Loom Search

Search

Overview

Full-text search on Lucene-based engines (Elasticsearch/OpenSearch) plus the leaner alternatives (Meilisearch/Typesense). The engine is easy to stand up and easy to get subtly wrong: analyzer mismatches, facet counts that fight their own filters, deep pagination that falls over at 10k, and relevance that looks fine on your three test queries and terrible in production. This skill targets those.

text (analyzed, full-text, scored) vs keyword (exact, aggregatable, sortable, filterable) is the decision under most of these. Get the mapping right first.

Analysis: the root of most bugs

An analyzer = optional char filters → one tokenizer → token filters. It runs at index time (on the stored field) and at query time (on the search string). The inverted index only ever contains analyzed tokens.

⚠ Index-time / query-time analyzer mismatch is the #1 silent search bug. If you index with an edge_ngram analyzer and also analyze the query with it, searching "cat" expands to c, ca, cat and matches "category", "catalog", "cathedral" — garbage relevance. The fix is almost always: aggressive analyzer at index time, plain analyzer at search time.

"name": {
  "type": "text",
  "analyzer": "autocomplete",          // edge_ngram — index time only
  "search_analyzer": "autocomplete_search"  // just lowercase — query time
}

• Reindex required to change the index-time analyzer (existing tokens are already committed). search_analyzer can change without reindex.
• normalizer = the keyword equivalent of an analyzer (lowercase/asciifold only, no tokenizer) so exact-match/aggregation fields can be case-insensitive.
• Verify what a field actually produces with GET /index/_analyze — don't guess.

POST /products/_analyze
{ "field": "name", "text": "Wireless Headphones" }   // shows the exact tokens indexed

Common token filters: lowercase, asciifolding (café→cafe), stop (drop the/a/is — omit for short-field/name search), stemmer/snowball (running→run), synonym/synonym_graph.

Mapping

PUT /products
{
  "settings": { "number_of_shards": 1, "number_of_replicas": 1 },
  "mappings": { "properties": {
    "name":        { "type": "text", "analyzer": "english",
                     "fields": { "raw": { "type": "keyword" } } },   // multi-field: search AND aggregate
    "category":    { "type": "keyword" },
    "price":       { "type": "float" },
    "created_at":  { "type": "date" }
  }}
}

• Multi-fields (name + name.raw) index one source field two ways — full-text on name, exact/sort/agg on name.raw. The standard pattern for "searchable and facetable."
• Use explicit mappings in production. Dynamic mapping infers types from the first doc seen — one stray "12" string makes a numeric field text forever (reindex to fix).
• Shard sizing: aim 10–50 GB/shard. Over-sharding (default was once 5) wastes heap and slows queries on small indices; start with 1 and scale by data size, not habit.

Query vs Filter Context (performance)

• filter — yes/no, no scoring, cached (bitset). Use for exact terms, ranges, booleans. Fast and reused.
• must/should — contribute to _score, not cached. Use only when relevance ranking matters.

{ "query": { "bool": {
  "must":   [ { "match": { "name": "headphones" } } ],          // scored
  "filter": [ { "range": { "price": { "gte": 50, "lte": 200 } } },
              { "term":  { "category": "electronics" } } ],       // cached, unscored
  "must_not": [ { "term": { "status": "discontinued" } } ]
}}}

⚠ Putting exact filters in must bloats scoring work and forfeits the filter cache for no relevance benefit. Ranges, terms, and boolean gates belong in filter.

Relevance

Default similarity is BM25 (replaced TF-IDF in ES 5+). Two knobs on the field's similarity:

• k1 (default 1.2) — term-frequency saturation. Higher = repeated terms keep helping; lower = TF saturates fast.
• b (default 0.75) — length normalization. Higher penalizes long fields more. For short fields (titles/names) a lower b often helps; b=0 ignores length entirely.

Tune via a custom similarity in settings only after boosting/field-weighting is exhausted — most "bad relevance" is a query/mapping problem, not a BM25 problem.

Field boosting (^) — cheap first lever: "fields": ["name^5", "brand^3", "description", "tags"].

multi_match types (pick deliberately):

function_score — inject business signal (popularity, recency) into _score:

{ "function_score": {
  "query": { "match": { "name": "laptop" } },
  "functions": [
    { "field_value_factor": { "field": "sales_count", "modifier": "log1p", "factor": 0.1 } },
    { "gauss": { "created_at": { "origin": "now", "scale": "30d", "decay": 0.5 } } }   // recency decay
  ],
  "score_mode": "sum", "boost_mode": "multiply"
}}

⚠ field_value_factor on a doc where the field is missing throws unless you set "missing": <default>. And an unbounded factor (raw sales_count) can swamp text relevance — always dampen with log1p/sqrt. Prefer gauss/exp decay for recency over hand-rolled math.

Tip: _search?explain=true (or _explain) shows the full score breakdown — use it instead of guessing why a doc ranks where it does. Newer ES also supports learning-to-rank / vector knn for hybrid semantic + lexical search.

Faceted Search — the post_filter gotcha

The classic faceted-UI requirement: when a user checks Category: Electronics, the result list filters to electronics, but the Category facet still shows counts for all categories (so they can switch). If you put the category filter in the main query, your category aggregation only counts electronics — the other options vanish and the UI breaks.

Correct pattern: filter the results with post_filter (applied after aggs are computed), and scope each aggregation with a filter agg that excludes its own facet's filter.

