filter
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
filter is Conditions that can be cached and reused.
Mostly:rdf:type(15), purpose(5), enables(4)
Maturity scale
raw canonical shape-checked rule-derived certifiedRdf:typein disputerdf:type
- Query Context[1]all time · Bcbbb3d7 Ccf6 4152 B195 B565faf22d60
- Query Context[2]all time · 870d36e1 74c7 4923 A45d 7839861584f0
- Consideration Factor[3]all time · Abf58a1b 4f1d 4caa 8cfe F563beaca75e
- Optimization Factor[4]all time · A7172c19 274b 4507 Bee6 74a913f617a3
- Concept[5]all time · 67b3880f 4304 41f2 A990 5fffd8b6b339
- Evaluation Factor[6]all time · A596011e E2a5 4f88 8b0e C0693c1c152b
- Query Technique[7]all time · F10d4f3d E383 4868 A4eb C95d9dac0976
- Concept[8]all time · 862c9573 384c 4fcf B141 Bb2857e60deb
- Elasticsearch Context[9]all time · 0a897c70 56d8 4e88 B17d 18d28ded0319
- Elasticsearch Feature[9]all time · 0a897c70 56d8 4e88 B17d 18d28ded0319
Inbound mentions (17)
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.
demonstratesDemonstrates(2)
- Filter Example
ex:filter-example - Python Code Example
ex:python-code-example
isBenefitOfIs Benefit of(2)
- Avoid Scoring
ex:avoid-scoring - Improve Performance
ex:improve-performance
asksCandidateToConsiderAsks Candidate to Consider(1)
- Sample Test Instructions
ex:sample-test-instructions
considersFactorConsiders Factor(1)
- Candidate Evaluation
ex:candidate-evaluation
considersFactorsConsiders Factors(1)
- Sample Test Instructions
ex:sample-test-instructions
enumeratesFactorsEnumerates Factors(1)
- Instruction 3
ex:instruction-3
hasContextHas Context(1)
- Bool Query
ex:bool-query
hasMemberHas Member(1)
- Optimization Factors
ex:optimization-factors
hasPartHas Part(1)
- Bool Query
ex:bool-query
hasRecommendationHas Recommendation(1)
- Efficient Query Design
ex:efficient-query-design
hasSubtopicHas Subtopic(1)
- Elasticsearch Query Optimization
ex:elasticsearch-query-optimization
hasTechniqueHas Technique(1)
- Part 1 Basic Query Optimization
ex:part-1-basic-query-optimization
improvedByImproved by(1)
- Repeated Queries
ex:repeated-queries
mentionsConceptMentions Concept(1)
- Documentation Intro
ex:documentation-intro
usesTechniqueUses Technique(1)
- Part 1 Basic Query Optimization
ex:part-1-basic-query-optimization
Other facts (26)
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 |
|---|---|---|
| Purpose | Improve Performance | [1] |
| Purpose | Filter Cache Benefit | [11] |
| Purpose | Avoid Scoring | [13] |
| Purpose | Avoid Scoring | [15] |
| Purpose | Improve Performance | [15] |
| Enables | Caching | [2] |
| Enables | Reuse | [2] |
| Enables | Avoid Scoring | [15] |
| Enables | Improve Performance | [15] |
| Has Property | Cacheable | [5] |
| Has Property | Reusable | [5] |
| Has Property | Filter Cacheable | [9] |
| Used in | Queries | [10] |
| Used in | Bool Query | [12] |
| Used in | Bool Query | [13] |
| Benefit | Performance Improvement | [5] |
| Benefit | Performance Improvement | [13] |
| Used for | Frequently Used Filters | [1] |
| Description | Conditions that can be cached and reused | [5] |
| Has Parameter | Cacheability | [5] |
| Is Technique for | Query Performance Improvement | [7] |
| Is Type of | Context | [9] |
| Can Be Cached | true | [10] |
| Improves | Repeated Queries | [10] |
| Part of | Bool Query | [14] |
| Uses | Bool Query | [15] |
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 (15)
ctx:claims/beam/bcbbb3d7-ccf6-4152-b195-b565faf22d60ctx:claims/beam/870d36e1-74c7-4923-a45d-7839861584f0- full textbeam-chunktext/plain1 KB
doc:beam/870d36e1-74c7-4923-a45d-7839861584f0Show excerpt
