Spark DataFrame Column Like: A Comprehensive Guide to Pattern Matching in Scala

Introduction

In this blog post, we'll explore how to filter data in Spark DataFrames based on pattern matching using the like() function and other related functions in Scala. By the end of this guide, you'll have a deep understanding of how to perform pattern matching on columns in Spark DataFrames using Scala, allowing you to create more powerful and flexible data processing pipelines.

Understanding Column Like

Pattern matching is a powerful feature that allows you to search for specific patterns within strings. In Spark DataFrames, you can use the like() function to filter rows based on whether a column's string value matches a specified pattern.

Filtering Data Using the like() Function

The like() function can be used in conjunction with the filter() or where() function to filter rows based on a specified pattern.

Example in spark

import org.apache.spark.sql.SparkSession 
        
val spark = SparkSession.builder() 
    .appName("DataFrameColumnLike") 
    .master("local") .getOrCreate() 
    
import spark.implicits._ 
val data = Seq(("Alice", "Apple"), 
    ("Bob", "Banana"), 
    ("Charlie", "Cherry"), 
    ("David", "Date")) 
    
val df = data.toDF("name", "fruit")

In this example, we create a DataFrame with two columns: "name" and "fruit".

Example in spark

val filteredDF = df.filter($"fruit".like("A%"))

In this example, we use the filter() function along with the like() function to filter rows where the "fruit" column has a string value that starts with the letter 'A'. The '%' symbol is used as a wildcard to match any number of characters.

Filtering Data Using the rlike() Function

The rlike() function can be used to filter rows based on whether a column's string value matches a specified regular expression pattern.

Example in spark

val filteredDF = df.filter($"fruit".rlike("^A"))

In this example, we use the filter() function along with the rlike() function to filter rows where the "fruit" column has a string value that starts with the letter 'A'. The regular expression pattern "^A" is used to match the start of the string.

Filtering Data Using SQL-style Syntax

You can use SQL-style syntax to filter data based on pattern matching using the selectExpr() or sql() functions.

Example in spark

val filteredDF = df.selectExpr("*").where("fruit LIKE 'A%'")

In this example, we use the selectExpr() and where() functions with SQL-style syntax to filter rows where the "fruit" column has a string value that starts with the letter 'A'.

Filtering Data Using Column Functions

You can use column functions, such as when() and otherwise() , in combination with the withColumn() function to perform pattern matching and create a new column based on the results.

Example in spark

import org.apache.spark.sql.functions._ 
        
val filteredDF = df.withColumn("starts_with_A", when($"fruit".like("A%"), true).otherwise(false))

In this example, we use the withColumn() function along with the when() and otherwise() functions to create a new column "starts_with_A" that indicates whether the "fruit" column value starts with the letter 'A'.

Conclusion

In this comprehensive blog post, we explored various ways to filter data in Spark DataFrames based on pattern matching using Scala, including the like() function, the rlike() function, SQL-style syntax, and column functions. With a deep understanding of how to perform pattern matching