Google Analytics（分析）API每次都会崩溃

Question

我正在使用Google Analytics（分析）API提取数据。我想将一年的数据转储到csv文件中。我也实现了拆分，以处理大数据。当我运行代码时，它开始将数据转储到csv中。但是，它崩溃并显示以下消息。每次我运行代码时都会发生这种情况。 此外，必须注意，我尝试提取数据1天，但仍然遇到相同的错误

googleapiclient.errors.HttpError：https://analyticsreporting.googleapis.com/v4/reports:batchGet?alt=json时返回“该服务当前不可用。“>

以下是我的代码。在这方面的任何帮助将不胜感激

import httplib2 as lib2
import google.oauth2.credentials
from google_auth_httplib2 import AuthorizedHttp
from datetime import datetime
import psycopg2

#Packages needed for connecting with Google API
from googleapiclient.discovery import build as google_build

#Data processing packages
import pandas
import numpy
import json
from datetime import datetime, timedelta


access_token = "***********"
refresh_token = "**********"
client_id = "***********"
client_secret = "*************"

token_uri = 'https://oauth2.googleapis.com/token'


token_expiry = datetime.now() - timedelta(days = 1)

#¯\_(ツ)_/¯
user_agent = 'my-user-agent/1.0'

credentials = google.oauth2.credentials.Credentials(access_token,
                                                    refresh_token=refresh_token,
                                                    token_uri='https://oauth2.googleapis.com/token',
                                                    client_id=client_id,
                                                    client_secret=client_secret)

#Authorize client
authorized = AuthorizedHttp(credentials=credentials)

api_name = 'analyticsreporting'
api_version = 'v4'

#Let's build the client
api_client_1dayactiveusers = google_build(serviceName=api_name, version=api_version, http=authorized)
pageToken_1dayactiveusers='firstcall'


# for user types
while pageToken_1dayactiveusers != None:
    sample_request = {
          'viewId': '**********',
          'dateRanges': {
              'startDate': datetime.strftime(datetime.now() - timedelta(days = 365),'%Y-%m-%d'),
              'endDate': datetime.strftime(datetime.now(),'%Y-%m-%d')
          },
          'dimensions': [{'name': 'ga:date'}],
          'metrics': [{'expression': 'ga:1dayUsers','alias':'onedayusers'}],
           'pageToken': pageToken_1dayactiveusers

    }

    response_1dayactiveusers = api_client_1dayactiveusers.reports().batchGet(
          body={
            'reportRequests': sample_request
          }).execute()
    print(response_1dayactiveusers)

    pageToken = response_1dayactiveusers.get("reports")[0].get('nextPageToken', None)
    print(pageToken)



    def parse_response(report):
      """Parses and prints the Analytics Reporting API V4 response"""
      # Initialize results, in list format because two dataframes might return
      result_list = []

      # Initialize empty data container for the two dateranges (if there are two that is)
      data_csv = []
      data_csv2 = []

      # Initialize header rows
      header_row = []

      # Get column headers, metric headers, and dimension headers.
      columnHeader = report.get('columnHeader', {})
      metricHeaders = columnHeader.get('metricHeader', {}).get('metricHeaderEntries', [])
      dimensionHeaders = columnHeader.get('dimensions', [])

      # Combine all of those headers into the header_row, which is in a list format
      for dheader in dimensionHeaders:
        header_row.append(dheader)
      for mheader in metricHeaders:
        header_row.append(mheader['name'])

      # Get data from each of the rows, and append them into a list
      rows = report.get('data', {}).get('rows', [])
      for row in rows:
        row_temp = []
        dimensions = row.get('dimensions', [])
        metrics = row.get('metrics', [])
        for d in dimensions:
          row_temp.append(d)
        for m in metrics[0]['values']:
          row_temp.append(m)
        data_csv.append(row_temp)



      # Putting those list formats into pandas dataframe, and append them into the final result
      result_df = pandas.DataFrame(data_csv, columns=header_row)
      result_list.append(result_df)


      return result_list

    response_data = response_1dayactiveusers.get('reports', [])[0]
    df = parse_response(response_data)[0]

    df.to_csv('/Users/ga_csv_2.csv', mode='a', header=False)