在 BigQuery 中，将对象的字符串化数组转换为非字符串化

Question

我正在将 .json 数据提取到 Google BigQuery 中，并且在提取时，来自 .json 的 arrays 和 objects 的数据类型都被转换为 string 列。 BigQuery 中的数据如下所示：

select 1 as id, '[]' as stringCol1, '[]' as stringCol2 union all
select 2 as id, null as stringCol1, null as stringCol2 union all
select 3 as id, "{'game': '22', 'year': 'sophomore'}" as stringCol1, "[{'teamName': 'teamA', 'teamAge': 37}, {'teamName': 'teamB', 'teamAge': 32]" as stringCol2 union all
select 4 as id, "{'game': '17', 'year': 'freshman'}" as stringCol1, "[{'teamName': 'teamA', 'teamAge': 32}, {'teamName': 'teamB', 'teamAge': 33]" as stringCol2 union all
select 5 as id, "{'game': '9', 'year': 'senior'}" as stringCol1, "[{'teamName': 'teamC', 'teamAge': 31}, {'teamName': 'teamD', 'teamAge': 17]" as stringCol2 union all
select 6 as id, "{'game': '234', 'year': 'junior'}" as stringCol1, "[{'teamName': 'teamC', 'teamAge': 42}, {'teamName': 'teamD', 'teamAge': 25]" as stringCol2

数据有点乱。

• 在stringCol1 中，有null 和'[]' 缺失数据的值。我想从这个字符串化对象创建两列 game 和 year。
• 对于stringCol2，这始终是一个包含两个对象的数组，具有相同的键（teamName 和teamAge，在这种情况下）。然后需要将其转换为 4 列 teamName1、teamAge1、teamName2、teamAge2。

This similar post 解决了将基本字符串化数组转换为非字符串化数组的问题，但这里的示例稍微复杂一些。特别是，其他帖子中的解决方案在这种情况下不起作用。

Answer 1

以下是 BigQuery 标准 SQL

#standardSQL
SELECT id,
  JSON_EXTRACT_SCALAR(stringCol1, '$.game') AS game,
  JSON_EXTRACT_SCALAR(stringCol1, '$.year') AS year,
  JSON_EXTRACT_SCALAR(t1, '$.teamName') AS teamName1,
  JSON_EXTRACT_SCALAR(t1, '$.teamAge') AS teamAge1,
  JSON_EXTRACT_SCALAR(t2, '$.teamName') AS teamName2,
  JSON_EXTRACT_SCALAR(t2, '$.teamAge') AS teamAge2
FROM `project.dataset.table`,
UNNEST([STRUCT(
  JSON_EXTRACT_ARRAY(stringCol2)[SAFE_OFFSET(0)] AS t1, 
  JSON_EXTRACT_ARRAY(stringCol2)[SAFE_OFFSET(1)] AS t2
)])   

如果适用于您问题中的示例数据

WITH `project.dataset.table` AS (
  SELECT 1 AS id, '[]' AS stringCol1, '[]' AS stringCol2 UNION ALL
  SELECT 2 AS id, NULL AS stringCol1, NULL AS stringCol2 UNION ALL
  SELECT 3 AS id, "{'game': '22', 'year': 'sophomore'}" AS stringCol1, "[{'teamName': 'teamA', 'teamAge': 37}, {'teamName': 'teamB', 'teamAge': 32}]" AS stringCol2 UNION ALL
  SELECT 4 AS id, "{'game': '17', 'year': 'freshman'}" AS stringCol1, "[{'teamName': 'teamA', 'teamAge': 32}, {'teamName': 'teamB', 'teamAge': 33}]" AS stringCol2 UNION ALL
  SELECT 5 AS id, "{'game': '9', 'year': 'senior'}" AS stringCol1, "[{'teamName': 'teamC', 'teamAge': 31}, {'teamName': 'teamD', 'teamAge': 17}]" AS stringCol2 UNION ALL
  SELECT 6 AS id, "{'game': '234', 'year': 'junior'}" AS stringCol1, "[{'teamName': 'teamC', 'teamAge': 42}, {'teamName': 'teamD', 'teamAge': 25}]" AS stringCol2
) 

输出是

Row id  game    year        teamName1   teamAge1    teamName2   teamAge2     
1   1   null    null        null        null        null        null     
2   2   null    null        null        null        null        null     
3   3   22      sophomore   teamA       37          teamB       32   
4   4   17      freshman    teamA       32          teamB       33   
5   5   9       senior      teamC       31          teamD       17   
6   6   234     junior      teamC       42          teamD       25      

上面可以有很多变体来提高例如可读性

#standardSQL
SELECT id,
  JSON_EXTRACT_SCALAR(stringCol1, '$.game') AS game,
  JSON_EXTRACT_SCALAR(stringCol1, '$.year') AS year,
  JSON_EXTRACT_SCALAR(t[SAFE_OFFSET(0)], '$.teamName') AS teamName1,
  JSON_EXTRACT_SCALAR(t[SAFE_OFFSET(0)], '$.teamAge') AS teamAge1,
  JSON_EXTRACT_SCALAR(t[SAFE_OFFSET(1)], '$.teamName') AS teamName2,
  JSON_EXTRACT_SCALAR(t[SAFE_OFFSET(1)], '$.teamAge') AS teamAge2
FROM `project.dataset.table`,
UNNEST([STRUCT(JSON_EXTRACT_ARRAY(stringCol2) AS t)])