How to Generate a Hash from Multiple Columns in PySpark

📘 Introduction

When processing massive datasets in PySpark, it’s often necessary to uniquely identify rows or efficiently detect changes across records. Using multiple columns as a composite key can quickly become cumbersome and inefficient — especially during joins or deduplication.

A better solution is to generate a single hash value derived from multiple columns. Hashing combines column values into a fixed-length identifier that’s easy to compare, compact to store, and quick to compute.

In this guide, you’ll learn how to generate a hash from multiple columns using PySpark’s built-in hash() and cryptographic functions like sha2, md5, and crc32. You’ll also see when to use each approach in real-world data engineering workflows.

💡 Why Generate a Hash from Multiple Columns?

You might want to generate a hash when you need to:

✅ Create a unique identifier for each record
✅ Simplify multi-column joins or key lookups
✅ Detect changes or duplicates between DataFrames
✅ Ensure data consistency across distributed systems

✅ Prerequisites

Before you start, make sure you have:

🐍☑️ Python installed
🔥☑️ A working PySpark environment

📦1️⃣ Install Libraries

Install the following Python packages using pip:

pip install pyspark

📥2️⃣ Import Libraries

Start by importing the required Python modules:

from pyspark.sql import SparkSession
from pyspark.sql.functions import hash, sha2, md5, concat_ws, crc32

⚙️3️⃣ Build a Spark Session

Initialize your Spark session — this is your entry point to PySpark:

spark = SparkSession.builder \
    .appName("HashMultipleColumnsDemo") \
    .getOrCreate()

✍️4️⃣ Create a Sample DataFrame

Let’s create a small DataFrame to demonstrate how to hash multiple columns:

data = [
    (1, "Alice", "NY", 1000),
    (2, "Bob", "CA", 2000),
    (3, "Charlie", "TX", 1500),
    (4, "David", "CA", 2200)
]

df = spark.createDataFrame(data, ["id", "name", "state", "salary"])
df.show()

Output:

+---+-------+-----+------+
| id|   name|state|salary|
+---+-------+-----+------+
|  1|  Alice|   NY|  1000|
|  2|    Bob|   CA|  2000|
|  3|Charlie|   TX|  1500|
|  4|  David|   CA|  2200|
+---+-------+-----+------+

🔢5️⃣ Generate a Hash Using the Built-in hash() Function

PySpark provides a native hash() function that computes a deterministic integer hash value for each row, based on one or more columns. It’s fast and ideal for joins or deduplication tasks.

hashed_df = df.withColumn("row_hash", hash(df.id, df.name, df.state, df.salary))
hashed_df.show()

Output:

+---+-------+-----+------+----------+
| id|   name|state|salary|  row_hash|
+---+-------+-----+------+----------+
|  1|  Alice|   NY|  1000| -14234968|
|  2|    Bob|   CA|  2000| 209673951|
|  3|Charlie|   TX|  1500| -95782344|
|  4|  David|   CA|  2200|  82348931|
+---+-------+-----+------+----------+

⚙️6️⃣ Generate a Hash Using SHA2 (256-bit Secure Hash)

If you need a consistent, string-based hash — for example, when comparing data across systems — you can use sha2()with concat_ws() to combine columns first.