I have seen and tried many existing StackOverflow posts regarding this issue but none work. I guess my JAVA heap space is not as large as expected for my large dataset, My dataset contains 6.5M rows. My Linux instance contains 64GB Ram with 4 cores. As per this suggestion I need to fix my code but I think making a dictionary from pyspark dataframe should not be very costly. Please advise me if any other way to compute that.
I just want to make a python dictionary from my pyspark dataframe, this is the content of my pyspark dataframe,
property_sql_df.show()
shows,
+--------------+------------+--------------------+--------------------+
| id|country_code| name| hash_of_cc_pn_li|
+--------------+------------+--------------------+--------------------+
| BOND-9129450| US|Scotron Home w/Ga...|90cb0946cf4139e12...|
| BOND-1742850| US|Sited in the Mead...|d5c301f00e9966483...|
| BOND-3211356| US|NEW LISTING - Com...|811fa26e240d726ec...|
| BOND-7630290| US|EC277- 9 Bedroom ...|d5c301f00e9966483...|
| BOND-7175508| US|East Hampton Retr...|90cb0946cf4139e12...|
+--------------+------------+--------------------+--------------------+
What I want is to make a dictionary with hash_of_cc_pn_li as key and id as a list value.
Expected Output
{
"90cb0946cf4139e12": ["BOND-9129450", "BOND-7175508"]
"d5c301f00e9966483": ["BOND-1742850","BOND-7630290"]
}
What I have tried so far,
%%time
duplicate_property_list = {}
for ind in property_sql_df.collect():
hashed_value = ind.hash_of_cc_pn_li
property_id = ind.id
if hashed_value in duplicate_property_list:
duplicate_property_list[hashed_value].append(property_id)
else:
duplicate_property_list[hashed_value] = [property_id]
What I get now on the console:
java.lang.OutOfMemoryError: Java heap space
and showing this error on Jupyter notebook output
ERROR:py4j.java_gateway:An error occurred while trying to connect to the Java server (127.0.0.1:33097)