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,
Way 1: causing java.lang.OutOfMemoryError: Java heap space
%%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]
Way 2: Not working because of missing native OFFSET on pyspark
%%time
i = 0
limit = 1000000
for offset in range(0, total_record,limit):
i = i + 1
if i != 1:
offset = offset + 1
duplicate_property_list = {}
duplicate_properties = {}
# Preparing dataframe
url = '''select id, hash_of_cc_pn_li from properties_df LIMIT {} OFFSET {}'''.format(limit,offset)
properties_sql_df = spark.sql(url)
# Grouping dataset
rows = properties_sql_df.groupBy("hash_of_cc_pn_li").agg(F.collect_set("id").alias("ids")).collect()
duplicate_property_list = { row.hash_of_cc_pn_li: row.ids for row in rows }
# Filter a dictionary to keep elements only where duplicate cound
duplicate_properties = filterTheDict(duplicate_property_list, lambda elem : len(elem[1]) >=2)
# Writing to file
with open('duplicate_detected/duplicate_property_list_all_'+str(i)+'.json', 'w') as fp:
json.dump(duplicate_property_list, fp)
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)
This is the followup question that I asked here: Creating dictionary from Pyspark dataframe showing OutOfMemoryError: Java heap space
Why not keep as much data and processing in Executors, rather than collecting to Driver? If I understand this correctly, you could use pyspark
transformations and aggregations and save directly to JSON, therefore leveraging executors, then load that JSON file (likely partitioned) back into Python as a dictionary. Admittedly, you introduce IO overhead, but this should allow you to get around your OOM heap space errors. Step-by-step:
import pyspark.sql.functions as f
spark = SparkSession.builder.getOrCreate()
data = [
("BOND-9129450", "90cb"),
("BOND-1742850", "d5c3"),
("BOND-3211356", "811f"),
("BOND-7630290", "d5c3"),
("BOND-7175508", "90cb"),
]
df = spark.createDataFrame(data, ["id", "hash_of_cc_pn_li"])
df.groupBy(
f.col("hash_of_cc_pn_li"),
).agg(
f.collect_set("id").alias("id") # use f.collect_list() here if you're not interested in deduplication of BOND-XXXXX values
).write.json("./test.json")
Inspecting the output path:
ls -l ./test.json
-rw-r--r-- 1 jovyan users 0 Jul 27 08:29 part-00000-1fb900a1-c624-4379-a652-8e5b9dee8651-c000.json
-rw-r--r-- 1 jovyan users 50 Jul 27 08:29 part-00039-1fb900a1-c624-4379-a652-8e5b9dee8651-c000.json
-rw-r--r-- 1 jovyan users 65 Jul 27 08:29 part-00043-1fb900a1-c624-4379-a652-8e5b9dee8651-c000.json
-rw-r--r-- 1 jovyan users 65 Jul 27 08:29 part-00159-1fb900a1-c624-4379-a652-8e5b9dee8651-c000.json
-rw-r--r-- 1 jovyan users 0 Jul 27 08:29 _SUCCESS
_SUCCESS
Loading to Python as dict
:
import json
from glob import glob
data = []
for file_name in glob('./test.json/*.json'):
with open(file_name) as f:
try:
data.append(json.load(f))
except json.JSONDecodeError: # there is definitely a better way - this is here because some partitions might be empty
pass
Finally
{item['hash_of_cc_pn_li']:item['id'] for item in data}
{'d5c3': ['BOND-7630290', 'BOND-1742850'],
'811f': ['BOND-3211356'],
'90cb': ['BOND-9129450', 'BOND-7175508']}
I hope this helps! Thank you for the good question!
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