v0.15.0

⏰ TLRU (Time-aware Least Recently Used) Policy!

Version 0.15.0 introduces the TLRU eviction policy, combining recency, frequency, and time-based factors for intelligent cache management:

New Features:

• ⏰ TLRU Policy - Time-aware LRU that considers age, frequency, and recency
• 🎯 Smart Eviction - Prioritizes entries approaching TTL expiration
• 📊 Triple Scoring - Combines frequency × position_weight × age_factor
• 🎚️ Frequency Weight - NEW: frequency_weight parameter to fine-tune recency vs frequency balance
• 🔄 Automatic Aging - Entries close to TTL get lower priority
• 💡 Backward Compatible - Without TTL, behaves like ARC
• ⚡ Optimal for Time-Sensitive Data - Perfect for caches with expiring content

Quick Start:

use cachelito::cache;

// Time-aware caching with TLRU
#[cache(policy = "tlru", limit = 100, ttl = 300)]
fn fetch_weather(city: String) -> WeatherData {
    // Entries approaching 5-minute TTL are prioritized for eviction
    fetch_from_api(city)
}

// TLRU without TTL behaves like ARC
#[cache(policy = "tlru", limit = 50)]
fn compute_expensive(n: u64) -> u64 {
    // Considers both frequency and recency
    expensive_calculation(n)
}

// NEW: Fine-tune with frequency_weight
#[cache(policy = "tlru", limit = 100, ttl = 300, frequency_weight = 1.5)]
fn fetch_popular_content(id: u64) -> Content {
    // frequency_weight > 1.0 emphasizes frequency over recency
    // Popular entries stay cached longer
    database.fetch(id)
}

How TLRU Works:

• Score Formula: frequency^weight × position_weight × age_factor
• Frequency Weight: Control balance between recency and frequency (default = 1.0)
  • < 1.0: Emphasize recency (good for time-sensitive data)
  • > 1.0: Emphasize frequency (good for popular content)
• Age Factor: Decreases as entry approaches TTL expiration (0.0 = expired, 1.0 = fresh)
• Eviction: Lowest score = first to evict
• Best For: Time-sensitive data, mixed access patterns, expiring content

What's Changed

• feat(GH-21): TLRU Policy by @josepdcs in #36

Full Changelog: 0.14.0...0.15.0