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Elasticsearch Search

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

Elasticsearch Search has 18 facts recorded in Dontopedia across 5 references, with 3 live disagreements.

18 facts·15 predicates·5 sources·3 in dispute

Mostly:rdf:type(2), uses index(2), has parameter(2)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Uses Indexin disputeusesIndex

  • synonyms[1]sourceall time · B0c69968 148d 412a 8238 E75eb88b5ed2
  • "test_index"[4]sourceall time · E3462606 2a58 4967 B7c7 2170e53b40d6

Has Parameterin disputehasParameter

Has Query StructurehasQueryStructure

Has AssertionhasAssertion

Assertsasserts

Searches FieldsearchesField

  • term[1]sourceall time · B0c69968 148d 412a 8238 E75eb88b5ed2

Uses Query TypeusesQueryType

  • match[1]all time · B0c69968 148d 412a 8238 E75eb88b5ed2

Performed byperformedBy

  • Es.search[3]sourceall time · 2a88f02e 0966 4c11 9f2f 5274939993fe

Request BodyrequestBody

  • query[5]sourceall time · 109fe33b 8545 4dfd 8086 98adca50d2c8

Target IndextargetIndex

  • test_index[5]sourceall time · 109fe33b 8545 4dfd 8086 98adca50d2c8

Uses BodyusesBody

Inbound mentions (6)

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.

performsPerforms(2)

assignedByAssigned by(1)

containsContains(1)

isUsedByIs Used by(1)

usedInUsed in(1)

Other facts (3)

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.

3 facts
PredicateValueRef
ReturnsResponse Object[2]
UsesMatch Phrase Query[2]
TargetsMy Index[2]

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.

18 undated
—
assertsbeam/b0c69968-148d-412a-8238-e75eb88b5ed2
ex:non-zero-hits
—
hasAssertionbeam/b0c69968-148d-412a-8238-e75eb88b5ed2
ex:non-zero-hits
—
hasParameterbeam/770c827d-4c85-4874-99a3-4f5191924dbd
ex:body-parameter
—
hasParameterbeam/770c827d-4c85-4874-99a3-4f5191924dbd
ex:index-parameter
—
hasQueryStructurebeam/b0c69968-148d-412a-8238-e75eb88b5ed2
ex:match-query-structure
—
performedBybeam/2a88f02e-0966-4c11-9f2f-5274939993fe
ex:es.search
—
typebeam/e3462606-2a58-4967-b7c7-2170e53b40d6
ex:ElasticsearchOperation
—
typebeam/b0c69968-148d-412a-8238-e75eb88b5ed2
ex:ElasticsearchQueryOperation
—
requestBodybeam/109fe33b-8545-4dfd-8086-98adca50d2c8
query
—
returnsbeam/770c827d-4c85-4874-99a3-4f5191924dbd
ex:response-object
—
searchesFieldbeam/b0c69968-148d-412a-8238-e75eb88b5ed2
term
—
targetIndexbeam/109fe33b-8545-4dfd-8086-98adca50d2c8
test_index
—
targetsbeam/770c827d-4c85-4874-99a3-4f5191924dbd
ex:my-index
—
usesbeam/770c827d-4c85-4874-99a3-4f5191924dbd
ex:match-phrase-query
—
usesBodybeam/e3462606-2a58-4967-b7c7-2170e53b40d6
ex:query-variable
—
usesIndexbeam/b0c69968-148d-412a-8238-e75eb88b5ed2
synonyms
—
usesIndexbeam/e3462606-2a58-4967-b7c7-2170e53b40d6
"test_index"
—
usesQueryTypebeam/b0c69968-148d-412a-8238-e75eb88b5ed2
match

References (5)

5 references
  1. [1]beam-chunk7 facts
    customctx:claims/beam/b0c69968-148d-412a-8238-e75eb88b5ed2
    • full textbeam-chunk
      text/plain1 KBdoc:beam/b0c69968-148d-412a-8238-e75eb88b5ed2
      Show excerpt
      print(f"Time to index 1000 documents: {end_time - start_time:.2f} seconds") # Run queries start_time = time.time() for doc in test_data: response = es.search(index='synonyms', body={ 'query': { 'match': { …
  2. [2]beam-chunk5 facts
    customctx:claims/beam/770c827d-4c85-4874-99a3-4f5191924dbd
    • full textbeam-chunk
      text/plain1 KBdoc:beam/770c827d-4c85-4874-99a3-4f5191924dbd
      Show 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…
  3. [3]beam-chunk1 fact
    customctx:claims/beam/2a88f02e-0966-4c11-9f2f-5274939993fe
    • full textbeam-chunk
      text/plain1 KBdoc:beam/2a88f02e-0966-4c11-9f2f-5274939993fe
      Show excerpt
      'term': 'hi' } } }) print(response['hits']['total']['value']) # Output: 1 ``` ### Explanation 1. **Thread Safety**: - Use a `threading.Lock` to ensure thread safety when adding and retrieving synonyms. 2. **E…
  4. [4]beam-chunk3 facts
    customctx:claims/beam/e3462606-2a58-4967-b7c7-2170e53b40d6
    • full textbeam-chunk
      text/plain1 KBdoc:beam/e3462606-2a58-4967-b7c7-2170e53b40d6
      Show excerpt
      response = es.search(index="test_index", body=query) print(response) ``` What are some strategies for scaling the Elasticsearch cluster to handle a large number of records? ->-> 1,15 [Turn 9913] Assistant: Designing a scalable architecture…
  5. [5]beam-chunk2 facts
    customctx:claims/beam/109fe33b-8545-4dfd-8086-98adca50d2c8
    • full textbeam-chunk
      text/plain1 KBdoc:beam/109fe33b-8545-4dfd-8086-98adca50d2c8
      Show excerpt
      response = es.search(index="test_index", body=query) print(response) ``` ### Summary To design a scalable architecture for your Elasticsearch cluster: 1. **Properly size and configure your nodes** with adequate resources. 2. **Optimize i…

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