メニュー
- Designing an Elasticsearch index mapping from scratch
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/UitbreidenOS/UitKit --skill elasticsearchコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SKILL.md を表示中
Guidelines and instructions for Agent execution state rollback rules
Guidelines and instructions for Agent execution step counters limits
Guidelines and instructions for Agent execution timeout limits setups
SOC 職業分類に基づく
| name | elasticsearch |
| description | - Designing an Elasticsearch index mapping from scratch |
search_afterExplicit mapping beats dynamic mapping in production. Dynamic mapping infers types at index time and can promote fields to the wrong type — a numeric ID inferred as long then hit with a string breaks indexing.
PUT /products
{
"settings": {
"number_of_shards": 3,
"number_of_replicas": 1,
"analysis"
Field type decisions:
| Use case | Type |
|---|---|
| Full-text search | text with appropriate analyzer |
| Exact match, aggregation, sorting | keyword |
| Same field for both | Multi-field: text + .keyword sub-field |
| Structured object, queryable | nested (separate Lucene docs) |
| Structured object, filter-only | flattened (cheaper, less flexible) |
| Numbers used in range queries | integer, long, float, double |
| Numbers used only for exact match | keyword (avoids numeric range overhead) |
| Geo queries | geo_point |
Nested vs flattened objects:
nested: each array element is a hidden separate Lucene document. Use when you need to query combinations within a single object (e.g., "attribute name = color AND value = red").flattened: entire object stored as keyword values. Use when you only need to filter on individual keys, not combinations.match vs term:
match: analyzed, for text fields — tokenizes the query string before searchingterm: exact, unanalyzed — use for keyword fields// Wrong: term on a text field (won't match tokenized content)
{ "term": { "name": "Blue Widget" } }
// Right: match on text, term on keyword
{ "match": { "name": "Blue Widget" } }
{ "term": { "category": "electronics" } }
Bool query structure:
{
"query": {
"bool": {
"must": [
{ "match": { "name": "wireless headphones" } }
],
"filter": [
{ "term": { "category": "audio" } },
{ "term": { "in_stock": true } },
{ "range": { "price": { "gte": 50, "lte": 300 } } }
],
"should": [
{ "term": { "tags": "featured" } }
],
"must_not": [
{ "term": { "tags": "discontinued" } }
],
"minimum_should_match": 0
}
}
}
must vs filter: Use filter for conditions that do not affect relevance score (exact match, range, boolean flags). Filter clauses are cached; must clauses are not. Queries that can go in filter should always go in filter.
Nested query:
{
"query": {
"nested": {
"path": "attributes",
"query": {
"bool": {
"must": [
{ "term": { "attributes.name": "color" } },
{ "term": { "attributes.value": "red" } }
]
}
}
}
}
}
Field boosting in multi_match:
{
"query": {
"multi_match": {
"query": "wireless headphones",
"fields": ["name^3", "description^1", "tags^2"],
"type": "best_fields",
"tie_breaker": 0.3
}
}
}
function_score — boost featured products and decay by age:
{
"query": {
"function_score": {
"query": { "match": { "name": "headphones" } },
"functions": [
{
"filter": { "term": { "tags": "featured" } },
"weight": 2.5
},
{
"gauss": {
"created_at": {
"origin": "now",
"scale": "30d",
"decay": 0.5
}
}
},
{
"field_value_factor": {
"field": "rating",
"modifier": "log1p",
"factor": 1.2,
"missing": 1
}
}
],
"score_mode": "multiply",
"boost_mode": "multiply"
}
}
}
BM25 parameters (k1, b) can be tuned per-index in settings:
{
"settings": {
"similarity": {
"default": {
"type": "BM25",
"k1": 1.5,
"b": 0.75
}
}
}
}
Increase k1 (default 1.2) to reward higher term frequency more. Decrease b (default 0.75) to reduce length normalization — useful when document length varies widely and you don't want short documents to dominate.
