Dontopedia

prometheus_client

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

prometheus_client has 27 facts recorded in Dontopedia across 9 references, with 3 live disagreements.

27 facts·12 predicates·9 sources·3 in dispute

Mostly:rdf:type(9), provides(3), import statement(1)

Maturity scale raw canonical shape-checked rule-derived certified

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.

isImportedFromIs Imported From(3)

belongsToManyBelongs to Many(2)

requiresRequires(2)

aliasForAlias for(1)

classOfClass of(1)

functionOfFunction of(1)

importsImports(1)

isImplementedByIs Implemented by(1)

isIntegratedWithIs Integrated With(1)

isProvidedByIs Provided by(1)

recommendsLibraryRecommends Library(1)

requiresLibraryRequires Library(1)

usesLibraryUses Library(1)

Other facts (22)

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.

22 facts
PredicateValueRef
Rdf:typePython Library[1]
Rdf:typePython Library[2]
Rdf:typeThird Party Library[3]
Rdf:typePython Library[4]
Rdf:typeLibrary[5]
Rdf:typePython Library[6]
Rdf:typeLibrary[7]
Rdf:typeLibrary[8]
Rdf:typePython Module[9]
ProvidesCounter Metric[5]
ProvidesHttp Server[5]
ProvidesMetric Decorator[8]
Import Statementfrom prometheus_client import start_http_server, Gauge[1]
Provides FunctionStart Http Server[1]
Provides ClassGauge[1]
Purposeexpose metrics from application[2]
Is Integrated WithLogging Framework[5]
LanguagePython[7]
Imported AsProm[8]
Is Monitoring LibraryExternal Library[8]
ImplementsMonitoring Strategy[8]
Is Used forMonitoring[8]

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/dbbff797-84ed-4730-a6e6-90ed61d1927c
ex:PythonLibrary
importStatementbeam/dbbff797-84ed-4730-a6e6-90ed61d1927c
from prometheus_client import start_http_server, Gauge
providesFunctionbeam/dbbff797-84ed-4730-a6e6-90ed61d1927c
ex:start-http-server
providesClassbeam/dbbff797-84ed-4730-a6e6-90ed61d1927c
ex:Gauge
labelbeam/dbbff797-84ed-4730-a6e6-90ed61d1927c
prometheus_client
typebeam/38560778-3ede-4ceb-8e27-66e99a32c394
ex:PythonLibrary
purposebeam/38560778-3ede-4ceb-8e27-66e99a32c394
expose metrics from application
labelbeam/38560778-3ede-4ceb-8e27-66e99a32c394
prometheus_client
typebeam/3e84946d-5b5f-4fb8-88c8-847b8697fefc
ex:ThirdPartyLibrary
typebeam/723ac183-3da8-4b70-bfa4-df2a9f02ca05
ex:PythonLibrary
labelbeam/723ac183-3da8-4b70-bfa4-df2a9f02ca05
prometheus_client
typebeam/286d2c11-7b35-44e9-8d9f-cc638ef96e94
ex:Library
labelbeam/286d2c11-7b35-44e9-8d9f-cc638ef96e94
prometheus_client
providesbeam/286d2c11-7b35-44e9-8d9f-cc638ef96e94
ex:counter-metric
providesbeam/286d2c11-7b35-44e9-8d9f-cc638ef96e94
ex:http-server
isIntegratedWithbeam/286d2c11-7b35-44e9-8d9f-cc638ef96e94
ex:logging-framework
typebeam/181eccfd-314d-4181-a9b1-b1b6691aab7e
ex:python-library
typebeam/f3dab0e0-7dee-4dd3-8606-8943a682a0a5
ex:Library
languagebeam/f3dab0e0-7dee-4dd3-8606-8943a682a0a5
ex:python
typebeam/9d46e98f-8c67-471e-8bbf-40d379ce4aab
ex:Library
labelbeam/9d46e98f-8c67-471e-8bbf-40d379ce4aab
prometheus_client
importedAsbeam/9d46e98f-8c67-471e-8bbf-40d379ce4aab
ex:prom
isMonitoringLibrarybeam/9d46e98f-8c67-471e-8bbf-40d379ce4aab
ex:external-library
implementsbeam/9d46e98f-8c67-471e-8bbf-40d379ce4aab
ex:monitoring-strategy
providesbeam/9d46e98f-8c67-471e-8bbf-40d379ce4aab
ex:metric-decorator
isUsedForbeam/9d46e98f-8c67-471e-8bbf-40d379ce4aab
ex:monitoring
typebeam/daf4bbd1-d90a-4b18-805a-01e7121471bb
ex:PythonModule

