AWS Glue pyspark UDF [英] AWS Glue pyspark UDF

问题描述

在AWS Glue中，我需要转换一个浮点值(摄氏度到华氏度)，并且正在使用UDF.

In AWS Glue, I need to convert a float value (celsius to fahrenheit) and am using an UDF.

以下是我的UDF:

toFahrenheit = udf(lambda x: '-1' if x in not_found else x * 9 / 5 + 32, StringType())

我在spark数据框中使用UDF的方式如下:

I am using the UDF as follows, in the spark dataframe:

weather_df.withColumn("new_tmax", toFahrenheit(weather_df["tmax"])).drop("tmax").withColumnRenamed("new_tmax","tmax")

运行代码时，我收到的错误消息为:

When I run the code, am getting the error message as :

IllegalArgumentException: u"requirement failed: The number of columns doesn't match.\nOld column names (11): station, name, latitude, longitude, elevation, date, awnd, prcp, snow, tmin, tmax\nNew column names (0): "

不确定如何调用UDF(这是python/pyspark的新功能)，并且未创建新的列架构，并且为空.

Not sure how to invoke the UDF, as am new to python / pyspark, and my new column schema is not created, and empty.

上面示例中的代码片段为:

The code snipped used for above sample is :

%pyspark
import sys
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.context import DynamicFrame
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from awsglue.job import Job
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType

glueContext = GlueContext(SparkContext.getOrCreate())

weather_raw = glueContext.create_dynamic_frame.from_catalog(database = "ohare-airport-2006", table_name = "ohare_intl_airport_2006_08_climate_csv")
print "cpnt : ", weather_raw.count()
weather_raw.printSchema()
weather_raw.toDF().show(10)

#UDF to convert the air temperature from celsius to fahrenheit (For sample transformation)
#toFahrenheit = udf((lambda c: c[1:], c * 9 / 5 + 32)
toFahrenheit = udf(lambda x: '-1' if x in not_found_cat else x * 9 / 5 + 32, StringType())

#Apply the UDF to maximum and minimum air temperature
wthdf = weather_df.withColumn("new_tmin", toFahrenheit(weather_df["tmin"])).withColumn("new_tmax", toFahrenheit(weather_df["tmax"])).drop("tmax").drop("tmin").withColumnRenamed("new_tmax","tmax").withColumnRenamed("new_tmin","tmin")

wthdf.toDF().show(5)

模式

 weather_df:
root
|-- station: string
|-- name: string
|-- latitude: double
|-- longitude: double
|-- elevation: double
|-- date: string
|-- awnd: double
|-- fmtm: string
|-- pgtm: string
|-- prcp: double
|-- snow: double
|-- snwd: long
|-- tavg: string
|-- tmax: long
|-- tmin: long

错误跟踪:

Traceback (most recent call last):
  File "/tmp/zeppelin_pyspark-3684249459612979499.py", line 349, in <module>
    raise Exception(traceback.format_exc())
Exception: Traceback (most recent call last):
  File "/tmp/zeppelin_pyspark-3684249459612979499.py", line 342, in <module>
    exec(code)
  File "<stdin>", line 3, in <module>
  File "/usr/lib/spark/python/pyspark/sql/dataframe.py", line 1558, in toDF
    jdf = self._jdf.toDF(self._jseq(cols))
  File "/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
    answer, self.gateway_client, self.target_id, self.name)
  File "/usr/lib/spark/python/pyspark/sql/utils.py", line 79, in deco
    raise IllegalArgumentException(s.split(': ', 1)[1], stackTrace)
IllegalArgumentException: u"requirement failed: The number of columns doesn't match.\nOld column names (11): station, name, latitude, longitude, elevation, date, awnd, prcp, snow, tmin, tmax\nNew column names (0): "

谢谢

推荐答案

上述解决方案(从Celcius到Fahrenheit)，以防万一供参考:

Solution for the above (Celcius to Fahrenheit), just in case for reference:

#UDF to convert the air temperature from celsius to fahrenheit
toFahrenheit = udf(lambda x: x * 9 / 5 + 32, StringType())

weather_in_Fahrenheit = weather_df.withColumn("new_tmax", toFahrenheit(weather_df["tmax"])).withColumn("new_tmin", toFahrenheit(weather_df["tmin"])).drop("tmax").drop("tmin").withColumnRenamed("new_tmax","tmax").withColumnRenamed("new_tmin","tmin")

weather_in_Fahrenheit.show(5)

原始数据示例:

+-----------+--------------------+---------+--------+---------+----+----+----+----+----------+
|    station|                name|elevation|latitude|longitude|prcp|snow|tmax|tmin|      date|
+-----------+--------------------+---------+--------+---------+----+----+----+----+----------+
|USW00094846|CHICAGO OHARE INT...|    201.8|  41.995| -87.9336| 0.0| 0.0|  25|  11|2013-01-01|
|USW00094846|CHICAGO OHARE INT...|    201.8|  41.995| -87.9336| 0.0| 0.0|  30|  10|2013-01-02|
|USW00094846|CHICAGO OHARE INT...|    201.8|  41.995| -87.9336| 0.0| 0.0|  29|  18|2013-01-03|
|USW00094846|CHICAGO OHARE INT...|    201.8|  41.995| -87.9336| 0.0| 0.0|  36|  13|2013-01-04|
|USW00094846|CHICAGO OHARE INT...|    201.8|  41.995| -87.9336|0.03| 0.4|  39|  18|2013-01-05|
|USW00094846|CHICAGO OHARE INT...|    201.8|  41.995| -87.9336| 0.0| 0.0|  36|  18|2013-01-06|
|USW00094846|CHICAGO OHARE INT...|    201.8|  41.995| -87.9336| 0.0| 0.0|  41|  15|2013-01-07|
|USW00094846|CHICAGO OHARE INT...|    201.8|  41.995| -87.9336| 0.0| 0.0|  44|  22|2013-01-08|
|USW00094846|CHICAGO OHARE INT...|    201.8|  41.995| -87.9336| 0.0| 0.0|  50|  27|2013-01-09|
|USW00094846|CHICAGO OHARE INT...|    201.8|  41.995| -87.9336|0.63| 0.0|  45|  22|2013-01-10|
+-----------+--------------------+---------+--------+---------+----+----+----+----+----------+

将UDF应用到华氏度之后:

After applying the UDF toFahrenheit:

+-----------+--------------------+--------+---------+---------+----------+-----+----+----+----+----+
|    station|                name|latitude|longitude|elevation|      date| awnd|prcp|snow|tmax|tmin|
+-----------+--------------------+--------+---------+---------+----------+-----+----+----+----+----+
|USW00094846|CHICAGO OHARE INT...|  41.995| -87.9336|    201.8|2013-01-01|  8.5| 0.0| 0.0|  77|  51|
|USW00094846|CHICAGO OHARE INT...|  41.995| -87.9336|    201.8|2013-01-02| 8.05| 0.0| 0.0|  86|  50|
|USW00094846|CHICAGO OHARE INT...|  41.995| -87.9336|    201.8|2013-01-03|11.41| 0.0| 0.0|  84|  64|
|USW00094846|CHICAGO OHARE INT...|  41.995| -87.9336|    201.8|2013-01-04| 13.2| 0.0| 0.0|  96|  55|
|USW00094846|CHICAGO OHARE INT...|  41.995| -87.9336|    201.8|2013-01-05| 9.62|0.03| 0.4| 102|  64|
+-----------+--------------------+--------+---------+---------+----------+-----+----+----+----+----+

这篇关于AWS Glue pyspark UDF的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！