Write-through caching
Write-through caching is a caching strategy that synchronously writes each update to both the cache and its backing store before acknowledging it.
Amazon Web Services in Action, Third Edition: An in-depth guide to AWS
Write-through caching is a caching technique that writes updated data synchronously to both the cache and the primary data store.
- How it is used
- Keeps cached application data aligned with database updates so later reads can use the caching layer.
- How deep it goes
- Explains the write-and-read flow, asynchronous-fill option, and memory limit, but gives no write-through configuration steps.
- Examples
- Uses a team-leaderboard capacity calculation and a figure comparing lazy loading with write-through caching.
- What you can do after
- Evaluate/understand when updating a cache alongside a data store helps freshness and when cache capacity poses a risk.
Advanced Algorithms and Data Structures
Write-ahead caching writes data to a cache before writing it to the underlying storage system.
- How it is used
- Serves read-intensive workloads that repeatedly access data written once or infrequently.
- How deep it goes
- Defines simultaneous cache and storage writes, then contrasts latency, data-loss risk, and alternative cache policies.
- Examples
- Uses session data as the representative workload: infrequent writes followed by many near-term reads.
- What you can do after
- Apply it to favor near-zero data-loss risk and fast repeated reads when slower writes are acceptable.
Read how Advanced Algorithms and Data Structures explains write-through caching →
Learn Concurrent Programming with Go
Cache write-through is a cache update policy that immediately mirrors writes to the corresponding location in main memory.
- How it is used
- Explains how shared-memory writes reach main memory and remain visible across processor caches.
- How deep it goes
- Describes the update sequence and its stale-cache limitation, then connects the remedy to cache-coherency protocols.
- Examples
- Uses two threads updating and reading one shared variable across processor caches, supported by an architecture figure.
- What you can do after
- Evaluate why mirroring writes to memory does not alone prevent stale values in other processor caches.
Read how Learn Concurrent Programming with Go explains write-through caching →
Related concepts
- write-behind cachingcontrasts with · Write-behind caching is a technique that records updates in a cache, acknowledges them, and later propagates them to a backing store.
- cachinguses · Caching is the practice of storing reusable data or computation results so later access can avoid repeating retrieval or computation.
- databaseuses · A database is an organized collection of persistent data managed by software for storage, retrieval, querying, and modification.
- memoryuses · Memory is a storage resource or mechanism that retains data or computational state for access during computation or later use.
- ACIDuses · ACID is the set of database transaction properties—atomicity, consistency, isolation, and durability—that characterize reliable transactional behavior.
- cache-aside cachingcontrasts with · Cache-aside caching is a pattern in which an application reads from a cache first, retrieves misses from backing storage, and populates the cache.
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