使用旧版 SQL 查询嵌套和重复字段
本文档详细介绍了如何使用旧版 SQL 查询语法来查询嵌套数据和重复数据。BigQuery 的首选查询语法是 GoogleSQL。如需了解如何在 GoogleSQL 中处理嵌套数据和重复数据，请参阅 GoogleSQL 迁移指南。
BigQuery 支持加载和导出 JSON 和 Avro 文件形式的嵌套数据和重复数据。对于许多旧版 SQL 查询，BigQuery 可自动展平数据。例如，许多 SELECT 语句可以在保持数据结构的同时检索嵌套字段或重复字段，WHERE 子句可以在保持数据结构的同时过滤数据。相反，ORDER BY 和 GROUP BY 子句则隐式展平查询数据。对于未隐式展平数据的情况，例如查询旧版 SQL 中的多个重复字段，可以使用 FLATTEN 和 WITHIN SQL 函数查询数据。
FLATTEN
在查询嵌套数据时，BigQuery 会自动为您展平表数据。 例如，我们来看看个人数据的示例架构：
Last modified Schema Total Rows Total Bytes Expiration
----------------- ----------------------------------- ------------ ------------- ------------
27 Sep 10:01:06 |- kind: string 4 794
|- fullName: string (required)
|- age: integer
|- gender: string
+- phoneNumber: record
| |- areaCode: integer
| |- number: integer
+- children: record (repeated)
| |- name: string
| |- gender: string
| |- age: integer
+- citiesLived: record (repeated)
| |- place: string
| +- yearsLived: integer (repeated)请注意，其中包含多个重复字段和嵌套字段。如果针对个人表运行如下所示的旧版 SQL 查询：
SELECT fullName AS name, age, gender, citiesLived.place, citiesLived.yearsLived FROM [dataset.tableId]
BigQuery 会以展平的输出形式返回数据：
+---------------+-----+--------+-------------------+------------------------+ | name | age | gender | citiesLived_place | citiesLived_yearsLived | +---------------+-----+--------+-------------------+------------------------+ | John Doe | 22 | Male | Seattle | 1995 | | John Doe | 22 | Male | Stockholm | 2005 | | Mike Jones | 35 | Male | Los Angeles | 1989 | | Mike Jones | 35 | Male | Los Angeles | 1993 | | Mike Jones | 35 | Male | Los Angeles | 1998 | | Mike Jones | 35 | Male | Los Angeles | 2002 | | Mike Jones | 35 | Male | Washington DC | 1990 | | Mike Jones | 35 | Male | Washington DC | 1993 | | Mike Jones | 35 | Male | Washington DC | 1998 | | Mike Jones | 35 | Male | Washington DC | 2008 | | Mike Jones | 35 | Male | Portland | 1993 | | Mike Jones | 35 | Male | Portland | 1998 | | Mike Jones | 35 | Male | Portland | 2003 | | Mike Jones | 35 | Male | Portland | 2005 | | Mike Jones | 35 | Male | Austin | 1973 | | Mike Jones | 35 | Male | Austin | 1998 | | Mike Jones | 35 | Male | Austin | 2001 | | Mike Jones | 35 | Male | Austin | 2005 | | Anna Karenina | 45 | Female | Stockholm | 1992 | | Anna Karenina | 45 | Female | Stockholm | 1998 | | Anna Karenina | 45 | Female | Stockholm | 2000 | | Anna Karenina | 45 | Female | Stockholm | 2010 | | Anna Karenina | 45 | Female | Moscow | 1998 | | Anna Karenina | 45 | Female | Moscow | 2001 | | Anna Karenina | 45 | Female | Moscow | 2005 | | Anna Karenina | 45 | Female | Austin | 1995 | | Anna Karenina | 45 | Female | Austin | 1999 | +---------------+-----+--------+-------------------+------------------------+
在此示例中，citiesLived.place 现在是 citiesLived_place，citiesLived.yearsLived 现在是 citiesLived_yearsLived。
虽然 BigQuery 可以自动展平嵌套字段，但在处理多个重复字段时您仍可能需要明确调用 FLATTEN。例如，如果尝试运行如下所示的旧版 SQL 查询：
SELECT fullName, age FROM [dataset.tableId] WHERE (citiesLived.yearsLived > 1995 ) AND (children.age > 3)
