Parquet column cannot be converted.

Question

In ADF dataflow using the derivedcolumn, I convert the columns to the appropriate datatypes.
i.e. string to date or to decimal...
the sink is in delta parquet.
Then in databricks I try to read the delta parquet but there is an error:

Parquet column cannot be converted. Column: [title_date], Expected: StringType, Found: INT32

note ethat I do not convert to int.
Any suggestions?

Thank you

Answer 1

Hello @arkiboys ,

Thanks for the question and using MS Q&A platform.

Is your cluster Databricks Runtime 7.3 LTS or above?

Cause:
If that is the case then this error usually occurs when the vectorized Parquet reader is decoding the decimal type column to a binary format

The vectorized Parquet reader is enabled by default in Databricks Runtime 7.3 and above for reading datasets in Parquet files. The read schema uses atomic data types: binary, boolean, date, string, and timestamp.

Note: Please note that this error only occurs if you have decimal type columns in the source data.

Resolution:

If you have decimal type columns in your source data, you should disable the vectorized Parquet reader.

Set spark.sql.parquet.enableVectorizedReader to false in the cluster’s Spark configuration to disable the vectorized Parquet reader at the cluster level.

You can also disable the vectorized Parquet reader at the notebook level by running:

    spark.conf.set("spark.sql.parquet.enableVectorizedReader","false")  

Important Note: The vectorized Parquet reader enables native record-level filtering using push-down filters, improving memory locality, and cache utilization. If you disable the vectorized Parquet reader, there may be a minor performance impact. You should only disable it, if you have decimal type columns in your source data.

For detailed info please refer to this document: Apache Spark job fails with Parquet column cannot be converted error

Hope this will help. Please let us know if any further queries.

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