) 
  ↓
#56 (Navigator) + #57 (Parsers) + #58 (Validators) + #59 (Format detection) + #60 (Memory)
  ↓
#38 (THIS ISSUE - Documentation)
```

**Why This Dependency Matters:**
- Cannot document performance without measurements
- Cannot document scalability without stress testing
- Cannot provide optimization guidance without baseline data
- Documentation must be evidence-based, not speculative

---

### 2. Create Comprehensive Performance Documentation

Update README.md with new "Performance & Scalability" section (~800-1,200 lines):

**Section Structure:**
1. Performance Overview
2. Component Performance Characteristics
3. Scalability Limits
4. Optimization Strategies
5. Benchmark Results
6. Performance Comparison Tables
7. Capacity Planning Guidance
8. Performance Monitoring Recommendations

---

### 3. Document Performance Overview

**Performance Overview** (~100-150 lines):

```markdown
# Performance & Scalability

## Overview

The ogc-client-CSAPI is designed for high-performance CSAPI operations with the following characteristics:

**Typical Performance** (measured on [benchmark hardware specs]):
- URL construction: X μs per URL
- Format detection: X μs per detection
- Single feature parsing: X ms per feature
- Collection parsing: X ms for 1,000 features
- Validation overhead: +X% with validation enabled
- Memory usage: X KB per feature, X MB for 1,000 features

**Design Principles:**
- ✅ Optional validation (default off for performance)
- ✅ Navigator caching (per collection, X% faster)
- ✅ Efficient format detection (O(1), X μs average)
- ✅ Lazy initialization (defer resource parsing until needed)
- ⚠️ JSON-only (no binary encodings for observation data)
- ⚠️ HTTP-only (no WebSocket streaming for real-time data)

**Performance Targets Met:**
- ✅ URL building: <X ms per request (good: <10ms, acceptable: <50ms)
- ✅ Parsing: <X ms per feature (good: <1ms, acceptable: <10ms)
- ✅ Memory: <X KB per feature (good: <10KB, acceptable: <100KB)
- ✅ Test coverage: 94.03% (excellent: >90%, good: >80%)
```

---

### 4. Document Component Performance Characteristics

**Navigator Performance** (~150-200 lines, from Issue #56):

```markdown
## Navigator Performance

### URL Construction

**Typical Latency:**
- System URL: X μs
- Deployment URL with bbox: X μs
- Datastream URL with complex query: X μs
- Collection URL with pagination: X μs

**Query Parameter Serialization:**
- Simple parameters (limit, offset): X μs
- Spatial parameters (bbox, geom): X μs
- Temporal parameters (datetime): X μs
- Complex filters (property queries): X μs

**Caching Performance:**
- First access (uncached): X μs
- Subsequent access (cached): X μs (X% faster)
- Cache hit rate: X% (typical usage pattern)

**Scalability:**
- Tested up to X collections cached
- Memory per cached navigator: X KB
- Recommended cache size: <X navigators

**Best Practices:**
- ✅ Reuse navigator instances (caching saves X% time)
- ✅ Use bbox instead of geom for simpler queries (X% faster)
- ✅ Batch requests when possible (reduces overhead)
```

**Parser Performance** (~200-250 lines, from Issues #57, #59):

```markdown
## Parser Performance

### Single Feature Parsing

**By Format:**
- GeoJSON (passthrough): X ms (baseline)
- SensorML→GeoJSON: X ms (+X% overhead for conversion)
- SWE Common: X ms

**By Resource Type:**
- System: X ms
- Deployment: X ms
- Procedure: X ms
- Datastream: X ms
- Observation: X ms

### Collection Parsing

**Scaling:**
- 10 features: X ms (X ms per feature)
- 100 features: X ms (X ms per feature)
- 1,000 features: X ms (X ms per feature)
- 10,000 features: X ms (X ms per feature)

**Scaling Characteristics:**
- O(n) linear scaling for collection size
- O(d) linear scaling for nesting depth
- No performance degradation observed up to 10,000 features

### Format Detection

**Detection Time:**
- Header detection (high confidence): X μs
- Body inspection (fallback): X μs
- Combined (best case): X μs
- Combined (worst case): X μs

**Detection Scenarios:**
- Best case (GeoJSON with header): X μs
- Medium case (SensorML with header): X μs
- Worst case (SWE without header): X μs

### Position Extraction

**By Position Type:**
- GeoJSON Point (passthrough): X μs
- GeoPose (create Point): X μs
- Vector (SWE, create Point): X μs
- DataRecord (SWE, create Point): X μs

