Computing the Levenshtein distance between two strings using PySpark – Examples included

pyspark.sql.functions.levenshtein

The Levenshtein function in PySpark computes the Levenshtein distance between two strings – that is, the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one word into the other. This function is invaluable in tasks involving fuzzy string matching, data deduplication, and data cleaning.

Imagine a scenario where a data analyst needs to reconcile customer names from two different databases to identify duplicates:

from pyspark.sql import SparkSession
from pyspark.sql.functions import levenshtein

# Initialize SparkSession
spark = SparkSession.builder \
    .appName("Levenshtein Demo @ Freshers.in") \
    .getOrCreate()

# Sample data with customer names from two different databases
data = [("Jonathan Smith", "Jonathon Smith"),
        ("Claire Saint", "Clare Sant"),
        ("Mark Spencer", "Marc Spencer"),
        ("Lucy Bane", "Lucy Bane")]

# Define DataFrame with names
df = spark.createDataFrame(data, ["DatabaseA_Name", "DatabaseB_Name"])

# Calculate the Levenshtein distance between the names
df_with_levenshtein = df.withColumn("Name_Match_Score", levenshtein(df["DatabaseA_Name"], df["DatabaseB_Name"]))
df_with_levenshtein.show(truncate=False)

Output:

+--------------+--------------+----------------+
|DatabaseA_Name|DatabaseB_Name|Name_Match_Score|
+--------------+--------------+----------------+
|Jonathan Smith|Jonathon Smith|1               |
|Claire Saint  |Clare Sant    |4               |
|Mark Spencer  |Marc Spencer  |1               |
|Lucy Bane     |Lucy Bane     |0               |
+--------------+--------------+----------------+

Benefits of using the Levenshtein function:

1. Improved Data Quality: It enables the identification and correction of errors, leading to higher data accuracy.
2. Efficient Matching: Provides a method for automated and efficient string comparison, saving time and resources.
3. Versatile Applications: Can be used across various industries, from healthcare to e-commerce, for maintaining data integrity.
4. Enhanced User Experience: In applications like search engines, it helps in returning relevant results even when the search terms are not exactly spelled correctly.

Scenarios for using the Levenshtein function:

1. Data Cleaning: Identifying and correcting typographical errors in text data.
2. Record Linkage: Associating records from different data sources by matching strings.
3. Search Enhancement: Improving the robustness of search functionality by allowing for close-match results.
4. Natural Language Processing (NLP): Evaluating and processing textual data for machine learning models.

Spark important urls to refer

1. Spark Examples
2. PySpark Blogs
3. Bigdata Blogs
4. Spark Interview Questions
5. Official Page