Optimizing Product Discovery When Storage Is Limited: Smart Indexing Strategies

Optimize product discovery when SSD capacity is tight — fast, cheaper search without buying bigger drives

Hook: If your internal product search is choking on a 1 TB SSD, returning irrelevant results or timing out, you don’t always need to upgrade hardware. In 2026, with SSD supply dynamics (including late‑2025 advances like SK Hynix’s PLC research) easing prices but still introducing endurance and QoS tradeoffs, the smarter route is to shrink and reorganize your indices. This guide shows how index pruning, compression, tiered indexing, and result caching combine to deliver fast product discovery on limited storage.

Executive summary — the plan in three lines

1. Reduce index footprint by pruning unused or low-value data and compressing stored fields and doc_values.
2. Split indices into hot/warm/cold tiers and offload cold data to object storage or cheaper drives.
3. Cut query work with smart caching (edge + in‑memory) and precomputed results for popular queries.

Why storage constraints still matter in 2026

Late‑2025 and early‑2026 semiconductor developments — including continuing advances in multi‑level cell tech and companies experimenting with PLCs — are helping SSD capacity grow and prices stabilize. But those devices can trade off latency, IOPS and endurance against raw bytes. For site search teams building product discovery under budget or on constrained hardware, that means two realities:

• Even with cheaper TBs, you still face performance and endurance tradeoffs (write amplification, GC, slower random reads) when indices grow unchecked.
• Cloud storage or new SSD types can lower costs, but they change failure and performance characteristics: treat them as tiers, not panaceas. See guidance on how cloud economics change your tradeoffs: Cloud per‑query cost caps and provider policies.

Core strategies (detailed)

1) Index pruning: keep only what drives discovery

Index pruning is the disciplined removal of nonessential data from the search index. Think of it as triage: keep the items that produce conversions and push the rest to cheaper storage or a fallback system.

• Traffic‑based pruning — use search analytics to drop or archive SKUs with zero traffic over X months. Typical rule: if a product hasn’t appeared in search results nor received clicks in 12 months and has no sales, archive.
• Field‑level pruning — stop storing large, rarely queried fields in the index (_source is heavy). Store images, long descriptions and rich specs in object storage and fetch them after the initial search hit.
• Term/Token pruning — remove low‑value tokens (rare typos, non‑semantic tokens) and reduce index bloat from noisy text like boilerplate user reviews or vendor notes.
• Document scoring pruning — compute a lightweight discovery score (freshness, CTR, revenue) and remove documents below a threshold from the main search index.

Example: Elasticsearch reindex script to drop heavy fields and only keep the searchable subset. (Escape quotes if pasting into a shell.)

POST _reindex
{
  "source": { "index": "products_v1" },
  "dest": { "index": "products_pruned_v2" },
  "script": {
    "source": "ctx._source.remove('long_description'); ctx._source.remove('images');"
  }
}

Pruning operational tips

• Run pruning as a periodic job: identify candidates via analytics, reindex into a pruned index, swap aliases.
• Archive rather than delete initially — keep a snapshot of pruned docs in object storage for recovery. See strategies for on‑demand archives and ephemeral recovery workflows in ephemeral workspace patterns.
• Use automation: tag low‑traffic SKUs in your catalog pipeline so they never enter the hot index.

2) Compression: squeeze more bytes without sacrificing relevance

Compression matters twice: it reduces storage and can improve read throughput by moving fewer bytes from disk to memory. Modern search engines and Lucene derivatives support multiple compression modes.

• Index codec — enable the best compression codec (for Lucene/Elasticsearch, use "best_compression" to trade CPU for space).
• Doc_values compression — represent numeric and keyword fields compactly; choose sparse representations for low‑cardinality fields.
• Delta & variable‑length encoding — apply to timestamps, numeric IDs, price history. Delta encoding is highly effective for sorted numeric series.
• Text token reduction — aggressive stemming and stopword lists reduce token counts. Consider edge‑ngrams only for necessary fields (autocomplete), not the whole index.
• Vector compression — for embedding‑based ranking, use product quantization or 8‑bit quantization to shrink vector indices. By 2026, 4–8 bit quantization is common in production vector stores; see practical notes on deploying compressed vectors in the context of local LLMs and agents (desktop LLM agent best practices).

