CSV files can come in all shapes and forms, with some presenting many errors that make the process of cleanly reading them inherently difficult. To help users read these files, DuckDB supports detailed error messages, the ability to skip faulty lines, and the possibility of storing faulty lines in a temporary table to assist users with a data cleaning step.

Structural Errors

DuckDB supports the detection and skipping of several different structural errors. In this section, we will go over each error with an example. For the examples, consider the following table:

CREATE TABLE people (name VARCHAR, birth_date DATE);

DuckDB detects the following error types:

• CAST: Casting errors occur when a column in the CSV file cannot be cast to the expected schema value. For example, the line Pedro,The 90s would cause an error since the string The 90s cannot be cast to a date.
• MISSING COLUMNS: This error occurs if a line in the CSV file has fewer columns than expected. In our example, we expect two columns; therefore, a row with just one value, e.g., Pedro, would cause this error.
• TOO MANY COLUMNS: This error occurs if a line in the CSV has more columns than expected. In our example, any line with more than two columns would cause this error, e.g., Pedro,01-01-1992,pdet.
• UNQUOTED VALUE: Quoted values in CSV lines must always be unquoted at the end; if a quoted value remains quoted throughout, it will cause an error. For example, assuming our scanner uses quote='"', the line "pedro"holanda, 01-01-1992 would present an unquoted value error.
• LINE SIZE OVER MAXIMUM: DuckDB has a parameter that sets the maximum line size a CSV file can have, which by default is set to 2,097,152 bytes. Assuming our scanner is set to max_line_size = 25, the line Pedro Holanda, 01-01-1992 would produce an error, as it exceeds 25 bytes.
• INVALID UNICODE: DuckDB only supports UTF-8 strings; thus, lines containing non-UTF-8 characters will produce an error. For example, the line pedro\xff\xff, 01-01-1992 would be problematic.

Anatomy of a CSV Error

By default, when performing a CSV read, if any structural errors are encountered, the scanner will immediately stop the scanning process and throw the error to the user. These errors are designed to provide as much information as possible to allow users to evaluate them directly in their CSV file.

This is an example for a full error message:

Conversion Error: CSV Error on Line: 5648
Original Line: Pedro,The 90s
Error when converting column "birth_date". date field value out of range: "The 90s", expected format is (DD-MM-YYYY)

Column date is being converted as type DATE
This type was auto-detected from the CSV file.
Possible solutions:
* Override the type for this column manually by setting the type explicitly, e.g. types={'birth_date': 'VARCHAR'}
* Set the sample size to a larger value to enable the auto-detection to scan more values, e.g. sample_size=-1
* Use a COPY statement to automatically derive types from an existing table.

  file= people.csv
  delimiter = , (Auto-Detected)
  quote = " (Auto-Detected)
  escape = " (Auto-Detected)
  new_line = \r\n (Auto-Detected)
  header = true (Auto-Detected)
  skip_rows = 0 (Auto-Detected)
  date_format = (DD-MM-YYYY) (Auto-Detected)
  timestamp_format =  (Auto-Detected)
  null_padding=0
  sample_size=20480
  ignore_errors=false
  all_varchar=0

The first block provides us with information regarding where the error occurred, including the line number, the original CSV line, and which field was problematic:

Conversion Error: CSV Error on Line: 5648
Original Line: Pedro,The 90s
Error when converting column "birth_date". date field value out of range: "The 90s", expected format is (DD-MM-YYYY)

The second block provides us with potential solutions:

Column date is being converted as type DATE
This type was auto-detected from the CSV file.
Possible solutions:
* Override the type for this column manually by setting the type explicitly, e.g. types={'birth_date': 'VARCHAR'}
* Set the sample size to a larger value to enable the auto-detection to scan more values, e.g. sample_size=-1
* Use a COPY statement to automatically derive types from an existing table.

Since the type of this field was auto-detected, it suggests defining the field as a VARCHAR or fully utilizing the dataset for type detection.

Finally, the last block presents some of the options used in the scanner that can cause errors, indicating whether they were auto-detected or manually set by the user.

Using the ignore_errors Option

There are cases where CSV files may have multiple structural errors, and users simply wish to skip these and read the correct data. Reading erroneous CSV files is possible by utilizing the ignore_errors option. With this option set, rows containing data that would otherwise cause the CSV parser to generate an error will be ignored. In our example, we will demonstrate a CAST error, but note that any of the errors described in our Structural Error section would cause the faulty line to be skipped.

For example, consider the following CSV file, faulty.csv:

Pedro,31
Oogie Boogie, three

If you read the CSV file, specifying that the first column is a VARCHAR and the second column is an INTEGER, loading the file would fail, as the string three cannot be converted to an INTEGER.

For example, the following query will throw a casting error.

FROM read_csv('faulty.csv', columns = {'name': 'VARCHAR', 'age': 'INTEGER'});

However, with ignore_errors set, the second row of the file is skipped, outputting only the complete first row. For example:

FROM read_csv(
    'faulty.csv',
    columns = {'name': 'VARCHAR', 'age': 'INTEGER'},
    ignore_errors = true
);

Outputs:

One should note that the CSV Parser is affected by the projection pushdown optimization. Hence, if we were to select only the name column, both rows would be considered valid, as the casting error on the age would never occur. For example:

SELECT name
FROM read_csv('faulty.csv', columns = {'name': 'VARCHAR', 'age': 'INTEGER'});

Outputs:

Retrieving Faulty CSV Lines

Being able to read faulty CSV files is important, but for many data cleaning operations, it is also necessary to know exactly which lines are corrupted and what errors the parser discovered on them. For scenarios like these, it is possible to use DuckDB’s CSV Rejects Table feature. By default, this feature creates two temporary tables.

1. reject_scans: Stores information regarding the parameters of the CSV Scanner
2. reject_errors: Stores information regarding each CSV faulty line and in which CSV Scanner they happened.

Note that any of the errors described in our Structural Error section will be stored in the rejects tables. Also, if a line has multiple errors, multiple entries will be stored for the same line, one for each error.

Reject Scans

The CSV Reject Scans Table returns the following information:

Reject Errors

The CSV Reject Errors Table returns the following information:

Parameters

The parameters listed below are used in the read_csv function to configure the CSV Rejects Table.

To store the information of the faulty CSV lines in a rejects table, the user must simply set the store_rejects option to true. For example:

FROM read_csv(
    'faulty.csv',
    columns = {'name': 'VARCHAR', 'age': 'INTEGER'},
    store_rejects = true
);

You can then query both the rejects_scans and reject_errors tables, to retrieve information about the rejected tuples. For example:

FROM rejects_table;

Outputs:

FROM rejects_table;

Outputs: