Apache Spark and Polars are powerful data processing libraries that cater to different needs. Spark excels in distributed computing and is widely used for big data processing, while Polars, a newer library, is designed for fast, single-machine data processing, leveraging Rust for performance. Sometimes, you might want to transform a Spark DataFrame into a Polars DataFrame to take advantage of Polars’ speed and efficiency for smaller datasets or specific operations. This article will guide you through the process.

Prerequisites

Before we dive in, ensure you have the following installed:

• Python (3.7 or above)
• Apache Spark (with PySpark)
• Polars (Python library)
  • You can install PySpark and Polars using pip:
  • pip install pyspark polars

Additionally, you’ll need a basic understanding of both Spark and Polars, along with familiarity with Python programming.

Loading Data into Spark DataFrame

Let’s start by loading some data into a Spark DataFrame. For this example, we’ll use a simple CSV file. This code initializes a Spark session and loads a CSV file into a Spark DataFrame.

data.csv

Code Example :

from pyspark.sql import SparkSession

# Initialize Spark session
spark = SparkSession.builder.appName("Spark to Polars").getOrCreate()

# Load data into Spark DataFrame
spark_df = spark.read.csv("data.csv", header=True, inferSchema=True)

# Show the first few rows
spark_df.show()

Output

+----+
|A\tB|
+----+
|1\ta|
|2\tb|
+----+

Transforming Spark DataFrame to Polars DataFrame

There are several ways to convert a Spark DataFrame to a Polars DataFrame. Here are three methods:

Method 1: Using Pandas as an Intermediary

One straightforward approach is to first convert the Spark DataFrame to a Pandas DataFrame and then to a Polars DataFrame.

import pandas as pd
import polars as pl

# Convert Spark DataFrame to Pandas DataFrame
print(type(spark))
pandas_df = spark_df.toPandas()

# Convert Pandas DataFrame to Polars DataFrame
polars_df = pl.from_pandas(pandas_df)

# Show the first few rows of Polars DataFrame
print(polars_df)
print(type(polars_df))

Output

<class 'pyspark.sql.session.SparkSession'>
shape: (2, 1)
┌─────┐
│ A    B  │
│ --- │
│ str │
╞═════╡
│ 1    a  │
│ 2    b  │
└─────┘
<class 'polars.dataframe.frame.DataFrame'>

Method 2: Using Arrow for Efficient Conversion

Apache Arrow provides a columnar memory format that enables efficient data interchange. PySpark supports Arrow for faster conversion to Pandas DataFrame, which can then be converted to a Polars DataFrame.

import pandas as pd
import polars as pl

# Enable Arrow-based conversion
spark.conf.set("spark.sql.execution.arrow.enabled", "true")

print(type(spark))

# Convert Spark DataFrame to Pandas DataFrame using Arrow
pandas_df = spark_df.toPandas()

# Convert Pandas DataFrame to Polars DataFrame
polars_df = pl.from_pandas(pandas_df)

# Show the first few rows of Polars DataFrame
print(polars_df)
print(type(polars_df))

Output

<class 'pyspark.sql.session.SparkSession'>
shape: (2, 1)
┌─────┐
│ A    B  │
│ --- │
│ str │
╞═════╡
│ 1    a  │
│ 2    b  │
└─────┘
<class 'polars.dataframe.frame.DataFrame'>

Method 3: Direct Conversion (Custom Implementation)

If performance is critical, you might consider writing a custom function to convert Spark DataFrame directly to Polars DataFrame without intermediate conversion to Pandas. This requires extracting data from Spark and loading it into Polars directly.

import polars as pl

def spark_to_polars(spark_df):
    columns = spark_df.columns
    pdf = spark_df.toPandas()
    data = {col: pdf[col].tolist() for col in columns}
    polars_df = pl.DataFrame(data)
    return polars_df

print(type(spark))

# Convert Spark DataFrame to Polars DataFrame
polars_df = spark_to_polars(spark_df)

# Show the first few rows of Polars DataFrame
print(polars_df)
print(type(polars_df))

Output

<class 'pyspark.sql.session.SparkSession'>
shape: (2, 1)
┌─────┐
│ A    B  │
│ --- │
│ str │
╞═════╡
│ 1    a  │
│ 2    b  │
└─────┘
<class 'polars.dataframe.frame.DataFrame'>

Conclusion

Transforming a Spark DataFrame to a Polars DataFrame can be achieved through various methods, each with its own trade-offs. Using Pandas as an intermediary is simple and effective, while leveraging Arrow can enhance performance. For those seeking the utmost efficiency, a custom implementation may be the best approach. With these methods, you can harness the power of both Spark and Polars in your data processing workflows.