CSV file

Options

See the following Apache Spark reference articles for supported read and write options.

Examples

These examples use the diamonds dataset. Specify the path to the dataset as well as any options that you would like.

Read file in any language

This notebook shows how to read a file, display sample data, and print the data schema using Scala, R, Python, and SQL.

Read CSV files notebook

Open notebook in new tab

Specify schema

When the schema of the CSV file is known, you can specify the desired schema to the CSV reader with the schema option.

Read CSV files with schema notebook

Open notebook in new tab

Verify correctness of the data

When reading CSV files with a specified schema, it is possible that the data in the files does not match the schema. For example, a field containing name of the city will not parse as an integer. The consequences depend on the mode that the parser runs in:

  • PERMISSIVE (default): nulls are inserted for fields that could not be parsed correctly
  • DROPMALFORMED: drops lines that contain fields that could not be parsed
  • FAILFAST: aborts the reading if any malformed data is found

To set the mode, use the mode option.

val diamonds_with_wrong_schema_drop_malformed = sqlContext.read.format("csv").option("mode", "PERMISSIVE")

In the PERMISSIVE mode it is possible to inspect the rows that could not be parsed correctly. To do that, you can add _corrupt_record column to the schema.

Find malformed rows notebook

Open notebook in new tab

Pitfalls of reading a subset of columns

The behavior of the CSV parser depends on the set of columns that are read. If the specified schema is incorrect, the results might differ considerably depending on the subset of columns that is accessed. The following notebook presents the most common pitfalls.

Caveats of reading a subset of columns of a CSV file notebook

Open notebook in new tab