Dealing with Column Count Mismatches in Snowflake: A Practical Approach Using ERROR_ON_COLUMN_COUNT_MISMATCH

When working with CSV files in Snowflake, especially when dealing with large and unstructured datasets, one common challenge is ensuring that the number of columns in the data matches the expected column count in the target table. Snowflake provides an option in the CREATE FILE FORMAT command called ERROR_ON_COLUMN_COUNT_MISMATCH to manage this potential issue.

This blog post will walk you through a practical use case for handling column count mismatches in CSV files using Snowflake's ERROR_ON_COLUMN_COUNT_MISMATCH parameter.

Scenario: Loading Data from CSV Files into Snowflake

Imagine you're responsible for loading daily transactional data into a Snowflake table. The source system provides CSV files, but occasionally, the number of columns in the files varies due to upstream system issues or formatting inconsistencies. For example, a CSV file might be missing a column, or an extra column might appear due to additional metadata.

The goal is to configure the file format in Snowflake so that data can still be loaded, even if the number of columns in the file doesn't match the table schema.

Solution: Using ERROR_ON_COLUMN_COUNT_MISMATCH

Snowflake's ERROR_ON_COLUMN_COUNT_MISMATCH parameter allows you to control how column mismatches are handled when loading data. By default, Snowflake expects the number of columns in the CSV file to match the number of columns in the table. When there is a mismatch, the load fails unless this parameter is configured.

The parameter can be set to FALSE to allow loading even when the number of columns in the CSV does not match. Snowflake will load the data into the table based on the columns that exist, and any missing columns will be set to NULL.

Here’s an example:

CREATE OR REPLACE FILE FORMAT col_mistmatch_csv_ff
TYPE = 'CSV'
COMPRESSION = 'NONE'
FIELD_DELIMITER = '|'
ERROR_ON_COLUMN_COUNT_MISMATCH = FALSE
SKIP_HEADER = 1;
        

How ERROR_ON_COLUMN_COUNT_MISMATCH Works:

1. Mismatch in Columns (Fewer Columns in CSV): If a row in the CSV file has fewer columns than the target table schema, Snowflake will load the data into the available columns and set the missing columns to NULL. For instance, if the table expects 5 columns but a CSV row has only 3, the remaining 2 columns will be filled with NULL.

Example CSV with missing columns:

ID|Name|Age 1|John|30 2|Jane|

In this case, for the second row, Age will be NULL.

2.Mismatch in Columns (Extra Columns in CSV):

If a row has more columns than the target table, Snowflake will load data only into the expected columns, and the extra data will be discarded.

ID|Name|Age|Country

• 1|John|30|USA
• 2|Jane|25|UK Here, the Country column will be discarded if the table has only three columns (ID, Name, Age).

Practical Use Case

Consider a scenario where you're loading customer order data into a table, but sometimes, due to system glitches, the CSV files contain fewer or extra columns than expected. Rather than rejecting the entire batch of data, Snowflake will gracefully handle this by inserting the available data and setting missing values to NULL. This is critical in ensuring that the data pipeline continues to operate without manual intervention for small inconsistencies in file formatting.

Benefits:

• Reduced Load Failures: Setting ERROR_ON_COLUMN_COUNT_MISMATCH to FALSE prevents load failures due to unexpected file structure variations.
• Flexibility: This setting provides flexibility in dealing with real-world data, where formatting issues might arise from upstream systems.
• Data Quality Control: You can still perform quality checks on the loaded data by querying rows where columns are NULL, enabling you to detect and correct any issues without stopping the data pipeline.

When to Set ERROR_ON_COLUMN_COUNT_MISMATCH to TRUE?

In some cases, you might want to enforce strict column count validation, especially if you require every row in the file to exactly match the table schema. In this case, set ERROR_ON_COLUMN_COUNT_MISMATCH to TRUE, and Snowflake will reject any rows where the column count doesn't match the table schema.