连接pyspark中多列的通用合并

Question

我必须合并许多 spark DataFrame。合并后，我想在具有相同名称的多个列之间执行合并。

我能够在 question 之后创建一个最小示例。

但是，我需要一段更通用的代码来支持：一组要合并的变量（在示例中为 set_vars = set(('var1','var2'))）和多个连接键（在示例中为 join_keys = set(('id'))）。

在pyspark 中是否有更简洁（更通用）的方法来获得此结果？

df1 =  spark.createDataFrame([ 
        ( 1, None ,  "aa"),
        ( 2 , "a", None ),
        ( 3 , "b",  None),
        ( 4 , "h",  None),],
        "id int, var1 string, var2 string",
       )

df2 =  spark.createDataFrame([ 
        ( 1, "f" ,  "Ba"),
        ( 2 , "a", "bb" ),
        ( 3 , "b",  None),],
        "id int, var1 string, var2 string",
       )

df1 = df1.alias("df1")
df2 = df2.alias("df2")

df3 = df1.join(df2, df1.id == df2.id, how='left').withColumn("var1_", coalesce("df1.var1", "df2.var1")).drop("var1").withColumnRenamed("var1_", "var1").withColumn("var2_", coalesce("df1.var2", "df2.var2")).drop("var2").withColumnRenamed("var2_", "var2")

Answer 1

我们可以通过将列作为列表传递给连接方法来避免重复列，而不是编写连接条件，请参阅link。 但是这里有一些常见的列，它们不是连接条件所必需的。我们可以使用 for 循环来概括您的代码。

spark = SparkSession.builder.master("local[*]").getOrCreate()

df1 =  spark.createDataFrame([
        ( 1, None ,  "aa"),
        ( 2 , "a", None ),
        ( 3 , "b",  None),
        ( 4 , "h",  None),],
        "id int, var1 string, var2 string",
       )

df2 =  spark.createDataFrame([
        ( 1, "f" ,  "Ba"),
        ( 2 , "a", "bb" ),
        ( 3 , "b",  None),],
        "id int, var1 string, var2 string",
       )

df1 = df1.alias("df1")
df2 = df2.alias("df2")

key_columns = ["id"]
# Get common columns between 2 dataframes excluding columns-
# -which are being used in joining conditions
other_common_columns = set(df1.columns).intersection(set(df2.columns))\
.difference(set(key_columns))

outputDF = df1.join(df2, key_columns, how='left')

for i in other_common_columns:
    outputDF = outputDF.withColumn(f"{i}_", coalesce(f"df1.{i}", f"df2.{i}"))\
.drop(i).withColumnRenamed(f"{i}_", i)

outputDF.show()

+---+----+----+
| id|var2|var1|
+---+----+----+
|  1|  aa|   f|
|  3|null|   b|
|  4|null|   h|
|  2|  bb|   a|
+---+----+----+