我如何迭代代碼存儲庫中的 json 文件並逐步將 append 迭代到數據集

Question

我通過數據連接將一個包含 100,000 個大約 100gb 的原始 json 文件的數據集導入鑄造廠。 我想使用Python Transforms raw file access轉換來讀取文件，將結構和結構的數組展平為 dataframe 作為對 df 的增量更新。 我想使用 *.json 文件的文檔中的以下示例中的內容，並使用@incremental()裝飾器將其轉換為增量更新。

>>> import csv
>>> from pyspark.sql import Row
>>> from transforms.api import transform, Input, Output
>>>
>>> @transform(
...     processed=Output('/examples/hair_eye_color_processed'),
...     hair_eye_color=Input('/examples/students_hair_eye_color_csv'),
... )
... def example_computation(hair_eye_color, processed):
...
...    def process_file(file_status):
...        with hair_eye_color.filesystem().open(file_status.path) as f:
...            r = csv.reader(f)
...
...            # Construct a pyspark.Row from our header row
...            header = next(r)
...            MyRow = Row(*header)
...
...            for row in csv.reader(f):
...                yield MyRow(*row)
...
...    files_df = hair_eye_color.filesystem().files('**/*.csv')
...    processed_df = files_df.rdd.flatMap(process_file).toDF()
...    processed.write_dataframe(processed_df)

在@Jeremy David Gamet 的幫助下，我能夠開發代碼來獲得我想要的數據集。

from transforms.api import transform, Input, Output
from  pyspark import *
import json


@transform(
     out=Output('foundry/outputdataset'),
     inpt=Input('foundry/inputdataset'),
 )
def update_set(ctx, inpt, out):
    spark = ctx.spark_session
    sc = spark.sparkContext

    filesystem = list(inpt.filesystem().ls())
    file_dates = []
    for files in filesystem:
        with inpt.filesystem().open(files.path,'r', encoding='utf-8-sig') as fi:
            data = json.load(fi)
        file_dates.append(data)

    json_object = json.dumps(file_dates)
    df_2 = spark.read.option("multiline", "true").json(sc.parallelize([json_object]))

    df_2.drop_duplicates()
# this code to [Flatten array column][1]
    df_2 = flatten(df_2)
    out.write_dataframe(df_2)

扁平化__df的代碼

上面的代碼適用於少數文件，因為文件超過 100,0000 我遇到以下錯誤：

Connection To Driver Lost 

This error indicates that connection to the driver was lost unexpectedly, which is often caused by the driver being terminated due to running out of memory. Common reasons for driver out-of-memory (OOM) errors include functions that materialize data to the driver such as .collect(), broadcasted joins, and using Pandas dataframes.

有什么辦法嗎？

Answer 1

我已經舉了一個例子來說明如何動態地完成這個作為另一個問題的答案。

這是該代碼答案的鏈接： 如何在 Palantir Foundry 中聯合多個動態輸入？ 以及相同代碼的副本：

from transforms.api import Input, Output, transform
from pyspark.sql import functions as F
import json
import logging


def transform_generator():
    transforms = []
    transf_dict = {## enter your dynamic mappings here ##}

    for value in transf_dict:
        @transform(
            out=Output(' path to your output here '.format(val=value)),
            inpt=Input(" path to input here ".format(val=value)),
        )
        def update_set(ctx, inpt, out):
            spark = ctx.spark_session
            sc = spark.sparkContext

            filesystem = list(inpt.filesystem().ls())
            file_dates = []
            for files in filesystem:
                with inpt.filesystem().open(files.path) as fi:
                    data = json.load(fi)
                file_dates.append(data)

            logging.info('info logs:')
            logging.info(file_dates)
            json_object = json.dumps(file_dates)
            df_2 = spark.read.option("multiline", "true").json(sc.parallelize([json_object]))
            df_2 = df_2.withColumn('upload_date', F.current_date())

            df_2.drop_duplicates()
            out.write_dataframe(df_2)
        transforms.append(update_logs)
    return transforms


TRANSFORMS = transform_generator()

如果有什么我可以澄清的，請告訴我。