如何在将JSON文件加载到BigQuery表时管理/处理模式更改

Here is how my input file looks like:

这是我的输入文件的样子：

{"Id": 1, "Address": {"Street":"MG Road","City":"Pune"}}
{"Id": 2, "Address": {"City":"Mumbai"}}
{"Id": 3, "Address": {"Street":"XYZ Road"}}
{"Id": 4}
{"Id": 5, "PhoneNumber": 12345678, "Address": {"Street":"ABCD Road", "City":"Bangalore"}}

In my dataflow pipeline, How I can I dynamically determine which fields are present in each row in order to adhere to the BigQuery table schema. e.g., In row #2, Street is missing. I want the entry for column Address.Street in the BigQuery to be "N/A" or null and don't want pipeline to fail because of schema change or missing data.

在我的数据流管道中，我如何动态确定每行中存在哪些字段以遵守BigQuery表模式。例如，在第2行，街道丢失了。我希望BigQuery中的列Address.Street的条目为“N / A”或null，并且不希望管道因架构更改或缺少数据而失败。

How can I handle this logic in my dataflow job before writing to BigQuery in Python?

在使用Python编写BigQuery之前，如何在数据流作业中处理此逻辑？

1 个解决方案

#1

I recommend writing your data into temp table with just one field line of type string

我建议只使用一个字符串类型的字段行将数据写入临时表

After you done with bringing your data to BigQuery temp table - now you can apply schema logic and query your data out of temp table to your final table

完成将数据导入BigQuery临时表后 - 现在您可以应用模式逻辑并将临时表中的数据查询到最终表

Below example is for BigQuery Standard SQL of how to apply schema logic against table with whole row in one field

下面的示例是关于如何在一个字段中对整个行应用模式逻辑的BigQuery Standard SQL

#standardSQL
WITH t AS (
  SELECT '{"Id": 1, "Address": {"Street":"MG Road","City":"Pune"}}' line UNION ALL
  SELECT '{"Id": 2, "Address": {"City":"Mumbai"}}' UNION ALL
  SELECT '{"Id": 3, "Address": {"Street":"XYZ Road"}}' UNION ALL
  SELECT '{"Id": 4}  ' UNION ALL
  SELECT '{"Id": 5, "PhoneNumber": 12345678, "Address": {"Street":"ABCD Road", "City":"Bangalore"}}' 
)
SELECT
  JSON_EXTRACT_SCALAR(line, '$.Id') id,
  JSON_EXTRACT_SCALAR(line, '$.PhoneNumber') PhoneNumber,
  JSON_EXTRACT_SCALAR(line, '$[Address].Street') Street,
  JSON_EXTRACT_SCALAR(line, '$[Address].City') City 
FROM t

with result as below

结果如下

Row id  PhoneNumber Street      City     
1   1   null        MG Road     Pune     
2   2   null        null        Mumbai   
3   3   null        XYZ Road    null     
4   4   null        null        null     
5   5   12345678    ABCD Road   Bangalore

#1