Big data & Spark
PySpark jobs, partitioning, batch processing, and large-dataset transform patterns.
How to Filter and Project Spark DataFrames with PySpark SQL
Simulate a SQL SELECT with WHERE using PySpark DataFrame select and filter to project columns and apply conditions.
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
spark = SparkSession.builder.appName("QueryFilterMock").master("local[2]").getOrCreate()
data = [
("Alice", 28, "Engineering"),
("Bob", 35, "Sales"),
("Carol", 32, "Engineering"),
("David", 25, "Marketing"),
("Eve", 29, "E…
How to Mock Hive Support in PySpark with unittest.mock
This code demonstrates how to mock Hive support in a PySpark environment using unittest.mock to simulate SQL queries returning fixed data.
from unittest.mock import Mock, patch
def get_hive_tables(spark):
"""Mock Hive support by returning a fixed list of tables."""
return spark.sql("SHOW TABLES").collect()
class HiveTable:
"""Simple class that mimics a Hive table row."""
def __init__(self, database, tableName):
self.database =…
How to select specific columns in Python with SQLite
A reusable function that connects to a SQLite database and returns only the requested columns from a given table.
import sqlite3
def select_pruned_columns(db_path, table, columns):
with sqlite3.connect(db_path) as conn:
cursor = conn.cursor()
col_list = ", ".join(columns)
query = f"SELECT {col_list} FROM {table}"
return cursor.execute(query).fetchall()
if __name__ == "__main__":
conn = sq…
Browse by section
Each section groups closely related Python snippets.
Big data & Spark — Python code examples
What you will find here
This page collects big data & spark snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.