Dontopedia

indexing_time

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

indexing_time is The time it takes to build the index from raw data..

46 facts·19 predicates·13 sources·5 in dispute

Mostly:rdf:type(12), has value for(6), measures(3)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Inbound mentions (29)

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.

measuresMeasures(4)

hasMemberHas Member(3)

containsMetricContains Metric(2)

measuredByMeasured by(2)

calculatesCalculates(1)

comparesMetricsCompares Metrics(1)

computesComputes(1)

containsContains(1)

containsElementContains Element(1)

describedByDescribed by(1)

hasMetricHas Metric(1)

hasOrderedMemberHas Ordered Member(1)

includesQuantitativeMetricIncludes Quantitative Metric(1)

includesTemporalMetricIncludes Temporal Metric(1)

mapsToMaps to(1)

measures-metricsMeasures Metrics(1)

monitorsMonitors(1)

orderedSuggestionOrdered Suggestion(1)

printsPrints(1)

secondElementSecond Element(1)

storesStores(1)

suggestedMetricSuggested Metric(1)

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.

26 facts
PredicateValueRef
Has Value forMilvus 2 3 0[3]
Has Value forFaiss 1 7 3[3]
Has Value forAnnoy 1 18 0[3]
Has Value forHnswlib 0 9 2[3]
Has Value forQdrant 0 8 1[3]
Has Value forWeaviate 1 14 0[3]
MeasuresIndexing Time for Vectors[1]
MeasuresIndex Construction[5]
MeasuresIndex Construction[8]
Has VariantWeaviate Index Time[11]
Has VariantFaiss Index Time[11]
Has Unitms[1]
Measurement PurposeNew Vector Indexing[1]
Unitmilliseconds[3]
Fully Populatedtrue[3]
DescriptionThe time it takes to build the index from raw data.[4]
Defined Astime taken to build the index from raw data[5]
Ordinal Position2[5]
Measures DurationConstruction Process[8]
Correlates WithSearch Time[8]
Is Part ofPerformance Metrics[9]
Is Stored inResults Dictionary[9]
Is Computed byEvaluate Database Function[9]
Maps toIndexing Time Key[9]
Is Calculated AsTime Difference[13]
Is Printed inIndex Documents Function[13]

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.

