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

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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 @") \

# 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"]))


|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
Author: user