问题描述
我目前在 BigQuery 中的工作流程如下:
My current workflow in BigQuery is as follows:
(1) 查询公共存储库中的数据(存储在美国)，(2) 将其写入我存储库中的表中，(3) 将 csv 导出到云存储桶中，以及 (4) 将 csv 下载到我在服务器上工作并且 (5) 在服务器上使用它.
(1) query data in a public repository (stored in the US), (2) write it to a table in my repository, (3) export a csv to a cloud bucket and (4) download the csv on the server I work on and (5) work with that on the server.
我现在的问题是我工作的服务器位于欧盟.因此，我必须支付相当多的费用才能在我的美国存储桶和我的欧盟服务器之间传输数据.我现在可以继续在欧盟找到我的存储桶，但是我仍然遇到了将数据从美国(BigQuery)传输到欧盟(存储桶)的问题.所以我也可以将bq中的数据集设置为位于欧盟，但是之后就无法查询了，因为公共存储库中的数据位于美国，并且不允许在不同位置之间进行查询.
The problem I have now, is that the server I work on is located in EU. Thus, I have to pay quite some fees for transfering data between my US bucket and my EU server. I could now go ahead and locate my bucket in EU, but then I still have the problem that I would transfer data from the US (BigQuery) to EU (bucket). So I could also set my dataset in bq to be located in the EU, but then I cant do any queries anylonger, because the data in the public repository is located in the US, and queries between different locations are not allowed.
有人知道如何解决这个问题吗?
Does anyone have an idea of how to approach this?
推荐答案
将 BigQuery 数据集从一个区域复制到另一个区域的一种方法是利用 存储数据传输服务.这并没有解决您仍然必须为bucket-to-bucket网络付费的事实流量，但可能会在将数据复制到欧盟服务器时为您节省一些 CPU 时间.
One way to copy a BigQuery dataset from one region to another is to take advantage of the Storage Data Transfer Service. It doesn't get around the fact that you still have to pay for bucket-to-bucket network traffic, but might save you some CPU time on copying data to a server in the EU.
流程是:
- 将所有 BigQuery 表提取到与表位于同一区域的存储桶中.(推荐 Avro 格式以获得最佳的数据类型保真度和最快的加载速度.)
- 运行存储传输作业，将提取的文件从起始位置存储区复制到目标位置的存储区.
- 将所有文件加载到位于目标位置的 BigQuery 数据集.
Python 示例:
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import sys
import time
import googleapiclient.discovery
from google.cloud import bigquery
import json
import pytz
PROJECT_ID = 'swast-scratch' # TODO: set this to your project name
FROM_LOCATION = 'US' # TODO: set this to the BigQuery location
FROM_DATASET = 'workflow_test_us' # TODO: set to BQ dataset name
FROM_BUCKET = 'swast-scratch-us' # TODO: set to bucket name in same location
TO_LOCATION = 'EU' # TODO: set this to the destination BigQuery location
TO_DATASET = 'workflow_test_eu' # TODO: set to destination dataset name
TO_BUCKET = 'swast-scratch-eu' # TODO: set to bucket name in destination loc
# Construct API clients.
bq_client = bigquery.Client(project=PROJECT_ID)
transfer_client = googleapiclient.discovery.build('storagetransfer', 'v1')
def extract_tables():
# Extract all tables in a dataset to a Cloud Storage bucket.
print('Extracting {}:{} to bucket {}'.format(
PROJECT_ID, FROM_DATASET, FROM_BUCKET))
tables = list(bq_client.list_tables(bq_client.dataset(FROM_DATASET)))
extract_jobs = []
for table in tables:
job_config = bigquery.ExtractJobConfig()
job_config.destination_format = bigquery.DestinationFormat.AVRO
extract_job = bq_client.extract_table(
table.reference,
['gs://{}/{}.avro'.format(FROM_BUCKET, table.table_id)],
location=FROM_LOCATION, # Available in 0.32.0 library.
job_config=job_config) # Starts the extract job.
extract_jobs.append(extract_job)
for job in extract_jobs:
job.result()
return tables
def transfer_buckets():
# Transfer files from one region to another using storage transfer service.
print('Transferring bucket {} to {}'.format(FROM_BUCKET, TO_BUCKET))
now = datetime.datetime.now(pytz.utc)
transfer_job = {
'description': '{}-{}-{}_once'.format(
PROJECT_ID, FROM_BUCKET, TO_BUCKET),
'status': 'ENABLED',
'projectId': PROJECT_ID,
'transferSpec': {
'transferOptions': {
'overwriteObjectsAlreadyExistingInSink': True,
},
'gcsDataSource': {
'bucketName': FROM_BUCKET,
},
'gcsDataSink': {
'bucketName': TO_BUCKET,
},
},
# Set start and end date to today (UTC) without a time part to start
# the job immediately.
'schedule': {
'scheduleStartDate': {
'year': now.year,
'month': now.month,
'day': now.day,
},
'scheduleEndDate': {
'year': now.year,
'month': now.month,
'day': now.day,
},
},
}
transfer_job = transfer_client.transferJobs().create(
body=transfer_job).execute()
print('Returned transferJob: {}'.format(
json.dumps(transfer_job, indent=4)))
# Find the operation created for the job.
job_filter = {
'project_id': PROJECT_ID,
'job_names': [transfer_job['name']],
}
# Wait until the operation has started.
response = {}
while ('operations' not in response) or (not response['operations']):
time.sleep(1)
response = transfer_client.transferOperations().list(
name='transferOperations', filter=json.dumps(job_filter)).execute()
operation = response['operations'][0]
print('Returned transferOperation: {}'.format(
json.dumps(operation, indent=4)))
# Wait for the transfer to complete.
print('Waiting ', end='')
while operation['metadata']['status'] == 'IN_PROGRESS':
print('.', end='')
sys.stdout.flush()
time.sleep(5)
operation = transfer_client.transferOperations().get(
name=operation['name']).execute()
print()
print('Finished transferOperation: {}'.format(
json.dumps(operation, indent=4)))
def load_tables(tables):
# Load all tables into the new dataset.
print('Loading tables from bucket {} to {}:{}'.format(
TO_BUCKET, PROJECT_ID, TO_DATASET))
load_jobs = []
for table in tables:
dest_table = bq_client.dataset(TO_DATASET).table(table.table_id)
job_config = bigquery.LoadJobConfig()
job_config.source_format = bigquery.SourceFormat.AVRO
load_job = bq_client.load_table_from_uri(
['gs://{}/{}.avro'.format(TO_BUCKET, table.table_id)],
dest_table,
location=TO_LOCATION, # Available in 0.32.0 library.
job_config=job_config) # Starts the load job.
load_jobs.append(load_job)
for job in load_jobs:
job.result()
# Actually run the script.
tables = extract_tables()
transfer_buckets()
load_tables(tables)
上述示例将 google-cloud-bigquery 库用于 BigQuery API，将 google-api-python-client 用于 Storage Data Transfer API.
The preceding sample uses google-cloud-bigquery library for BigQuery API and google-api-python-client for Storage Data Transfer API.
请注意，此示例不考虑分区表.
Note that this sample does not account for partitioned tables.
这篇关于如何最好地处理存储在 Google BigQuery 中不同位置的数据?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！