I am using pyspark to do some text analysis on a table in Hive. I use the following code
from pyspark.sql import SQLContext, Row, HiveContext
from pyspark.sql.functions import col, udf, StringType
from pyspark.sql.types import *
from pyspark import SparkContext
hc = HiveContext(sc)
df=hc.sql("select * from table1")
def cleaning_text(sentence):
sentence=sentence.lower()
sentence=re.sub('\'',' ',sentence)
cleaned=' '.join([w for w in cleaned.split() if not len(w)<=2 ])
return cleaned
org_val=udf(cleaning_text,StringType())
data=df.withColumn("cleaned",org_val(df.text))
data_1=data.select('uniqueid','cleaned','parsed')#2630789 #2022395
tokenizer = Tokenizer(inputCol="cleaned", outputCol="words")
wordsData = tokenizer.transform(data_1)
hc.sql("SET spark.sql.hive.convertMetastoreParquet=false")
hc.sql("create table table2 (uniqueid string, cleaned string, parsed string)")
wordsData.insertInto('table2')
I can do
words_data.show(2)
however as I try to export it, it gives me this error
INFO FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
Exception in thread "stdout writer for python" 17/02/02 15:18:44 ERROR Utils: Uncaught exception in thread stdout writer for python
java.lang.OutOfMemoryError: Java heap space
I do not mind if this gets exported as a text file too.
I was running this script on the spark shell which defaults to driver memory of 1g.
I changed it by running the statement below while starting the spark shell
pyspark --driver-memory 10g
This solved my problem