BigQuery 会返回类似于以下内容的错误：
Cannot query the cross product of repeated fields children.age and citiesLived.yearsLived
如需在多个重复字段中查询，则需要展平以下字段之一：
SELECT fullName, age, gender, citiesLived.place FROM (FLATTEN([dataset.tableId], children)) WHERE (citiesLived.yearsLived > 1995) AND (children.age > 3) GROUP BY fullName, age, gender, citiesLived.place
返回结果：
+------------+-----+--------+-------------------+ | fullName | age | gender | citiesLived_place | +------------+-----+--------+-------------------+ | John Doe | 22 | Male | Stockholm | | Mike Jones | 35 | Male | Los Angeles | | Mike Jones | 35 | Male | Washington DC | | Mike Jones | 35 | Male | Portland | | Mike Jones | 35 | Male | Austin | +------------+-----+--------+-------------------+
WITHIN 子句
WITHIN 关键字专用于聚合函数，以便在记录和嵌套字段内的子字段和重复字段中进行聚合。在指定 WITHIN 关键字时，需要指定要聚合的范围：
WITHIN RECORD：对记录所含重复值中的数据进行聚合。WITHIN node_name：对指定节点所含重复值中的数据进行聚合，其中的节点是聚合函数中字段的父节点。
假设您要查找上个示例中每个人的子女数。为此，您可以计算每个记录包含的 children.name 数：
SELECT fullName, COUNT(children.name) WITHIN RECORD AS numberOfChildren FROM [dataset.tableId];
您将得到以下结果：
+---------------+------------------+ | fullName | numberOfChildren | +---------------+------------------+ | John Doe | 2 | | Jane Austen | 2 | | Mike Jones | 3 | | Anna Karenina | 0 | +---------------+------------------+
如需进行比较，请尝试列出所有子女的姓名：
SELECT fullName, children.name FROM [dataset.tableId]
+---------------+---------------+ | fullName | children_name | +---------------+---------------+ | John Doe | Jane | | John Doe | John | | Jane Austen | Josh | | Jane Austen | Jim | | Mike Jones | Earl | | Mike Jones | Sam | | Mike Jones | Kit | | Anna Karenina | None | +---------------+---------------+
这与我们的 WITHIN RECORD 查询结果匹配；John Doe 确实有两个名叫 Jane 和 John 的子女，Jane Austen 有两个名叫 Josh 和 Jim 的子女，Mike Jones 有三个名叫 Earl、Sam 和 Kit 的子女，而 Anna Karenina 没有子女。
现在，假设您想要查找某人在不同地方居住过的次数。您可以使用 WITHIN 子句在某一特定节点中聚合：
SELECT fullName, COUNT(citiesLived.place) WITHIN RECORD AS numberOfPlacesLived, citiesLived.place, COUNT(citiesLived.yearsLived) WITHIN citiesLived AS numberOfTimesInEachCity, FROM [dataset.tableId];
+---------------+---------------------+-------------------+-------------------------+ | fullName | numberOfPlacesLived | citiesLived_place | numberOfTimesInEachCity | +---------------+---------------------+-------------------+-------------------------+ | John Doe | 2 | Seattle | 1 | | John Doe | 2 | Stockholm | 1 | | Mike Jones | 4 | Los Angeles | 4 | | Mike Jones | 4 | Washington DC | 4 | | Mike Jones | 4 | Portland | 4 | | Mike Jones | 4 | Austin | 4 | | Anna Karenina | 3 | Stockholm | 4 | | Anna Karenina | 3 | Moscow | 3 | | Anna Karenina | 3 | Austin | 2 | +---------------+---------------------+-------------------+-------------------------+
此查询执行以下操作：
- 对
citiesLived.place执行WITHIN RECORD，计算每人居住过的地点数 - 对
citiesLived.yearsLived执行WITHIN，计算每个人在每个城市居住过的次数（仅在citiesLived中统计）。
对嵌套和重复字段使用限定范围的聚合是 BigQuery 最强大的功能之一，通常可以避免在查询中出现成本高昂的联接。