用SparkSQL和PySpark完成按时间字段顺序将字符串字段中的值组合在一起分组显示

用SparkSQL和PySpark完成以下数据转换。
源数据：
userid,page_name,visit_time
1,A,2021-2-1
2,B,2024-1-1
1,C,2020-5-4
2,D,2028-9-1

目的数据：
user_id,page_name_path
1,C->A
2,B->D

PySpark：

from pyspark.sql import SparkSession
from pyspark.sql import functions as F
from pyspark.sql.window import Window

# 初始化SparkSession（如果在已有环境中可以直接使用已有的spark对象）
spark = SparkSession.builder.appName("DataTransformation").getOrCreate()

# 创建示例数据的DataFrame
data = [
    (1, "A", "2021-2-1"),
    (2, "B", "2024-1-1"),
    (1, "C", "2020-5-4"),
    (2, "D", "2028-9-1")
]
columns = ["userid", "page_name", "visit_time"]
df = spark.createDataFrame(data, columns)

# 将visit_time转换为日期类型，方便后续排序
df = df.withColumn("visit_time", F.to_date(F.col("visit_time")))

# 按照userid分区，根据visit_time排序创建窗口
window_spec = Window.partitionBy("userid").orderBy("visit_time")

# 使用collect_list函数收集每个userid对应的page_name列表，然后使用concat_ws函数将其拼接为指定格式
result_df = df.withColumn("page_name_list", F.collect_list("page_name").over(window_spec)) \
             .groupBy("userid") \
             .agg(F.concat_ws("->", F.col("page_name_list")).alias("page_name_path")) \
             .select("userid", "page_name_path")

# 重命名userid列为user_id（和目标数据列名一致）
result_df = result_df.withColumnRenamed("userid", "user_id")

# 展示结果
result_df.show()

SparkSQL：

SELECT userid AS user_id,
       CONCAT_WS('->', collect_list(page_name) OVER (PARTITION BY userid ORDER BY visit_time)) AS page_name_path
FROM page_visits
GROUP BY userid