Apache Spark is a powerful distributed data processing framework that has gained immense popularity for its ability to handle large-scale data analytics. Spark SQL is a module within Apache Spark that allows users to execute SQL queries on structured data, which can be in the form of a DataFrame or a SQL table. One of the key features of Spark SQL is its support for pattern matching using the LIKE operator, combined with wildcards, which can be very useful for filtering data based on specific patterns. This comprehensive guide will explore HOW to use the LIKE operator with wildcards in Spark SQL, providing insight into its syntax, usage, and nuances, along with examples in Scala.
Understanding LIKE Operator and Wildcards in Spark SQL
The LIKE operator in Spark SQL is used for pattern matching in string columns, allowing users to filter and select rows that match certain patterns defined by the user. It is often combined with wildcard characters to define those patterns. In Spark SQL, there are two main wildcard characters:
- Percent sign (%) – The percent sign represents zero, one, or multiple characters.
- Underscore (_) – The underscore represents exactly one character.
When using the LIKE operator with these wildcards, you can construct patterns that match a wide variety of string conditions. Let’s demonstrate some typical use cases and their respective patterns.
Pattern Matching with Percent (%) Wildcard
The percent wildcard (%) is used when you want to match any sequence of characters (including the possibility of no characters at all) in a specific position of the string. Here are some examples:
'data%': Matches any string that starts with ‘data’ followed by any number of characters.'%spark': Matches any string that ends with ‘spark’.'%bigdata%': Matches any string that contains the substring ‘bigdata’.
Pattern Matching with Underscore (_) Wildcard
The underscore wildcard (_) is used when you want to match exactly one character at a specific position in the string. Examples include:
'data_': Matches any five-character string that starts with ‘data’.'_park': Matches any five-character string that ends with ‘park’.'bi_data': Matches any seven-character string that starts with ‘bi’, then any character, then ‘data’.
Combining Wildcards
You can combine both wildcards in a single pattern to construct more complex matching conditions. For instance, the pattern '%data_' matches any string that contains ‘data’ followed by any one character.
Setting Up the Spark Session
Before diving into examples, let’s set up a Spark session. Assuming you have Spark installed, here’s how to initiate it:
import org.apache.spark.sql.SparkSession
val spark = SparkSession
.builder()
.appName("Spark SQL LIKE with Wildcard Guide")
.config("spark.master", "local")
.getOrCreate()
import spark.implicits._
With the Spark session initiated, let’s create a DataFrame to work with:
val data = Seq(
("Alice", "Data Scientist"),
("Bob", "Data Engineer"),
("Cindy", "Database Administrator"),
("David", "Data Analyst"),
("Eve", "Deep Learning Engineer")
)
val df = data.toDF("name", "occupation")
df.show()
The output after running the above code snippet would be:
+-----+-----------------------+
| name| occupation|
+-----+-----------------------+
|Alice| Data Scientist|
| Bob| Data Engineer|
|Cindy|Database Administrator|
|David| Data Analyst|
| Eve|Deep Learning Engineer|
+-----+-----------------------+
Using LIKE for Pattern Matching
Now, we’ll explore different ways of using the LIKE operator with wildcards in Spark SQL query expressions.
Example 1: Starting With a Pattern
Matches names that start with ‘Da’:
df.filter($"occupation".like("Da%")).show()
Output:
+-----+-------------+
| name| occupation|
+-----+-------------+
|Alice|Data Scientist|
| Bob| Data Engineer|
|David| Data Analyst|
+-----+-------------+
Example 2: Ending With a Pattern
Matches occupations that end with ‘Engineer’:
df.filter($"occupation".like("%Engineer")).show()
Output:
+----+-------------------+
|name| occupation|
+----+-------------------+
| Bob| Data Engineer|
| Eve|Deep Learning Engineer|
+----+-------------------+