Cache Hit Ratio

The ratio is calculated as Cache Hits / (Cache Hits + Cache Misses) . A hit occurs when the requested data is found in the cache; a miss requires a fallback to the primary data store.

• High Ratio (>90%) : Indicates effective cache warming and relevant eviction policies.
• Low Ratio (<50%) : Suggests cache churn, insufficient size, or poor temporal locality.
• Impact : A 1% increase in hit ratio can reduce P99 latency by milliseconds.

The Cache Eviction Policy directly governs the hit ratio by determining which objects to discard under memory pressure.

• LRU (Least Recently Used) : Optimizes for temporal locality; ideal for recency-biased access patterns.
• TTL (Time-to-Live) : Forces expiration based on absolute time; critical for Semantic Caches to maintain factual freshness.
• LFU (Least Frequently Used) : Tracks access frequency; prevents one-hit-wonders from polluting the cache.

A traditional exact-match cache has a low hit ratio for natural language queries. A Semantic Cache stores vector embeddings and retrieves based on cosine similarity.

• Mechanism : If a new query embedding is within a threshold distance of a cached query, the cached answer is returned.
• Benefit : Dramatically increases hit ratio for paraphrased questions.
• Risk : Overly broad thresholds cause factual inaccuracies (false hits).

A Cache Stampede is a pathological scenario where a popular entry expires, causing a flood of concurrent requests to hit the backend.

• Trigger : High-traffic key expiration with no lock mechanism.
• Solution : Probabilistic early expiration recomputes the value before the hard expiry, or request coalescing merges concurrent misses into a single backend call.
• Metric : A sudden drop in hit ratio often signals a stampede event.

In transformer inference, the KV-Cache stores key-value tensors to avoid recomputing prior tokens. The hit ratio here is binary per sequence.

• Prefix Caching : If multiple requests share a common system prompt, the KV-Cache hit ratio for that prefix is 100%.
• Memory Trade-off : A high hit ratio requires large GPU memory allocation; eviction of old sequences frees memory but drops the ratio.
• Impact : Directly reduces Time-to-First-Token (TTFT) for long context windows.

An Embedding Cache stores computed vectors for documents to bypass the encoder model. The hit ratio directly translates to GPU cost savings.

• Static Documents : Near 100% hit ratio after initial indexing.
• Dynamic Content : Requires cache invalidation on document update to prevent stale vectors.
• Cost Model : Cost = (1 - Hit Ratio) * GPU Inference Cost. A 95% ratio eliminates 95% of encoding overhead.

The algorithm that determines which items to remove from a full cache to make space for new entries. The choice of policy directly impacts cache hit ratio by controlling how long valuable data persists.

• LRU (Least Recently Used): Discards the data with the oldest access timestamp, optimizing for temporal locality
• LFU (Least Frequently Used): Evicts items with the lowest access count, favoring popular content
• TTL-based: Removes entries after a fixed time-to-live expires, regardless of access patterns
• Two-tier strategies often combine LRU for recency with TTL for data freshness guarantees

The proactive process of pre-loading a cache with anticipated data before it is requested by users. Effective warming strategies prevent the cold start problem, where a newly deployed or flushed cache initially serves 0% of requests from cache, causing latency spikes.

• Boot-time hydration: Loading embeddings or query results from a snapshot on service startup
• Predictive pre-fetching: Analyzing historical access patterns to warm entries ahead of peak traffic
• Incremental warming: Gradually populating the cache to avoid overwhelming backend stores during initialization

A cascading failure mode where a popular cache entry expires, causing a flood of concurrent requests to simultaneously hit the backend database. This can collapse a system even when the cache hit ratio was previously high.

• Probabilistic early expiration: Refresh entries at randomized intervals before hard expiry
• Locking/mutex on miss: Only one request regenerates the value while others wait
• External recomputation: Use a background job to refresh hot keys asynchronously, avoiding request-triggered stampedes entirely

A caching layer that stores query-answer pairs based on semantic similarity rather than exact string matching. This dramatically improves effective cache hit ratio for natural language queries where users phrase the same intent differently.

• Stores embeddings of previous queries and compares cosine similarity of incoming requests
• A similarity threshold (e.g., 0.95) determines whether a cached response is reused
• Reduces LLM inference costs by avoiding redundant generation for paraphrased questions
• Commonly paired with vector databases like FAISS or Milvus for low-latency lookup

A performance metric indicating the maximum response time experienced by 99% of requests. While cache hit ratio measures effectiveness, P99 latency measures the user-facing outcome. A high hit ratio that still produces tail latency spikes signals cache stampedes or slow cache storage.

• P50 (median): Typical user experience, often fast when cache is working
• P99: Worst-case for 1% of users, revealing cache miss penalties and backend bottlenecks
• Monitoring P99 alongside hit ratio exposes whether misses are truly degrading user experience

A storage layer that persists computed vector embeddings for documents or queries to eliminate redundant neural network inference. In RAG pipelines, embedding computation is often the most expensive step before retrieval.

• Caches document embeddings to avoid re-encoding unchanging knowledge base content
• Caches query embeddings for frequently asked questions with identical semantic intent
• Can improve effective cache hit ratio at the computation layer, reducing GPU utilization even when the final answer differs
• Typically implemented with a distributed key-value store like Redis for sub-millisecond access