**Memory Overhead:**
- Point creation: X bytes per Point
- Coordinates array: 24 bytes (3 × 8-byte numbers)
```

**Validation Performance** (~150-200 lines, from Issue #58):

```markdown
## Validation Performance

### Validation Overhead

**By Validator:**
- GeoJSON validation: +X% overhead
- SWE validation (no constraints): +X% overhead
- SWE validation (with constraints): +X% overhead
- SensorML validation: +X% overhead (not integrated)

**By Resource Type:**
- System validation: X ms per feature
- Deployment validation: X ms per feature
- Datastream validation: X ms per feature

**Collection Validation:**
- 10 features: X ms (+X% overhead)
- 100 features: X ms (+X% overhead)
- 1,000 features: X ms (+X% overhead)

### Constraint Validation Cost

**SWE Constraint Types:**
- Interval checking: X μs per check
- Pattern/regex matching: X μs per check
- Significant figures: X μs per check
- Token list validation: X μs per check

**Best Practices:**
- ✅ Disable validation for trusted sources (X% faster)
- ✅ Enable validation in development (catch errors early)
- ⚠️ Constraint validation expensive (consider disabling for performance-critical code)
```

**Memory Usage** (~150-200 lines, from Issue #60):

```markdown
## Memory Usage

### Per-Feature Memory

**By Resource Type:**
- System: X KB per feature
- Deployment: X KB per feature
- Procedure: X KB per feature
- Datastream: X KB per feature
- Observation: X KB per feature

### Collection Memory

**Scaling:**
- 10 features: X KB (X KB per feature)
- 100 features: X KB (X KB per feature)
- 1,000 features: X MB (X KB per feature)
- 10,000 features: X MB (X KB per feature)

**Memory Characteristics:**
- O(n) linear scaling with collection size
- O(d) linear scaling with nesting depth
- Peak memory: X × steady-state during parsing
- GC frequency: Every X features parsed

### Nesting Depth Memory

**SWE DataRecord Nesting:**
- 1 level: X KB
- 2 levels: X KB
- 3 levels: X KB
- 5 levels: X KB
- 10 levels: X KB (not recommended)

**Best Practices:**
- ✅ Limit nesting to 5 levels (X KB overhead per level)
- ✅ Use streaming for >10,000 features (avoid loading all in memory)
- ⚠️ Deep nesting (>10 levels) may cause stack overflow
```

---

### 5. Document Scalability Limits

**Scalability Limits** (~100-150 lines):

```markdown
## Scalability Limits

### Collection Size Limits

**Practical Limits:**
- **Small collections**: <100 features (<X MB memory)
- **Medium collections**: 100-1,000 features (X-Y MB memory)
- **Large collections**: 1,000-10,000 features (Y-Z MB memory)
- **Very large**: >10,000 features (>Z MB) - **consider streaming**

**Performance Degradation:**
- No degradation observed up to 10,000 features
- Linear scaling (O(n)) confirmed for collection size
- GC frequency increases with size (every X features)

### Nesting Depth Limits

**SWE DataRecord:**
- **Recommended**: ≤5 levels deep (X KB per level)
- **Maximum safe**: ≤10 levels deep (X KB per level)
- **Unsafe**: >10 levels (risk of stack overflow)

**Recursive Parsing:**
- Call stack depth: X levels before overflow
- Memory per level: X KB
- Tested up to 20 levels (stress test)

### Concurrent Request Limits

**Navigator Caching:**
- Tested up to X concurrent navigators
- Memory per navigator: X KB
- Recommended limit: <X navigators

**Parser Instances:**
- Parsers are stateless (safe for concurrent use)
- No limit on concurrent parsing operations
- Memory is per-operation, not per-parser

### Memory Constraints

**For Memory-Limited Environments:**
- **<512 MB**: Limit to X features per parse operation
- **512 MB - 2 GB**: Limit to X features per parse operation
- **>2 GB**: No practical limit (up to 10,000+ features)

**Embedded/Mobile:**
- Minimum heap: X MB (library + small dataset)
- Recommended heap: X MB (library + medium dataset)
- Disable validation (saves X% memory)
```

---

### 6. Document Optimization Strategies

**Optimization Strategies** (~200-250 lines):

```markdown
## Optimization Strategies

### 1. Caching Strategy

**Navigator Caching:**
- ✅ **DO**: Reuse navigator instances (X% faster)
- ✅ **DO**: Cache per collection (not per request)
- ❌ **DON'T**: Create new navigator for each request (X% slower)

**Example:**
```typescript
// Good: Reuse navigator (cached)
const navigator = new TypedCSAPINavigator(collection);
const systems = await navigator.getSystems();
const deployments = await navigator.getDeployments(); // Same navigator

// Bad: Create new navigator each time (uncached)
const systems = await new TypedCSAPINavigator(collection).getSystems();
const deployments = await new TypedCSAPINavigator(collection).getDeployments();
```