Elasticsearch index setting for best compression:

PUT /products_compressed
{
  "settings": {
    "index": {
      "codec": "best_compression",
      "refresh_interval": "30s"
    }
  }
}

Compression tradeoffs

• CPU vs space: more compression saves SSD but increases CPU on reads and merges—measure it.
• Latency sensitivity: compress heavy, cold fields, but keep hot, high‑QPS fields quicker to access.

3) Tiered indexing: match data to storage and life cycle

Tiered indexing separates hot, warm and cold data across storage media and access patterns. This is the most powerful long‑term lever when SSD capacity is limited.

• Hot tier (SSD NVMe): active products and facets, low latency, small footprint by pruning and compression.
• Warm tier (SATA SSD or large NVMe with lower IOPS): less frequently queried seasonal products.
• Cold/frozen tier (object store / frozen indices): archive indices exposed via slow queries or asynchronous retrieval.

Implementing tiering:

1. Define ILM (Index Lifecycle Management) rules that roll over indices based on size or age. Roll small monthly indices into warm/cold tiers.
2. Use index aliases so your search layer queries a single alias while the system routes to the right physical tier.
3. For frozen indices, rely on on‑demand thawing or use a search service that supports “frozen on object store” reads. For patterns on distributing small fast indexes to the edge and keeping cold stores centralized, see work on rapid edge content publishing.

PUT /_ilm/policy/products_policy
{
  "policy": {
    "phases": {
      "hot": {"min_age": "0ms", "actions": {"rollover": {"max_size": "50gb"}}},
      "warm": {"min_age": "7d", "actions": {"forcemerge": {"max_num_segments": 1}}},
      "cold": {"min_age": "30d", "actions": {"freeze": {}}}
    }
  }
}

Tiering operational tips

• Use metrics to tune ages and sizes. Don’t pick arbitrary durations — adjust based on query patterns.
• For limited SSD capacity, keep only the last N daily/weekly indices hot and relegate older indices to warm or cold.
• Provide a UI fallback for “slow search” across cold data (e.g., an “advanced search” option that informs users results may be delayed).

4) Result caching and query‑side optimizations

Caching reduces read pressure and user‑perceived latency. Combine engine caches with application and edge caches for maximal effect.

• Shard/query caching — use your search engine’s request cache for identical queries. Tune cache sizes by monitoring hit rate.
• Application cache (Redis/KeyDB) — store top‑N results for popular queries and autocomplete prefixes. Cache keys by query + filters signature.
• Edge/CDN caching — cache JSON responses for highly repeatable queries at the CDN layer with short TTL (e.g., 60–300s).
• Precomputed facets and aggregates — compute and cache facet counts offline for heavy facets instead of computing at query time.
• Partial search — return a ranked hot set from the hot index immediately and progressively load warm/cold hits asynchronously.

Simple Redis caching pattern (pseudo):

# python pseudo-code
key = f"search:{hash(query + json.dumps(filters))}"
result = redis.get(key)
if not result:
    result = search_engine.query(query, filters)
    redis.set(key, json.dumps(result), ex=60)  # 60s TTL
return json.loads(result)

Cache invalidation

Invalidate cache keys when products are updated, created, or deleted. Use message queues (Kafka, RabbitMQ) to emit change events; subscribers delete related keys.

Putting it all together — a practical case study

Scenario: an online catalog with 2M SKUs, limited to 1 TB NVMe. Baseline index size: ~800 GB with average query latency 180 ms and frequent timeouts during peak. Goal: keep hot search node within 400 GB and latency < 120 ms.

1. Run analytics for 30 days and tag 600k SKUs as low‑traffic. Archive them to object storage and remove from hot index. → saves 240 GB.
2. Reindex hot catalogue with best_compression and remove long_description and images from stored fields. → additional 280 GB saved.
3. Quantize embedding vectors from 32‑bit floats to 8‑bit PQ for semantic ranking and move vector store to a dedicated node. → saves 40 GB. For practical tips on compressed vectors and local model deployments, consult notes on desktop LLM and vector workflows.
4. Introduce Redis cache for top 10k queries and CDN edge caching for autocomplete. Cache hit ratio reaches 65%, lowering direct searches to the engine. Monitor edge behavior as in edge observability patterns.
5. Apply ILM: keep last 3 daily indices hot, move weekly indices to warm, archive monthly to cold S3-backed indices.

