对于input_table中的每一行，应创建Xoutput_table中的行数，其中X=年中的天数（从StartDate开始）
Info字段应包含Y字符数，其中Y= X*2，如果字符数较少，则字段应填充额外的#字符。
在output_tableAM和PM列中，将以正确的顺序填充Info字符，以便每个AM和PM字段将恰好有1个字符。
下面是代码：

from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import IntegerType, StringType, DateType, StructField, StructType, TimestampType, ArrayType

# Connection details for input table
url="..."
user="..."
password="..."
input_table="..."
output_table="..."

# Define schema for input table
input_schema = StructType([
    StructField("ID1", IntegerType(), True),
    StructField("ID2", IntegerType(), True),
    StructField("StartDate", TimestampType(), True),
    StructField("Info", StringType(), True),
    StructField("Extracted", TimestampType(), True)
])

# Define schema for output table
output_schema = StructType([
    StructField("ID1", IntegerType(), True),
    StructField("ID2", IntegerType(), True),
    StructField("Date", DateType(), True),
    StructField("AM", StringType(), True),
    StructField("PM", StringType(), True),
    StructField("CurrentYear", StringType(), True)
])

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

# Register UDF for padding marks
pad_marks_udf = udf(lambda info, days: marks.ljust(days, '#')[:days], StringType())

# Register UDF for creating rows
create_rows_udf = udf(lambda start_date, marks, days: [(start_date + i, info[i], info[i + 1]) for i in range(0, days, 2)],
                     ArrayType(StructType([
                         StructField("Date", DateType(), True),
                         StructField("AM", StringType(), True),
                         StructField("PM", StringType(), True),
                     ])))

# Define function to pad marks and create rows
def process_row(row):
    id1 = row["ID1"]
    id2 = row["ID2"]
    start_date = row["StartDate"]
    info= row["info"]
    extracted = row["Extracted"]

    # Calculate number of days * 2
    days = (start_date.year % 4 == 0 and 366 or 365) * 2

    # Pad info
    padded_info = pad_info_udf(info, days)

    # Create rows
    rows = create_rows_udf(start_date, padded_info, days)

    # Prepare output rows
    output_rows = []
    for r in rows:
        date = r["Date"]
        am = r["AM"]
        pm = r["PM"]
        current_year = f"{current_year .year}/{current_year .year + 1}"

        output_rows.append((id1, id2, date, am, pm, current_year))

    return output_rows

# Load input table as DataFrame
df_input = spark.read \
    .format("jdbc") \
    .option("url", url) \
    .option("dbtable", input_table) \
    .option("user", user) \
    .option("password", password) \
    .schema(input_schema) \
    .load()

# Apply processing to input table
output_rows = df_input.rdd.flatMap(process_row)

# Create DataFrame from output rows
df_output = spark.createDataFrame(output_rows, output_schema)

# Write DataFrame to output table
df_output.write \
    .format("jdbc") \
    .option("url", url) \
    .option("user", user) \
    .option("password", password) \
    .option("dbtable", output_table) \
    .mode("append") \
    .save()

类似的代码在Python中运行没有问题，但当转换为PySpark时抛出一个AssertionError。它不需要在input_table中做任何修改，并在output_table中添加修改过的行。