如何在 Apache Spark 中为两个具有不同结构的 DataFrame 实现 NOT IN

Question

我在 Java 应用程序中使用 Apache Spark。我有两个 DataFrame：df1 和 df2。 df1 包含 Row 以及 email、firstName 和 lastName。 df2 包含 Row 和 email。

我想创建一个 DataFrame: df3，其中包含 df1 中的所有行，df2 中不存在该电子邮件。

有没有办法用 Apache Spark 做到这一点？我尝试从 df1 和 df2 创建 JavaRDD<String>，方法是将它们转换为 toJavaRDD() 并过滤 df1 以包含所有电子邮件，然后使用 subtract，但我不知道如何将新的 JavaRDD 映射到 ds1 并获得 DataFrame.

基本上我需要 df1 中的所有行，其电子邮件不在 df2.

中

DataFrame customers = sqlContext.cassandraSql("SELECT email, first_name, last_name FROM customer ");

DataFrame customersWhoOrderedTheProduct = sqlContext.cassandraSql("SELECT email FROM customer_bought_product " +
                            "WHERE product_id = '" + productId + "'");

JavaRDD<String> customersBoughtEmail = customersWhoOrderedTheProduct.toJavaRDD().map(row -> row.getString(0));

List<String> notBoughtEmails = customers.javaRDD()
                        .map(row -> row.getString(0))
                        .subtract(customersBoughtEmail).collect();

Answer 1

Spark 2.0.0+

可以直接使用NOT IN。

Spark < 2.0.0

可以用outer join和filter来表达

val customers = sc.parallelize(Seq(
  ("john@example.com", "John", "Doe"),
  ("jane@example.com", "Jane", "Doe")
)).toDF("email", "first_name", "last_name")

val customersWhoOrderedTheProduct = sc.parallelize(Seq(
  Tuple1("jane@example.com")
)).toDF("email")

val customersWhoHaventOrderedTheProduct = customers.join(
    customersWhoOrderedTheProduct.select($"email".alias("email_")),
    $"email" === $"email_", "leftouter")
 .where($"email_".isNull).drop("email_")

customersWhoHaventOrderedTheProduct.show

// +----------------+----------+---------+
// |           email|first_name|last_name|
// +----------------+----------+---------+
// |john@example.com|      John|      Doe|
// +----------------+----------+---------+

原始 SQL 等效：

customers.registerTempTable("customers")
customersWhoOrderedTheProduct.registerTempTable(
  "customersWhoOrderedTheProduct")

val query = """SELECT c.* FROM customers c LEFT OUTER JOIN  
                 customersWhoOrderedTheProduct o
               ON c.email = o.email
               WHERE o.email IS NULL"""

sqlContext.sql(query).show

// +----------------+----------+---------+
// |           email|first_name|last_name|
// +----------------+----------+---------+
// |john@example.com|      John|      Doe|
// +----------------+----------+---------+

Answer 2

我在 python 中做到了，此外我建议您使用整数作为键而不是字符串。

from pyspark.sql.types import *

samples = sc.parallelize([
    ("abonsanto@fakemail.com", "Alberto", "Bonsanto"), ("mbonsanto@fakemail.com", "Miguel", "Bonsanto"),
    ("stranger@fakemail.com", "Stranger", "Weirdo"), ("dbonsanto@fakemail.com", "Dakota", "Bonsanto")
])

keys = sc.parallelize(
    [("abonsanto@fakemail.com",), ("mbonsanto@fakemail.com",), ("dbonsanto@fakemail.com",)]
)

complex_schema = StructType([
    StructField("email", StringType(), True),
    StructField("first_name", StringType(), True),
    StructField("last_name", StringType(), True)
])

simple_schema = StructType([
    StructField("email", StringType(), True)
])

df1 = sqlContext.createDataFrame(samples, complex_schema)
df2 = sqlContext.createDataFrame(keys, simple_schema)

df1.show()
df2.show()

df3 = df1.join(df2, df1.email == df2.email, "left_outer").where(df2.email.isNull()).show()

如何在 Apache Spark 中为两个具有不同结构的 DataFrame 实现 NOT IN

How to implement NOT IN for two DataFrames with different structure in Apache Spark

java

sql

apache-spark

apache-spark-sql