# PySpark Incremental Transform: write\_dataframe schema Error

**URL:** <https://community.palantir.com/t/pyspark-incremental-transform-write-dataframe-schema-error/4300>\
**Category:** Ask the Community\
**Tags:** transforms-python, transforms-python-builds\
**Created:** [June 23, 2025, 3:41pm UTC](https://community.palantir.com/t/pyspark-incremental-transform-write-dataframe-schema-error/4300 "2025-06-23T15:41:53Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![helen](https://avatars.discourse-cdn.com/v4/letter/h/a88e57/32.png) [@helen](https://community.palantir.com/u/helen)\
**Post date:** [June 23, 2025, 3:41pm UTC](https://community.palantir.com/t/pyspark-incremental-transform-write-dataframe-schema-error/4300/1 "2025-06-23T15:41:53Z")

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Hi Everyone,

I’m encountering a persistent schema mismatch error when writing a DataFrame in a PySpark transform using `@incremental()`. My goal is to append new rows to an already existing output dataset.

The transform fails with the following error:

`com.palantir.logsafe.exceptions.SafeIllegalStateException: Attempting to write a schema with smaller amount of columns than schema of the existing dataset: {existingSchema=StructType(... actual schema, 14 columns ...), newSchema=StructType(... actual schema, 6 columns ...)}`

However, when I print the schema right before the `output_table.write_dataframe(df)` call, it shows all 14 expected columns, but the `newSchema` reported in the error shows only 6 columns.

I suspect the error might be related to a `pivot` operation performed earlier in the transformation, somehow affecting the schema that PySpark internally tracks, even if `printSchema()` displays the correct one at the end.

Here’s a breakdown of my transformation steps and what I’ve tried:

**Transformation Steps (Simplified):**

1. **Join:** I join several DataFrames together.
2. **Transformations:** Apply various column-level transformations (e.g., `withColumn`, `cast`).
3. **Explode:** An array column is exploded, potentially creating many rows from a single original row.
4. **Pivot:** I perform a `pivot` operation on a categorical column, which expands distinct values of that column into new columns.
5. **Schema Check & Alignment:** Before the final write, I have logic to explicitly align the DataFrame’s schema with my `schema_variable`. This includes adding missing columns with `null` values and ensuring data types match.

**What I’ve Tried:**

- **Confirmed `df.printSchema()`:** `df.printSchema()` consistently shows all 14 columns with their correct data types right before the write statement.
- **Explicit Schema Definition and Alignment:**
  - I explicitly define the target schema using `T.StructType(all_fields)`, which correctly displays all 14 columns when printed.
  - I also implemented a schema alignment step, as shown in the code snippet below.

- **Broke Down Transformation into Multiple Spark Jobs** : I also tried to break down the transformation by persisting the DataFrame after the **Join** and **Transformations** steps, writing it to a dataset, and then reading it back in for the subsequent **Explode** and **Pivot** operations. And the Error persist in the Expldoe and Pivot step.
- **Referenced Foundry Incremental Examples:** I’ve tried to adapt patterns from the [incremental-examples#merge-and-append-with-varying-schemas](https://www.palantir.com/docs/foundry/transforms-python/incremental-examples#merge-and-append-with-varying-schemas) documentation.

**Specific Concerns about the Pivot Step:**

My primary suspicion is the `pivot` step. While `df.printSchema()` after the pivot and subsequent alignment looks correct, I’m wondering if the underlying Spark plan or some internal metadata related to the pivot operation is retaining a “slimmer” schema, which then gets reported to the Foundry backend.

**Questions:**

1. Given that `df.printSchema()` reports the correct 14-column schema, what could cause the backend in Foundry to see a “slimmer” (6-column) schema during the `@incremental()` write? Is there a known issue with PySpark’s schema inference or internal representation after complex operations like `pivot` that might manifest specifically with Foundry’s `write_dataframe`?
2. Are there any specific considerations or best practices when using `pivot` in PySpark transforms that are then written incrementally to Foundry datasets that could lead to this kind of schema mismatch?
3. Any other known causes, debugging tips, or methods?

Thanks for any insights!

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<div class="post-metadata">

**Author:** ![anaritarc](https://avatars.discourse-cdn.com/v4/letter/a/ed655f/32.png) [@anaritarc](https://community.palantir.com/u/anaritarc)\
**Post date:** [September 17, 2025, 5:48pm UTC](https://community.palantir.com/t/pyspark-incremental-transform-write-dataframe-schema-error/4300/2 "2025-09-17T17:48:07Z")

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Hi,

One way you could do some debugging, and hopefully solve your issue, is by adding something like this:

```python
if previous_df.schema != df_final.schema:
   common_columns = set(previous_df.columns).intersection(set(df_final.columns))
        # Cache schema lookups to avoid repeated access
   previous_schema = {col.name: col.dataType for col in previous_df.schema}
   final_schema = {col.name: col.dataType for col in df_final.schema} 
   # Go through the output dtypes of the columns
   # Check if the column exists in both previous_df and df_final schemas
   for col_name in common_columns:
       prev_dtype = previous_schema[col_name]
       final_dtype = final_schema[col_name]
       # Check if the dtype of the output matches the df that we have
       # if it isnt
       if prev_dtype != final_dtype:
           # check if the output is an array if yes
           if isinstance(prev_dtype, ArrayType) and isinstance(final_dtype, StringType):
              # convert to array
              df_final = df_final.withColumn(col_name, F.split(F.col(col_name), " "))
           if isinstance(prev_dtype, StringType) and isinstance(final_dtype, ArrayType)
              # convert to string
              df_final = df_final.withColumn(col_name, F.col(col_name).cast("string"))
# Merge the old and new dataframes
combined_df = previous_df.unionByName(df_final, allowMissingColumns=True) 
# Write the combined dataframe
outputTbl.set_mode('replace')
outputTbl.write_dataframe(combined_df)

```

This code is a bit heavy, but you will be able to verify many possible scenarios once you build the dataset and do an analysis of it.

Is it a dtype issue, is a column with non normalised name and the you have ‘-’ from different keyboards, etc.

Hope it helps

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<div class="post-metadata">

**Author:** ![system](https://yyz1.discourse-cdn.com/palantir/user_avatar/community.palantir.com/system/32/3_2.png) [@system](https://community.palantir.com/u/system)\
**Post date:** [September 12, 2026, 5:48pm UTC](https://community.palantir.com/t/pyspark-incremental-transform-write-dataframe-schema-error/4300/3 "2026-09-12T17:48:53Z")

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Closed on September 12, 2026, 5:48pm UTC

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