使用 UDF 处理多列时堆栈溢出 [英] Stack Overflow while processing several columns with a UDF

问题描述

我有一个 DataFrame，其中包含许多 str 类型的列，我想对所有这些列应用一个函数，而不重命名它们的名称或添加更多列，我尝试使用 for-in 循环执行 withColumn(参见下面的示例)，但通常当我运行代码时，它会显示 Stack Overflow(它很少工作)，这个 DataFrame 一点也不大，它只有 ~15000 条记录.

I have a DataFrame with many columns of str type, and I want to apply a function to all those columns, without renaming their names or adding more columns, I tried using a for-in loop executing withColumn (see example bellow), but normally when I run the code, it shows a Stack Overflow (it rarely works), this DataFrame is not big at all, it has just ~15000 records.

# df is a DataFrame
def lowerCase(string):
    return string.strip().lower()

lowerCaseUDF = udf(lowerCase, StringType())

for (columnName, kind) in df.dtypes:
    if(kind == "string"):
        df = df.withColumn(columnName, lowerCaseUDF(df[columnName]))

df.select("Tipo_unidad").distinct().show()

完整的错误很长，因此我决定只粘贴一些行.但您可以在此处找到完整的跟踪完整跟踪

The complete error is very long, therefore I decided to paste only some lines. But you can find the full trace here Complete Trace

Py4JJavaError:调用 o516.showString 时发生错误.:org.apache.spark.SparkException:由于阶段失败，作业中止:阶段 2.0 中的任务 1 失败了 4 次，最近失败:丢失任务 1.3在 2.0 阶段(TID 38，worker2.mcbo.mood.com.ve):java.lang.StackOverflowError 在java.io.ObjectInputStream$BlockDataInputStream.readByte(ObjectInputStream.java:2774)

Py4JJavaError: An error occurred while calling o516.showString. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 2.0 failed 4 times, most recent failure: Lost task 1.3 in stage 2.0 (TID 38, worker2.mcbo.mood.com.ve): java.lang.StackOverflowError at java.io.ObjectInputStream$BlockDataInputStream.readByte(ObjectInputStream.java:2774)

我认为这个问题的产生是因为这段代码启动了许多作业(string 类型的每一列一个)，你能告诉我另一种选择吗?或者我做错了什么?

I am thinking that this problem is produced because this code launches many jobs (one for each column of type string), could you show me another alternative or what I am doing wrong?

推荐答案

试试这个:

from pyspark.sql.functions import col, lower, trim

exprs = [
    lower(trim(col(c))).alias(c) if t == "string" else col(c) 
    for (c, t) in df.dtypes
]

df.select(*exprs)

与您当前的解决方案相比，这种方法有两个主要优点:

This approach has two main advantages over you current solution:

• 它只需要作为单个投影(没有最有可能导致 SO 的不断增长的谱系)而不是每个字符串列的投影.
• 它仅直接操作内部表示而不将数据传递给 Python (BatchPythonProcessing).

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