Caching strategies (for example, write-through, read-through, lazy loading, TTL)

Various caching strategies can be employed, each with its advantages and use cases. Understanding these strategies is essential for candidates preparing for the AWS Certified Developer – Associate (DVA-C02) exam. Here, we discuss the core caching strategies, such as write-through, read-through, lazy loading, and Time-To-Live (TTL), which are relevant to AWS technologies like Amazon ElastiCache or DAX (DynamoDB Accelerator).

Write-Through Caching

Definition: Write-through caching ensures that the cache is updated when the database is written to. This method prioritizes consistency of the data between the cache and the data store.

How it Works: When an application writes data, the write operation is directed to the cache first. Once written to the cache, the same write is propagated to the underlying data store.

Advantages:

• Ensures data consistency by updating the cache and the persistent storage simultaneously.
• Write operations may be slightly slower due to the dual-write operation, but read operations are fast and consistent with the latest write.

Disadvantages:

• Increased latency for write operations since both cache and datastore are updated together.
• Inefficient for write-heavy applications that don’t benefit as much from cache reading.

Example AWS Service: Amazon ElastiCache for Redis or Memcached with a write-through configuration.

Read-Through Caching

Definition: With read-through caching, the cache acts as the primary reader of data. If the requested data is not in the cache, it’s fetched from the data store and also cached for future access.

How it Works: Upon a read miss, the application queries the cache. If the data isn’t present, the cache system retrieves the data from the underlying store, places it in the cache, and returns it to the requester.

Advantages:

• Reduces the load on the data store, as subsequent reads for the same data will hit the cache.
• Data in the cache is always up-to-date from the last read, ensuring consistency.

Disadvantages:

• There can be a latency spike on a cache miss since the data must be fetched from the slow data store.
• Complexity increases as the application must handle cache population on a cache miss.

Example AWS Service: Amazon ElastiCache for Redis or DAX for Amazon DynamoDB, configured for read-through operations.

Lazy Loading (Cache-Aside)

Definition: Lazy loading populates the cache with data only when that data is requested. If the data is in the cache, it is returned; if not, it is fetched from the data store, returned to the client, and added to the cache.

How it Works: The application first checks if the data is in the cache. If it’s a miss, the data is retrieved from the data store and then written to the cache for future access.

Advantages:

• On-demand caching keeps the cache size minimal by only storing the data that is needed.
• Eliminates caching of unnecessary data, saving memory and potentially reducing costs.

Disadvantages:

• Cache misses result in an initial delay as the data has to be fetched from the data store and then cached.
• Data in the cache may become stale if not updated or expired proactively.

Example AWS Service: Amazon ElastiCache for Redis, implemented with lazy loading patterns.

Time-To-Live (TTL)

Definition: TTL is a caching strategy where each cache entry is given a lifespan. Once the lifespan expires, the data is either removed from the cache or marked as stale, depending on the cache’s eviction policies.

How it Works: TTL values are attached to cache entries. After the TTL has elapsed, the data is considered stale and is either automatically deleted or updated depending on the cache configuration.

Advantages:

• Helps in maintaining the freshness of data by automatically evicting old entries.
• Prevents indefinite growth of the cache, which can lead to memory issues.

Disadvantages:

• Determining the proper TTL value can be challenging; too short may lead to frequent cache misses, too long may cause stale data.
• Requires an additional logic layer to handle the eviction and optional refresh of data entries.

Example AWS Service: Amazon ElastiCache and Amazon DAX both support TTL policies for cache entries.

By implementing these caching strategies, AWS developers can optimize application performance, reduce database load, and provide a faster user experience. The AWS Certified Developer – Associate exam candidates must understand these patterns, when to use them, and their impact on system design.