Make your code work spontaneously 🙃

A lightweight PHP library for probabilistic code execution and deterministic feature distribution. Perfect for A/B testing, gradual feature rollouts, performance sampling, and controlled chaos engineering.

Quick Start

use function Antikirra\probability;

// Random execution - 30% chance to log debug info
if (probability(0.3)) {
    error_log("Debug: processing request #{$requestId}");
}

// Deterministic execution - same user always gets same experience
if (probability(0.5, "new_checkout_user_{$userId}")) {
    return renderNewCheckout();
}

// Gradual rollout - increase from 10% to 100% over time
if (probability(0.1, "feature_ai_search_user_{$userId}")) {
    enableAISearch();
}

Install

composer require antikirra/probability:^3.0

🚀 Key Features

• Zero Dependencies - Pure PHP implementation
• Deterministic Distribution - Consistent results for the same input keys
• High Performance - Minimal overhead, suitable for high-traffic applications
• Simple API - Just one function with intuitive parameters
• Battle-tested - Production-ready with predictable behavior at scale

💡 Use Cases

• Performance Sampling - Log only a fraction of requests to reduce storage costs while maintaining system visibility. Sample database queries, API calls, or user interactions for performance monitoring without overwhelming your logging infrastructure.

• A/B Testing - Run controlled experiments with consistent user experience. Test new features, UI changes, or algorithms on a specific percentage of users while ensuring each user always sees the same variant throughout their session.

• Feature Flags - Gradually roll out new features with fine-grained control. Start with a small percentage of users and increase over time, or enable features for specific user segments based on subscription tiers or other criteria.

• Chaos Engineering - Test system resilience by introducing controlled failures. Simulate random delays, service outages, or cache misses to ensure your application handles edge cases gracefully in production.

• Rate Limiting - Implement soft rate limits without additional infrastructure. Control access to expensive operations or API endpoints based on user tiers, preventing abuse while maintaining a smooth experience for legitimate users.

• Load Balancing - Distribute traffic across different backend services or database replicas probabilistically, achieving simple load distribution without complex routing rules.

• Canary Deployments - Route a small percentage of traffic to new application versions or infrastructure, monitoring for issues before full rollout.

• Analytics Sampling - Reduce analytics data volume and costs by tracking only a representative sample of events while maintaining statistical significance.

• Content Variation - Test different content strategies, email templates, or notification messages to optimize engagement metrics.

• Resource Optimization - Selectively enable resource-intensive features like real-time updates, advanced search, or AI-powered suggestions based on server load or user priority.

🔬 How It Works

The library uses two strategies for probability calculation:

1. Pure Random (No Key)

When called without a key, uses PHP's mt_rand() for true randomness:

probability(0.25); // 25% chance, different result each time

2. Deterministic (With Key)

When provided with a key, uses CRC32 hashing for consistent results:

probability(0.25, 'unique_key'); // Same result for same key

Technical Details:

• Uses crc32() to hash the key into a 32-bit unsigned integer (0 to 4,294,967,295)
• Normalizes the hash by dividing by MAX_UINT32 (4294967295) to get a value between 0.0 and 1.0
• Compares normalized value against the probability threshold
• Same key → same hash → same normalized value → deterministic result

The deterministic approach ensures:

• Same input always produces same output
• Uniform distribution across large datasets
• No need for external storage or coordination
• Fast performance (CRC32 is optimized in PHP)

📖 API Reference

function probability(float $probability, string $key = ''): bool

Parameters

• $probability (float): A value between 0.0 and 1.0

  • 0.0 = Never returns true (0% chance)
  • 0.5 = Returns true half the time (50% chance)
  • 1.0 = Always returns true (100% chance)

• $key (string|null): Optional. When provided, ensures deterministic behavior

  • Same key always produces same result
  • Different keys distribute uniformly

Returns

• bool: true if the event should occur, false otherwise

Examples

// 15% random chance
probability(0.15);

// Deterministic 30% for user with id 123
probability(0.30, "user_123");

// Combining feature and user for unique distribution
probability(0.25, "feature_checkout_user_123");

🎯 Best Practices

1. Use Meaningful Keys

// ❌ Bad - too generic
probability(0.5, "test");

// ✅ Good - specific and unique
probability(0.5, "homepage_redesign_user_$userId");

2. Separate Features

// ❌ Bad - same users get all features
if (probability(0.2, $userId)) { /* feature A */ }
if (probability(0.2, $userId)) { /* feature B */ }

// ✅ Good - different user groups per feature
if (probability(0.2, "feature_a_$userId")) { /* feature A */ }
if (probability(0.2, "feature_b_$userId")) { /* feature B */ }

3. Consider Scale

// For high-frequency operations, use very small probabilities
if (probability(0.001)) { // 0.1% - suitable for millions of requests
    $metrics->record($data);
}

📊 When to Use: Random vs Deterministic

Scenario	Use Random (no key)	Use Deterministic (with key)
Performance sampling	✅ Sample random requests	❌ Would sample same requests
Logging/Debugging	✅ Random sampling	❌ Not needed for logs
A/B Testing	❌ Inconsistent UX	✅ User sees same variant
Feature Rollout	❌ Unpredictable access	✅ Stable feature access
Chaos Engineering	✅ Random failures	⚠️ Depends on use case
Load Testing	✅ Random distribution	❌ Predictable patterns
Canary Deployment	❌ Unstable routing	✅ Consistent routing
User Segmentation	❌ Segments change	✅ Stable segments