**HTTP Caching:**
- Global HTTP cache shared across navigators
- ETags and conditional requests supported (if server provides)
- Cache invalidation: Manual or time-based

### 2. Validation Strategy

**When to Enable Validation:**
- ✅ **Development**: Always enable (catch errors early)
- ✅ **Testing**: Always enable (verify data quality)
- ⚠️ **Production (untrusted)**: Enable (security > performance)
- ❌ **Production (trusted)**: Disable (performance > validation)

**Validation Overhead:**
- GeoJSON: +X%
- SWE (no constraints): +X%
- SWE (with constraints): +X%

**Example:**
```typescript
// Development: Enable validation
const systems = await navigator.getSystems({ validate: true, strict: true });

// Production (trusted source): Disable validation
const systems = await navigator.getSystems({ validate: false });
```

### 3. Format Selection

**Format Performance:**
- **Fastest**: GeoJSON (native format, no conversion)
- **Medium**: SensorML (conversion overhead: +X%)
- **Slowest**: SWE Common (complex parsing: +X%)

**Best Practices:**
- ✅ Prefer GeoJSON when available (fastest)
- ⚠️ Use SensorML only when GeoJSON unavailable
- ⚠️ SWE Common for observations/commands only

### 4. Collection Handling

**Streaming vs. Batch:**
- **Small (<100)**: Load entire collection (simple, fast)
- **Medium (100-1,000)**: Load entire collection or paginate
- **Large (1,000-10,000)**: Paginate (X features per page)
- **Very large (>10,000)**: **Implement streaming** (avoid memory issues)

**Pagination Strategy:**
```typescript
// Good: Paginate large collections
let offset = 0;
const limit = 100;
while (true) {
  const result = await navigator.getSystems({ offset, limit });
  processBatch(result.data);
  if (result.data.length < limit) break;
  offset += limit;
}
```

### 5. Lazy Loading

**Parser Instantiation:**
- Parsers instantiated lazily (on first use)
- Reduces initial memory footprint
- No performance penalty (instantiation is fast)

**Resource Selection:**
- Only import needed resource parsers
- Use tree-shaking to eliminate unused code
- Bundle size reduction: X% (if only using 3 of 10 resources)

### 6. Memory Optimization

**For Large Datasets:**
- ✅ Process in batches (don't hold all in memory)
- ✅ Release references after processing (allow GC)
- ✅ Increase Node.js heap size: `--max-old-space-size=4096`

**For Deeply Nested Data:**
- ⚠️ Limit SWE DataRecord nesting to 5 levels
- ⚠️ Monitor stack depth in recursive parsing
- ❌ Avoid >10 levels (risk of stack overflow)

### 7. Bundle Size Optimization

**For Browser Applications:**
- ✅ Use tree-shaking (eliminate unused resources)
- ✅ Code-split by resource type (load on demand)
- ✅ Minify and compress (gzip reduces X%)
- ⚠️ Navigator.ts is 79 KB (consider lazy loading)

**Estimated Bundle Sizes:**
- Full library: ~250-300 KB (minified: ~X KB, gzipped: ~X KB)
- With tree-shaking (3 resources): ~X KB (X% reduction)
```

---

### 7. Document Benchmark Results

**Benchmark Results** (~150-200 lines):

```markdown
## Benchmark Results

**Benchmark Environment:**
- Hardware: [CPU model, RAM, OS]
- Node.js: [version]
- Date: [benchmark date]

### Navigator Benchmarks

| Operation | Iterations | Avg Time | Ops/Sec | Variance |
|-----------|-----------|----------|---------|----------|
| System URL | 10,000 | X μs | X,XXX | ±X% |
| Deployment URL | 10,000 | X μs | X,XXX | ±X% |
| Complex query | 10,000 | X μs | X,XXX | ±X% |
| Cached access | 10,000 | X μs | X,XXX | ±X% |

### Parser Benchmarks

| Operation | Iterations | Avg Time | Ops/Sec | Variance |
|-----------|-----------|----------|---------|----------|
| GeoJSON System | 1,000 | X ms | X,XXX | ±X% |
| SensorML→GeoJSON | 1,000 | X ms | X,XXX | ±X% |
| SWE Quantity | 1,000 | X ms | X,XXX | ±X% |
| Collection (100) | 100 | X ms | X,XXX | ±X% |
| Collection (1,000) | 10 | X ms | X,XXX | ±X% |

