Caching Techniques in OODB

2.1 What is Caching?

Caching involves keeping frequently accessed data in memory to speed up data retrieval. In OODB, it helps minimize the time spent accessing data from disk storage.

2.2 Types of Caching

• Memory Caching: Storing data in RAM for fast access.
• Disk Caching: Using disk storage to cache data that doesn't fit in memory.
• Distributed Caching: Sharing cached data across multiple nodes in a network.

3.1 Cache-aside

In the cache-aside strategy, the application code is responsible for loading data into the cache. When data is requested, the application checks the cache first; if not found, it retrieves it from the database and stores it in the cache.

3.2 Read-through Caching

With read-through caching, the cache automatically retrieves data from the database when it is not present in the cache. This strategy simplifies the application code.

3.3 Write-through Caching

In write-through caching, all writes to the database also update the cache simultaneously. This ensures that the cache is always up-to-date.

3.4 Write-behind Caching

In write-behind caching, writes are first made to the cache, and then the updates are asynchronously written to the database. This improves performance but may lead to data inconsistency if not managed properly.

5.1 Example of Cache-aside in Python

class Database:
    def get_data(self, key):
        # Simulate a database call
        return f"Data for {key}"

class Cache:
    def __init__(self):
        self.cache = {}

    def get(self, key):
        return self.cache.get(key)

    def set(self, key, value):
        self.cache[key] = value

db = Database()
cache = Cache()

def get_data(key):
    # Check the cache first
    data = cache.get(key)
    if data is None:
        # If not in cache, fetch from database
        data = db.get_data(key)
        cache.set(key, data)
    return data

# Fetch data
print(get_data("item1"))  # First time fetches from DB
print(get_data("item1"))  # Second time fetches from Cache