Databricks Community

NiraliGandhi · ‎08-13-2024

This is not making it consistent when we perform aggregation on multiple columns and thus it is hindering metadata driven transformation because of inconsistency.

How can we request Databricks/pyspark to include this ? and is there any known work around meanwhile ?

mark_ott · Monday

When using PySpark or Databricks to perform a pivot operation with only a single aggregation, you may notice that the alias is not applied as expected, leading to inconsistencies, especially when trying to automate or apply metadata-driven frameworks. This inconsistency is a known limitation and can be frustrating for users seeking predictable column names from the pivot transformation.

Why the Alias Is Not Applied

When you specify only one aggregation in a .pivot() in Databricks or PySpark, the engine generates the column name based solely on the aggregation expression (for example, sum(value)), rather than honoring any alias you provide. However, if there are multiple aggregations, Databricks combines the column alias and the aggregation alias (e.g., value_sum), improving consistency. This default behavior can hinder workflows where predictable naming is expected for downstream processing or automation.

Workarounds

There are several common workarounds to this issue:

Manual Renaming: Use the .withColumnRenamed() method after the pivot operation to rename columns according to your needed aliases. This is straightforward but requires you to know the generated column names ahead of time.
Dynamic Renaming with List Comprehensions: After pivoting, use the dataframe's .columns attribute to programmatically rename columns, often with regex or string replacement techniques.
Multiple Aggregations: If you specify more than one aggregation in the .agg() after the pivot, the alias is more likely to be honored. As a workaround, include a dummy aggregation and drop it later.
SQL Syntax Pivot Alias: Using Databricks SQL, you can provide explicit column and aggregation aliases directly in the PIVOT clause, which tends to be more consistent than the DataFrame API.

Requesting a Feature/Filing a Bug

To formally request this to be addressed in Databricks or Spark:

Databricks Feature Request: You can submit enhancement ideas or bug reports via the official Databricks Aha! Ideas portal. Visit https://databricks.aha.io/ and register your suggestion for more consistent alias application in pivots.
Spark JIRA: For the open-source PySpark project, create an issue on their JIRA (https://issues.apache.org/jira/projects/SPARK/issues), describing your use case and the inconsistency.

Example Workaround

python

pivot_df = (
    df.groupBy("some_column")
      .pivot("pivot_column")
      .agg(F.sum("value").alias("total_value"))
)

# Rename columns after pivot
for col in pivot_df.columns:
    if col.startswith("sum("):
        pivot_df = pivot_df.withColumnRenamed(col, "desired_alias")

Or, use SQL:

sql

SELECT *
FROM table
PIVOT (
  SUM(value) AS total_value
  FOR pivot_column IN ('A', 'B')
)

This enforces the alias more reliably.

Summary Table: Workaround Methods

Method	Description	Effort	Predictability
Manual withColumnRenamed	Rename after pivot using code	Moderate	High
Dynamic Regex Rename	Programmatically rename columns	Moderate	High (but fragile)
Add Dummy Aggregation	Add another agg to use aliases	Low	Medium
SQL Pivot Aliasing	Use SQL and specify aliases	Low	High

For maximum future-proofing, submitting a feature request via the official Databricks portal is recommended. In the meantime, renaming columns after the pivot via code is the most widely used solution.