Read-Through vs Write-Through Cache PDF

205.2 Read-Through vs Write-Through Cache Read-through and write-through caching are two caching strategies used to manage how data is synchronized between a cache and a primary storage system. They play crucial roles in system performance optimization, especially in applications where data access...

205.2 Read-Through vs Write-Through Cache Read-through and write-through caching are two caching strategies used to manage how data is synchronized between a cache and a primary storage system. They play crucial roles in system performance optimization, especially in applications where data access speed is critical. Read-Through Cache Read-Through Cache ** Definition: In a read-through cache, data is loaded into the cache on demand, typically when a ** read request occurs for data that is not already in the cache. ** Process:** When a read request is made, the cache first checks if the data is available (cache hit). If the data is not in the cache (cache miss), the cache system reads the data from the  primary storage, stores it in the cache, and then returns the data to the client. Subsequent read requests for the same data will be served directly from the cache until the data expires or is evicted. ** Pros: ** Transparency: The application only needs to interact with the cache, simplifying data access logic. Reduced Latency on Hits: Frequently accessed data is served quickly from the cache. Reduces Load on Primary Storage: Frequent read operations are offloaded from the primary storage. ** Cons: ** Cache Miss Penalty: The first request for data not in the cache incurs latency due to fetching from the data store. Read-Through Cache Example: Online Product Catalog ** Scenario: Imagine an e-commerce website with an extensive online product catalog. ** ** Read-Through Process: ** Cache Miss: When a customer searches for a product that is not currently in the cache, the system experiences a cache miss. Fetching and Caching: The system then fetches the product details from the primary database (like a SQL database) and stores this information in the cache. Subsequent Requests: The next time any customer searches for the same product, the system delivers the product information directly from the cache, significantly faster than querying the primary database. ** Benefits in this Scenario: ** Reduced Database Load: Frequent queries for popular products are served from the cache, reducing the load on the primary database. Improved Read Performance: After initial caching, product information retrieval is much faster. Write-Through Cache Write-Through Cache ** Definition: In a write-through cache, data is written simultaneously to the cache and the primary ** storage system. This approach ensures that the cache always contains the most recent data. ** Process: ** (This is the /Parallel Writes Approach:) [[ ]] When a write request is made, the data is first written to the cache. Simultaneously, the same data is written to the primary storage. Read requests can then be served from the cache, which contains the up-to-date data. ** Pros: ** Data Consistency: Provides strong consistency between the cache and the primary storage. No Data Loss on Crash: Since data is written to the primary storage, there’s no risk of data loss if the cache fails. Simplified Writes: The application only interacts with the cache for write operations. ** Cons: ** Latency on Write Operations: Each write operation incurs latency as it requires simultaneous writing to both the cache and the primary storage. Higher Load on Primary Storage: Every write request impacts the primary storage.  ! ❗ Write-Through Cache Example: Banking System Transaction ** Scenario: Consider a banking system processing financial transactions. ** ** Write-Through Process: ** Transaction Execution: When a user makes a transaction, such as a deposit, the transaction details are written to the cache. Simultaneous Database Write: Simultaneously, the transaction is also recorded in the primary database. Consistent Data: This ensures that the cached data is always up-to-date with the database. If the user immediately checks their balance, the updated balance is already in the cache for fast retrieval. ** Benefits in this Scenario: ** Data Integrity: Crucial in banking, as it ensures that the cache and the primary database are always synchronized, reducing the risk of discrepancies. Reliability: In the event of a cache system failure, the data is safe in the primary database. … ** Two Common Implementations: ** 1. Cache-First Approach: ** * * ** The application writes data to the cache. * The cache synchronously writes this data to the primary storage. ** ** * ** Advantage: The application only needs to interact with the cache. ** ** Risk: If the cache fails before writing the data to the primary storage, data loss can ** *** occur. *** 2. Parallel Writes Approach: *** * ** The application writes the data simultaneously to both the cache and the primary storage. ** Advantage: Eliminates data loss if the cache fails because the database already ** has the latest data. ** Trade-Off: The application must handle two write operations, potentially ** increasing write latency and complexity. Also, the application no longer only interacts with the cache; it must also write to the database directly. … Key Differences In the Read-Through Cache (Product Catalog), the emphasis is on efficiently loading and ** ** * serving read-heavy data after the initial request, which is ideal for data that is read frequently * * ** but updated less often. ** * In the Write-Through Cache (Banking System), the focus is on maintaining high data integrity ** ** * and consistency between the cache and the database, which is essential for transactional data * *** where every write is critical. *** ** Data Synchronization Point: ** ** Read-through caching synchronizes data at the point of reading, ** * ** ** * * while write-through caching synchronizes data at the point of writing. * ** ** * ** ** * ** Performance Impact: ** ** Read-through caching improves read performance after the initial load, ** * * * whereas write-through caching ensures write reliability but may have slower write * ** ** * * *** *** * performance. * ** Use Case Alignment: ** ** Read-through is ideal for read-heavy workloads with infrequent data updates, ** * * * whereas write-through is suitable for environments where data integrity and consistency * ** ** * ** ** ** ** are crucial, especially for write operations. * * When to Use Each Strategy * *** Read-Through: * ** * Ideal when read operations are frequent, and data changes infrequently. ** ** ** ~~ ~~ ~~ ~~** * Useful when you can tolerate eventual consistency for reads. (Since you have to wait till * ** ** * the TTL expires to get the latest from the database) *** Write-Through: * ** * Suitable when data consistency is critical, and you cannot afford stale data. ** ** ~~ ~~ * * Necessary when the system requires immediate propagation of updates. ** ** * Summary Table ~ Aspect ~ ~** Read-Through ** ~ ~** Write-Through ** ~ ** Focus ** Read operations Write operations ** Data Retrieval ** Cache fetches from data store on N/A misses ** Data Update ** N/A Cache writes to data store *** synchronously *** ** Consistency ** May have stale data *** *** Strong consistency ** Performance ** Fast reads on hits, slower on misses *** *** * Slower writes due to synchronous * update ** Use Case ** *** Read-heavy applications *** Applications requiring data integrity Conclusion ** Read-through caching is optimal for scenarios where read performance is crucial and the data ** *** *** can be loaded into the cache on the first read request. ** Write-through caching is suited for applications where data integrity and consistency on ** *** *** *** write operations are paramount. *** * Both strategies enhance performance but in different aspects of data handling – ** ** * *** read-through for read efficiency (since its lazy-loading mechanism aligns perfectly with *** *** read-heavy scenarios where you want the cache to focus only on frequently accessed data *** — in contrast, write-through has write overhead so not ideal for purely read-heavy workloads) , and write-through for reliable writes. *** ***