Spark Join: DataFrame API vs SQL Methods

There are typically two ways to perform a join operation in Spark: using the DataFrame API or using SQL statements.

1. Performing a Join operation using the DataFrame API.

// 创建两个DataFrame
val df1 = spark.read.csv("path/to/first.csv")
val df2 = spark.read.csv("path/to/second.csv")

// 执行Join操作
val result = df1.join(df2, df1("key") === df2("key"), "inner")

1. Performing a Join operation with SQL statements.

// 创建临时表
df1.createOrReplaceTempView("table1")
df2.createOrReplaceTempView("table2")

// 执行Join操作
val result = spark.sql("SELECT * FROM table1 JOIN table2 ON table1.key = table2.key")

When performing a Join operation, it is important to choose the appropriate Join type (such as inner join, outer join, left join, right join, etc.), as well as the columns to be joined. Additionally, ensure that the data types of the columns being joined are consistent, otherwise runtime errors may occur.