{
  "query": { "bool": { "filter": [ { "range": { "price": { "gte": 50 } } } ] } },  // filters that affect ALL facets
  "aggs": {
    "categories": { "terms": { "field": "category" } }   // NOT filtered by category → shows all options
  },
  "post_filter": { "term": { "category": "electronics" } } // narrows hits only, after aggs
}

• Rule of thumb: a filter that should affect a facet's own counts goes in the main query; a filter driven by that facet goes in post_filter (single facet) or per-agg filter (multiple interacting facets).
• size: 0 when you only need aggregation counts (skip fetching hits).
• ⚠ terms aggregation doc_count can be approximate on sharded indices (each shard returns its top-N, then merges). Raise shard_size or accept the small error; don't build billing on facet counts.

Pagination — deep-paging cliff

⚠ from/size is capped at 10,000 (index.max_result_window) and gets quadratically expensive before then: every shard must collect from + size hits and the coordinator sorts them all. Fine for page 1–20 of a UI; fatal for scrolling/export.

search_after requires a deterministic total sort including a unique field (usually _id or a tiebreaker) — otherwise pages overlap or skip.

Autocomplete — pick the right mechanism

• Completion suggester rebuilds from a dedicated completion field and doesn't reflect deletes until reindex/rebuild — it's a suggestion structure, not your live index.
• edge_ngram: set min_gram/max_gram and only at index time (query analyzer = lowercase), or you hit the mismatch bug above.
• Add fuzziness for typos: completion suggester fuzzy.fuzziness: 1, or match fuzziness: AUTO (0 edits for ≤2 chars, 1 for 3–5, 2 for >5 — Levenshtein/Damerau distance). Fuzziness is expensive; cap max_expansions and avoid fuzziness: 2 on long high-QPS queries.
• Client: debounce 200–300 ms, require ≥2 chars, cancel stale in-flight requests.

Zero-Downtime Reindex (alias pattern)

Never point your app at a concrete index name — always at an alias. Reindexing (new analyzer, mapping change, shard count) then becomes atomic and reversible.

1. Create products_v2 with the new mapping/settings.
2. POST /_reindex  { source: products_v1, dest: products_v2 }   (optionally with a transform script)
3. Atomically swap the alias:

POST /_aliases
{ "actions": [
  { "remove": { "index": "products_v1", "alias": "products" } },
  { "add":    { "index": "products_v2", "alias": "products" } }
]}

• The alias swap is a single atomic step — no window where products points at nothing.
• ⚠ Writes arriving during the reindex land only in v1. For live indexing, dual-write to both (or the alias) during the migration, or reindex a quiesced/append-only snapshot then catch up by _reindex with a range on updated_at.
• Roll back by swapping the alias back — v1 is untouched.
• Use index templates so time-series indices (logs-*) get consistent mappings automatically on rollover.

Indexing Throughput

• Bulk API (_bulk, or helpers.bulk in Python) for imports — never one doc per request. Tune batch size to ~5–15 MB per bulk.
• During heavy bulk load: set number_of_replicas: 0 and refresh_interval: -1, then restore afterward — refresh-per-doc is a major throughput killer.
• Provide explicit _ids only if you need upserts/dedup; letting ES auto-generate is faster (skips a get-before-write).
• refresh_interval (default 1s) is why new docs aren't searchable instantly — that's near-real-time, not real-time. Force with ?refresh=wait_for only in tests.

Engine Choice

• Elasticsearch/OpenSearch — max flexibility, aggregations, scale, vector search; operationally heavy. OpenSearch is the Apache-2.0 fork after ES's license change; APIs largely overlap but have diverged — check version docs.
• Meilisearch / Typesense — typo-tolerant, instant-search-first, sane defaults, tiny ops footprint; far less flexible aggregation/relevance control. Excellent for product/site search and autocomplete where you don't need ES's analytics.

Reference Implementation (Node, bool + facets + highlight)

async search(query, filters = {}, page = 1, pageSize = 20) {
  const must = query ? [{ multi_match: {
    query, fields: ["name^3", "description", "tags^2"], type: "best_fields", fuzziness: "AUTO" } }]
    : [{ match_all: {} }];
  const filter = [];
  if (filters.category) filter.push({ term: { category: filters.category } });
  if (filters.priceMin || filters.priceMax) filter.push({ range: { price: {
    ...(filters.priceMin && { gte: filters.priceMin }),
    ...(filters.priceMax && { lte: filters.priceMax }) } } });

  const res = await this.client.search({ index: "products", body: {
    from: (page - 1) * pageSize, size: pageSize,      // ⚠ shallow paging only (<10k)
    query: { bool: { must, filter } },
    aggs: { categories: { terms: { field: "category", size: 20 } },
            price_stats: { stats: { field: "price" } } },
    highlight: { fields: { name: {}, description: { fragment_size: 150 } } },
  }});
  return {
    hits: res.hits.hits.map(h => ({ ...h._source, _score: h._score, highlight: h.highlight })),
    total: res.hits.total.value, aggregations: res.aggregations,
  };
}

⚠ Highlight fragments go straight into dangerouslySetInnerHTML in most UIs — ES escapes the surrounding text, but confirm you're not concatenating unescaped user content around it (XSS).

Verification Checklist

• text vs keyword chosen per field; facet/sort fields are keyword (or .raw multi-field)
• Explicit mapping in prod; no dynamic-mapping type drift
• Index-time vs query-time analyzers deliberate (autocomplete/ngram analyzed at index time only); verified with _analyze
• Exact/range/boolean predicates in filter context (cached), not must
• Facets show all options while results narrow — post_filter/per-agg filter, not everything in the main query
• Pagination beyond a few thousand uses search_after (+PIT for consistency), not from/size
• Autocomplete mechanism matches the need; edge_ngram not applied at query time; debounced client
• Reindex goes through an alias with an atomic swap; in-flight writes handled
• Bulk indexing with replicas/refresh tuned; not one-doc-per-request
• function_score fields handle missing and dampen magnitude (log1p/decay); relevance tested on representative queries, not 3 happy-path ones
• terms agg count approximation understood; not used where exact counts are load-bearing