Related Issues/PRs

Fixes #19460

What changes are proposed in this pull request?

Searching in the dataset schema table threw TypeError: s.toLowerCase is not a function when the dataset had pandas MultiIndex columns. MultiIndex column names are stored as arrays (e.g., ["foo", "a"]) rather than strings, causing the search filter to call toLowerCase() on arrays.

Changes:

• Updated hasFilter function to handle both string and array column names
• Convert array names to dot-separated strings before search (e.g., ["foo", "a"] → "foo.a")
• Display array names as dot-separated strings in the table
• Use inline type definition for schema prop instead of exported interface
• Added 8 comprehensive unit tests covering regular and MultiIndex column scenarios

Example:

import pandas as pd
from mlflow.data.pandas_dataset import from_pandas

df = pd.DataFrame(dict(a=range(10), b=range(20, 30)))
df.columns = pd.MultiIndex.from_tuples([("foo", "a"), ("foo", "b")])
ds = from_pandas(df, name="test")
# Searching for "foo" now works correctly in the UI

Before (crashed):

After (works):

Searching for "foo" filters to foo.a and foo.b:

Searching for "bar" filters to bar.c and bar.d:

Regular datasets continue to work as expected:

How is this PR tested?

• Existing unit/integration tests
• New unit/integration tests
• Manual tests

Tests added:

• 8 unit tests for ExperimentViewDatasetSchemaTable component
• Tests cover regular columns, MultiIndex columns, filtering by name and type
• All tests passing ✅

Does this PR require documentation update?

• No. You can skip the rest of this section.
• Yes. I've updated:
  • Examples
  • API references
  • Instructions

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

Fixed crash when searching dataset schema with pandas MultiIndex columns. Search now correctly handles hierarchical column names.

What component(s), interfaces, languages, and integrations does this PR affect?

Components

• area/tracking: Tracking Service, tracking client APIs, autologging
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/evaluation: MLflow model evaluation features, evaluation metrics, and evaluation workflows
• area/gateway: MLflow AI Gateway client APIs, server, and third-party integrations
• area/prompts: MLflow prompt engineering features, prompt templates, and prompt management
• area/tracing: MLflow Tracing features, tracing APIs, and LLM tracing functionality
• area/projects: MLproject format, project running backends
• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/build: Build and test infrastructure for MLflow
• area/docs: MLflow documentation pages

How 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" section
• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes

Should this PR be included in the next patch release?

• Yes (this PR will be cherry-picked and included in the next patch release)
• No (this PR will be included in the next minor release)

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