[Feature Request] Expose full partition metadata list programmatically in DeltaTable

[amtaftp]

Feature request

Which Delta project/connector is this regarding?

• Spark
• Standalone
• Flink
• Kernel
• Other (fill in here)

Overview

Delta Lake’s Python API currently surfaces partition column names via:

from delta.tables import DeltaTable
dt = DeltaTable.forName(spark, "tbl_sales_bronze")
dt.detail().show()

However, it lacks a method to list actual partition values (e.g. year=2025/month=06/day=11) programmatically via the API—instead relying on SQL (SHOW PARTITIONS) or a Spark DataFrame distinct query.

A full partition metadata listing ([{ 'year':'2025', 'month':'06', 'day':'11' }, …]) would be highly valuable.

Motivation

Use case in Microsoft Fabric Notebooks / Lakehouses: Fabric does not support SHOW PARTITIONS on OneLake-based Delta tables

Users need a reliable API method (e.g. DeltaTable.partitions()) to introspect partition metadata without executing Spark SQL queries.

Supports tooling and audit workflows that need to iterate through all partition combinations stored in a Delta Lake.

Reduces complexity and bridging between Python-only and SQL hybrid approaches.

Further details

The delta-rs Python API which is not supported by MS Fabric already implements:

dt = DeltaTable(...)
dt.partitions()  # -> list[dict[str, str]]
which returns a list of partition-value dictionaries (e.g., [{ 'year': '2020', 'month': '1', 'day': '1' }, …])

Willingness to contribute

The Delta Lake Community encourages new feature contributions. Would you or another member of your organization be willing to contribute an implementation of this feature?

• Yes. I can contribute this feature independently.
• Yes. I would be willing to contribute this feature with guidance from the Delta Lake community.
• No. I cannot contribute this feature at this time.