### Validation Benchmarks

| Operation | Iterations | Avg Time | Ops/Sec | Variance |
|-----------|-----------|----------|---------|----------|
| No validation | 1,000 | X ms | X,XXX | ±X% |
| GeoJSON validation | 1,000 | X ms | X,XXX | ±X% |
| SWE validation | 1,000 | X ms | X,XXX | ±X% |
| Constraint validation | 1,000 | X ms | X,XXX | ±X% |

### Memory Benchmarks

| Operation | Memory Usage | GC Events | Notes |
|-----------|--------------|-----------|-------|
| Single feature | X KB | 0 | Baseline |
| Collection (100) | X KB | X | X KB per feature |
| Collection (1,000) | X MB | X | X KB per feature |
| Collection (10,000) | X MB | X | X KB per feature |
| Nesting (5 levels) | X KB | 0 | X KB per level |

### Regression Tests

**Performance Regression Detection:**
- Benchmarks run on every PR
- Alert if any benchmark >10% slower
- Alert if memory usage >20% higher
- Historical data tracked in CI/CD

**Baseline Commit:** [commit SHA]
**Last Updated:** [date]
```

---

### 8. Document Performance Comparison Tables

**Performance Comparison** (~100-150 lines):

```markdown
## Performance Comparisons

### Format Performance

| Format | Parse Time | Conversion | Memory | Best For |
|--------|-----------|------------|--------|----------|
| **GeoJSON** | X ms (fastest) | None | X KB | Direct consumption, web apps |
| **SensorML** | X ms (+X%) | To GeoJSON | X KB | Sensor metadata, procedures |
| **SWE Common** | X ms (+X%) | None | X KB | Observations, datastreams |

### Validator Performance

| Validator | Overhead | Constraint Cost | Best For |
|-----------|----------|-----------------|----------|
| **GeoJSON** | +X% | N/A | Feature validation, required properties |
| **SWE (simple)** | +X% | N/A | Type checking, required properties |
| **SWE (constraints)** | +X% | +X% | Data quality, interval/pattern validation |
| **SensorML** | N/A | N/A | Not integrated (known limitation) |

### Resource Type Performance

| Resource | Parse Time | Memory | Validation | Notes |
|----------|-----------|--------|------------|-------|
| **System** | X ms | X KB | X ms | Common, medium complexity |
| **Deployment** | X ms | X KB | X ms | Geometry extraction |
| **Procedure** | X ms | X KB | X ms | All process types supported |
| **Datastream** | X ms | X KB | X ms | SWE schema extraction |
| **Observation** | X ms | X KB | X ms | SWE-only, high volume |

### Collection Scaling

| Size | Parse Time | Memory | Throughput | Notes |
|------|-----------|--------|------------|-------|
| **10** | X ms | X KB | X,XXX/sec | Fast |
| **100** | X ms | X KB | X,XXX/sec | Good |
| **1,000** | X ms | X MB | X,XXX/sec | Acceptable |
| **10,000** | X ms | X MB | X,XXX/sec | Consider streaming |
```

---

### 9. Document Capacity Planning Guidance

**Capacity Planning** (~100-150 lines):

```markdown
## Capacity Planning

### Client-Side Deployment

**Browser Applications:**
- **Initial Load**: ~X KB (gzipped library)
- **Memory Footprint**: ~X MB (library + small dataset)
- **Recommended**: Tree-shake unused resources (X% reduction)
- **Mobile**: Consider lazy loading (defer X KB until needed)

**Node.js Applications:**
- **Initial Load**: ~X ms (library import)
- **Memory Footprint**: ~X MB (library + parsers)
- **Concurrent Operations**: No limit (parsers are stateless)
- **Recommended**: Increase heap size for large datasets

### Server-Side Deployment

**Resource Requirements (per instance):**
- **CPU**: X% per concurrent request (X ms parse time)
- **Memory**: X MB base + (X KB × features)
- **Throughput**: ~X,XXX requests/sec (simple queries)
- **Concurrent Requests**: Limited by memory (X MB per request)

**Scaling Estimates:**

| Users | Requests/Min | Memory | CPU | Instances |
|-------|--------------|--------|-----|-----------|
| 10 | 60 | X MB | X% | 1 |
| 100 | 600 | X MB | X% | 1-2 |
| 1,000 | 6,000 | X MB | X% | X-Y |
| 10,000 | 60,000 | X MB | X% | Y-Z |

**Bottleneck Analysis:**
- **CPU**: Parsing is CPU-bound (X ms per feature)
- **Memory**: Collections held in memory (X KB per feature)
- **Network**: Bandwidth dominates for large collections
- **I/O**: Disk/database access typically slower than parsing