"bool": { "must": [ { "match": { "title": "example" } }, { "match": { "content": "example" } } ], "filter": [ { "term": { "status": "active" }} # Assuming there's a status field that can be fil…
ctx:claims/beam/abf58a1b-4f1d-4caa-8cfe-f563beaca75ectx:claims/beam/a7172c19-274b-4507-bee6-74a913f617a3ctx:claims/beam/67b3880f-4304-41f2-a990-5fffd8b6b339- full textbeam-chunktext/plain1 KB
doc:beam/67b3880f-4304-41f2-a990-5fffd8b6b339Show excerpt
- Understanding when to use `match`, `term`, `bool`, `filter`, etc. - Proper use of `must`, `should`, `must_not`, and `filter` clauses. 2. **Filter Context**: - Using `filter` context for conditions that can be cached and reused. …
ctx:claims/beam/a596011e-e2a5-4f88-8b0e-c0693c1c152b- full textbeam-chunktext/plain997 B
doc:beam/a596011e-e2a5-4f88-8b0e-c0693c1c152bShow excerpt
- Consider factors such as query type, filter context, field selection, result size control, and performance metrics. ### Conclusion By following this structure, you can effectively evaluate the candidates' query optimization skills and e…
ctx:claims/beam/f10d4f3d-e383-4868-a4eb-c95d9dac0976- full textbeam-chunktext/plain1 KB
doc:beam/f10d4f3d-e383-4868-a4eb-c95d9dac0976Show excerpt
[Turn 3233] Assistant: Great! I'm glad you found the structure and instructions helpful. Implementing these changes should provide a comprehensive way to evaluate the candidates' query optimization skills. Here's a quick recap of the key c…
ctx:claims/beam/862c9573-384c-4fcf-b141-bb2857e60deb- full textbeam-chunktext/plain1 KB
doc:beam/862c9573-384c-4fcf-b141-bb2857e60debShow excerpt
- Consider factors such as query type, filter context, field selection, result size control, and performance metrics. ### Example Usage Here are the complete test functions with detailed instructions: ```python from elasticsearch import …
ctx:claims/beam/0a897c70-56d8-4e88-b17d-18d28ded0319- full textbeam-chunktext/plain1 KB
doc:beam/0a897c70-56d8-4e88-b17d-18d28ded0319Show excerpt
I'm seeing an average latency of 200ms, which seems a bit high ->-> 6,12 [Turn 5145] Assistant: Certainly! Reducing query latency in Elasticsearch involves several strategies, including optimizing your query, tuning your index settings, an…
ctx:claims/beam/8df2418b-59d6-46c1-acb8-8a0b398a2016ctx: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/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86- full textbeam-chunktext/plain1 KB
doc:beam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86Show excerpt
Ensure your queries are optimized for performance. 1. **Use Efficient Query Types**: Prefer `term` and `terms` queries over `match` and `match_phrase` queries when possible. ```json { "query": { "bool": { "mu…
ctx:claims/beam/b5493bfc-15b0-462f-9e72-cb64b5007812ctx:claims/beam/9b8f6129-279b-4ba5-b802-69921d2c1ae5- full textbeam-chunktext/plain1 KB
doc:beam/9b8f6129-279b-4ba5-b802-69921d2c1ae5Show excerpt
- **Replicas**: Use replicas to improve read performance and availability. Typically, 1 replica is sufficient, but you can adjust based on your needs. ### 2. **Data Distribution and Routing** - **Index Settings**: Configure index settin…
ctx:claims/beam/63484f14-f077-4119-aad4-2ec5f59e1801
See also
- Query Context
- Frequently Used Filters
- Improve Performance
- Caching
- Reuse
- Consideration Factor
- Optimization Factor
- Concept
- Cacheability
- Cacheable
- Reusable
- Performance Improvement
- Evaluation Factor
- Query Technique
- Query Performance Improvement
- Elasticsearch Context
- Filter Cacheable
- Context
- Elasticsearch Feature
- Query Component
- Queries
- Repeated Queries
- Filter Cache Benefit
- Bool Query
- Avoid Scoring
- Recommendation
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