Terms aggregation (faceted navigation):
{
"aggs": {
"by_category": {
"terms": {
"field": "category",
"size": 20,
"order": { "_count": "desc" }
}
},
"price_ranges": {
"range": {
"field": "price",
"ranges": [
{ "to": 50 },
{ "from": 50, "to": 150 },
{ "from": 150, "to": 500 },
{ "from": 500 }
]
}
},
"avg_rating": {
"avg": { "field": "rating" }
}
}
}
Nested aggregation (facet on nested objects):
{
"aggs": {
"attributes": {
"nested": { "path": "attributes" },
"aggs": {
"attribute_names": {
"terms": { "field": "attributes.name" },
"aggs": {
"attribute_values": {
"terms": { "field": "attributes.value" }
}
}
}
}
}
}
}
Date histogram (time-series analytics):
{
"aggs": {
"orders_over_time": {
"date_histogram": {
"field": "created_at",
"calendar_interval": "1d",
"time_zone": "UTC",
"min_doc_count": 0
},
"aggs": {
"revenue": { "sum": { "field": "price" } }
}
}
}
}
Standard from/size — simple but limited to 10,000 docs:
{ "from": 0, "size": 20 }
search_after — cursor-based, no deep pagination limit. Requires a unique sort field:
{
"sort": [
{ "created_at": "desc" },
{ "id": "asc" }
],
"search_after": ["2024-03-15T10:00:00Z", "prod_abc123"],
"size": 20
}
Pass the last hit's sort values as search_after for the next page. More efficient than scroll for real-time pagination (user-facing search).
Scroll API — use for batch export / reindexing, not user-facing pagination. Holds a search context open:
POST /products/_search?scroll=1m
{ "size": 1000, "query": { "match_all": {} } }
Then: POST /_search/scroll with the scroll_id. Clear when done.
For time-series data (logs, events, metrics):
PUT /_ilm/policy/logs-policy
{
"policy": {
"phases": {
"hot": { "actions": { "rollover": { "max_size": "50gb", "max_age": "7d" } } },
"warm": { "min_age": "7d", "actions": { "forcemerge": { "max_num_segments": 1 }, "allocate": { "require": { "data": "warm" } } } },
"cold": { "min_age": "30d", "actions": { "allocate": { "require": { "data": "cold" } } } },
"delete": { "min_age": "90d", "actions": { "delete": {} } }
}
}
}
keyword, numeric, and date fields — they power sorting and aggregations. Only disable for fields never used for these operations.text fields used in aggregations. Avoid it — add a .keyword sub-field instead._cat/thread_pool/search for queue depth; a backed-up queue means undersized cluster or too many concurrent queries.POST /index/_forcemerge?max_num_segments=1 to reduce segment count and improve query performance."refresh_interval": "-1", bulk index, then restore to "1s". Avoids creating a new segment per refresh cycle.Design a product catalog index with full-text search, faceted filtering by category, price, and rating, and a relevance boost for featured products.
Index mapping: Use text with an edge_ngram analyzer on the name field for prefix matching. Use keyword on category, tags. Use scaled_float for price. Add a nested type for attributes.
Search query:
{
"query": {
"function_score": {
"query": {
"bool": {
"must": [
{
"multi_match": {
"query": "bluetooth speaker",
"fields": ["name^3", "description"],
"type": "best_fields"
}
}
],
"filter": [
{ "term": { "category": "audio" } },
{ "term": { "in_stock": true } },
{ "range": { "price": { "gte": 30, "lte": 200 } } },
{ "range": { "rating": { "gte": 4.0 } } }
]
}
},
"functions": [
{ "filter": { "term": { "tags": "featured" } }, "weight": 2.0 },
{ "field_value_factor": { "field": "rating", "modifier": "sqrt", "factor": 1.5, "missing": 1 } }
],
"score_mode": "sum",
"boost_mode": "multiply"
}
},
"aggs": {
"categories": { "terms": { "field": "category", "size": 10 } },
"price_histogram": { "histogram": { "field": "price", "interval": 50 } },
"avg_rating": { "avg": { "field": "rating" } }
},
"sort": [{ "_score": "desc" }],
"size": 24
}
This query: matches the search text with boosted name field, filters to in-stock audio products in the price/rating range, doubles the score for featured-tagged products, and returns category/price/rating aggregations for the facet panel alongside the results.