match_phrase
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
match_phrase has 16 facts recorded in Dontopedia across 6 references, with 2 live disagreements.
Mostly:rdf:type(5), is used for(1), field(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
discouragesDiscourages(1)
- Use Efficient Query Types
ex:use-efficient-query-types
preferredOverPreferred Over(1)
- Term Query
ex:term-query
prefers-overPrefers Over(1)
- Use Term Queries
ex:use-term-queries
recommendsQueryTypeRecommends Query Type(1)
- Query Optimization
ex:query-optimization
usesUses(1)
- Elasticsearch Search
ex:elasticsearch-search
Other facts (12)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Query Type | [1] |
| Rdf:type | Query Type | [2] |
| Rdf:type | Elasticsearch Query Type | [3] |
| Rdf:type | Query Type | [4] |
| Rdf:type | Elasticsearch Query Type | [5] |
| Is Used for | Text Search | [1] |
| Field | text | [2] |
| Query Value | sample | [2] |
| Searches for | Sample Term | [2] |
| Searches | Text Field | [3] |
| Has Field | Text Field | [3] |
| Used When | phrase matching needed | [6] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (6)
ctx:claims/beam/df7c58f3-fbec-47d0-9088-2916d03b14b6- full textbeam-chunktext/plain1 KB
doc:beam/df7c58f3-fbec-47d0-9088-2916d03b14b6Show excerpt
"number_of_shards": 5, "number_of_replicas": 1, "analysis": { "analyzer": { "default": { "type": "standard", " stopwords…
ctx:claims/beam/d180d2a5-12cd-414f-b30b-7f699289a6d3- full textbeam-chunktext/plain1 KB
doc:beam/d180d2a5-12cd-414f-b30b-7f699289a6d3Show excerpt
# Prepare bulk indexing data actions = [ { "_index": "my_index", "_source": {"id": i, "text": "This is a sample document"} } for i in range(1000000) ] # Perform bulk indexing helpers.bulk(es, actions) # Enable …
ctx:claims/beam/770c827d-4c85-4874-99a3-4f5191924dbd- full textbeam-chunktext/plain1 KB
doc:beam/770c827d-4c85-4874-99a3-4f5191924dbdShow excerpt
You can also instrument your application to log search latencies and then visualize these logs using tools like Grafana or Kibana. #### Example Python Code with Logging ```python import time from elasticsearch import Elasticsearch import l…
ctx:claims/beam/2157dee9-e970-4d48-9c1b-078d02e8d4d8- full textbeam-chunktext/plain1 KB
doc:beam/2157dee9-e970-4d48-9c1b-078d02e8d4d8Show excerpt
- **Index Shards**: Ensure that the number of shards is appropriate for your data volume. Too many shards can lead to performance degradation. ```json PUT /your-index-name/_settings { "number_of_shards": 5 } ``` ### 2. Query…
ctx:claims/beam/63484f14-f077-4119-aad4-2ec5f59e1801ctx:claims/beam/06b4c25a-1508-496d-a7cb-ac62d70720b0- full textbeam-chunktext/plain1 KB
doc:beam/06b4c25a-1508-496d-a7cb-ac62d70720b0Show excerpt
'index.refresh_interval': '30s', 'number_of_shards': 1, 'number_of_replicas': 0, 'analysis': { 'analyzer': { 'synonym_analyzer': { 'type': 'custom', …
See also
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