数据流批处理作业无法扩展

如何解决数据流批处理作业无法扩展

尽管Dataflow将目标工作人员设置为1000，但我的Dataflow作业（工作ID：2020-08-18_07_55_15-1415306428650650914914）并没有超过1名工作人员。

该作业配置为查询Google Patents BigQuery数据集，使用ParDo自定义函数和转换器（huggingface）库对文本进行标记，序列化结果，并将所有内容写入巨型木地板文件。

我以为（昨天完成工作后，映射了一个函数而不是使用beam.DoFn类）是问题，这是一些非并行化的对象消除了缩放；因此，将标记化过程重构为一个类。

以下是脚本，该脚本是使用以下命令从命令行运行的：

python bq_to_parquet_pipeline_w_class.py --extra_package transformers-3.0.2.tar.gz

脚本：

    import os
    import re
    import argparse
    
    import google.auth
    import apache_beam as beam
    from apache_beam.options import pipeline_options
    from apache_beam.options.pipeline_options import GoogleCloudOptions
    from apache_beam.options.pipeline_options import PipelineOptions
    from apache_beam.options.pipeline_options import SetupOptions
    from apache_beam.runners import DataflowRunner
    
    
    from apache_beam.io.gcp.internal.clients import bigquery
    import pyarrow as pa
    import pickle
    from transformers import AutoTokenizer
    
    
    print('Defining TokDoFn')
    class TokDoFn(beam.DoFn):
        def __init__(self,tok_version,block_size=200):
            self.tok = AutoTokenizer.from_pretrained(tok_version)
            self.block_size = block_size
    
        def process(self,x):
            txt = x['abs_text'] + ' ' + x['desc_text'] + ' ' + x['claims_text']
            enc = self.tok.encode(txt)
    
            for idx,token in enumerate(enc):
                chunk = enc[idx:idx + self.block_size]
                serialized = pickle.dumps(chunk)
                yield serialized
    
    
    def run(argv=None,save_main_session=True):
        query_big = '''
        with data as (
          SELECT 
            (select text from unnest(abstract_localized) limit 1) abs_text,(select text from unnest(description_localized) limit 1) desc_text,(select text from unnest(claims_localized) limit 1) claims_text,publication_date,filing_date,grant_date,application_kind,ipc
          FROM `patents-public-data.patents.publications` 
        )
    
        select *
        FROM data
        WHERE
          abs_text is not null 
          AND desc_text is not null
          AND claims_text is not null
          AND ipc is not null
        '''
    
        query_sample = '''
        SELECT *
        FROM `client_name.patent_data.patent_samples`
        LIMIT 2;
        '''
    
        print('Start Run()')
        parser = argparse.ArgumentParser()
        known_args,pipeline_args = parser.parse_known_args(argv)
    
        '''
        Configure Options
        '''
        # Setting up the Apache Beam pipeline options.
        # We use the save_main_session option because one or more DoFn's in this
        # workflow rely on global context (e.g.,a module imported at module level).
        options = PipelineOptions(pipeline_args)
        options.view_as(SetupOptions).save_main_session = save_main_session
    
        # Sets the project to the default project in your current Google Cloud environment.
        _,options.view_as(GoogleCloudOptions).project = google.auth.default()
    
        # Sets the Google Cloud Region in which Cloud Dataflow runs.
        options.view_as(GoogleCloudOptions).region = 'us-central1'
    
    
        # IMPORTANT! Adjust the following to choose a Cloud Storage location.
        dataflow_gcs_location = 'gs://client_name/dataset_cleaned_pq_classTok'
        # Dataflow Staging Location. This location is used to stage the Dataflow Pipeline and SDK binary.
        options.view_as(GoogleCloudOptions).staging_location = f'{dataflow_gcs_location}/staging'
    
        # Dataflow Temp Location. This location is used to store temporary files or intermediate results before finally outputting to the sink.
        options.view_as(GoogleCloudOptions).temp_location = f'{dataflow_gcs_location}/temp'
    
        # The directory to store the output files of the job.
        output_gcs_location = f'{dataflow_gcs_location}/output'
    
        print('Options configured per GCP Notebook Examples')
        print('Configuring BQ Table Schema for Beam')
    
    
        #Write Schema (to PQ):
        schema = pa.schema([
            ('block',pa.binary())
        ])
    
        print('Starting pipeline...')
        with beam.Pipeline(runner=DataflowRunner(),options=options) as p:
            res = (p
                   | 'QueryTable' >> beam.io.Read(beam.io.BigQuerySource(query=query_big,use_standard_sql=True))
                   | beam.ParDo(TokDoFn(tok_version='gpt2',block_size=200))
                   | beam.Map(lambda x: {'block': x})
                   | beam.io.WriteToParquet(os.path.join(output_gcs_location,f'pq_out'),schema,record_batch_size=1000)
                   )
            print('Pipeline built. Running...')
    
    if __name__ == '__main__':
        import logging
        logging.getLogger().setLevel(logging.INFO)
        logging.getLogger("transformers.tokenization_utils_base").setLevel(logging.ERROR)
        run()

解决方法

解决方案是双重的：

我执行工作时超出了以下配额，所有配额都在“ Compute Engine API”下（请在此处查看您的配额：https://console.cloud.google.com/iam-admin/quotas）：

• CPU（我要求增加到50个）
• 永久磁盘标准（GB）（我要求增加到12,500）
• In_Use_IP_Address（我要求增加到50）

注意：如果您在作业运行时读取控制台输出，则超出的配额应打印为INFO行。

按照彼得·金的建议，我在命令中传递了--max_num_workers标志：

python bq_to_parquet_pipeline_w_class.py --extra_package transformers-3.0.2.tar.gz --max_num_workers 22

然后我开始扩展！

总而言之，如果有一种方法可以在达到配额时通过Dataflow控制台提示用户，并提供一种简便的方法来请求增加（和建议的补充）配额，以及有关应增加多少的建议。