离开反加入Spark吗? [英] Left Anti join in Spark?

问题描述

我已经定义了两个表，

 val tableName = "table1"
    val tableName2 = "table2"

    val format = new SimpleDateFormat("yyyy-MM-dd")
      val data = List(
        List("mike", 26, true),
        List("susan", 26, false),
        List("john", 33, true)
      )
    val data2 = List(
        List("mike", "grade1", 45, "baseball", new java.sql.Date(format.parse("1957-12-10").getTime)),
        List("john", "grade2", 33, "soccer", new java.sql.Date(format.parse("1978-06-07").getTime)),
        List("john", "grade2", 32, "golf", new java.sql.Date(format.parse("1978-06-07").getTime)),
        List("mike", "grade2", 26, "basketball", new java.sql.Date(format.parse("1978-06-07").getTime)),
        List("lena", "grade2", 23, "baseball", new java.sql.Date(format.parse("1978-06-07").getTime))
      )

      val rdd = sparkContext.parallelize(data).map(Row.fromSeq(_))
      val rdd2 = sparkContext.parallelize(data2).map(Row.fromSeq(_))
      val schema = StructType(Array(
        StructField("name", StringType, true),
        StructField("age", IntegerType, true),
        StructField("isBoy", BooleanType, false)
      ))
    val schema2 = StructType(Array(
        StructField("name", StringType, true),
        StructField("grade", StringType, true),
        StructField("howold", IntegerType, true),
        StructField("hobby", StringType, true),
        StructField("birthday", DateType, false)
      ))

      val df = sqlContext.createDataFrame(rdd, schema)
      val df2 = sqlContext.createDataFrame(rdd2, schema2)
      df.createOrReplaceTempView(tableName)
      df2.createOrReplaceTempView(tableName2)

我正在尝试建立查询以从table1返回没有在table2中具有匹配行的行. 我尝试使用以下查询来做到这一点:

I'm trying to build query to return rows from table1 that doesn't have matching row in table2. I've tried to do it using this query:

Select * from table1 LEFT JOIN table2 ON table1.name = table2.name AND table1.age = table2.howold AND table2.name IS NULL AND table2.howold IS NULL

但这只是给了我来自table1的所有行:

but this just gives me all rows from table1:

列表({"name":"john"，"age":33，"isBoy":true}， {"name":"susan"，"age":26，"isBoy":false}， {"name":"mike"，"age":26，"isBoy":true})

List({"name":"john","age":33,"isBoy":true}, {"name":"susan","age":26,"isBoy":false}, {"name":"mike","age":26,"isBoy":true})

如何有效地在Spark中进行这种类型的联接?

How to make this type of join in Spark efficiently?

我正在寻找一个SQL查询，因为我需要能够指定要在两个表之间进行比较的列，而不仅仅是像在其他建议问题中那样逐行比较.像使用减法，除其他外.

I'm looking for an SQL query because I need to be able to specify columns which to compare between two tables, not just compare row by row like it is done in other recommended questions. Like using subtract, except etc.

推荐答案

您可以使用左反"连接类型-通过DataFrame API或SQL(DataFrame API支持SQL支持的所有内容，包括您需要的任何连接条件) ):

You can use the "left anti" join type - either with DataFrame API or with SQL (DataFrame API supports everything that SQL supports, including any join condition you need):

DataFrame API:

DataFrame API:

df.as("table1").join(
  df2.as("table2"),
  $"table1.name" === $"table2.name" && $"table1.age" === $"table2.howold",
  "leftanti"
)

SQL:

sqlContext.sql(
  """SELECT table1.* FROM table1
    | LEFT ANTI JOIN table2
    | ON table1.name = table2.name AND table1.age = table2.howold
  """.stripMargin)

注意:还值得注意的是，存在一种更短，更简明的方式来创建示例数据，而无需分别指定模式，使用元组和隐式的toDF方法，然后修复"需要时自动推断的模式:

NOTE: it's also worth noting that there's a shorter, more concise way of creating the sample data without specifying the schema separately, using tuples and the implicit toDF method, and then "fixing" the automatically-inferred schema where needed:

import spark.implicits._
val df = List(
  ("mike", 26, true),
  ("susan", 26, false),
  ("john", 33, true)
).toDF("name", "age", "isBoy")

val df2 = List(
  ("mike", "grade1", 45, "baseball", new java.sql.Date(format.parse("1957-12-10").getTime)),
  ("john", "grade2", 33, "soccer", new java.sql.Date(format.parse("1978-06-07").getTime)),
  ("john", "grade2", 32, "golf", new java.sql.Date(format.parse("1978-06-07").getTime)),
  ("mike", "grade2", 26, "basketball", new java.sql.Date(format.parse("1978-06-07").getTime)),
  ("lena", "grade2", 23, "baseball", new java.sql.Date(format.parse("1978-06-07").getTime))
).toDF("name", "grade", "howold", "hobby", "birthday").withColumn("birthday", $"birthday".cast(DateType))

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