查询 JSON 字符串
本文介绍可用于查询和转换以 JSON 字符串形式存储的半结构化数据的 Databricks SQL 运算符。
注意
利用此功能，可读取半结构化数据，而无需平展文件。 但是，为了获得最佳的读取查询性能，Databricks 建议提取具有正确数据类型的嵌套列。
可以使用语法 <column-name>:<extraction-path> 从包含 JSON 字符串的字段中提取列，其中 <column-name> 是字符串列名，而 <extraction-path> 是要提取的字段的路径。 返回的结果为字符串。
创建具有高度嵌套数据的表
运行以下查询以创建具有高度嵌套数据的表。 本文中的示例均引用此表。
CREATE TABLE store_data AS SELECT
'{
"store":{
"fruit": [
{"weight":8,"type":"apple"},
{"weight":9,"type":"pear"}
],
"basket":[
[1,2,{"b":"y","a":"x"}],
[3,4],
[5,6]
],
"book":[
{
"author":"Nigel Rees",
"title":"Sayings of the Century",
"category":"reference",
"price":8.95
},
{
"author":"Herman Melville",
"title":"Moby Dick",
"category":"fiction",
"price":8.99,
"isbn":"0-553-21311-3"
},
{
"author":"J. R. R. Tolkien",
"title":"The Lord of the Rings",
"category":"fiction",
"reader":[
{"age":25,"name":"bob"},
{"age":26,"name":"jack"}
],
"price":22.99,
"isbn":"0-395-19395-8"
}
],
"bicycle":{
"price":19.95,
"color":"red"
}
},
"owner":"amy",
"zip code":"94025",
"fb:testid":"1234"
}' as raw
提取顶级列
若要提取列，请在提取路径中指定 JSON 字段的名称。
可以在括号中提供列名。 括号内引用的列区分大小写。 列名的引用也可不区分大小写。
SELECT raw:owner, RAW:owner FROM store_data
+-------+-------+
| owner | owner |
+-------+-------+
| amy | amy |
+-------+-------+
-- References are case sensitive when you use brackets
SELECT raw:OWNER case_insensitive, raw:['OWNER'] case_sensitive FROM store_data
+------------------+----------------+
| case_insensitive | case_sensitive |
+------------------+----------------+
| amy | null |
+------------------+----------------+
使用反引号转义空格和特殊字符。 字段名称匹配（不区分大小写）。
-- Use backticks to escape special characters. References are case insensitive when you use backticks.
-- Use brackets to make them case sensitive.
SELECT raw:`zip code`, raw:`Zip Code`, raw:['fb:testid'] FROM store_data
+----------+----------+-----------+
| zip code | Zip Code | fb:testid |
+----------+----------+-----------+
| 94025 | 94025 | 1234 |
+----------+----------+-----------+
注意
如果 JSON 记录包含多个由于不区分大小写而与提取路径匹配的列，则会出现要求使用方括号这一错误。 如果行之间的列匹配，则不会遇到任何错误。 {"foo":"bar", "Foo":"bar"} 会引发错误，但以下内容将不会引发错误：
{"foo":"bar"}
{"Foo":"bar"}
提取嵌套字段
可以通过点表示法或使用方括号指定嵌套字段。 使用方括号时，各列区分大小写。
-- Use dot notation
SELECT raw:store.bicycle FROM store_data
-- the column returned is a string
+------------------+
| bicycle |
+------------------+
| { |
| "price":19.95, |
| "color":"red" |
| } |
+------------------+
-- Use brackets
SELECT raw:store['bicycle'], raw:store['BICYCLE'] FROM store_data
+------------------+---------+
| bicycle | BICYCLE |
+------------------+---------+
| { | null |
| "price":19.95, | |
| "color":"red" | |
| } | |
+------------------+---------+
从数组中提取值
可以使用方括号对数组中的元素进行索引。 索引从 0 开始。 可以使用星号 (*)，后跟点或括号表示法来从数组中的所有元素中提取子字段。
-- Index elements
SELECT raw:store.fruit[0], raw:store.fruit[1] FROM store_data
+------------------+-----------------+
| fruit | fruit |
+------------------+-----------------+
| { | { |
| "weight":8, | "weight":9, |
| "type":"apple" | "type":"pear" |
| } | } |
+------------------+-----------------+
-- Extract subfields from arrays
SELECT raw:store.book[*].isbn FROM store_data
+--------------------+
| isbn |
+--------------------+
| [ |
| null, |
| "0-553-21311-3", |
| "0-395-19395-8" |
| ] |
+--------------------+
-- Access arrays within arrays or structs within arrays
SELECT
raw:store.basket[*],
raw:store.basket[*][0] first_of_baskets,
raw:store.basket[0][*] first_basket,
raw:store.basket[*][*] all_elements_flattened,
raw:store.basket[0][2].b subfield
FROM store_data
+----------------------------+------------------+---------------------+---------------------------------+----------+
| basket | first_of_baskets | first_basket | all_elements_flattened | subfield |
+----------------------------+------------------+---------------------+---------------------------------+----------+
| [ | [ | [ | [1,2,{"b":"y","a":"x"},3,4,5,6] | y |
| [1,2,{"b":"y","a":"x"}], | 1, | 1, | | |
| [3,4], | 3, | 2, | | |
| [5,6] | 5 | {"b":"y","a":"x"} | | |
| ] | ] | ] | | |
+----------------------------+------------------+---------------------+---------------------------------+----------+
强制转换值
可以使用 :: 将值转换为基本数据类型。 使用 from_json 方法将嵌套结果强制转换为更复杂的数据类型，例如数组或结构。
-- price is returned as a double, not a string
SELECT raw:store.bicycle.price::double FROM store_data
+------------------+
| price |
+------------------+
| 19.95 |
+------------------+
-- use from_json to cast into more complex types
SELECT from_json(raw:store.bicycle, 'price double, color string') bicycle FROM store_data
-- the column returned is a struct containing the columns price and color
+------------------+
| bicycle |
+------------------+
| { |
| "price":19.95, |
| "color":"red" |
| } |
+------------------+
SELECT from_json(raw:store.basket[*], 'array<array<string>>') baskets FROM store_data
-- the column returned is an array of string arrays
+------------------------------------------+
| basket |
+------------------------------------------+
| [ |
| ["1","2","{\"b\":\"y\",\"a\":\"x\"}]", |
| ["3","4"], |
| ["5","6"] |
| ] |
+------------------------------------------+
NULL 行为
如果存在具有 null 值的 JSON 字段，则会收到该列的 SQL null 值，而不是 null 文本值。
select '{"key":null}':key is null sql_null, '{"key":null}':key == 'null' text_null
+-------------+-----------+
| sql_null | text_null |
+-------------+-----------+
| true | null |
+-------------+-----------+
使用 Spark SQL 运算符转换嵌套数据
Apache Spark 有许多用于处理复杂嵌套数据的内置函数。 以下笔记本包含示例。
此外，当内置的 Spark 运算符无法以所需的方式转换数据时，高阶函数会提供许多其他选项。