如何使用pyspark从Spark获取批量行

ni65a41a  于 2023-08-02  发布在  Spark
关注(0)|答案(2)|浏览(127)

我有一个包含超过60亿行数据的Spark RDD,我想使用train_on_batch来训练深度学习模型。我无法将所有行都放入内存,因此我希望一次获得10K左右的数据,以批量处理成64或128的块(取决于模型大小)。我目前使用的是rdd.sample(),但我不认为这能保证得到所有行。有没有更好的方法来划分数据,使其更易于管理,以便我可以编写一个生成器函数来获取批处理?我的代码如下:

data_df = spark.read.parquet(PARQUET_FILE)
print(f'RDD Count: {data_df.count()}') # 6B+
data_sample = data_df.sample(True, 0.0000015).take(6400) 
sample_df = data_sample.toPandas()

def get_batch():
  for row in sample_df.itertuples():
    # TODO: put together a batch size of BATCH_SIZE
    yield row

for i in range(10):
    print(next(get_batch()))

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hgqdbh6s

hgqdbh6s1#

试试这个:

from pyspark.sql import functions as F
 sample_dict = {}

 # Read the parquet file
 df = spark.read.parquet("parquet file")

 # add the partition_number as a column
 df = df.withColumn('partition_num', F.spark_partition_id())
 df.persist()

 total_partition = [int(row.partition_num) for row in 
 df.select('partition_num').distinct().collect()]

 for each_df in total_partition:
     sample_dict[each_df] = df.where(df.partition_num == each_df)

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z9smfwbn

z9smfwbn2#

我不相信Spark让你抵消或分页您的数据。
但你可以添加一个索引,然后分页,首先:

from pyspark.sql.functions import lit
    data_df = spark.read.parquet(PARQUET_FILE)
    count = data_df.count()
    chunk_size = 10000

    # Just adding a column for the ids
    df_new_schema = data_df.withColumn('pres_id', lit(1))
    
    # Adding the ids to the rdd 
    rdd_with_index = data_df.rdd.zipWithIndex().map(lambda (row,rowId): (list(row) + [rowId+1]))
    
    # Creating a dataframe with index
    df_with_index = spark.createDataFrame(rdd_with_index,schema=df_new_schema.schema)
    
    # Iterating into the chunks
    for page_num in range(0, count+1, chunk_size):
        initial_page = page_num*chunk_size
        final_page = initial_page + chunk_size 
        where_query = ('pres_id > {0} and pres_id <= {1}').format(initial_page,final_page)
        chunk_df = df_with_index.where(where_query).toPandas()
        train_on_batch(chunk_df) # <== Your function here

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这不是最佳的,因为使用了pandas Dataframe ,它会严重利用spark,但会解决你的问题。
如果id影响到您的功能,请不要忘记删除它。

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