如何暂存 GCP/Apache Beam 数据流模板? [英] How to I stage a GCP/Apache Beam Dataflow template?

问题描述

好的，我必须在这里遗漏一些东西.将管道作为模板进行暂存需要什么?当我尝试通过 这些说明，它运行模块但不进行任何操作.它似乎按预期运行而没有错误，但我没有看到任何文件实际上被添加到存储桶位置，在我的 --template_location 中侦听.我的python代码应该出现在那里吗?我猜对吗?我已经确定我已经安装了所有的 Beam 和谷歌云 SDK，但也许我遗漏了什么?您需要做什么来暂存此数据流模板?我也可以手动将文件放入存储桶并从那里运行吗?以下是我目前正在使用的模板:

Ok I have to be missing something here. What do i need to stage a pipeline as a template? When I try to stage my template with via these instructions, it runs the module but doesn't stage anything., it appears to function as expected without errors, but I don't see any files actually get added to the bucket location listen in my --template_location. Should my python code be showing up there? I assume so right? I have made sure i have all the beam and google cloud SDKs installed, but maybe I'm missing something? What do you need to do to stage this dataflow template? Also can I manually just drop the file in a bucket and run it from there? The following is the template I am currently playing with:

import json
import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.io.gcp.bigquery import parse_table_schema_from_json

GC_PROJECT = 'my-proj'
BUCKET = 'test-bucket'
STAGING_BUCKET = '%s/test' % BUCKET
TEMP_BUCKET = '%s/test' % BUCKET
# RUNNER = 'DataflowRunner'
RUNNER = 'DirectRunner'

# pipeline_args = ['--save_main_session']
pipeline_args = []
pipeline_args.append('--project=%s' % GC_PROJECT)
pipeline_args.append('--runner=%s' % RUNNER)
pipeline_args.append('--staging_location=gs://%s' % STAGING_BUCKET)
pipeline_args.append('--temp_location=gs://%s' % TEMP_BUCKET)

BQ_DATASET = 'lake'
BQ_TABLE = 'whatever'

SCHEMA_OBJ = [
    {"name": "id", "type": "STRING", "description": ""},
    {"name": "value", "type": "STRING", "description": ""}
]


class ContactUploadOptions(PipelineOptions):
    @classmethod
    def _add_argparse_args(cls, parser):
        parser.add_value_provider_argument(
            '--infile',
            type=str,
            help='path of input file',
            default='gs://%s/data_files/test.csv' % BUCKET)

def run(argv=None):
    print('running')
    p = beam.Pipeline(options=PipelineOptions(pipeline_args))
    lines = (p
             | beam.Create([
                {"id": "some random name", "value": "i dont know"},
                {"id": "id2", "value": "whatever man"}]))

    schema_str = '{"fields": ' + json.dumps(SCHEMA_OBJ) + '}'
    schema = parse_table_schema_from_json(schema_str)
    output_destination = '%s.%s' % (BQ_DATASET, BQ_TABLE)
    (lines
        | 'Write lines to BigQuery' >> beam.io.WriteToBigQuery(
            output_destination,
            schema=schema,
            create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,
            write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND))

    p.run().wait_until_finish()


if __name__ == '__main__':
    run(pipeline_args)

此外，如果有人可以链接一些 sdk 文档/资源来解释上面的暂存说明如何/为什么应该起作用，那就太棒了！

Also, if someone could link some sdk documentaion/resources that explain how/why the staging instructions above are supposed to work, that would be awesome!

推荐答案

临时位置是在运行作业时加载临时文件的位置.您没有提到将在其中创建模板的template_location".

The temp location is where the temporary files will be loaded while running the job. You have not mentioned the "template_location" where template will be created.

请参阅创建模板和运行模板

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