Principles and Implementation of Caching Mechanism

Principles and Implementation of Caching Mechanism

Caching mechanism is a core technology in backend frameworks for performance enhancement. It works by storing frequently accessed data copies in high-speed storage to reduce direct access to slower data sources (such as databases).

I. Basic Concepts of Caching
A cache is essentially a key-value store in memory. Its workflow is as follows:

  1. When an application needs data, it first queries the cache.
  2. If the data exists in the cache (cache hit), it is returned directly.
  3. If it does not exist (cache miss), the data is fetched from the data source and written into the cache.
  4. Subsequent identical requests can retrieve the data directly from the cache.

II. Detailed Cache Eviction Policies
When cache space is insufficient, decisions must be made on which data to remove:

  1. LRU (Least Recently Used)

    • Principle: Prioritizes evicting data that has not been accessed for the longest time.
    • Implementation: Uses a hash table combined with a doubly linked list.
    class LRUCache:
    def __init__(self, capacity):
        self.cache = {}          # Stores key-value pairs
        self.order = DoublyLinkedList()  # Maintains access order
        self.capacity = capacity

    def get(self, key):
        if key not in self.cache:
            return None
        node = self.cache[key]
        self.order.move_to_head(node)  # Moves to the head of the list to indicate recent use
        return node.value

  2. LFU (Least Frequently Used)

    • Principle: Evicts data with the lowest access frequency.
    • Implementation: Requires maintaining access frequency counters.
    • Complexity: Needs to balance frequency updates and lookup efficiency.

III. Cache Penetration Problem and Solutions

  1. Problem Description: A large number of queries for non-existent data cause requests to directly hit the database.
  2. Solutions:
    • Bloom Filter: Quickly determines whether data exists.
    • Cache Null Values: Briefly caches non-existent data as well.

IV. Cache Avalanche Response Strategies

  1. Problem Description: A large number of cache entries expire simultaneously, causing a sudden surge in database pressure.
  2. Solutions:
    • Set Random Expiration Times: Avoid simultaneous expiration.
    • Hot Data Never Expires: Combined with periodic asynchronous updates.
    • Circuit Breaker Mechanism: Temporarily rejects requests when database pressure is high.

V. Multi-Level Cache Architecture Design
A typical multi-level cache includes:

  1. Application-Level Cache (Local Memory)
  2. Distributed Cache (e.g., Redis Cluster)
  3. CDN Cache (Static Resources)
  4. Browser Cache

Each level of cache has different hit rates and latency characteristics. Appropriate caching strategies need to be designed based on business requirements.

VI. Cache Consistency Guarantee

  1. Write Strategies:
    • Write-Through: Simultaneously updates the cache and the database.
    • Write-Back: Updates the cache first, then asynchronously batches writes to the database.
  2. Invalidation Strategy: Invalidates the cache when data is updated, and reloads it upon the next read.

By rationally applying these caching techniques, system performance can be significantly improved. However, attention must be paid to the increased complexity and data consistency issues introduced by caching.