When building data pipelines with Flyte, you might encounter a checksum mismatch error during the automatic conversion of a pandas DataFrame into a StructuredDataset (typically in Parquet format). This issue occurs when a task returns a DataFrame, which Flyte serializes and writes to object storage. On subsequent deserialization, even minor variations in the conversion process may lead to a checksum mismatch, causing your workflow to fail.

In this post, I present two simplified examples. The first snippet reproduces the error by returning a DataFrame between tasks, while the second snippet shows the ideal solution: writing the DataFrame to a CSV file and returning a FlyteFile instead.

1. Simplest Code That Reproduces the Error

In this example, one task creates and returns a DataFrame and a downstream task consumes it. In a Flyte environment, this automatic serialization–deserialization process may trigger a checksum mismatch error.

import pandas as pd
from flytekit import task, workflow

@task
def create_df() -> pd.DataFrame:
    """
    Create a simple DataFrame.

    Returns
    -------
    pd.DataFrame
        A DataFrame with sample data.
    """
    data = {'col1': [1, 2, 3], 'col2': ['a', 'b', 'c']}
    return pd.DataFrame(data)

@task
def consume_df(df: pd.DataFrame) -> int:
    """
    Consume the DataFrame and return the number of rows.

    Parameters
    ----------
    df : pd.DataFrame
        Input DataFrame.

    Returns
    -------
    int
        The number of rows in the DataFrame.
    """
    return len(df)

@workflow
def df_workflow() -> int:
    """
    Workflow that passes a DataFrame between tasks.
    
    Returns
    -------
    int
        The row count of the DataFrame.
    """
    df = create_df()
    return consume_df(df)

Note: When running this workflow within a proper Flyte environment, the automatic conversion of the DataFrame to a StructuredDataset (Parquet) may trigger a checksum mismatch error during deserialization.

2. Ideal Code That Avoids the Error

The best practice is to bypass the automatic StructuredDataset conversion by persisting the DataFrame as a CSV file and returning a file artifact (FlyteFile). This approach avoids the extra serialization step that is prone to checksum mismatches.

import pandas as pd
from pathlib import Path
from flytekit import task, workflow, FlyteFile

@task
def create_and_save_df(output_file: str) -> FlyteFile:
    """
    Create a simple DataFrame and save it to CSV.

    Parameters
    ----------
    output_file : str
        The file name where the DataFrame is saved.

    Returns
    -------
    FlyteFile
        A FlyteFile reference pointing to the saved CSV file.
    """
    data = {'col1': [1, 2, 3], 'col2': ['a', 'b', 'c']}
    df = pd.DataFrame(data)
    path = Path(output_file)
    df.to_csv(path, index=False)
    return FlyteFile(str(path))

@task
def consume_file(file: FlyteFile) -> int:
    """
    Read the CSV file and return the number of rows.

    Parameters
    ----------
    file : FlyteFile
        The file artifact containing the CSV data.

    Returns
    -------
    int
        The number of rows in the CSV file.
    """
    df = pd.read_csv(file.download())
    return len(df)

@workflow
def file_workflow() -> int:
    """
    Workflow that uses a CSV file artifact to avoid DataFrame serialization.

    Returns
    -------
    int
        The row count from the CSV file.
    """
    file = create_and_save_df(output_file="sample.csv")
    return consume_file(file)

3. Conclusion

Checksum mismatches in Flyte often arise from the automatic conversion of DataFrames into StructuredDatasets. By writing your DataFrame to a CSV file and returning a FlyteFile instead, you can bypass this extra conversion step and avoid checksum errors.

These simplified examples illustrate both the problematic pattern and the ideal solution. Adjust these patterns according to your pipeline’s requirements to ensure robust and error-free data processing with Flyte.