Google bigquery 从bigquery取消列数据嵌套？

我试图在大查询中从我的数据中获取2列。以下是我的疑问：

SELECT user_id, ep FROM table_name limit 3

现在，event_params是一个嵌套列。它有一个键和值。以下是数据的外观：

user_id   ep.key     ep.value.string_value    ep.value.int_value
1         origin             fcm                   null
2         origin             fcm                   null
3         screen             null                   4
          origin             auto                  null
             id              null                   97

大查询如何将列ep划分为键和值（存储在字符串或int中）。我需要以下格式的数据：

user_id ep.key  ep.value
1        origin   fcm
2        origin   fcm
3        screen   4
         origin   auto
           id     97

---

下面是BigQuery标准SQL

#standardSQL
SELECT user_id, 
  ARRAY(
    SELECT AS STRUCT ep.key AS key, 
      COALESCE(ep.value.string_value, CAST(ep.value.int_value AS STRING)) AS value
    FROM UNNEST(ep) ep
  ) ep
FROM `project.dataset.table_name`

您可以使用问题中的样本数据测试、播放上述内容，如下例所示

#standardSQL
WITH `project.dataset.table_name` AS (
  SELECT 1  user_id, [STRUCT<key STRING, value STRUCT<string_value STRING, int_value INT64>>('origin', STRUCT('fcm', NULL))] ep UNION ALL
  SELECT 2  user_id, [STRUCT<key STRING, value STRUCT<string_value STRING, int_value INT64>>('origin', STRUCT('fcm', NULL))] ep UNION ALL
  SELECT 3  user_id, [STRUCT<key STRING, value STRUCT<string_value STRING, int_value INT64>>('screen', STRUCT(NULL, 4)),
    STRUCT('origin', STRUCT('auto', NULL)),
    STRUCT('id', STRUCT(NULL, 97))
  ] ep   
)
SELECT user_id, 
  ARRAY(
    SELECT AS STRUCT ep.key AS key, 
      COALESCE(ep.value.string_value, CAST(ep.value.int_value AS STRING)) AS value
    FROM UNNEST(ep) ep
  ) ep
FROM `project.dataset.table_name`   

如果您需要使用相同的用户id对所有行进行分组，则另一个选项非常有用

#standardSQL
SELECT user_id, 
  ARRAY_AGG(STRUCT( ep.key AS key, 
    COALESCE(ep.value.string_value, CAST(ep.value.int_value AS STRING)) AS value
  )) ep
FROM `project.dataset.table_name`, UNNEST(ep) ep
GROUP BY user_id  

如以下示例所示，示例数据中有额外的行

#standardSQL
WITH `project.dataset.table_name` AS (
  SELECT 1  user_id, [STRUCT<key STRING, value STRUCT<string_value STRING, int_value INT64>>('origin', STRUCT('fcm', NULL))] ep UNION ALL
  SELECT 1  user_id, [STRUCT<key STRING, value STRUCT<string_value STRING, int_value INT64>>('origin2', STRUCT('fcm2', NULL))] ep UNION ALL
  SELECT 2  user_id, [STRUCT<key STRING, value STRUCT<string_value STRING, int_value INT64>>('origin', STRUCT('fcm', NULL))] ep UNION ALL
  SELECT 3  user_id, [STRUCT<key STRING, value STRUCT<string_value STRING, int_value INT64>>('screen', STRUCT(NULL, 4)),
    STRUCT('origin', STRUCT('auto', NULL)),
    STRUCT('id', STRUCT(NULL, 97))
  ] ep   
)
SELECT user_id, 
  ARRAY_AGG(STRUCT( ep.key AS key, 
    COALESCE(ep.value.string_value, CAST(ep.value.int_value AS STRING)) AS value
  )) ep
FROM `project.dataset.table_name`, UNNEST(ep) ep
GROUP BY user_id  

若您对相同的数据运行第一个选项，您将得到下面的结果

Row user_id ep.key  ep.value     
1   1       origin  fcm  
            origin2 fcm2     
2   2       origin  fcm  
3   3       screen  4    
            origin  auto     
            id      97     

Row user_id ep.key  ep.value     
1   1       origin  fcm  
2   1       origin2 fcm2     
3   2       origin  fcm  
4   3       screen  4    
            origin  auto     
            id      97   

Row user_id ep.key  ep.value     
1   1       origin  fcm  
2   1       origin2 fcm2     
3   2       origin  fcm  
4   3       screen  4    
            origin  auto     
            id      97