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我正在尝试使用 Python 中的 Apache Beam 将 BigQuery 表记录写为 GCS 存储桶中的 JSON 文件。

我有一个 BigQuery 表 -my_project.my_dataset.my_table像这样

在此处输入图像描述

我希望将表记录/条目写入 GCS 存储桶位置中的 JSON 文件 - “gs://my_core_bucket/data/my_data.json”

预期的 JSON 格式：


[
    {"id":"1","values":{"name":"abc","address":"Mumbai","phn":"1111111111"}},
    {"id":"2","values":{"name":"def","address":"Kolkata","phn":"2222222222"}},
    {"id":"3","values":{"name":"ghi","address":"Chennai","phn":"3333333333"}},
    {"id":"4","values":{"name":"jkl","address":"Delhi","phn":"4444444444"}}
]

但是，通过我当前的 apache 管道实现，我看到创建的 JSON 文件在文件“gs://my_core_bucket/data/my_data.json”中有类似这样的条目

{"id":"1","values":{"name":"abc","address":"Mumbai","phn":"1111111111"}}
{"id":"2","values":{"name":"def","address":"Kolkata","phn":"2222222222"}}
{"id":"3","values":{"name":"ghi","address":"Chennai","phn":"3333333333"}}
{"id":"4","values":{"name":"jkl","address":"Delhi","phn":"4444444444"}}

如何创建一个干净的 JSON 文件，将 BigQuery 记录作为 JSON 数组元素？

这是我的管道代码。

import os
import json
import logging

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions


class PrepareData(beam.DoFn):
    def process(self, record):  # sample record - {"id": "1", "name": "abc", "address": "Mumbai", "phn": "1111111111"}        
        rec_columns = [ "id", "name", "address", "phn", "country", "age"]   # all columns of the bigquery table 

        rec_keys = list(record.keys())  # ["id", "name", "address", "phn"]  # columns needed for processing  

        values = {}

        for x in range(len(rec_keys)):
            key = rec_keys[x]

            if key != "id" and key in rec_columns:
                values[key] = record[key]

        return [{"id": record['id'], "values": values}]


class MainClass:
    def run_pipe(self):
        try:        
            project = "my_project"
            dataset = "my_dataset"
            table = "my_table"
            region = "us-central1"
            job_name = "create-json-file"
            temp_location = "gs://my_core_bucket/dataflow/temp_location/"
            runner = "DataflowRunner"
            
            # set pipeline options
            argv = [
                f'--project={project}',
                f'--region={region}',
                f'--job_name={job_name}',
                f'--temp_location={temp_location}',
                f'--runner={runner}'
            ]
            
            # json file name
            file_name = "gs://my_core_bucket/data/my_data"

            # create pipeline 
            p = beam.Pipeline(argv=argv)

            # query to read table data
            query = f"SELECT id, name, address, phn FROM `{project}.{dataset}.{table}` LIMIT 4"

            bq_data = p | 'Read Table' >> beam.io.Read(beam.io.ReadFromBigQuery(query=query, use_standard_sql=True))

            # bq_data will be in the form 
            # {"id": "1", "name": "abc", "address": "Mumbai", "phn": "1111111111"}
            # {"id": "2", "name": "def", "address": "Kolkata", "phn": "2222222222"}
            # {"id": "3", "name": "ghi", "address": "Chennai", "phn": "3333333333"}
            # {"id": "4", "name": "jkl", "address": "Delhi", "phn": "4444444444"}
            
            # alter data in the form needed for downstream process
            prepared_data = bq_data | beam.ParDo(PrepareData())

            # write formatted pcollection as JSON file
            prepared_data | 'JSON format' >> beam.Map(json.dumps)
            prepared_data | 'Write Output' >> beam.io.WriteToText(file_name, file_name_suffix=".json", shard_name_template='')

            # execute pipeline
            p.run().wait_until_finish()
        except Exception as e:
            logging.error(f"Exception in run_pipe - {str(e)}")


if __name__ == "__main__":
    main_cls = MainClass()
    main_cls.run_pipe()
4

2 回答 2

2

请按照评论中的建议，尝试将所有结果合二为一。为了成功序列化set作为组合过程获得的结果，您可以使用自定义序列化程序。

您的代码可能如下所示：

import os
import json
import logging

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions


# Based on https://stackoverflow.com/questions/8230315/how-to-json-serialize-sets
class SetEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, set):
            return list(obj)
        return json.JSONEncoder.default(self, obj)


