When working with text data in big data environments, measuring the similarity between strings can be essential. One of the most commonly used metrics for this is the Levenshtein distance, which calculates the number of insertions, deletions, and substitutions required to transform one string into another. In this post, we’ll demonstrate how to implement a User Defined Function (UDF) in Apache Spark to compute the Levenshtein distance efficiently.

Prerequisites

To follow along with this tutorial, ensure you have Apache Spark installed and the python-Levenshtein package available. You can install it using:

pip install python-Levenshtein

Implementing the Levenshtein UDF in PySpark

First, let’s create a Spark session and define our UDF:

from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import IntegerType
import Levenshtein

# Initialize Spark session
spark = SparkSession.builder \
    .appName("Levenshtein UDF") \
    .getOrCreate()

# Function to compute Levenshtein distance
def levenshtein_distance(str1, str2):
    return Levenshtein.distance(str1, str2) if str1 and str2 else None

# Register as a UDF
levenshtein_udf = udf(levenshtein_distance, IntegerType())

Applying the UDF in a Spark DataFrame

Now that we have defined our UDF, let’s create a sample DataFrame and apply the function to compute the Levenshtein distance between pairs of strings.

# Create sample DataFrame
data = [("spark", "shark"), ("java", "scala"), ("hadoop", "hadooop")]
columns = ["string1", "string2"]
df = spark.createDataFrame(data, columns)

# Apply UDF
df_with_distance = df.withColumn("levenshtein_distance", levenshtein_udf(df["string1"], df["string2"]))

# Show results
df_with_distance.show()

Expected Output

The resulting DataFrame will include the Levenshtein distance for each pair of strings:

+-------+-------+-------------------+
|string1|string2|levenshtein_distance|
+-------+-------+-------------------+
|  spark|  shark|                  1|
|   java|  scala|                  4|
| hadoop| hadooop|                  1|
+-------+-------+-------------------+

Conclusion

Using a UDF in Spark allows you to efficiently compute the Levenshtein distance between strings within a distributed environment. This technique is particularly useful in applications such as text deduplication, fuzzy matching, and spell correction. Try it out in your Spark projects and let us know how you use it!