Apache Spark 4.0 introduces a new data source API called V2 and even more now we can use python to create custom data sources. This is a great feature that allows us to create custom data sources that can be used in any pyspark projects.
This project is intended to collect all custom pyspark formats that I have created for my projects.
Here is what we have so far:
- http-csv : A custom data source that reads CSV files from HTTP.
You are welcome to contribute with new formats or improvements in the existing ones.
Usage:
pip install pyspark==4.0.0.dev2
pip install pysparkformatYou also can use this package in Databricks notebooks. Tested with Databricks Runtime 15.4 LTS. Just install it using the following command to general-purpose cluster:
%pip install pysparkformatfrom pyspark.sql import SparkSession
from pysparkformat.http.csv import HTTPCSVDataSource
# you can comment the following line if you are running this code in Databricks
spark = SparkSession.builder.appName("custom-datasource-example").getOrCreate()
# uncomment to disable format check for Databricks Runtime
# spark.conf.set("spark.databricks.delta.formatCheck.enabled", False)
spark.dataSource.register(HTTPCSVDataSource)
url = "https://www.stats.govt.nz/assets/Uploads/Annual-enterprise-survey/Annual-enterprise-survey-2023-financial-year-provisional/Download-data/annual-enterprise-survey-2023-financial-year-provisional.csv"
df = spark.read.format("http-csv").option("header", True).load(url)
df.show() # or use display(df) in Databricks
