How to read csv with spark

To read a CSV file in Spark, you can use the read method of the SparkSession object, which is the entry point to Spark’s SQL functionality. Here is an example code snippet:

from pyspark.sql import SparkSession

# create a SparkSession object
spark = SparkSession.builder.appName("CSVReader").getOrCreate()

# read the CSV file as a DataFrame
df = spark.read.format("csv").option("header", "true").load("path/to/csv/file.csv")

# show the first 20 rows of the DataFrame
df.show(20)

In this example, we are using the format method to specify that the file is in CSV format, and the option method to specify that the file has a header row. You can also specify other options such as the delimiter, encoding, etc. by passing them as key-value pairs to the option method. Finally, the load method is used to load the CSV file into a DataFrame, which is a distributed collection of data organized into named columns.

You can also use other methods such as csv and text to read CSV files in Spark, depending on your specific requirements.

When reading a CSV file in Spark, there are several options that you can specify to control how the file is parsed and loaded. Here is a list of some of the most commonly used options:

1. header: Specifies whether the CSV file has a header row. If set to true, the first row of the file is used as the column names of the resulting DataFrame. If set to false or omitted, Spark will infer the column names based on the data types of the columns.
2. inferSchema: Specifies whether to infer the data types of the columns from the data in the file. If set to true, Spark will examine the first few rows of the file to determine the data types of the columns. If set to false or omitted, all columns will be read as strings.
3. delimiter: Specifies the delimiter character used to separate the fields in the CSV file. The default delimiter is a comma (,), but you can specify a different character such as a tab (\t) or a pipe (|).
4. quote: Specifies the character used to enclose fields that contain the delimiter character. The default quote character is a double quote ("), but you can specify a different character such as a single quote (').
5. escape: Specifies the character used to escape special characters within a field. The default escape character is a backslash (\), but you can specify a different character if necessary.
6. nullValue: Specifies the string representation of a null value in the file. If a field in the CSV file contains this value, it will be interpreted as a null value in the resulting DataFrame. The default null value is an empty string (""), but you can specify a different value such as null or NA.
7. nanValue: Specifies the string representation of a NaN (Not a Number) value in the file. If a field in the CSV file contains this value, it will be interpreted as a NaN value in the resulting DataFrame. The default NaN value is "NaN", but you can specify a different value if necessary.
8. positiveInf: Specifies the string representation of positive infinity in the file. If a field in the CSV file contains this value, it will be interpreted as positive infinity in the resulting DataFrame. The default positive infinity value is "Inf", but you can specify a different value if necessary.
9. negativeInf: Specifies the string representation of negative infinity in the file. If a field in the CSV file contains this value, it will be interpreted as negative infinity in the resulting DataFrame. The default negative infinity value is "-Inf", but you can specify a different value if necessary.

These are some of the most commonly used options when reading a CSV file in Spark. You can find a complete list of options and their descriptions in the Spark documentation.