How to Read CSV Files with Quoted Fields Containing Embedded Commas in Spark?

Handling CSV files with quoted fields that contain embedded commas is a common requirement when working with data import in Spark. Let’s delve into how to manage this using PySpark, which ensures the proper parsing of such fields.

Reading CSV Files with Quoted Fields and Embedded Commas

To read CSV files that have quoted fields with embedded commas, you can leverage the `spark.read` method with appropriate options to correctly handle the quotes. You specifically need to set the `quote` and `escape` parameters and ensure the correct separator is defined.

Example Using PySpark

Consider a CSV file named `example.csv` with the following content:

name,age,description
John,30,"Software engineer, team lead"
Jane,25,"Data scientist, Ph.D."

Here’s how you can read this file correctly with PySpark:

from pyspark.sql import SparkSession

# Initialize the Spark session
spark = SparkSession.builder \
    .appName("Read CSV with Embedded Commas") \
    .getOrCreate()

# Read the CSV file with specific options
df = spark.read.csv('example.csv', header=True, quote='"', escape='"', inferSchema=True)

# Show the DataFrame content
df.show()

Let’s break down the parameters used:

• header=True: Indicates the first row is a header.
• quote='”‘: Sets the quotation character. It is required for fields that contain the delimiter character.
• escape='”‘: Sets the escape character. It ensures that any embedded quote characters within the quoted field will be handled properly.
• inferSchema=True: Automatically infers data types of the columns.

Expected Output

When you run the code, it should produce the following output in the Spark DataFrame:

+----+---+---------------------------+
|name|age|description                |
+----+---+---------------------------+
|John| 30|Software engineer, team lead|
|Jane| 25|Data scientist, Ph.D.       |
+----+---+---------------------------+

This output shows that the embedded commas in the “description” field are handled correctly, preserving the integrity of the data.

Conclusion

By setting the `quote` and `escape` options appropriately, you can ensure that Spark correctly reads CSV files with quoted fields containing embedded commas. This approach is crucial when dealing with complex data imports and helps maintain data consistency.