PySpark .show() fails with “Python worker exited unexpectedly” on Windows (Python 3.14)

Body

I am facing a PySpark error on Windows while calling .show() on a DataFrame. The job fails with a Python worker crash.

Environment

OS: Windows 10

Spark: Apache Spark (PySpark)

IDE: VS Code

Python version: 3.14

Code

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from pyspark.sql import SparkSession from pyspark.sql.types import StructType, StructField, StringType spark = SparkSession.builder \ .appName("Test") \ .master("local[*]") \ .getOrCreate() emp_schema = StructType([ StructField("employee_id", StringType(), True), StructField("department_id", StringType(), True), StructField("name", StringType(), True), StructField("age", StringType(), True), StructField("gender", StringType(), True), StructField("salary", StringType(), True), StructField("hire_date", StringType(), True) ]) emp_data = [ ["001", "101", "John Doe", "30", "Male", "50000", "2015-01-01"], ["002", "101", "Jane Smith", "25", "Female", "45000", "2016-02-15"], ["003", "102", "Bob Brown", "35", "Male", "55000", "2014-05-01"], ["004", "102", "Alice Lee", "28", "Female", "48000", "2017-09-30"], ["005", "103", "Jack Chan", "40", "Male", "60000", "2013-04-01"], ["006", "103", "Jill Wong", "32", "Female", "52000", "2018-07-01"], ["007", "101", "James Johnson", "42", "Male", "70000", "2012-03-15"], ["008", "102", "Kate Kim", "29", "Female", "51000", "2019-10-01"], ["009", "103", "Tom Tan", "33", "Male", "58000", "2016-06-01"], ["010", "104", "Lisa Lee", "27", "Female", "47000", "2018-08-01"], ["011", "104", "David Park", "38", "Male", "65000", "2015-11-01"], ["012", "105", "Susan Chen", "31", "Female", "54000", "2017-02-15"], ["013", "106", "Brian Kim", "45", "Male", "75000", "2011-07-01"], ["014", "107", "Emily Lee", "26", "Female", "46000", "2019-01-01"], ["015", "106", "Michael Lee", "37", "Male", "63000", "2014-09-30"], ["016", "107", "Kelly Zhang", "30", "Female", "49000", "2018-04-01"], ["017", "105", "George Wang", "34", "Male", "57000", "2016-03-15"], ["018", "104", "Nancy Liu", "29", "Female", "50000", "2017-06-01"], ["019", "103", "Steven Chen", "36", "Male", "62000", "2015-08-01"], ["020", "102", "Grace Kim", "32", "Female", "53000", "2018-11-01"] ] from pyspark.sql.types import StructType, StructField, StringType emp_schema = "employee_id string, department_id string, name string, age string, gender string, salary string, hire_date string" emp = spark.createDataFrame(emp_data, emp_schema) emp.show()