References (9)

9 references
  1. ctx:claims/beam/dbbff797-84ed-4730-a6e6-90ed61d1927c
    • full textbeam-chunk
      text/plain1 KBdoc:beam/dbbff797-84ed-4730-a6e6-90ed61d1927c
      Show excerpt
      risk_tracker.add_metric(Metric("Latency and Throughput", 3)) risk_tracker.add_metric(Metric("LLM Integration Complexity", 4)) risk_tracker.add_metric(Metric("Data Privacy and Compliance", 2)) risk_tracker.add_metric(Metric("Document Types a
  2. ctx:claims/beam/38560778-3ede-4ceb-8e27-66e99a32c394
    • full textbeam-chunk
      text/plain1 KBdoc:beam/38560778-3ede-4ceb-8e27-66e99a32c394
      Show excerpt
      for future in concurrent.futures.as_completed(futures): user_id = futures[future] try: response, response_time = future.result() response_times.append(response_t
  3. ctx:claims/beam/3e84946d-5b5f-4fb8-88c8-847b8697fefc
    • full textbeam-chunk
      text/plain1 KBdoc:beam/3e84946d-5b5f-4fb8-88c8-847b8697fefc
      Show excerpt
      # Create a metric metric = prometheus_client.Counter('my_metric', 'My metric') # Increment the metric metric.inc() # Print the metric print(prometheus_client.generate_latest()) ``` I'm getting this error: "error generating metric". How do
  4. ctx:claims/beam/723ac183-3da8-4b70-bfa4-df2a9f02ca05
    • full textbeam-chunk
      text/plain1 KBdoc:beam/723ac183-3da8-4b70-bfa4-df2a9f02ca05
      Show excerpt
      my_counter = Counter('my_metric', 'My metric') # Increment the metric my_counter.inc() # Start the HTTP server to expose metrics start_http_server(port=8000) # Run indefinitely to keep the server alive while True: pass ``` ### Expla
  5. ctx:claims/beam/286d2c11-7b35-44e9-8d9f-cc638ef96e94
    • full textbeam-chunk
      text/plain1 KBdoc:beam/286d2c11-7b35-44e9-8d9f-cc638ef96e94
      Show excerpt
      Here's an example of how you might integrate Prometheus metrics with an existing logging framework in Python: #### Step 1: Set Up Logging First, set up your logging framework: ```python import logging # Configure logging logging.basicCon
  6. ctx:claims/beam/181eccfd-314d-4181-a9b1-b1b6691aab7e
    • full textbeam-chunk
      text/plain1 KBdoc:beam/181eccfd-314d-4181-a9b1-b1b6691aab7e
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      logging.basicConfig(level=logging.INFO, filename=log_file, filemode='w', format='%(asctime)s - %(levelname)s - %(message)s') start_http_server(port=prometheus_port) ``` - **Error Handling:** Implement proper error handling to catch
  7. ctx:claims/beam/f3dab0e0-7dee-4dd3-8606-8943a682a0a5
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f3dab0e0-7dee-4dd3-8606-8943a682a0a5
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      - Part of the Prometheus ecosystem, Alertmanager handles alerts sent by client applications such as the Prometheus server. It manages alert delivery and deduplication, and supports various notification channels like email, Slack, and Pag
  8. ctx:claims/beam/9d46e98f-8c67-471e-8bbf-40d379ce4aab
    • full textbeam-chunk
      text/plain1 KBdoc:beam/9d46e98f-8c67-471e-8bbf-40d379ce4aab
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      def test_process_query(self): self.assertEqual(process_query("example"), "Processed example") def test_process_query_with_retry(self): self.assertEqual(process_query_with_retry("example"), "Processed example") if _
  9. ctx:claims/beam/daf4bbd1-d90a-4b18-805a-01e7121471bb
    • full textbeam-chunk
      text/plain1 KBdoc:beam/daf4bbd1-d90a-4b18-805a-01e7121471bb
      Show excerpt
      from prometheus_client import start_http_server, Summary, Counter app = FastAPI() # Prometheus metrics REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request') TOTAL_REQUESTS = Counter('total_requests', 'Total

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