如何在从socket源数据创建dataframe时指定架构？

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType

# 创建SparkSession
spark = SparkSession.builder.getOrCreate()

# 定义架构
schema = StructType([
    StructField("name", StringType(), True),
    StructField("age", StringType(), True),
    StructField("city", StringType(), True)
])

# 从socket源数据创建DataFrame，并应用指定的架构
socketDF = spark \
    .readStream \
    .format("socket") \
    .option("host", "localhost") \
    .option("port", 9999) \
    .load() \
    .selectExpr("CAST(value AS STRING)") \
    .selectExpr("split(value, ',') AS data") \
    .selectExpr("data[0] AS name", "data[1] AS age", "data[2] AS city") \
    .selectExpr("CAST(name AS STRING)", "CAST(age AS STRING)", "CAST(city AS STRING)") \
    .selectExpr("name", "age", "city")

# 打印DataFrame的架构
socketDF.printSchema()

# 启动流式查询
query = socketDF \
    .writeStream \
    .outputMode("append") \
    .format("console") \
    .start()

# 等待流式查询结束
query.awaitTermination()