Adjust the dual exporter logic for tracing#19320
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@kriscon-db Thank you for the contribution! Could you fix the following issue(s)? ⚠ DCO checkThe DCO check failed. Please sign off your commit(s) by following the instructions here. See https://github.com/mlflow/mlflow/blob/master/CONTRIBUTING.md#sign-your-work for more details. |
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LGTM with a minor suggestion
mlflow/tracing/provider.py
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| # If dual export is set AND we have already added a set_destination processor, return. | ||
| # If dual export is set but no set_destination processor added, skip return and go | ||
| # to default processing to catch the default processor, if present. | ||
| if (not MLFLOW_TRACE_ENABLE_OTLP_DUAL_EXPORT.get()) or added_destination_processor: |
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Can we check the processors list has non-otlp one, rather than introducing a new flag? We may add another precedence processor other than destination.
| if (not MLFLOW_TRACE_ENABLE_OTLP_DUAL_EXPORT.get()) or added_destination_processor: | ||
| return processors | ||
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| # Finally, default MLflow-based processors (inference table in serving, else tracking URI). |
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make sure that users can not configure both trace destination processor AND inference table/default mlflow processor in the below code block
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If trace destination is set, then the only way to not return is to pass the OLTP and dual export check which would force a return in the 2nd block. I don't believe this is a possible outcome with the current logic.
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
Change tracing precedence order to allow for dual exporting even if set_destination is called.
How is this PR tested?
Does this PR require documentation update?
Release Notes
Is this a user-facing change?
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/tracking: Tracking Service, tracking client APIs, autologgingarea/models: MLmodel format, model serialization/deserialization, flavorsarea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/evaluation: MLflow model evaluation features, evaluation metrics, and evaluation workflowsarea/gateway: MLflow AI Gateway client APIs, server, and third-party integrationsarea/prompts: MLflow prompt engineering features, prompt templates, and prompt managementarea/tracing: MLflow Tracing features, tracing APIs, and LLM tracing functionalityarea/projects: MLproject format, project running backendsarea/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/build: Build and test infrastructure for MLflowarea/docs: MLflow documentation pagesHow should the PR be classified in the release notes? Choose one:
rn/none- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" sectionrn/breaking-change- The PR will be mentioned in the "Breaking Changes" sectionrn/feature- A new user-facing feature worth mentioning in the release notesrn/bug-fix- A user-facing bug fix worth mentioning in the release notesrn/documentation- A user-facing documentation change worth mentioning in the release notesShould this PR be included in the next patch release?
Yesshould be selected for bug fixes, documentation updates, and other small changes.Noshould be selected for new features and larger changes. If you're unsure about the release classification of this PR, leave this unchecked to let the maintainers decide.What is a minor/patch release?
Bug fixes, doc updates and new features usually go into minor releases.
Bug fixes and doc updates usually go into patch releases.