如何在将 JSON 文件加载到 BigQuery 表中时管理/处理

How to manage/handle schema changes while loading JSON file into BigQuery table(如何在将 JSON 文件加载到 BigQuery 表中时管理/处理架构更改)

问题描述

这是我的输入文件的样子:

Here is how my input file looks like:

{"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"}}

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符合 BigQuery 表架构.例如，在第 2 行中，缺少 Street.我希望 BigQuery 中列 Address.Street 的条目为 "N/A" 或 null 并且不希望管道失败由于架构更改或缺少数据.

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.

在使用 Python 写入 BigQuery 之前，如何在我的数据流作业中处理此逻辑?

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

推荐答案

我建议将您的数据写入临时表，其中只有一个 line 类型为 string 的字段

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

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

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

以下示例是 BigQuery 标准 SQL，说明如何对一个字段中包含整行的表应用架构逻辑

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

#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  

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      

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