如何使用PySpark转换结构化流？

这看起来似乎很明显，但是在回顾文档和示例时，我不确定是否可以找到一种方法来使用PySpark进行结构化流转换。

例如：

from pyspark.sql import SparkSession

spark = (
    SparkSession
    .builder
    .appName('StreamingWordCount')
    .getOrCreate()
)

raw_records = (
    spark
    .readStream
    .format('socket')
    .option('host', 'localhost')
    .option('port', 9999)
    .load()
)

# I realize there's a SQL function for upper-case, just illustrating a sample
# use of an arbitrary map function
records = raw_records.rdd.map(lambda w: w.upper()).toDF()

counts = (
    records
    .groupBy(records.value)
    .count()
)

query = (
    counts
    .writeStream
    .outputMode('complete')
    .format('console')
    .start()
)
query.awaitTermination()

Queries with streaming sources must be executed with writeStream.start

from pyspark.sql.functions import udf

@udf
def to_upper(s)
    return s.upper()

raw_records.select(to_upper("value"))