使用 Google SQL 或 BIGQUERY 反透视多列

Question

我一直在尝试从 google 工作表 table 中将 table 像下面的那样转入 (EAV) 或 Date、ldap、Shift：

        Date    Ldap1   Ldap2   Ldap3 </p>
       2020-04-01   Shift A Shift B Shift C </p>
       2020-04-02   Shift A Shift B Shift C</p>
       2020-04-03   Shift A Shift B Shift C</p>

我的初始代码（从其他地方安排的）可以工作，但随着列数增加到 400，我需要执行大约 400 次。有什么想法可以使它更快更实用吗？




      SELECT date,  "ldap1" AS id_name, ldap1 AS id
      FROM yt_tns_vendor_ops_ham_cog_leads.workflow_test
      UNION ALL
      SELECT date,  "ldap2" AS id_name, ldap2 AS id 
      FROM yt_tns_vendor_ops_ham_cog_leads.workflow_test
      ORDER BY date, id_name

结果：

date    id_name id
2020-04-01  Ldap1   Shift A
2020-04-01  Ldap2   Shift B
2020-04-02  Ldap1   Shift A
2020-04-02  Ldap2   Shift B
2020-04-03  Ldap1   Shift A
2020-04-03  Ldap2   Shift B
2020-04-04  Ldap1   Shift A
2020-04-04  Ldap2   Shift B
2020-04-05  Ldap1   Shift A
2020-04-05  Ldap2   Shift B
2020-04-06  Ldap1   Shift A
2020-04-06  Ldap2   Shift B
2020-04-07  Ldap1   WO
2020-04-07  Ldap2   WO
2020-04-08  Ldap1   WO
2020-04-08  Ldap2   WO
2020-04-09  Ldap1   Shift A
2020-04-09  Ldap2   Shift B
2020-04-10  Ldap1   Shift A
2020-04-10  Ldap2   Shift B
2020-04-11  Ldap1   Shift A
2020-04-11  Ldap2   Shift B
2020-04-12  Ldap1   Shift A
2020-04-12  Ldap2   Shift B
2020-04-13  Ldap1   Shift A
2020-04-13  Ldap2   Shift B
2020-04-14  Ldap1   Shift A
2020-04-14  Ldap2   Shift B
2020-04-15  Ldap1   WO
2020-04-15  Ldap2   WO
2020-04-16  Ldap1   WO
2020-04-16  Ldap2   WO
2020-04-17  Ldap1   Shift A
2020-04-17  Ldap2   Shift B
2020-04-18  Ldap1   Shift A

Answer 1

以下适用于 BigQuery 标准 SQL

#standardSQL
SELECT `date`,
  SPLIT(kv, ':')[OFFSET(0)] id_name,
  SPLIT(kv, ':')[OFFSET(1)] id
FROM `project.dataset.table` t,
UNNEST(SPLIT(REGEXP_REPLACE(TO_JSON_STRING(t), r'[{}"]', ''))) kv
WHERE SPLIT(kv, ':')[OFFSET(0)] != 'date'

如果要应用于您问题中的样本数据，如下例所示

#standardSQL
WITH `project.dataset.table` AS (
  SELECT DATE '2020-04-01' `date`, 'Shift A' ldap1, 'Shift B' ldap2,  'Shift C' ldap3 UNION ALL
  SELECT '2020-04-02', 'Shift A', 'Shift B',  'Shift C' UNION ALL
  SELECT '2020-04-03', 'Shift A', 'Shift B',  'Shift C' 
)
SELECT `date`,
  SPLIT(kv, ':')[OFFSET(0)] id_name,
  SPLIT(kv, ':')[OFFSET(1)] id
FROM `project.dataset.table` t,
UNNEST(SPLIT(REGEXP_REPLACE(TO_JSON_STRING(t), r'[{}"]', ''))) kv
WHERE SPLIT(kv, ':')[OFFSET(0)] != 'date'

结果是

Row date        id_name id   
1   2020-04-01  ldap1   Shift A  
2   2020-04-01  ldap2   Shift B  
3   2020-04-01  ldap3   Shift C  
4   2020-04-02  ldap1   Shift A  
5   2020-04-02  ldap2   Shift B  
6   2020-04-02  ldap3   Shift C  
7   2020-04-03  ldap1   Shift A  
8   2020-04-03  ldap2   Shift B  
9   2020-04-03  ldap3   Shift C

以上查询可以进一步重构（取决于您的喜好）

#standardSQL
SELECT `date`, id_name, id
FROM `project.dataset.table` t,
UNNEST(SPLIT(REGEXP_REPLACE(TO_JSON_STRING(t), r'[{}"]', ''))) kv,
UNNEST([STRUCT(SPLIT(kv, ':')[OFFSET(0)] AS id_name, SPLIT(kv, ':')[OFFSET(1)] AS id)])
WHERE id_name != 'date'

这个避免了多余的部分并且稍微不那么冗长

使用 Google SQL 或 BIGQUERY 反透视多列

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