💻 Real-World Examples

Laravel: Feature Flag Middleware

namespace App\Http\Middleware;

use Closure;
use function Antikirra\probability;

class FeatureFlag
{
    public function handle($request, Closure $next, $feature, $percentage)
    {
        $userId = $request->user()?->id ?? $request->ip();
        $key = "{$feature}_user_{$userId}";

        if (!probability((float)$percentage, $key)) {
            abort(404); // Feature not enabled for this user
        }

        return $next($request);
    }
}

// Usage in routes:
// Route::get('/beta', ...)->middleware('feature:beta_dashboard,0.1');

Symfony: Performance Monitoring

use function Antikirra\probability;
use Psr\Log\LoggerInterface;

class DatabaseQueryLogger
{
    public function __construct(
        private LoggerInterface $logger,
        private float $samplingRate = 0.01 // 1% of queries
    ) {}

    public function logQuery(string $sql, float $duration): void
    {
        // Random sampling - no need for deterministic behavior
        if (!probability($this->samplingRate)) {
            return;
        }

        $this->logger->info('Query executed', [
            'sql' => $sql,
            'duration' => $duration,
            'sampled' => true
        ]);
    }
}

WordPress: A/B Testing

use function Antikirra\probability;

function show_homepage_variant() {
    $user_id = get_current_user_id() ?: $_SERVER['REMOTE_ADDR'];
    $key = "homepage_redesign_user_{$user_id}";

    // 50% of users see new design, consistently
    if (probability(0.5, $key)) {
        get_template_part('homepage', 'new');
    } else {
        get_template_part('homepage', 'classic');
    }
}

API Rate Limiting by Tier

use function Antikirra\probability;

class ApiRateLimiter
{
    public function allowRequest(User $user, string $endpoint): bool
    {
        $limits = [
            'free' => 0.1,    // 10% of requests allowed
            'basic' => 0.5,   // 50% of requests allowed
            'premium' => 1.0  // 100% of requests allowed
        ];

        $probability = $limits[$user->tier] ?? 0;
        $key = "api_{$endpoint}_{$user->id}_" . date('YmdH'); // Hourly bucket

        return probability($probability, $key);
    }
}

🧪 Testing

The library includes a comprehensive Pest test suite covering edge cases, statistical correctness, and deterministic behavior.

# Install dev dependencies
composer install

# Run tests
composer test
# or
./vendor/bin/pest

# Run with coverage (requires Xdebug or PCOV)
./vendor/bin/pest --coverage

Test coverage includes:

• Edge cases (0.0, 1.0, epsilon boundaries)
• Input validation and error handling
• Deterministic key behavior
• Statistical correctness over large sample sizes
• Hash collision handling
• Type coercion

⚡ Performance

Benchmarks on PHP 8.4 (Apple M4):

Operation	Time per call	Ops/sec
Random (no key)	~0.14 μs	~7.0M
Deterministic (with key)	~0.16 μs	~6.2M

Memory usage: 0 bytes (no allocations)

The library is optimized for high-throughput scenarios:

• Fast-path optimization for edge cases (0.0, 1.0)
• Minimal function calls
• No object instantiation
• CRC32 is faster than other hash functions

Run php benchmark.php to test performance on your hardware.

❓ FAQ / Troubleshooting

Why do I get different results in different environments?

Q: Same key returns different results on different servers.

A: This is expected! CRC32 implementation is consistent, but you might be using different keys. Ensure you're using the exact same key string across environments.

// ❌ This will differ between users
probability(0.5, $userId); // If $userId is different

// ✅ This will be consistent for same user
probability(0.5, "feature_x_user_{$userId}");

Why is my A/B test showing 52% instead of 50%?

Q: I'm using probability(0.5, $userId) but getting uneven distribution.

A: With small sample sizes, variance is normal. The distribution converges to 50% with larger samples (law of large numbers). For 100 users, expect 45-55%. For 10,000 users, expect 49-51%.

Can I use this for cryptographic purposes?

Q: Is this secure for generating random tokens?

A: No! This library is NOT cryptographically secure. CRC32 is predictable and mt_rand() is not suitable for security. Use random_bytes() or random_int() for security purposes.

How do I gradually increase rollout percentage?

Q: I want to go from 10% to 50% to 100%.

A: Just change the probability value in your code/config. Users in the 0-10% hash range stay enabled, users in 10-50% get added, etc.

// Week 1: 10% rollout
if (probability(0.1, "feature_x_user_{$userId}")) { ... }

// Week 2: 50% rollout (includes original 10%)
if (probability(0.5, "feature_x_user_{$userId}")) { ... }

// Week 3: 100% rollout
if (probability(1.0, "feature_x_user_{$userId}")) { ... }

What about hash collisions?

Q: Can different keys produce the same result?

A: Yes, CRC32 has only 2³² (~4.3 billion) possible values. With many keys, collisions are possible but rare for typical use cases. For most applications this is acceptable. If you need collision-resistant hashing, fork and replace CRC32 with MD5 or SHA256.

Why not use a database for feature flags?

Q: Isn't a feature flag service better?

A: Depends on your needs:

• Use this library: Simple rollouts, performance sampling, no persistence needed, minimal dependencies
• Use feature flag service: Complex targeting, runtime changes, analytics, team collaboration

This library excels at simplicity and performance, not flexibility.