### Embedded/IoT Deployment

**Minimum Requirements:**
- **RAM**: X MB (library + small dataset)
- **CPU**: X MHz (acceptable parse time)
- **Flash/ROM**: ~X KB (minified library)

**Constraints:**
- Disable validation (saves X% memory)
- Limit collection size (<X features)
- Use GeoJSON only (smallest overhead)
- Consider batch processing (process X features at a time)

### Monitoring Recommendations

**Metrics to Track:**
- Parse time (p50, p95, p99)
- Memory usage (heap, RSS)
- GC frequency and pause time
- Cache hit rate (navigators)
- Error rate (validation failures)

**Alerts:**
- Parse time >X ms (degradation)
- Memory usage >X% of limit (leak or overload)
- GC frequency >X per minute (memory pressure)
- Error rate >X% (data quality issues)
```

---

### 10. Document Performance Monitoring

**Performance Monitoring** (~50-100 lines):

```markdown
## Performance Monitoring

### Recommended Metrics

**Application Performance:**
```typescript
import { performance } from 'perf_hooks';

// Measure parse time
const start = performance.now();
const result = await navigator.getSystems({ limit: 100 });
const parseTime = performance.now() - start;
console.log(`Parse time: ${parseTime.toFixed(2)} ms`);

// Measure memory usage
const memBefore = process.memoryUsage();
await navigator.getSystems({ limit: 1000 });
const memAfter = process.memoryUsage();
const memDelta = (memAfter.heapUsed - memBefore.heapUsed) / 1024 / 1024;
console.log(`Memory delta: ${memDelta.toFixed(2)} MB`);
```

**Production Monitoring:**
- Integrate with APM (New Relic, Datadog, etc.)
- Track parse time percentiles (p50, p95, p99)
- Monitor memory growth over time
- Alert on performance regressions

### Profiling

**Node.js Profiling:**
```bash
# CPU profiling
node --prof app.js
node --prof-process isolate-*.log > profile.txt

# Heap profiling
node --inspect app.js
# Open chrome://inspect in Chrome
# Take heap snapshots before/after operations
```

**Chrome DevTools:**
```bash
# Browser profiling
npm run build
# Load in browser with DevTools open
# Performance tab: Record → Perform operation → Stop
# Memory tab: Take heap snapshots
```
```

---

### 11. Add Performance FAQ

**Performance FAQ** (~100-150 lines):

```markdown
## Performance FAQ

### Q: How fast is ogc-client-CSAPI?

**A:** Typical performance for common operations:
- URL construction: <X μs
- Single feature parsing: <X ms
- Collection (100 features): <X ms
- Validation overhead: +X%

For detailed benchmarks, see the [Benchmark Results](#benchmark-results) section.

### Q: What's the maximum collection size?

**A:** Tested up to 10,000 features with linear scaling (O(n)). Practical limits:
- **Recommended**: <1,000 features per request
- **Maximum**: 10,000 features (X MB memory)
- **Beyond**: Use pagination or streaming

### Q: Should I enable validation in production?

**A:** Depends on data source:
- ✅ **Trusted source**: Disable (X% faster)
- ⚠️ **Untrusted source**: Enable (security > performance)
- ✅ **Development**: Always enable (catch errors early)

### Q: How much memory does the library use?

**A:** Memory usage scales with dataset size:
- Library alone: ~X MB
- Single feature: ~X KB
- 100 features: ~X KB
- 1,000 features: ~X MB
- 10,000 features: ~X MB

### Q: Which format is fastest?

**A:** GeoJSON is fastest (native format, no conversion):
- GeoJSON: X ms (baseline)
- SensorML: X ms (+X% for conversion)
- SWE Common: X ms (+X% for parsing)

### Q: How do I optimize for mobile/embedded?

**A:** Several optimization strategies:
- ✅ Disable validation (`validate: false`)
- ✅ Use tree-shaking (remove unused resources)
- ✅ Limit collection sizes (<100 features)
- ✅ Use GeoJSON only (smallest overhead)
- ✅ Paginate large datasets (X features per page)

### Q: What causes performance degradation?

**A:** Common performance bottlenecks:
- 🐌 Large collections (>1,000 features) - use pagination
- 🐌 Deep nesting (>5 levels) - limit SWE DataRecord depth
- 🐌 Constraint validation - disable if not needed
- 🐌 SensorML conversion - use GeoJSON when available
- 🐌 Creating new navigators - reuse cached instances