Result: hot index ~160 GB, warm ~120 GB on cheaper SSD, archived ~520 GB on object storage. Query latency falls to 85 ms for hot results; tail latency improves and SSD write amplification is reduced because hot index is smaller and compaction less frequent.

Monitoring and metrics you must track

Measure these continuously:

• Index size by index and by field (identify heavy fields).
• Documents in hot/warm/cold indices.
• Cache hit ratio (shard cache + app cache + CDN).
• Query latency p50/p95/p99 and errors/timeouts.
• SSD health metrics: TBW, write amplification, IOPS, GC stalls.
• Search analytics: query frequency, zero‑result queries, CTR per query.

Prometheus example queries:

# p50 and p95 for search latency
histogram_quantile(0.50, sum(rate(search_request_duration_seconds_bucket[5m])) by (le))
histogram_quantile(0.95, sum(rate(search_request_duration_seconds_bucket[5m])) by (le))

# index size by name
sum(elasticsearch_index_store_size_bytes) by (index)

If you run these metrics at the edge or across hybrid deployments, consider operational guidance from rapid edge publishing and observability work: edge publishing playbooks and edge observability.

Performance tuning checklist — priority ordering

1. Run search analytics and identify low‑value docs to prune.
2. Remove heavy stored fields and offload large blobs to object storage.
3. Enable index compression and tune refresh and merge settings for offline reindex windows.
4. Implement ILM: hot/warm/cold separation and index rollover.
5. Deploy Redis caching for top queries and CDN caching for static search responses.
6. Quantize vectors and consider dedicated vector nodes or third‑party vector stores.
7. Monitor SSD metrics and adjust write patterns and refresh intervals to reduce wear.

2026 trends and what to watch next

Short‑term trends shaping search architecture in 2026:

• Wider availability of PLC/QLC drives is reducing price per TB — but endurance and GC characteristics mean you should still tier storage.
• Vector quantization is moving from research to mainstream; expect 4–8 bit compressed vectors to be standard in managed search offerings. See practical notes from teams building local agents and models: desktop LLM agent best practices.
• Cloud providers and search SaaS increasingly offer native frozen indices on object stores with acceptable cold read latencies — perfect for large catalogs. Read about changing cloud economics: cloud per‑query cost news.
• Edge AI and embeddings at the edge will push hybrid approaches: small hot indexes near users, large cold stores in central regions. Emerging work on hybrid edge inference is worth watching: edge inference experiments.

Bottom line: even as hardware improves, efficient index management remains the most cost‑effective lever.

Rule of thumb: reduce index bytes before buying more bytes — every GB you shave off the hot index improves latency, reduces SSD wear, and saves money.

Actionable takeaways

• Start with analytics: prune low‑value SKUs and heavy fields first.
• Apply best compression selectively; measure CPU impact under peak load.
• Adopt tiered indices: hot on fast NVMe, warm on cheaper SSDs, cold on object store.
• Cache aggressively for popular queries and autocomplete; use Redis + CDN.
• Quantize vectors and isolate vector workloads to avoid inflating general search indices.

Next steps (quick plan you can run this week)

1. Generate a 30‑day search analytics report (top queries, zero‑result queries, high volume terms, low traffic SKUs).
2. Identify the top 10 heavy fields by size and plan a reindex to drop or externalize them.
3. Create a lightweight ILM policy that rolls indices by size and moves older indices to a warm tier.
4. Implement Redis cache for the top 1,000 queries and measure cache hit rates.

Call to action

If you want a hands‑on plan tailored to your catalog, export your search analytics (top queries, index sizes, doc counts) and run a free index audit. We’ll map a 6‑week plan: pruning, compression, tiering and caching, and estimate SSD savings and latency improvements. Ready to reduce index bloat and get faster product discovery without a hardware refresh?

Contact us for a technical audit and a step‑by‑step migration playbook designed for constrained SSD capacity.

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