带有字典参数的Spark UDF失败

问题描述

我在Spark数据框中有一个列(myCol)，其值为1,2，并且我想用该值的描述创建一个新列，例如1->'A'，2->'B'等

I have a column (myCol) in a Spark dataframe that has values 1,2 and I want to create a new column with the description of this values like 1-> 'A', 2->'B' etc

我知道这可以通过联接来完成，但是我尝试了一下，因为它看起来更优雅:

I know that this can be done with a join but I tried this because it seems more elegant:

dictionary= { 1:'A' , 2:'B' }

add_descriptions = udf(lambda x , dictionary: dictionary[x] if x in dictionary.keys() else None)

df.withColumn("description",add_descriptions(df.myCol,dictionary))

它失败并显示错误

lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 323, in get_return_value
py4j.protocol.Py4JError: An error occurred while calling z:org.apache.spark.sql.function  s.col. Trace:
py4j.Py4JException: Method col([class java.util.HashMap]) does not exist
        at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:318)
        at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:339)
        at py4j.Gateway.invoke(Gateway.java:274)
        at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
        at py4j.commands.CallCommand.execute(CallCommand.java:79)
        at py4j.GatewayConnection.run(GatewayConnection.java:214)
        at java.lang.Thread.run(Thread.java:745)

使用字典作为参数的用户定义函数是否不可能?

Is it not possible to have a user difined function with dictionaries as arguments?

推荐答案

有可能，您只需要做一些不同的事情即可.

It is possible, you just have to do it a bit differently.

dictionary= { 1:'A' , 2:'B' }

def add_descriptions(in_dict):
    def f(x):
        return in_dict.get(x)
    return udf(f)

df.withColumn(
    "description",
    add_descriptions(dictionary)(df.myCol)
)

如果要直接在UDF中添加字典，因为UDF仅接受列作为参数，则需要有一个map列来替换字典.

If you want to add directly your dict in the UDF, as UDFs only accept columns as argument, you need to have a map column to replace your dict.

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