### Q: How do I detect performance regressions?

**A:** Performance monitoring strategies:
- ✅ Run benchmarks on every PR (automated)
- ✅ Track parse time percentiles (p50, p95, p99)
- ✅ Monitor memory growth over time
- ✅ Set alerts for >10% slower or >20% more memory
- ✅ Profile with Chrome DevTools or Node.js profiler

### Q: Is there a performance SLA?

**A:** Performance targets (not guarantees):
- **Good**: Parse time <X ms per feature, memory <X KB per feature
- **Acceptable**: Parse time <X ms per feature, memory <X KB per feature
- **Poor**: Parse time >X ms per feature, memory >X KB per feature

Actual performance depends on hardware, dataset complexity, and configuration.
```

---

### 12. Integrate with CI/CD

Add performance documentation to CI/CD workflow:

**Documentation Generation:**
```yaml
# .github/workflows/docs.yml
- name: Generate performance documentation
  run: |
    npm run bench:all
    npm run docs:performance
```

**Documentation Verification:**
```yaml
# Verify performance documentation is up-to-date
- name: Check performance docs
  run: |
    npm run bench:summary
    git diff --exit-code docs/performance.md
```

**PR Comments:**
```yaml
# Post performance summary to PRs
- name: Performance summary
  run: |
    npm run bench:compare
    # Post results as PR comment
```

---

## Acceptance Criteria

### Prerequisites (1 item)
- [ ] ✅ Work items #32-37 (Issues #55-60) are complete with all benchmark data available

### Documentation Structure (8 items)
- [ ] Created "Performance & Scalability" section in README.md (~800-1,200 lines)
- [ ] Documented performance overview with typical latency, throughput, memory
- [ ] Documented component performance (Navigator, Parsers, Validators, Format detection, Memory)
- [ ] Documented scalability limits (collection size, nesting depth, concurrent requests, memory constraints)
- [ ] Documented optimization strategies (caching, validation, format selection, collection handling, lazy loading, memory, bundle size)
- [ ] Documented benchmark results with tables
- [ ] Documented performance comparisons (format, validator, resource type, collection scaling)
- [ ] Documented capacity planning guidance (client-side, server-side, embedded/IoT, monitoring)

### Performance Overview (5 items)
- [ ] Documented typical performance metrics (URL construction, parsing, validation, memory)
- [ ] Documented design principles (optional validation, caching, format detection, lazy initialization)
- [ ] Documented performance targets (good/acceptable/poor thresholds)
- [ ] Documented known limitations (JSON-only, HTTP-only)
- [ ] Provided benchmark environment details

### Component Performance (20 items)
- [ ] Documented Navigator URL construction latency
- [ ] Documented Navigator query serialization performance
- [ ] Documented Navigator caching performance (cache hit benefit)
- [ ] Documented single feature parsing by format (GeoJSON, SensorML, SWE)
- [ ] Documented single feature parsing by resource type (System, Deployment, Procedure, etc.)
- [ ] Documented collection parsing scaling (10, 100, 1,000, 10,000 features)
- [ ] Documented format detection latency (header vs body, best/worst case)
- [ ] Documented position extraction performance by type
- [ ] Documented validation overhead by validator (GeoJSON, SWE, SensorML)
- [ ] Documented validation overhead by resource type
- [ ] Documented constraint validation cost (intervals, patterns, significant figures)
- [ ] Documented memory per feature by resource type
- [ ] Documented collection memory scaling (10, 100, 1,000, 10,000 features)
- [ ] Documented nesting depth memory (1, 2, 3, 5, 10 levels)
- [ ] Documented GC frequency and overhead
- [ ] Documented memory leak detection results
- [ ] Provided performance best practices for each component
- [ ] Included code examples demonstrating performance optimization
- [ ] Referenced specific benchmark issues for detailed data
- [ ] Cross-referenced architecture features (Issue #23) with performance data

### Scalability Limits (8 items)
- [ ] Documented practical collection size limits (small/medium/large/very large)
- [ ] Documented nesting depth limits (recommended/maximum safe/unsafe)
- [ ] Documented concurrent request limits
- [ ] Documented memory constraints by environment (<512MB, 512MB-2GB, >2GB)
- [ ] Documented performance degradation characteristics
- [ ] Documented when to use streaming vs batch processing
- [ ] Provided embedded/mobile specific limits
- [ ] Documented scaling characteristics (linear O(n) vs superlinear)

### Optimization Strategies (10 items)
- [ ] Documented caching strategy with examples
- [ ] Documented validation strategy (when to enable/disable)
- [ ] Documented format selection guidance (GeoJSON vs SensorML vs SWE)
- [ ] Documented collection handling strategies (streaming vs batch, pagination)
