Dontopedia

Pool Map Call

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-05.)

Pool Map Call has 16 facts recorded in Dontopedia across 3 references.

16 facts·16 predicates·3 sources

Mostly:uses function(1), distributes across(1), purpose(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (7)

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.

areCoordinatedByAre Coordinated by(1)

calledByCalled by(1)

callsCalls(1)

executesMapExecutes Map(1)

hasComponentHas Component(1)

isInputToIs Input to(1)

scheduledByScheduled by(1)

Other facts (16)

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.

16 facts
PredicateValueRef
Uses FunctionIngest Document[1]
Distributes AcrossWorker Processes[1]
PurposeParallel Processing[1]
Operates onDocuments[1]
EnablesParallel Processing[1]
ProducesProcessed Documents[1]
CoordinatesWorker Processes[1]
Is Higher Order Functiontrue[1]
Rdf:typeMethod Call[2]
Calls FunctionIngest Document Function[2]
Applies toBatch Variable[2]
Passes FunctionIngest Document Function[3]
Passes CollectionDocuments List[3]
Applies Function toDocuments List[3]
Parallel Executiontrue[3]
SchedulesIngest Document Function[3]

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.

usesFunctionbeam/3cca2fbf-b6c9-4756-9e7d-11034944be68
ex:ingest-document
distributesAcrossbeam/3cca2fbf-b6c9-4756-9e7d-11034944be68
ex:worker-processes
purposebeam/3cca2fbf-b6c9-4756-9e7d-11034944be68
ex:parallel-processing
operatesOnbeam/3cca2fbf-b6c9-4756-9e7d-11034944be68
ex:documents
enablesbeam/3cca2fbf-b6c9-4756-9e7d-11034944be68
ex:parallel-processing
producesbeam/3cca2fbf-b6c9-4756-9e7d-11034944be68
ex:processed-documents
coordinatesbeam/3cca2fbf-b6c9-4756-9e7d-11034944be68
ex:worker-processes
isHigherOrderFunctionbeam/3cca2fbf-b6c9-4756-9e7d-11034944be68
true
typebeam/033a8e69-4536-4bb5-95fa-8622b141c188
ex:MethodCall
callsFunctionbeam/033a8e69-4536-4bb5-95fa-8622b141c188
ex:ingest-document-function
appliesTobeam/033a8e69-4536-4bb5-95fa-8622b141c188
ex:batch-variable
passesFunctionbeam/c74e97dd-23f2-45e9-9ec1-958b9896a948
ex:ingest-document-function
passesCollectionbeam/c74e97dd-23f2-45e9-9ec1-958b9896a948
ex:documents-list
appliesFunctionTobeam/c74e97dd-23f2-45e9-9ec1-958b9896a948
ex:documents-list
parallelExecutionbeam/c74e97dd-23f2-45e9-9ec1-958b9896a948
true
schedulesbeam/c74e97dd-23f2-45e9-9ec1-958b9896a948
ex:ingest-document-function

References (3)

3 references
  1. ctx:claims/beam/3cca2fbf-b6c9-4756-9e7d-11034944be68
    • full textbeam-chunk
      text/plain1 KBdoc:beam/3cca2fbf-b6c9-4756-9e7d-11034944be68
      Show excerpt
      - `pool.map(ingest_document, documents)`: Distributes the documents across the worker processes for parallel processing. 2. **Simulated Ingestion**: - `time.sleep(0.01)`: Simulates the ingestion time for each document. 3. **Logging*
  2. ctx:claims/beam/033a8e69-4536-4bb5-95fa-8622b141c188
    • full textbeam-chunk
      text/plain1 KBdoc:beam/033a8e69-4536-4bb5-95fa-8622b141c188
      Show excerpt
      for i in range(0, len(documents), batch_size): batch = documents[i:i + batch_size] with Pool(processes=os.cpu_count()) as pool: pool.map(ingest_document, batch) def main(): documents = [f"document_{i}" f
  3. ctx:claims/beam/c74e97dd-23f2-45e9-9ec1-958b9896a948
    • full textbeam-chunk
      text/plain1 KBdoc:beam/c74e97dd-23f2-45e9-9ec1-958b9896a948
      Show excerpt
      4. **Monitoring and Logging**: Implement monitoring and logging to ensure high uptime and diagnose issues quickly. ### Example Implementation Let's modify your code to use multiprocessing to handle the ingestion of documents concurrently.

See also

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