Sure, let’s dive into the differences between `==` and `===` in both Scala and Apache Spark.
Difference Between == and === in Scala and Spark
In Scala
In Scala, `==` is used to compare two objects for equality. It internally calls the `equals` method. The `==` operator is part of the Scala standard library and is used for general equality testing.
On the other hand, `===` is not a standard Scala operator. It is specific to certain libraries, like Cats, which use the `===` operator to signify type-safe equality comparison. This means you need to bring in an additional library to use `===` in Scala.
Example in Scala:
Here, we’ll compare two strings using `==` and `===` (assuming we have the Cats library for `===`).
import cats.implicits._
val str1 = "Hello"
val str2 = "Hello"
val str3 = "World"
// Using == in Scala
val result1 = (str1 == str2) // true
val result2 = (str1 == str3) // false
// Using === in Scala (requires Cats library)
val result3 = (str1 === str2) // true
val result4 = (str1 === str3) // false
println(s"Using ==: result1 = $result1, result2 = $result2")
println(s"Using ===: result3 = $result3, result4 = $result4")
Using ==: result1 = true, result2 = false
Using ===: result3 = true, result4 = false
In Apache Spark
In the context of Spark, which uses the DataFrame API, `==` and `===` serve different purposes:
==: This is a comparison operator in Scala, but when used in the context of Spark DataFrames, it is not used for column comparison.===: This is a method provided by Spark’s Column class for equality comparisons of columns within DataFrames.
In Spark, `===` is part of the `Column` class and is used to compare the values of two columns for equality. It returns a new column with boolean values.
Example in PySpark:
Here is an example of using `===` to compare two columns in a DataFrame:
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
spark = SparkSession.builder.appName("ExampleApp").getOrCreate()
data = [(1, "Alice"), (2, "Bob"), (1, "Alice")]
df = spark.createDataFrame(data, ["ID", "Name"])
# Using === to compare columns
result_df = df.withColumn("Is_Same", col("ID") == col("Name"))
result_df.show()
# Using == wrongly raises an error
# result_df_wrong = df.withColumn("Is_Same", df.col("ID") == 1)
# result_df_wrong.show()
+---+-----+-------+
| ID| Name|Is_Same|
+---+-----+-------+
| 1|Alice| false|
| 2| Bob| false|
| 1|Alice| false|
+---+-----+-------+
Example in Scala with Spark:
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
val spark = SparkSession.builder.appName("ExampleApp").getOrCreate()
import spark.implicits._
val data = Seq((1, "Alice"), (2, "Bob"), (1, "Alice"))
val df = data.toDF("ID", "Name")
// Using === to compare columns
val resultDF = df.withColumn("Is_Same", $"ID" === $"Name")
resultDF.show()
// Using == wrongly raises an error
// val resultDFWrong = df.withColumn("Is_Same", df("ID") == 1)
// resultDFWrong.show()
+---+-----+-------+
| ID| Name|Is_Same|
+---+-----+-------+
| 1|Alice| false|
| 2| Bob| false|
| 1|Alice| false|
+---+-----+-------+
Both examples show that in the context of Spark, you should use `===` rather than `==` for column comparisons in DataFrames.