Question

使用Python。在将CSV文件处理到Big Query时，有什么方法可以添加额外的字段。我想添加一个带有当前日期的date_loaded字段吗？

我使用过的Google代码示例..

# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'my_dataset'

dataset_ref = client.dataset(dataset_id)
job_config = bigquery.LoadJobConfig()
job_config.schema = [
    bigquery.SchemaField('name', 'STRING'),
    bigquery.SchemaField('post_abbr', 'STRING')
]
job_config.skip_leading_rows = 1    
# The source format defaults to CSV, so the line below is optional.
job_config.source_format = bigquery.SourceFormat.CSV
uri = 'gs://cloud-samples-data/bigquery/us-states/us-states.csv'
    load_job = client.load_table_from_uri(
    uri,
    dataset_ref.table('us_states'),
    job_config=job_config)  # API request
print('Starting job {}'.format(load_job.job_id))

load_job.result()  # Waits for table load to complete.
print('Job finished.')

destination_table = client.get_table(dataset_ref.table('us_states'))
print('Loaded {} rows.'.format(destination_table.num_rows))

Answer 1

通过修改此Python example以适合您的问题，您可以打开并从本地PC读取原始CSV文件，通过添加一列对其进行编辑，并在每行末尾附加时间戳，以免出现空白列。 This link说明了如何使用自定义日期和时间在Python中获取时间戳。

然后将结果数据写入输出文件，并将其加载到Google Storage。 Here可以找到有关如何从Python文件运行外部命令的信息。

我希望这会有所帮助。

#Import the dependencies
import csv,datetime,subprocess
from google.cloud import bigquery

#Replace the values for variables with the appropriate ones
#Name of the input csv file
csv_in_name = 'us-states.csv'
#Name of the output csv file to avoid messing up the original
csv_out_name = 'out_file_us-states.csv'
#Name of the NEW COLUMN NAME to be added
new_col_name = 'date_loaded'
#Type of the new column
col_type = 'DATETIME'
#Name of your bucket
bucket_id = 'YOUR BUCKET ID'
#Your dataset name
ds_id = 'YOUR DATASET ID'
#The destination table name
destination_table_name = 'TABLE NAME'


# read and write csv files
with open(csv_in_name,'r') as r_csvfile:
    with open(csv_out_name,'w') as w_csvfile:

        dict_reader = csv.DictReader(r_csvfile,delimiter=',')
        #add new column with existing
        fieldnames = dict_reader.fieldnames + [new_col_name]
        writer_csv = csv.DictWriter(w_csvfile,fieldnames,delimiter=',')
        writer_csv.writeheader()


        for row in dict_reader:
#Put the timestamp after the last comma so that the column is not empty
            row[new_col_name] = datetime.datetime.now()
            writer_csv.writerow(row)

#Copy the file to your Google Storage bucket
subprocess.call('gsutil cp ' + csv_out_name + ' gs://' + bucket_id , shell=True)


client = bigquery.Client()

dataset_ref = client.dataset(ds_id)
job_config = bigquery.LoadJobConfig()
#Add a new column to the schema!
job_config.schema = [
    bigquery.SchemaField('name', 'STRING'),
    bigquery.SchemaField('post_abbr', 'STRING'),
    bigquery.SchemaField(new_col_name, col_type)
]
job_config.skip_leading_rows = 1
# The source format defaults to CSV, so the line below is optional.
job_config.source_format = bigquery.SourceFormat.CSV
#Address string of the output csv file
uri = 'gs://' + bucket_id + '/' + csv_out_name
load_job = client.load_table_from_uri(uri,dataset_ref.table(destination_table_name),job_config=job_config)  # API request
print('Starting job {}'.format(load_job.job_id))

load_job.result()  # Waits for table load to complete.
print('Job finished.')

destination_table = client.get_table(dataset_ref.table(destination_table_name))
print('Loaded {} rows.'.format(destination_table.num_rows))

Answer 2

您可以在加载时继续加载数据，但是可以加载到名为old_table的表中。

加载后，您可以运行以下内容：

bq --location=US query --destination_table mydataset.newtable --use_legacy_sql=false --replace=true 'select *, current_date() as date_loaded from mydataset.old_table'

这基本上将旧表的内容加载到date_loaded末尾的新列new_table中。这样，您现在有了一个新列，而无需下载本地或所有混乱情况。

将CSV上传到大型查询时添加日期加载字段

2 个答案: