#FBR (Frequency-Based Replacement) Cache

FBR is a frequency-based replacement cache.

#Overview

FBR (Frequency-Based Replacement) is a cache eviction strategy based on access frequency. It tracks the access frequency for each key, and when the cache is full, it evicts the key with the lowest access frequency.

#Features

• Hit Rate: 78%
• Memory Usage: Medium
• Concurrency: Medium
• Implementation Complexity: Medium

#Use Cases

• Frequency-based access
• Hot data retention
• Cold data eviction
• Scenarios needing clear distinction between hot and cold data

#Quick Start

#Installation

go get github.com/lazygophers/utils/cache/fbr

#Basic Usage

package main

import (
    "fmt"
    "github.com/lazygophers/utils/cache/fbr"
)

func main() {
    // Create cache with capacity of 1000
    cache := fbr.New(1000)

    // Set values
    cache.Set("key1", "value1")
    cache.Set("key2", "value2")

    // Get values
    if value, ok := cache.Get("key1"); ok {
        fmt.Println("Found:", value)
    }

    // Delete values
    cache.Delete("key1")

    // Clear cache
    cache.Clear()
}

#How It Works

FBR maintains access frequency for each key:

Key1: access count = 5  (hot data)
Key2: access count = 1  (cold data)
Key3: access count = 0  (never accessed)

When evicting, prioritizes evicting keys with the lowest access frequency.

#API Reference

#Constructors

// Create new FBR cache
func New(capacity int) *FBR

// Create new FBR cache with options
func NewWithOpts(opts Options) *FBR

#Main Methods

// Set key-value pair
func (c *FBR) Set(key string, value interface{})

// Get value
func (c *FBR) Get(key string) (interface{}, bool)

// Delete key
func (c *FBR) Delete(key string)

// Clear cache
func (c *FBR) Clear()

// Get statistics
func (c *FBR) Stats() Stats

#Performance Characteristics

• Time Complexity:
  • Set: O(log n)
  • Get: O(log n)
  • Delete: O(log n)
• Space Complexity: O(n), where n is cache capacity

#Best Practices

1. Hot data retention: FBR can effectively retain hot data
2. Cold data eviction: Suitable for scenarios needing to quickly evict cold data
3. Large frequency differences: Suitable for scenarios with large access frequency differences

#Related Documentation

• Cache Overview
• LFU Cache
• ALFU Cache