Backing store

As explained in Latency

A backing store is the authoritative storage layer from which a cache obtains values that it does not contain or whose cached copies have expired.

A backing store is the authoritative storage layer from which a cache obtains values that are absent or expired. It may be a database or service containing the primary copy of the data, while the cache keeps temporary copies closer to callers to reduce access latency. section 6.3 section 6.0

The backing store determines how caching behaves when data is unavailable or changes:

• On a cache miss, the application or cache retrieves the value from the backing store, depending on the caching strategy. section 6.2 section 6.3
• Read-through caching makes the cache query the backing store, transform the result into a cacheable format, update itself, and return the value. section 6.3.2
• Write-through caching synchronously propagates cache updates to the backing store, trading write latency for closer synchronization and possible transactional guarantees. section 6.3.3
• Expiration, replacement, or a low cache hit ratio increases backing-store traffic; a high ratio reduces slow lookups, while excessive caching can still add overhead. section 6.11 section 6.2

Read-through database lookup

An application requests a key from a read-through cache. On a miss, the cache queries the database, converts the result into JSON, stores it, and returns it. The application does not need to manage the database lookup, but the cache becomes coupled to the data model and backing-store format. section 6.3.2

Figure 6.2 With read-through caching, the client looks up a key from the cache. Unlike with cache-aside caching, the read-through cache queries the database and updates itself on a cache miss.

Trace the cache-to-database path during a read-through miss: client lookup, backing-store query, cache update, and response. section 6.3.2

How Latency covers it

How deep it goes

Explains cache misses, read-through and write-through flows, serialization, latency, consistency, TTL, and hit-ratio effects.

Examples

Uses databases, services, SQL results converted to JSON, and customer searches with negative results as concrete cases.

What it assumes

Assumes familiarity with caches and cache keys while defining hits, misses, temporary copies, and primary data storage.

What you can do after

Evaluate caching strategies by tracing miss paths, measuring hit ratios, and weighing backing-store latency against consistency and complexity.

How it connects

Connects backing stores to caching strategies, cache misses, cache hits, TTL, cache consistency, and cache replacement policies.

liveBookmanning.com

Read Latency on liveBook Get the book at manning.com

Related concepts

• read-through cachingused by · Read-through caching is a strategy in which a cache retrieves missing data from a backing store, stores it, and returns it to the requester.
• cachingconfigured by · Caching is the practice of storing reusable data or computation results so later access can avoid repeating retrieval or computation.
• cache missused by · A cache miss occurs when a lookup or access finds no usable requested item in the relevant cache, requiring retrieval or computation elsewhere.
• databaseuses · A database is an organized collection of persistent data managed by software for storage, retrieval, querying, and modification.

This explainer was generated by Manning's knowledge graph from the text of Latency. The section links point to the parts of the book it draws on.