我是新的编程与Spark结构化流。我用了这个后就犯了这个错误 F.approx_count_distinct ，这是我的密码。我的问题是，我想得到一个Dataframe，检测欺诈，但首先我需要检查是否有人与相同的 card_number . 有人能帮我吗？提前谢谢。

from pyspark.sql import SparkSession
from pyspark import SparkContext, SparkConf
from pyspark.sql.functions import from_json, col
from pyspark.sql import functions as F
from pyspark.sql.functions import when
from pyspark.sql.types import *
conf = SparkConf().setAppName("Pruebas").setMaster("local")
sc = SparkContext(conf=conf)
sc.setLogLevel("ERROR")
sparkSQL = SparkSession \
.builder \
.appName("SparkSQL") \
.master("local") \
.getOrCreate()
broker="localhost:9092"
topic = "transacts"

# Construir el dataframe de streaming

df = sparkSQL \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", broker) \
.option("failOnDataLoss", "false") \
.option("subscribe", topic) \
.option("startingOffsets", "latest") \
.option("includeTImestamp", "true") \
.load()

# Definir el esquema que utilizaremos en el json

schema = StructType([ StructField("card_owner", StringType(), True),
StructField("card_number", StringType(), True),
StructField("geography", StringType(), True),
StructField("target", StringType(), True),
StructField("amount",  StringType(), True),
StructField("currency", StringType(), True)])

# decodificar el json

# al decodificar el json nos genera una serie de subcolumnas dentro del campo value

df = df.withColumn("value", from_json(df["value"].cast("string"), schema))
df.printSchema()

# seleccionamos el timestamp del mensaje y las columnas del json

df = df.select("timestamp","value.*")
df1 = df.groupBy(df.card_number).agg(F.approx_count_distinct(df.card_owner).alias('titulares')).filter((F.col('titulares')>1))

df1 = df1.selectExpr("'a' as key", "to_json(struct(*)) as value")

query= df1.writeStream\
.outputMode("complete")\
.format("kafka")\
.option("topic","aux_topic1")\
.option("kafka.bootstrap.servers", "localhost:9092")\
.option("checkpointLocation","hdfs://localhost:9000/checkpoints")\
.start()

# query.awaitTermination(200)

# Paso de json a df

topic1= "aux_topic1"
df1 = sparkSQL \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", broker) \
.option("failOnDataLoss", "false") \
.option("subscribe", topic1) \
.option("startingOffsets", "latest") \
.option("includeTImestamp", "true") \
.load()

# Definir el esquema que utilizaremos en el json

schema = StructType([ StructField("card_number", StringType(), True),
StructField("titulares", StringType(), True)])

# decodificar el json

df1 = df1.withColumn("value", from_json(df1["value"].cast("string"), schema))
df1.printSchema()

df1 = df1.select("timestamp","value.*")
df2=df.join(df1, on="card_number")

# Mostrar por pantalla

query1= df2.writeStream\
.outputMode("append")\
.format("console")\
.queryName("test")\
.start()
query1.awaitTermination()