- [ ] Documented lazy loading benefits
- [ ] Documented memory optimization techniques
- [ ] Documented bundle size optimization (tree-shaking, code-splitting)
- [ ] Provided performance comparison tables
- [ ] Included code examples for each strategy
- [ ] Cross-referenced with architectural features (Issue #23)

### Benchmark Results (6 items)
- [ ] Documented benchmark environment (hardware, Node.js version, date)
- [ ] Included Navigator benchmark table
- [ ] Included Parser benchmark table
- [ ] Included Validation benchmark table
- [ ] Included Memory benchmark table
- [ ] Documented regression test approach

### Performance Comparisons (4 items)
- [ ] Created format performance comparison table
- [ ] Created validator performance comparison table
- [ ] Created resource type performance comparison table
- [ ] Created collection scaling comparison table

### Capacity Planning (6 items)
- [ ] Documented client-side deployment requirements (browser, Node.js)
- [ ] Documented server-side deployment requirements (CPU, memory, throughput)
- [ ] Created scaling estimates table (users, requests/min, resources)
- [ ] Documented embedded/IoT requirements and constraints
- [ ] Provided bottleneck analysis
- [ ] Documented monitoring recommendations

### Performance FAQ (10 items)
- [ ] Answered "How fast is ogc-client-CSAPI?"
- [ ] Answered "What's the maximum collection size?"
- [ ] Answered "Should I enable validation in production?"
- [ ] Answered "How much memory does the library use?"
- [ ] Answered "Which format is fastest?"
- [ ] Answered "How do I optimize for mobile/embedded?"
- [ ] Answered "What causes performance degradation?"
- [ ] Answered "How do I detect performance regressions?"
- [ ] Answered "Is there a performance SLA?"
- [ ] Added other relevant FAQs based on benchmark findings

### CI/CD Integration (3 items)
- [ ] Added performance documentation generation to CI/CD
- [ ] Added documentation verification (ensure up-to-date)
- [ ] Added PR comment with performance summary

### Cross-References (4 items)
- [ ] Linked to Issue #19 (test coverage)
- [ ] Linked to Issue #23 (architecture)
- [ ] Linked to Issues #55-60 (component benchmarks)
- [ ] Referenced specific files from architecture validation

## Implementation Notes

### Files to Create

**None** - Only documentation updates to existing README.md

### Files to Modify

**README.md** (~800-1,200 lines added):
- New "Performance & Scalability" section after main features
- 12 subsections covering all performance aspects
- Tables, code examples, and FAQs
- Links to benchmark issues and architecture validation

### Files to Reference

**Benchmark Issues** (for data):
- Issue #55 (Benchmark infrastructure)
- Issue #56 (Navigator performance)
- Issue #57 (Parser performance)
- Issue #58 (Validator performance)
- Issue #59 (Format detection performance)
- Issue #60 (Memory usage)

**Validation Issues** (for context):
- Issue #19 (Test coverage - 94.03%, 832+ tests)
- Issue #23 (Architecture - layered design, performance features)

**Source Files** (for examples):
- navigator.ts (caching implementation)
- parsers/base.ts (optional validation)
- formats.ts (format detection)
- typed-navigator.ts (high-level API)

### Content Organization

**Section Order:**
1. Performance Overview (high-level summary)
2. Component Performance (detailed metrics)
3. Scalability Limits (practical boundaries)
4. Optimization Strategies (how to improve)
5. Benchmark Results (raw data tables)
6. Performance Comparisons (side-by-side)
7. Capacity Planning (deployment guidance)
8. Performance Monitoring (ongoing tracking)
9. Performance FAQ (quick answers)

### Writing Guidelines

**Style:**
- ✅ Evidence-based (use actual benchmark data)
- ✅ Actionable (provide specific recommendations)
- ✅ Contextual (explain why performance matters)
- ✅ Honest (document limitations and tradeoffs)

**Format:**
- Use tables for comparative data
- Use code examples for optimization strategies
- Use bullet points for lists
- Use emojis sparingly (✅/❌/⚠️ for status)

**Maintenance:**
- Update after each benchmark run
- Version benchmarks by commit SHA
- Document benchmark environment changes
- Track performance trends over time

### Dependencies

**CRITICAL DEPENDENCIES:**
- **REQUIRES** work item #32 (Issue #55) - Benchmark infrastructure
- **REQUIRES** work item #33 (Issue #56) - Navigator benchmarks
- **REQUIRES** work item #34 (Issue #57) - Parser benchmarks
- **REQUIRES** work item #35 (Issue #58) - Validator benchmarks
- **REQUIRES** work item #36 (Issue #59) - Format detection benchmarks
- **REQUIRES** work item #37 (Issue #60) - Memory benchmarks

**Why These Dependencies Matter:**
- Cannot document performance without measurements
- Cannot provide optimization guidance without baseline
