从大型 Pyspark dataframe 创建字典，显示 OutOfMemoryError: Java 堆空间

Question

我已经看到并尝试了许多关于此问题的现有StackOverflow 帖子，但没有一个有效。 我猜我的 JAVA 堆空间没有我的大型数据集预期的那么大，我的数据集包含 650 万行。 我的 Linux 实例包含 64GB Ram with 4 cores 。 根据这个建议，我需要修复我的代码，但我认为从 pyspark dataframe 制作字典应该不会很昂贵。 如果有其他方法可以计算，请告诉我。

I just want to make a python dictionary from my pyspark dataframe, this is the content of my pyspark dataframe,

property_sql_df.show()显示，

+--------------+------------+--------------------+--------------------+
|            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...|
+--------------+------------+--------------------+--------------------+

我想要的是制作一个字典，其中 hash_of_cc_pn_li 作为键，id 作为列表值。

预计 Output

{
  "90cb0946cf4139e12": ["BOND-9129450", "BOND-7175508"]
  "d5c301f00e9966483": ["BOND-1742850","BOND-7630290"]
}

到目前为止我所尝试的，

方式1：导致java.lang.OutOfMemoryError: Java堆空间

%%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] 

方式 2：由于缺少 pyspark 上的原生 OFFSET 而无法工作

%%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)

我现在在控制台上得到什么：

java.lang.OutOfMemoryError: Java 堆空间

并在Jupyter 笔记本 output上显示此错误

ERROR:py4j.java_gateway:An error occurred while trying to connect to the Java server (127.0.0.1:33097)

这是我在这里问的后续问题： 从 Pyspark dataframe 显示 OutOfMemoryError: Java 堆空间创建字典

Answer 1

为什么不在 Executors 中保留尽可能多的数据和处理，而不是收集到 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. 诚然，您引入了 IO 开销，但这应该可以让您绕过 OOM 堆空间错误。 一步步：

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")

检查 output 路径：

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

作为dict加载到 Python ：

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

最后

{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']}

我希望这有帮助！ 谢谢你的好问题！