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IBM.ILOG.CPLEX.Optimization.Studio.v12.6.Cracked-EAT 64 Bit

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IBM.ILOG.CPLEX.Optimization.Studio.v12.6.Cracked-EAT

Your code only works for me if you only output one record. The udf fails if you try to output both.
Create a new script with the following code.
from pyspark import SparkContext, SparkConf, SQLContext
from pyspark.sql import SparkSession
from pyspark.sql.types import *
from pyspark.sql.functions import udf, col, lit
from pyspark.sql.functions import col, lit, when
import os

# A dictionary that store the data type that i want to store.
COL_TYPES = {“String”:StringType, “Number”:IntegerType}
# A list of columns to drop.
DROP_COLUMNS = [“category”]

mydata = spark.createDataFrame(
[
(1,1,1,”This is a test”,”This is another test”),
(2,2,2,”This is another one”,”This is another one”),
(3,3,3,”this is a test”,”this is another test”)
],
[“id”,”category”,”message”,”name”]
)

myschema = StructType([
StructField(“id”, IntegerType(), True),
StructField(“category”, StringType(), True),
StructField(“message”, StringType(), True),
StructField(“name”, StringType(), True)
])

# Create spark context object
sc = SparkContext(appName=”udf_example”)
# Create sql context
sqlContext = SQLContext(sc)

# Read data from text file
mydf = sqlContext.read.text(“s3://data01/messagefile.txt”)

# Create new column with the data type that we want
col_

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