typebeam/9f797393-50e3-41f0-a90a-ffaea027f129
ex:PerformanceMetric
measuresbeam/9f797393-50e3-41f0-a90a-ffaea027f129
ex:indexing-time-for-vectors
hasUnitbeam/9f797393-50e3-41f0-a90a-ffaea027f129
ms
measurementPurposebeam/9f797393-50e3-41f0-a90a-ffaea027f129
ex:new-vector-indexing
typebeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
ex:PerformanceMetric
labelbeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
indexing_time
typebeam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
ex:PerformanceMetric
labelbeam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
Indexing Time
hasValueForbeam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
ex:milvus-2-3-0
hasValueForbeam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
ex:faiss-1-7-3
hasValueForbeam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
ex:annoy-1-18-0
hasValueForbeam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
ex:hnswlib-0-9-2
hasValueForbeam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
ex:qdrant-0-8-1
hasValueForbeam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
ex:weaviate-1-14-0
unitbeam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
milliseconds
fullyPopulatedbeam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
true
typebeam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01
ex:PerformanceMetric
labelbeam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01
Indexing Time
descriptionbeam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01
The time it takes to build the index from raw data.
typebeam/692b18d5-3f23-4553-a43b-eff0a0815c04
ex:PerformanceMetric
labelbeam/692b18d5-3f23-4553-a43b-eff0a0815c04
Indexing Time
definedAsbeam/692b18d5-3f23-4553-a43b-eff0a0815c04
time taken to build the index from raw data
measuresbeam/692b18d5-3f23-4553-a43b-eff0a0815c04
ex:index-construction
ordinalPositionbeam/692b18d5-3f23-4553-a43b-eff0a0815c04
2
typebeam/d26a5287-fb4f-4619-b610-ba0ca857b51f
ex:PerformanceMetric
typebeam/1ff666a3-024a-43b9-a61b-238256feb9fd
ex:Metric
labelbeam/1ff666a3-024a-43b9-a61b-238256feb9fd
Indexing Time
typebeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
ex:PerformanceMetric
labelbeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
Indexing Time
measuresbeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
ex:index-construction
measuresDurationbeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
ex:construction-process
correlatesWithbeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
ex:search-time
typebeam/82230382-8bc4-4da4-8f74-b604a44e2862
ex:Metric
labelbeam/82230382-8bc4-4da4-8f74-b604a44e2862
Indexing Time
isPartOfbeam/82230382-8bc4-4da4-8f74-b604a44e2862
ex:performance-metrics
isStoredInbeam/82230382-8bc4-4da4-8f74-b604a44e2862
ex:results-dictionary
isComputedBybeam/82230382-8bc4-4da4-8f74-b604a44e2862
ex:evaluate-database-function
mapsTobeam/82230382-8bc4-4da4-8f74-b604a44e2862
ex:indexing-time-key
typebeam/5e937662-abc6-4623-b5b6-7b168728e324
ex:performance-metric
typebeam/7a9ac19a-33f6-4bf6-abb1-90a9206a55a1
ex:PerformanceMetric
hasVariantbeam/7a9ac19a-33f6-4bf6-abb1-90a9206a55a1
ex:weaviate_index_time
hasVariantbeam/7a9ac19a-33f6-4bf6-abb1-90a9206a55a1
ex:faiss_index_time
typebeam/cc7f1022-6680-4382-82c0-198c5bd4b914
ex:LatencyMetric
labelbeam/cc7f1022-6680-4382-82c0-198c5bd4b914
Indexing Time
isCalculatedAsbeam/db3d2131-4d92-4987-a533-fcf237e4ca37
ex:time-difference
isPrintedInbeam/db3d2131-4d92-4987-a533-fcf237e4ca37
ex:index-documents-function

References (13)

