Reading CSV Files in Databricks Using SQL
Databricks recommends using the read_files table-valued function for SQL users to read CSV files. This function is available in Databricks Runtime 13.3 LTS and above. If you use SQL to read CSV data directly without using temporary views or read_files, you cannot specify data source options or the schema for the data.
Here’s an example of how to use read_files to read a CSV file:
SELECT * FROM read_files(
format = 'csv',
header = true,
path = '/path/to/your/file.csv'
);
Alternatively, you can create a temporary view to read the CSV file:
CREATE TEMPORARY VIEW temp_view
USING CSV
OPTIONS (header "true", path "/path/to/your/file.csv");
SELECT * FROM temp_view;
Frequently Asked Questions
- Q: How do I handle malformed records in CSV files?
A: You can use the mode option to handle malformed records. The modes are PERMISSIVE (default), DROPMALFORMED, and FAILFAST. For example, you can set the mode to PERMISSIVE to insert nulls for fields that cannot be parsed correctly. - Q: Can I specify a custom schema for my CSV data?
A: Yes, you can specify a custom schema when using the DataFrame API. However, if you are using SQL without read_files or temporary views, you cannot specify a schema. - Q: How do I skip rows when reading a CSV file in Databricks using SQL?
A: You can use Spark SQL’s read_files function with the skipRows option to skip rows. Alternatively, you can create a temporary view and use SQL to filter out the rows you want to skip. - Q: What are the limitations of reading CSV files directly in SQL without using temporary views or read_files?
A: The limitations include not being able to specify data source options or the schema for the data. - Q: How do I inspect rows that could not be parsed correctly in PERMISSIVE mode?
A: You can either provide a custom path to the badRecordsPath option to record corrupt records to a file or add the column _corrupt_record to the schema to review corrupt records in the resultant DataFrame. - Q: Can I use other programming languages like Python or Scala to read CSV files in Databricks?
A: Yes, Databricks supports reading CSV files using Python, Scala, and R in addition to SQL. - Q: How do I ensure that my CSV file is correctly uploaded to Databricks DBFS?
A: After uploading your file to Databricks DBFS, you can verify its successful upload by loading it into a DataFrame and displaying its contents.
Bottom Line: Reading CSV files in Databricks using SQL is efficient with the read_files function, which allows for flexible data source options and schema specification. For more complex data handling, consider using other supported languages like Python or Scala.