# utility function for list combination
class ListCombineFn(beam.CombineFn):
    def create_accumulator(self):
        return []

    def add_input(self, accumulator, input):
        accumulator.append(input)
        return accumulator

    def merge_accumulators(self, accumulators):
        merged = []
        for accum in accumulators:
            merged += accum
        return merged

    def extract_output(self, accumulator):
        return accumulator



class PrepareData(beam.DoFn):
    def process(self, record):  # sample record - {"id": "1", "name": "abc", "address": "Mumbai", "phn": "1111111111"}        
        rec_columns = [ "id", "name", "address", "phn", "country", "age"]   # all columns of the bigquery table 

        rec_keys = list(record.keys())  # ["id", "name", "address", "phn"]  # columns needed for processing  

        values = {}

        for x in range(len(rec_keys)):
            key = rec_keys[x]

            if key != "id" and key in rec_columns:
                values[key] = record[key]

        return [{"id": record['id'], "values": values}]


class MainClass:
    def run_pipe(self):
        try:        
            project = "my_project"
            dataset = "my_dataset"
            table = "my_table"
            region = "us-central1"
            job_name = "create-json-file"
            temp_location = "gs://my_core_bucket/dataflow/temp_location/"
            runner = "DataflowRunner"
            
            # set pipeline options
            argv = [
                f'--project={project}',
                f'--region={region}',
                f'--job_name={job_name}',
                f'--temp_location={temp_location}',
                f'--runner={runner}'
            ]
            
            # json file name
            file_name = "gs://my_core_bucket/data/my_data"

            # create pipeline 
            p = beam.Pipeline(argv=argv)

            # query to read table data
            query = f"SELECT id, name, address, phn FROM `{project}.{dataset}.{table}` LIMIT 4"

            bq_data = p | 'Read Table' >> beam.io.Read(beam.io.ReadFromBigQuery(query=query, use_standard_sql=True))

            # bq_data will be in the form 
            # {"id": "1", "name": "abc", "address": "Mumbai", "phn": "1111111111"}
            # {"id": "2", "name": "def", "address": "Kolkata", "phn": "2222222222"}
            # {"id": "3", "name": "ghi", "address": "Chennai", "phn": "3333333333"}
            # {"id": "4", "name": "jkl", "address": "Delhi", "phn": "4444444444"}
            
            # alter data in the form needed for downstream process
            prepared_data = bq_data | beam.ParDo(PrepareData())

            # combine all the results in one PCollection
            # see https://beam.apache.org/documentation/transforms/python/aggregation/combineglobally/
            prepared_data | 'Combine results' >> beam.CombineGlobally(ListCombineFn())

            # write formatted pcollection as JSON file. We will use a 
            # custom encoder for se serialization
            prepared_data | 'JSON format' >> beam.Map(json.dumps, cls=SetEncoder)
            prepared_data | 'Write Output' >> beam.io.WriteToText(file_name, file_name_suffix=".json", shard_name_template='')

            # execute pipeline
            p.run().wait_until_finish()
        except Exception as e:
            logging.error(f"Exception in run_pipe - {str(e)}")


if __name__ == "__main__":
    main_cls = MainClass()
    main_cls.run_pipe()
于 2021-07-12T22:02:08.943 回答
0

您可以直接在 BigQuery 中执行此操作，然后使用 Dataflow 按原样打印结果。

只更改查询

query = f"Select ARRAY_AGG(str) from (SELECT struct(id as id, name as name, address as address, phn as phn) as str FROM `{project}.{dataset}.{table}` LIMIT 4)"

请记住，

  • BigQuery 处理总是比数据流处理（或同等芯片上的其他处理）更快、更便宜
  • Dataflow 将始终构建有效的 JSON（您的 JSON 无效，您不能从数组开始）
于 2021-07-13T08:03:20.873 回答