13 references
  1. ctx:claims/beam/9f797393-50e3-41f0-a90a-ffaea027f129
    • full textbeam-chunk
      text/plain1 KBdoc:beam/9f797393-50e3-41f0-a90a-ffaea027f129
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      'storage_efficiency': storage_efficiency, 'scalability': scalability, 'ease_of_use': ease_of_use, 'cost': cost } for library, metrics in results.items(): print(f"Library: {library}") print(f"Sear
  2. ctx:claims/beam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
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      8. **Ease of Integration**: How easy it is to integrate the database into your existing system. 9. **Community Support**: The level of community support and documentation available. 10. **Cost**: The financial cost associated with using the
  3. ctx:claims/beam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
    • full textbeam-chunk
      text/plain1 KBdoc:beam/9423e542-ef27-4b6c-82c7-f95a6bf87bd7
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      matrix.loc['Qdrant 0.8.1', 'search_time'] = 190 matrix.loc['Weaviate 1.14.0', 'search_time'] = 210 # Add more sample data for other metrics matrix.loc['Milvus 2.3.0', 'index_size'] = 1000 matrix.loc['Faiss 1.7.3', 'index_size'] = 1200 matr
  4. ctx:claims/beam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01
    • full textbeam-chunk
      text/plain1 KBdoc:beam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01
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      matrix.loc['Faiss 1.7.3', 'search_time'] = 200 matrix.loc['Annoy 1.18.0', 'search_time'] = 250 matrix.loc['Hnswlib 0.9.2', 'search_time'] = 220 matrix.loc['Qdrant 0.8.1', 'search_time'] = 190 matrix.loc['Weaviate 1.14.0', 'search_time'] = 2
  5. ctx:claims/beam/692b18d5-3f23-4553-a43b-eff0a0815c04
    • full textbeam-chunk
      text/plain1 KBdoc:beam/692b18d5-3f23-4553-a43b-eff0a0815c04
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      How can I expand this matrix to cover more performance metrics and make a more informed decision? ->-> 3,3 [Turn 2211] Assistant: To expand your comparison matrix and make a more informed decision about which sparse retrieval engine to use
  6. ctx:claims/beam/d26a5287-fb4f-4619-b610-ba0ca857b51f
    • full textbeam-chunk
      text/plain1 KBdoc:beam/d26a5287-fb4f-4619-b610-ba0ca857b51f
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      matrix.loc['Dense Passage Retriever', 'f1_score'] = .72 matrix.loc['Sparse Retrieval', 'f1_score'] = 0.92 matrix.loc['Faiss', 'f1_score'] = 0.62 matrix.loc['Hnswlib', 'f1_score'] = 0.82 matrix.loc['Qdrant', 'f1_score'] = 0.72 matrix.loc['D
  7. ctx:claims/beam/1ff666a3-024a-43b9-a61b-238256feb9fd
    • full textbeam-chunk
      text/plain1 KBdoc:beam/1ff666a3-024a-43b9-a61b-238256feb9fd
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      matrix.loc['Weaviate 1.14.0', 'indexing_time'] = 360 matrix.loc['Milvus 2.3.0', 'memory_usage'] = 500 matrix.loc['Faiss 1.7.3', 'memory_usage'] = 550 matrix.loc['Annoy 1.18.0', 'memory_usage'] = 600 matrix.loc['Hnswlib 0.9.2', 'memory_usag
  8. ctx:claims/beam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
    • full textbeam-chunk
      text/plain1 KBdoc:beam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
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      matrix = pd.DataFrame(index=databases, columns=metrics) # Fill in the matrix with sample data matrix.loc['Milvus 2.3.0', 'search_time'] = 180 matrix.loc['Faiss 1.7.3', 'search_time'] = 200 matrix.loc['Annoy 1.18.0', 'search_time'] = 250 ma
  9. ctx:claims/beam/82230382-8bc4-4da4-8f74-b604a44e2862
    • full textbeam-chunk
      text/plain1 KBdoc:beam/82230382-8bc4-4da4-8f74-b604a44e2862
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      16. **Security Features**: Availability of security features such as encryption, access control, etc. ### Improved Evaluation Script Here's an improved version of your evaluation script that includes more comprehensive metrics and a struct
  10. ctx:claims/beam/5e937662-abc6-4623-b5b6-7b168728e324
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5e937662-abc6-4623-b5b6-7b168728e324
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      print(f"Weaviate time: {weaviate_time} seconds") print(f"FAISS time: {faiss_time} seconds") ``` ->-> 5,10 [Turn 4941] Assistant: Certainly! To properly benchmark Weaviate and FAISS, you'll want to measure both the indexing time and the sea
  11. ctx:claims/beam/7a9ac19a-33f6-4bf6-abb1-90a9206a55a1
  12. ctx:claims/beam/cc7f1022-6680-4382-82c0-198c5bd4b914
    • full textbeam-chunk
      text/plain1 KBdoc:beam/cc7f1022-6680-4382-82c0-198c5bd4b914
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      To ensure your queries are performing optimally, consider the following: 1. **Timeouts**: Set appropriate timeouts for your queries. 2. **Scroll API**: Use the Scroll API for large result sets to avoid overwhelming the cluster. ### Exampl
  13. ctx:claims/beam/db3d2131-4d92-4987-a533-fcf237e4ca37
    • full textbeam-chunk
      text/plain1 KBdoc:beam/db3d2131-4d92-4987-a533-fcf237e4ca37
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      By addressing these points, you can ensure that your implementation meets GDPR compliance requirements and is more secure. [Turn 8700] User: I'm trying to boost the throughput of my indexing system to handle 600 docs/sec, up from 400. I've

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