Caching Strategies

Why this matters

Caching makes NLP services faster, cheaper, and more reliable. In real workloads, many requests repeat or are similar enough to reuse work. Smart caching reduces latency spikes, protects upstream models during traffic bursts, and cuts inference costs.

• Customer support bots: repeat intents and FAQs benefit from response caches.
• Search/RAG: reusing embeddings and retrieval results avoids re-encoding on every query.
• Localization/translation: many strings repeat across pages and versions.

Concept explained simply

Caching is remembering previous work so you can answer identical (or near-identical) requests quickly. The hard parts are choosing the right place to remember (client, edge, service, or database), how long to remember (TTL), and how to forget safely (invalidation). For NLP, also include the model version, tokenization, decoding parameters, and any retrieval index version in your cache key so you never serve incompatible results.

Mental model

Think of caching as a layered backpack:

• Top pocket (client/edge): tiny, fastest. Use for exact repeats and static content.
• Middle pocket (service memory, Redis): larger, fast. Use for hot keys.
• Bottom pocket (disk, object store): largest, slower. Use for warm start and long-lived data.

Pack items with clear labels: input hash + model/version + parameters + pipeline versions. When you change any label, it becomes a new item, avoiding stale results.

Core strategies

1. Key design
   • Include: endpoint, model_name, model_version, decoding_params, tokenizer_version, locale, pipeline_stage, input_hash, postprocess_version, rag_index_version.
   • Normalize input before hashing: trim, lower/Unicode normalize where appropriate, collapse whitespace, sort JSON keys.
   • Hash: stable hash (e.g., SHA-256) of normalized input + param bundle.
2. TTL and invalidation
   • Use short TTLs for volatile content (minutes–hours), longer for stable assets (days–weeks).
   • Bump versions on model or pipeline updates to invalidate safely.
   • Support manual purge for critical fixes.
3. Multi-tier caching
   • Client/edge cache for GET-like idempotent endpoints and static prompts.
   • Service memory (LRU) for microseconds access to the top N hot keys.
   • Redis/Memcached for cross-instance sharing and eviction policies (LRU/LFU).
   • Disk/object store for warm starts, batch precomputes, and cold backups.
4. Determinism matters
   • Cache model outputs only when they are deterministic or acceptable to reuse. With sampling (e.g., temperature > 0), cache only if a seed or exact params are part of the key, or cache earlier pipeline stages (tokenization, retrieval) instead.
5. RAG-specific caches
   • Document embeddings cache keyed by (doc_id, embedding_model, version).
   • Query embedding cache keyed by (normalized_query, model, version).
   • Retrieval result cache keyed by (normalized_query_hash, index_version, top_k, filter_set).
   • If grounding data changes, bump index_version to invalidate.
6. Warm-up and preload
   • Preload top queries, top intents, and common UI strings before traffic peaks.
   • After deployment, replay the last hour's top misses to build a warm cache.
7. Capacity and eviction
   • Start with LFU or LRU; monitor hit rate, memory usage, and evictions.
   • Segment caches: small hot cache for outputs, medium cache for embeddings, larger store for retrieval results.
8. Safety and privacy
   • Avoid caching raw PII; if required, hash or tokenize sensitive fields and encrypt at rest.
   • Scope keys per tenant or user when results are personalized.

Worked examples

1) Intent classification API

Scenario: Classify short user messages (deterministic model).

• Normalize: lowercase, strip punctuation except emoji, trim spaces.
• Key: intent_v2|distilbert-intent|tok1.3|lang=en|thresh=0.8|input_sha256=...
• TTL: 7 days. Invalidate when model_version changes.
• Placement: service memory (top 10k) + Redis (1M).

2) RAG Q&A pipeline

Stages to cache:

• Document embeddings: key (doc_id|embed_model=v3|norm=v2) with long TTL; invalidate on re-index.
• Query embeddings: key (normalized_query|embed_model=v3).
• Retrieval results: key (qhash|index_version=v12|topk=8|filters=plan:pro).

Do not cache final answers if they include user-specific data; instead cache only retrieval results and use fast generation.

3) Translation microservice

Use deterministic decoding (beam_size=5, no sampling).

• Key: translate|model=t5xl-v1|beam=5|max_len=256|src=en|tgt=de|tok=2.1|src_sha256=...
• TTL: 30 days for static UI strings; 1 day for content streams.
• Prewarm: top 5k strings from analytics.

Who this is for

• NLP engineers and MLEs deploying models to production.
• Backend engineers integrating NLP APIs.
• Data/ML platform engineers optimizing cost and latency.

Prerequisites

• Basic understanding of HTTP services and stateless APIs.
• Familiarity with NLP pipelines (tokenization, embeddings, retrieval, generation).
• Awareness of model versioning and deployment processes.

Learning path

1. Map your pipeline stages and mark deterministic vs stochastic steps.
2. Define normalization rules and a versioned cache key schema.
3. Add a Redis tier with LRU/LFU and observable metrics.
4. Introduce per-stage TTLs and version-based invalidation.
5. Implement warm-up, capacity planning, and privacy safeguards.

Practical projects

• Build a model-output cache for a sentiment API with versioned keys and a dashboard for hit rate and p95 latency.
• Create an embedding cache for a RAG system; add automatic invalidation when the index version changes.
• Implement a multi-tier cache (service memory + Redis + disk) and measure savings during a traffic spike simulation.

Common mistakes (and how to self-check)

• Missing parameters in the key: If decoding params change and cache still hits, your key is incomplete. Self-check: log and diff the params used in cache hit vs miss.
• Caching non-deterministic outputs: If temperature, top_p, or sampling seed vary, hits may confuse users. Self-check: enforce deterministic settings or scope cache to exact params.
• Forgetting to invalidate on model update: Self-check: keys include model_version; observe hit-rate drop to near-zero after upgrade.
• Overly long TTLs on personalized content: Self-check: verify keys include tenant/user scope or set short TTLs.
• Ignoring normalization: Self-check: measure duplicate keys for whitespace/casing variants; normalize and compare hit rate.

Exercises

Complete these before taking the quick test.

Exercise 1 — Design a robust cache key

Design a cache key and TTL policy for a sentiment classification endpoint. Assumptions: deterministic model; inputs are short texts; English only; tokenizer v1.4; model v3.2; threshold=0.7; post-processing v2. Include input normalization in your plan and outline where you would place the cache (service memory vs Redis) and why.

• Deliverable: a key template, normalization rules, TTL, and placement rationale.

Exercise 2 — Capacity estimate

Estimate memory for caching 1,000,000 unique model outputs for 30 days. Assume: average response 900 bytes; cache key 120 bytes; store overhead 48 bytes per item. Add 30% headroom. Recommend a Redis node size.

• Deliverable: total bytes, GB estimate, and a node size recommendation.

Mini challenge

Your product sees a traffic spike at lunch (3x). Propose a warm-up plan for the top 2,000 queries and an automatic backfill when hit rate falls below 50%. Include where the warm keys live and how you expire them safely afterward.

Next steps

• Instrument cache metrics and create alerts for hit rate and eviction spikes.
• Pilot multi-tier caching in a canary service and compare latency/cost baselines.
• Extend caching to upstream stages (tokenization, embeddings, retrieval) where outputs are most reusable.

Ready? Quick test

The quick test is available to everyone. Note: only logged-in users get saved progress.