Mirrored Table - Failed to read parquet file because the column segment for column is too large

[JoshBlade]

I have a table that I'm mirroring into fabric. The source contains an nvarch(2048) field and in the mirrored warehouse, it is a varchar(8000).

Queries that include this column are throwing an error: 
Failed to read parquet file because the column segment for column '{MyColumnName}' is too large

I remirrored the table yesteray and it seemed better briefly, but today I'm getting the error again. 

[v-kpoloju-msft · Accepted solution]

Hi @JoshBlade,
Thanks for reaching out to the Microsoft fabric community forum.I would also take a moment to personally thanks @nilendraFabric, for actively participating in the community forum and his inputs.

After reviewing the details you provided, I have identified few workarounds that may help resolve the issue. Please follow these steps:
The error “Failed to read parquet file because the column segment for column is too large” in Microsoft Fabric could be due to a data type mismatch or corruption in the Parquet file.

• Ensure your table is properly partitioned. Proper partitioning can deal with enormous datasets by dividing them into smaller, more manageable pieces. This can decrease the size of each Parquet file and prevent the "column segment too large" problem.
• Enable auto compaction to ensure small files in Delta table partitions are automatically compacted with each write. This feature helps maintain optimal file sizes automatically, eliminating the need for manual intervention.
• If your table schema keeps changing, ensure that schema evolution is accomplished correctly. You may enable automatic schema evolution on your system to prevent changes from causing errors.
• Use data skipping to reduce data reads on queries. This can be achieved by storing the data files in such a way that the query engine can skip unused data.

Kindly check the following documentation links for additional information:
FAILED_READ_FILE error class - Azure Databricks | Microsoft Learn
OPTIMIZE - Azure Databricks - Databricks SQL | Microsoft Learn

If this post helps, then please give us ‘Kudos’ and consider Accept it as a solution to help the other members find it more quickly.

Best Regards.

[v-kpoloju-msft]

Hi @JoshBlade,

May I ask if you have resolved this issue? If so, please mark the helpful reply and accept it as the solution. This will be helpful for other community members who have similar problems to solve it faster.

Thank you.

[v-kpoloju-msft]

Hi @JoshBlade,

I wanted to check if you had the opportunity to review the information provided. Please feel free to contact us if you have any further questions. If my response has addressed your query, please accept it as a solution and give a 'Kudos' so other members can easily find it.

Thank you.

[v-kpoloju-msft]

Hi @JoshBlade,

I hope this information is helpful. Please let me know if you have any further questions or if you'd like to discuss this further. If this answers your question, please Accept it as a solution and give it a 'Kudos' so others can find it easily.

Thank you.

[nilendraFabric]

hello @JoshBlade 

Fabric stores mirrored tables as Delta Lake tables in OneLake

This means standard Delta Lake optimization features, including `OPTIMIZE`, are fully supported

try running OPTIMIZE on your table

This will 

Compacts small files into larger, analytics-friendly sizes (default target: 128MB).
• Applies V-Order, a write-time optimization that sorts and compresses Parquet files for up to 50% faster reads.
• Reduces the risk of “column segment too large” errors by reorganizing data into balanced Parquet files.

OPTIMIZE [YourMirroredTable] VORDER;

If this helps please share the output and accept the answer