pysparkformat 0.0.9

Collection of Apache Spark Custom Data Formats

pysparkformat: PySpark Data Source Formats

This project provides a collection of custom data source formats for Apache Spark 4.0+ and Databricks, leveraging the new V2 data source PySpark API.

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Supported Formats

Currently, the following formats are supported:

http-csv

This format reads in parallel CSV directly from a URL.

Options

The following options can be specified when using the http-csv format:

Name	Description	Type	Default
header	Indicates whether the CSV file contains a header row.	boolean	false
sep	The field delimiter character.	string	,
encoding	The character encoding of the file.	string	utf-8
quote	The quote character.	string	"
escape	The escape character.	string	\
maxLineSize	The maximum length of a line (in bytes).	integer	10000
partitionSize	The size of each data partition (in bytes).	integer	1048576

http-json

This format reads in parallel JSON Lines directly from a URL. You must specify the schema when using this format.

Options

Name	Description	Type	Default
maxLineSize	The maximum length of a line (in bytes).	integer	10000
partitionSize	The size of each data partition (in bytes).	integer	1048576

Installation

This requires PySpark 4.0 or later to be installed:

pip install pyspark==4.0.0.dev2

Install the package using pip:

pip install pysparkformat

For Databricks:

Install within a Databricks notebook using:

%pip install pysparkformat

This has been tested with Databricks Runtime 15.4 LTS and later.

Usage Example: http-csv

This example demonstrates reading a CSV file from a URL using the http-csv format.

from pyspark.sql import SparkSession
from pysparkformat.http.csv import HTTPCSVDataSource

# Initialize SparkSession (only needed if not running in Databricks)
spark = SparkSession.builder.appName("http-csv-example").getOrCreate()

# You may need to disable format checking depending on your cluster configuration
spark.conf.set("spark.databricks.delta.formatCheck.enabled", False)

# Register the custom data source
spark.dataSource.register(HTTPCSVDataSource)

# URL of the CSV file
url = "https://raw.githubusercontent.com/aig/pysparkformat/refs/heads/master/tests/data/valid-with-header.csv"

# Read the data
df = spark.read.format("http-csv") \
             .option("header", True) \
             .load(url)

# Display the DataFrame (use `display(df)` in Databricks)
df.show()

Usage Example: http-json

from pyspark.sql import SparkSession
from pysparkformat.http.json import HTTPJSONDataSource

# Initialize SparkSession (only needed if not running in Databricks)
spark = SparkSession.builder.appName("http-json-example").getOrCreate()

# You may need to disable format checking depending on your cluster configuration
spark.conf.set("spark.databricks.delta.formatCheck.enabled", False)

# Register the custom data source
spark.dataSource.register(HTTPJSONDataSource)

# URL of the JSON file
url = "https://raw.githubusercontent.com/aig/pysparkformat/refs/heads/master/tests/data/valid-nested.jsonl"

# Read the data (you must specify the schema at the moment)
df = spark.read.format("http-json") \
             .schema("name string, wins array<array<string>>") \
             .load(url)

# Display the DataFrame (use `display(df)` in Databricks)
df.show()

Contributing

Contributions are welcome! We encourage the addition of new custom data source formats and improvements to existing ones.