- Documentation must be evidence-based, not speculative
- All data comes from benchmark results

### Testing Requirements

**Documentation Validation:**
- All benchmark data accurate (verify against source issues)
- All code examples compile and run correctly
- All links work (issues, files, commits)
- All tables formatted correctly (Markdown rendering)
- Performance claims match benchmark results

**Regression Prevention:**
- CI/CD verifies documentation up-to-date
- Performance regressions trigger documentation updates
- Benchmark environment documented (reproducibility)

### Caveats

**Performance Variability:**
- Benchmarks run on specific hardware (document specs)
- Results vary by Node.js version, CPU, memory
- Production performance may differ from benchmarks
- Network latency typically dominates client-side performance

**Documentation Maintenance:**
- Performance documentation requires ongoing maintenance
- Update after significant code changes
- Re-run benchmarks periodically (quarterly?)
- Track performance trends over time

**Benchmark Accuracy:**
- Benchmarks show relative performance (not absolute)
- Microbenchmarks may not reflect real-world usage
- Use benchmarks for guidance, not guarantees
- Validate performance in production environments

## Priority Justification

**Priority: Low**

**Why Low Priority:**
1. **Depends on Other Work**: Cannot complete until #32-37 (benchmark issues) are done
2. **No Functional Impact**: Library works correctly without performance docs
3. **Test Coverage Excellent**: 94%+ coverage means quality is already high
4. **Architecture Validated**: Issue #23 confirmed excellent architecture
5. **Documentation Effort**: Requires aggregating and synthesizing benchmark data (8-15 hours)

**Why Still Important:**
1. **User Guidance**: Users need performance characteristics for decision-making
2. **Capacity Planning**: Server operators need resource estimates
3. **Optimization Baseline**: Establishes baseline for detecting regressions
4. **Production Readiness**: Complete performance documentation signals production quality
5. **Competitive Analysis**: Performance docs help users compare with alternatives

**Impact if Not Addressed:**
- ⚠️ Users cannot estimate resource requirements
- ⚠️ Cannot plan server capacity
- ⚠️ Unknown if suitable for mobile/embedded
- ⚠️ No performance regression detection baseline
- ✅ Library still works correctly (functional quality not affected)
- ✅ Architecture already validated (Issue #23)

**Effort Estimate:** 8-15 hours (after #32-37 complete)
- Data aggregation: 3-5 hours (collect from 6 benchmark issues)
- Documentation writing: 4-8 hours (~1,000 lines with tables/examples)
- Review and validation: 1-2 hours (verify accuracy)
- CI/CD integration: 0.5-1 hour

**When to Prioritize Higher:**
- If users request performance documentation
- If preparing for public release (documentation completeness matters)
- If competing with other libraries (performance comparison important)
- If seeing production performance issues (need baseline for investigation)
- If onboarding new developers (performance guidance accelerates learning)

Document performance characteristics and scalability limits #61

Description

Problem

Context

Detailed Findings

1. Architecture Validated but Performance Not Documented

2. Test Coverage Excellent but Performance Tests Missing

3. Known Performance Features Undocumented

4. Code Size but No Performance Impact Documentation

5. Architectural Weaknesses with Performance Implications

6. Dependencies for Performance Documentation

7. Performance Considerations vs. Reality

Proposed Solution

1. Establish Prerequisites (DEPENDS ON #32-37)

2. Create Comprehensive Performance Documentation

3. Document Performance Overview

4. Document Component Performance Characteristics

5. Document Scalability Limits

6. Document Optimization Strategies

2. Validation Strategy

3. Format Selection

4. Collection Handling

5. Lazy Loading

6. Memory Optimization

7. Bundle Size Optimization

8. Document Performance Comparison Tables

9. Document Capacity Planning Guidance

10. Document Performance Monitoring

Profiling

12. Integrate with CI/CD

Acceptance Criteria

Prerequisites (1 item)

Documentation Structure (8 items)

Performance Overview (5 items)

Component Performance (20 items)

Scalability Limits (8 items)

Optimization Strategies (10 items)

Benchmark Results (6 items)

Performance Comparisons (4 items)

Capacity Planning (6 items)

Performance FAQ (10 items)

CI/CD Integration (3 items)

Cross-References (4 items)

Implementation Notes

Files to Create

Files to Modify

Files to Reference

Content Organization

Writing Guidelines

Dependencies

Testing Requirements

Caveats

Priority Justification

Metadata

Metadata

Assignees

Labels

Projects

Milestone

Relationships

Development

Issue actions