从扳手读取的谷歌数据流

Question

我正在尝试从Google扳手数据库中读取一个表，并将其写入文本文件以使用python sdk和google dataflow进行备份。我写了以下脚本：

    from __future__ import absolute_import

import argparse
import itertools
import logging
import re
import time
import datetime as dt
import logging

import apache_beam as beam
from apache_beam.io import iobase
from apache_beam.io import WriteToText
from apache_beam.io.range_trackers import OffsetRangeTracker, UnsplittableRangeTracker
from apache_beam.metrics import Metrics
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import StandardOptions, SetupOptions
from apache_beam.options.pipeline_options import GoogleCloudOptions

from google.cloud.spanner.client import Client
from google.cloud.spanner.keyset import KeySet

BUCKET_URL = 'gs://my_bucket'
OUTPUT = '%s/output/' % BUCKET_URL
PROJECT_ID = 'my_project'
INSTANCE_ID = 'my_instance'
DATABASE_ID = 'my_db'
JOB_NAME = 'spanner-backup'
TABLE = 'my_table'


class SpannerSource(iobase.BoundedSource):
    def __init__(self):
    logging.info('Enter __init__')

    self.spannerOptions = {
        "id": PROJECT_ID,
        "instance": INSTANCE_ID,
        "database": DATABASE_ID
    }
    self.SpannerClient = Client

    def estimate_size(self):
    logging.info('Enter estimate_size')
    return 1

    def get_range_tracker(self, start_position=None, stop_position=None):
    logging.info('Enter get_range_tracker')
    if start_position is None:
       start_position = 0
    if stop_position is None:
       stop_position = OffsetRangeTracker.OFFSET_INFINITY

    range_tracker = OffsetRangeTracker(start_position, stop_position)
    return UnsplittableRangeTracker(range_tracker)

    def read(self, range_tracker):  # This is not called when using the dataflowRunner !
    logging.info('Enter read')
    # instantiate spanner client
    spanner_client = self.SpannerClient(self.spannerOptions["id"])
    instance = spanner_client.instance(self.spannerOptions["instance"])
    database = instance.database(self.spannerOptions["database"])

    # read from table
    table_fields = database.execute_sql("SELECT t.column_name FROM information_schema.columns AS t WHERE t.table_name = '%s'" % TABLE)
    table_fields.consume_all()
    self.columns = [x[0] for x in table_fields]
    keyset = KeySet(all_=True)
    results = database.read(table=TABLE, columns=self.columns, keyset=keyset)

    # iterator over rows
    results.consume_all()
    for row in results:
        JSON_row = {
        self.columns[i]: row[i] for i in range(len(self.columns))
        }
        yield JSON_row

    def split(self, start_position=None, stop_position=None):
    # this should not be called since the source is unspittable
    logging.info('Enter split')
    if start_position is None:
        start_position = 0
    if stop_position is None:
        stop_position = 1

    # Because the source is unsplittable (for now), only a single source is returned
    yield iobase.SourceBundle(
        weight=1,
        source=self,
        start_position=start_position,
        stop_position=stop_position)


def run(argv=None):
  """Main entry point"""
  pipeline_options = PipelineOptions()
  google_cloud_options = pipeline_options.view_as(GoogleCloudOptions)
  google_cloud_options.project = PROJECT_ID
  google_cloud_options.job_name = JOB_NAME
  google_cloud_options.staging_location = '%s/staging' % BUCKET_URL
  google_cloud_options.temp_location = '%s/tmp' % BUCKET_URL

  #pipeline_options.view_as(StandardOptions).runner = 'DirectRunner'
  pipeline_options.view_as(StandardOptions).runner = 'DataflowRunner'
  p = beam.Pipeline(options=pipeline_options)

  output = p | 'Get Rows from Spanner' >> beam.io.Read(SpannerSource())
  iso_datetime = dt.datetime.now().replace(microsecond=0).isoformat()
  output | 'Store in GCS' >> WriteToText(file_path_prefix=OUTPUT + iso_datetime + '-' + TABLE, file_name_suffix='')  # if this line is commented, job completes but does not do anything


  result = p.run()
  result.wait_until_finish()


if __name__ == '__main__':
  logging.getLogger().setLevel(logging.INFO)
  run()

但是，此脚本只能在DirectRunner上正确运行：当我让它在DataflowRunner上运行时，它会运行一段时间而没有任何输出，然后退出并出现错误：

“执行失败步骤失败14 […]工作流程失败。原因：[…]工人失去了与服务的联系。”

有时，它会一直持续下去，而不会创建输出。

此外，如果我注释“输出= …”行，则作业完成，但实际上并未读取数据。

似乎dataflowRunner调用源的函数“ estimate_size”，而不调用函数“ read”或“ get_range_tracker”。

有人对导致这种情况的原因有任何想法吗？我知道有一个（更完整的）带有实验性扳手源/接收器的Java SDK，但如果可能的话，我宁愿使用python。

谢谢

Answer 1

Google当前添加了对Backup Spanner和Dataflow的支持，您可以在创建DataFlow作业时选择相关模板。

有关更多信息：https :
//cloud.google.com/blog/products/gcp/cloud-spanner-adds-import-export-
functionality-to-ease-data-
movement