要实现自定义的Pyspark分解(用于结构数组),可以按照以下步骤进行:
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, explode
from pyspark.sql.types import StructType, StructField, StringType, IntegerType
spark = SparkSession.builder.appName("Custom Pyspark Decompose").getOrCreate()
data = [("A", [("a1", 1), ("a2", 2), ("a3", 3)]),
("B", [("b1", 4), ("b2", 5), ("b3", 6)])]
df = spark.createDataFrame(data, ["col1", "col2"])
def custom_decompose(array_col):
result = []
for item in array_col:
result.append((item[0], item[1]))
return result
spark.udf.register("custom_decompose", custom_decompose, ArrayType(StructType([
StructField("col3", StringType()),
StructField("col4", IntegerType())
]))))
df = df.withColumn("decomposed_col", explode(expr("custom_decompose(col2)")))
df = df.select("col1", "decomposed_col.col3", "decomposed_col.col4")
至此,我们成功实现了自定义的Pyspark分解(用于结构数组),其中一个分解中有4列。请注意,这只是一个示例,你可以根据实际需求进行修改和扩展。
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