Question

使用Python 2.7，我不会将查询从BigQuery传递给具有specific formating请求的ML Predict。

首先：是否有更简单的方法直接从BigQuery查询转换为正确格式的JSON，因此可以将其传递给requests.post()而不是通过pandas（据我所知，pandas仍然不支持GCP标准）？

第二：有没有办法构建查询以直接转换为JSON格式，然后修改JSON以反映ML Predict JSON要求？

目前我的代码如下：

#I used the bigquery to dataframe option here to view the output.
#I would like to not use pandas in the end code.
logs = log_data.execute(output_options=bq.QueryOutput.dataframe()).result()
data = logs.to_json(orient='index')
print data

'{"0":{"end_time":"2018-04-19","device":"iPad","device_os":"iOS","device_os_version":"5.1.1","latency":0.150959,"megacycles":140.0,"cost":"1.3075e-08","device_brand":"Apple","device_family":"iPad","browser_version":"5.1","app":"567","ua_parse":"0"}}'

#The JSON needs to be in this format according to google documentation.
#data =  {
#  'instances': [
#    {
#      'key':'',
#      'end_time': '2018-04-19',
#      'device': 'iPad',
#      'device_os': 'iOS',
#      'device_os_version': '5.1.1',
#      'latency': 0.150959,
#      'megacycles':140.0,
#      'cost':'1.3075e-08',
#      'device_brand':'Apple',
#      'device_family':'iPad',
#      'browser_version':'5.1',
#      'app':'567',
#      'ua_parse':'40.9.8'
#    }
#  ]
#}

所以我需要更改的是前导键'0'到'instances'，我应该全部设置为传入`requests.post（）。

有没有办法实现这个目标？

编辑 - 添加BigQuery查询：

%%bq query --n log_data
WITH `my.table` AS (
  SELECT ARRAY<STRUCT<end_time STRING, device STRING, device_os STRING, device_os_version STRING, latency FLOAT64, megacycles FLOAT64,
  cost STRING, device_brand STRING, device_family STRING, browser_version STRING, app STRING, ua_parse STRING>>[] instances
)
SELECT TO_JSON_STRING(t)
FROM `my.table` AS t
WHERE end_time >='2018-04-19'
LIMIT 1

data = log_data.execute().result()

感谢@MikhailBerlyant我调整了我的查询和代码，看起来像这样：

%%bq query --n log_data
SELECT [TO_JSON_STRING(t)] AS instance
FROM `yourproject.yourdataset.yourtable` AS t
WHERE end_time >='2018-04-19'
LIMIT 1

但是当我运行执行logs = log_data.execute().result()时，我得到了这个

传递到request.post

时会导致此错误

TypeError: QueryResultsTable job_zfVEiPdf2W6msBlT6bBLgMusF49E is not JSON serializable

在execut（）中有没有办法只返回json？

Answer 1

首先：是否有更简单的方法直接从BigQuery查询转到正确格式的JSON

见下面的例子

#standardSQL
WITH yourTable AS (
  SELECT ARRAY<STRUCT<id INT64, type STRING>>[(1, 'abc'), (2, 'xyz')] instances
)
SELECT TO_JSON_STRING(t)
FROM yourTable t

结果是您要求的格式：

{"instances":[{"id":1,"type":"abc"},{"id":2,"type":"xyz"}]}

上面演示了查询及其工作方式在你真实的情况下 - 你应该使用类似下面的东西

SELECT TO_JSON_STRING(t)
FROM `yourproject.yourdataset.yourtable` AS t
WHERE end_time >='2018-04-19'
LIMIT 1

希望这会有所帮助：o）

根据评论进行更新

SELECT [TO_JSON_STRING(t)] AS instance
FROM `yourproject.yourdataset.yourtable` t    
WHERE end_time >='2018-04-19'
LIMIT 1

Answer 2

我想添加这个，以防有人遇到同样的问题，或者至少有问题，一旦你有了查询就去了。

我能够编写一个函数，以Google ML Predict希望将其传递给requests.post（）的方式格式化查询。这很可能是实现这一目标的可怕方法，但我无法找到以正确格式从BigQuery到ML Predict的直接方式。

def logs(query):
  client = gcb.Client()
  query_job = client.query(query)
  CSV_COLUMNS ='end_time,device,device_os,device_os_version,latency,megacycles,cost,device_brand,device_family,browser_version,app,ua_parse'.split(',')

  for row in query_job.result():
    var = list(row)
    l1 = dict(zip(CSV_COLUMNS,var))
    l1.update({'key':''})
    l2 = {'instances':[l1]}
  return l2

将Biquery查询格式化为ML适当的JSON以通过ML预测

2 个答案: