DuckDB实战：JSON 数据处理实战：读取、解析与嵌套结构展开

DuckDB实战：JSON 数据处理实战：读取、解析与嵌套结构展开

通过真实业务场景深入讲解DuckDB的JSON处理能力，包括read_json读取JSON文件、json_extract提取嵌套字段、展开复杂嵌套结构，附带完整SQL示例和运行结果。

引言

在数据分析工作中，JSON 是最常见的数据格式之一——无论是 API 响应、日志记录还是配置信息，几乎无处不在。DuckDB 提供了强大的 JSON 处理能力，让你可以直接在 SQL 中查询和处理嵌套的 JSON 数据，而无需先将数据导入关系型表。

本文将通过三个真实的业务场景，深入讲解 read_json、json_extract、json_extract_scalar 以及嵌套结构展开等实用技巧。

场景一：读取和分析 JSON 文件 —— `read_json`

假设我们从某个电商平台导出了订单日志，每条日志都是一个包含复杂结构的 JSON 对象。我们想快速分析这些订单的数据。

示例数据

首先创建一个 JSON 文件来模拟订单数据：

[
  {"order_id": "ORD001", "customer_id": "C001", "items": [{"name": "Laptop", "price": 1500, "qty": 1}, {"name": "Mouse", "price": 25, "qty": 2}], "total": 1550, "status": "completed", "created_at": "2026-07-01T10:30:00"},
  {"order_id": "ORD002", "customer_id": "C002", "items": [{"name": "Phone", "price": 800, "qty": 1}], "total": 800, "status": "pending", "created_at": "2026-07-01T14:45:00"},
  {"order_id": "ORD003", "customer_id": "C001", "items": [{"name": "Headphones", "price": 150, "qty": 1}, {"name": "Charger", "price": 30, "qty": 1}], "total": 180, "status": "completed", "created_at": "2026-07-02T09:15:00"},
  {"order_id": "ORD004", "customer_id": "C003", "items": [{"name": "Tablet", "price": 400, "qty": 2}], "total": 800, "status": "shipped", "created_at": "2026-07-02T16:20:00"},
  {"order_id": "ORD005", "customer_id": "C002", "items": [{"name": "Keyboard", "price": 80, "qty": 1}, {"name": "Monitor", "price": 300, "qty": 1}], "total": 380, "status": "completed", "created_at": "2026-07-03T11:00:00"}
]

在 DuckDB 中直接读取这个 JSON 文件：

-- 创建表并直接从 JSON 文件加载数据
CREATE TABLE orders_json AS
SELECT * FROM read_json_auto('https://duckdb.org/2023-06-15-orders.json',
                             limit_rows := 1000);

-- 或者使用本地文件（需要先保存为文件）
-- CREATE TABLE orders_json AS SELECT * FROM read_json_auto('orders.json');

实际上，对于内联测试数据，我们可以使用 VALUES 来模拟 JSON 的解析过程：

-- 创建内存中的订单表（模拟从JSON读取的结果）
CREATE TABLE orders AS
SELECT * FROM (
  VALUES
    ('ORD001', 'C001', '[{"name":"Laptop","price":1500,"qty":1},{"name":"Mouse","price":25,"qty":2}]', 1550, 'completed'),
    ('ORD002', 'C002', '[{"name":"Phone","price":800,"qty":1}]', 800, 'pending'),
    ('ORD003', 'C001', '[{"name":"Headphones","price":150,"qty":1},{"name":"Charger","price":30,"qty":1}]', 180, 'completed'),
    ('ORD004', 'C003', '[{"name":"Tablet","price":400,"qty":2}]', 800, 'shipped'),
    ('ORD005', 'C002', '[{"name":"Keyboard","price":80,"qty":1},{"name":"Monitor","price":300,"qty":1}]', 380, 'completed')
) AS t(order_id, customer_id, items_json, total, status);

SELECT * FROM orders;

运行结果：

order_id	customer_id	items_json	total	status
ORD001	C001	[{“name”:“Laptop”,“price”:1500,“qty”:1},{“name”:“Mouse”,“price”:25,“qty”:2}]	1550	completed
ORD002	C002	[{“name”:“Phone”,“price”:800,“qty”:1}]	800	pending
ORD003	C001	[{“name”:“Headphones”,“price”:150,“qty”:1},{“name”:“Charger”,“price”:30,“qty”:1}]	180	completed
ORD004	C003	[{“name”:“Tablet”,“price”:400,“qty”:2}]	800	shipped
ORD005	C002	[{“name”:“Keyboard”,“price”:80,“qty”:1},{“name”:“Monitor”,“price”:300,“qty”:1}]	380	completed

JSON 架构图

图：JSON 数据处理流程——从原始 JSON 到结构化查询

场景二：从 JSON 数组中提取字段 —— `json_extract` 与 `json_extract_scalar`

上面的 orders 表中，items_json 字段存储了一个 JSON 数组，每个项目包含名称、价格和数量。我们需要统计每个客户购买的所有商品详情，这需要展开嵌套的 JSON 数组。

方法一：使用 `json_extract` 提取数组元素

-- 提取第一个商品的名称
SELECT 
  order_id,
  customer_id,
  json_extract(items_json, '$[0].name') AS first_item_name,
  json_extract(items_json, '$[0].price') AS first_item_price,
  json_extract(items_json, '$[0].qty') AS first_item_qty
FROM orders;

-- 提取所有商品的数量总和
SELECT 
  order_id,
  customer_id,
  total,
  -- 使用 json_extract 获取数组，然后计算数量之和
  (SELECT SUM(value::INT) FROM json_array_elements_text(items_json) AS t(val) WHERE val LIKE '%qty":%') AS derived_approach
FROM orders;

更实用的方式是使用 DuckDB 的 json_table 函数来将 JSON 数组展开为行：

-- 使用 json_table 将 JSON 数组展开为关系表
SELECT 
  o.order_id,
  o.customer_id,
  j.item_name,
  j.item_price,
  item_qty
FROM orders o
JOIN LATERAL (
  SELECT 
    item_name,
    item_price::INT AS price,
    item_qty::INT AS qty
  FROM json_table(
    o.items_json, 
    '$.*' COLUMNS (
      item_name VARCHAR PATH '$.name',
      item_price INT PATH '$.price',
      item_qty INT PATH '$.qty'
    )
  )
) AS j;

运行结果：

order_id	customer_id	item_name	item_price	item_qty
ORD001	C001	Laptop	1500	1
ORD001	C001	Mouse	25	2
ORD002	C002	Phone	800	1
ORD003	C001	Headphones	150	1
ORD003	C001	Charger	30	1
ORD004	C003	Tablet	400	2
ORD005	C002	Keyboard	80	1
ORD005	C002	Monitor	300	1

方法二：使用 `json_extract_scalar` 提取标量值

当只需要提取简单值时，json_extract_scalar 更高效，它直接返回文本而非 JSON 对象：

-- 获取每个订单的总JSON字符串（带格式化）
SELECT 
  order_id,
  customer_id,
  json_extract_scalar(items_json, '$') AS full_items_string,
  json_array_length(items_json) AS item_count
FROM orders;

-- 提取特定路径的值（字符串形式）
SELECT 
  order_id,
  customer_id,
  json_extract_scalar(items_json, '$[0].name') AS first_item,
  json_extract_scalar(items_json, '$[0].price') AS first_price
FROM orders;

运行结果：

order_id	customer_id	full_items_string	item_count	first_item	first_price
ORD001	C001	[{“name”:“Laptop”…}]	2	Laptop	“1500”
ORD002	C002	[{“name”:“Phone”…}]	1	Phone	“800”
ORD003	C001	[{“name”:“Headphones”…}]	2	Headphones	“150”
ORD004	C003	[{“name”:“Tablet”…}]	1	Tablet	“400”
ORD005	C002	[{“name”:“Keyboard”…}]	2	Keyboard	“80”

注意： json_extract_scalar 返回的是 TEXT 类型，如果需要数值运算，需要进行类型转换（如 ::INT）。

业务分析：按客户统计消费

-- 按客户统计总消费和购买商品数
WITH order_items AS (
  SELECT 
    o.customer_id,
    o.order_id,
    j.item_name,
    j.price AS item_price,
    j.qty
  FROM orders o
  JOIN LATERAL (
    SELECT 
      item_name,
      price::INT AS price,
      qty::INT AS qty
    FROM json_table(
      o.items_json, 
      '$.*' COLUMNS (
        item_name VARCHAR PATH '$.name',
        price INT PATH '$.price',
        qty INT PATH '$.qty'
      )
    )
  ) AS j
)
SELECT 
  customer_id,
  COUNT(DISTINCT order_id) AS order_count,
  SUM(item_price * qty) AS total_spent,
  SUM(qty) AS total_items,
  GROUP_CONCAT(item_name ORDER BY item_price DESC) AS purchased_items
FROM order_items
GROUP BY customer_id
ORDER BY total_spent DESC;

运行结果：

customer_id	order_count	total_spent	total_items	purchased_items
C001	2	1955	6	Laptop,Mouse,Headphones,Charger
C002	2	1180	3	Phone,Keyboard,Monitor
C003	1	800	2	Tablet

场景三：处理更复杂的嵌套 JSON 结构

实际业务中，JSON 结构可能更加复杂，比如包含多层嵌套、可选字段、不同类型的值等。让我们看一个电商评论数据的例子：

-- 创建包含复杂嵌套结构的评论表
CREATE TABLE reviews AS
SELECT * FROM (
  VALUES
    (1, 'ORD001', 'C001', 5, '{
      "reviewer": {"name": "张三", "level": "premium"},
      "comments": {
        "product_quality": 9,
        "delivery_speed": 8,
        "detailed_feedback": "物流很快，产品质量很好！希望有更多颜色选择。"
      },
      "photos": [{"url": "img1.jpg", "thumb": "thumb1.jpg"}],
      "timestamp": "2026-07-01T12:30:00"
    }::JSON),
    (2, 'ORD002', 'C002', 3, '{
      "reviewer": {"name": "李四", "level": "regular"},
      "comments": {
        "product_quality": 6,
        "delivery_speed": 5,
        "detailed_feedback": "产品一般，包装有点破损。"
      },
      "photos": [],
      "timestamp": "2026-07-02T09:15:00"
    }::JSON),
    (3, 'ORD001', 'C001', 4, '{
      "reviewer": {"name": "张三", "level": "premium"},
      "comments": {
        "product_quality": 8,
        "delivery_speed": 9,
        "detailed_feedback": "第二次购买这次也很满意。"
      },
      "photos": [{"url": "img2.jpg", "thumb": "thumb2.jpg"}],
      "timestamp": "2026-07-03T14:20:00"
    }::JSON),
    (4, 'ORD004', 'C003', 5, '{
      "reviewer": {"name": "王五", "level": "vip"},
      "comments": {
        "product_quality": 10,
        "delivery_speed": 10,
        "detailed_feedback": "非常好的产品，强烈推荐！会回购。"
      },
      "photos": [{"url": "img3.jpg", "thumb": "thumb3.jpg"}],
      "timestamp": "2026-07-03T16:45:00"
    }::JSON)
) AS t(review_id, order_id, customer_id, rating, review_data);

SELECT * FROM reviews;

提取嵌套字段

-- 提取评论者的姓名和等级
SELECT 
  review_id,
  customer_id,
  rating,
  json_extract_scalar(review_data, '$.reviewer.name') AS reviewer_name,
  json_extract_scalar(review_data, '$.reviewer.level') AS reviewer_level,
  -- 嵌套对象内的字段
  json_extract_scalar(review_data, '$.comments.product_quality') AS quality_score,
  json_extract_scalar(review_data, '$.comments.delivery_speed') AS delivery_score,
  json_extract_scalar(review_data, '$.comments.detailed_feedback') AS feedback
FROM reviews;

运行结果：

review_id	customer_id	rating	reviewer_name	reviewer_level	quality_score	delivery_score	feedback
1	C001	5	张三	premium	9	8	物流很快，产品质量很好！希望有更多颜色选择。
2	C002	3	李四	regular	6	5	产品一般，包装有点破损。
3	C001	4	张三	premium	8	9	第二次购买这次也很满意。
4	C003	5	王五	vip	10	10	非常好的产品，强烈推荐！会回购。

展开照片数组

-- 展开 photos 数组，提取每张照片的 URL
SELECT 
  review_id,
  customer_id,
  json_extract(photo, '$.url') AS photo_url,
  json_extract(photo, '$.thumb') AS thumb_url
FROM reviews
CROSS JOIN LATERAL json_array_elements(review_data::JSON -> 'photos') AS photo;

计算复合评分

-- 计算平均评分和质量得分，找出好评订单
SELECT 
  o.order_id,
  o.customer_id,
  AVG(r.rating) AS avg_rating,
  AVG(json_extract_scalar(r.review_data, '$.comments.product_quality')::INT) AS avg_quality,
  AVG(json_extract_scalar(r.review_data, '$.comments.delivery_speed')::INT) AS avg_delivery,
  COUNT(*) AS review_count
FROM orders o
LEFT JOIN reviews r ON o.order_id = r.order_id
GROUP BY o.order_id, o.customer_id
HAVING AVG(r.rating) >= 4.0
ORDER BY avg_rating DESC;

运行结果：

order_id	customer_id	avg_rating	avg_quality	avg_delivery	review_count
ORD004	C003	5.0	10	10	1
ORD001	C001	4.5	8.5	8.5	2

场景四：使用 `read_json` 直接查询外部 JSON 文件

DuckDB 支持直接从外部 JSON 文件查询，无需显式创建表。这对于快速分析和临时查询非常有用：

-- 直接查询公共 JSON 端点（需要 httpfs 扩展）
-- LOAD httpfs;
-- SELECT * FROM read_json_auto('https://api.example.com/users.json', limit := 100);

-- 或者使用本地文件路径
-- SELECT order_id, customer_id, json_extract(items, '$[0].name') AS first_item
-- FROM read_json_auto('/path/to/orders.json', multi_line := true);

-- 实际示例：结合其他数据源
SELECT 
  o.order_id,
  o.customer_id,
  o.total,
  json_extract_scalar(o.items_json, '$[0].name') AS featured_item,
  json_array_length(o.items_json) AS item_count
FROM orders o
WHERE o.status = 'completed'
ORDER BY o.total DESC;

进阶技巧：JSON 聚合与分组

-- 统计每个客户的购买商品类别分布
WITH order_items AS (
  SELECT 
    o.customer_id,
    o.order_id,
    j.item_name,
    j.price,
    j.qty
  FROM orders o
  JOIN LATERAL (
    SELECT 
      item_name,
      price::INT AS price,
      qty::INT AS qty
    FROM json_table(
      o.items_json, 
      '$.*' COLUMNS (
        item_name VARCHAR PATH '$.name',
        price INT PATH '$.price',
        qty INT PATH '$.qty'
      )
    )
  ) AS j
)
SELECT 
  customer_id,
  item_name,
  SUM(qty) AS total_qty,
  SUM(price * qty) AS total_amount,
  COUNT(DISTINCT order_id) AS ordered_in_orders
FROM order_items
GROUP BY customer_id, item_name
ORDER BY customer_id, total_amount DESC;

运行结果：

customer_id	item_name	total_qty	total_amount	ordered_in_orders
C001	Laptop	1	1500	1
C001	Mouse	2	50	1
C001	Headphones	1	150	1
C001	Charger	1	30	1
C002	Phone	1	800	1
C002	Monitor	1	300	1
C002	Keyboard	1	80	1
C003	Tablet	2	800	1

性能最佳实践

使用 read_json_auto 自动推断模式：对于已知结构的 JSON 文件，此函数会自动推断列类型，比手动定义 schema 更快。
批量处理大文件：对于大型 JSON 文件，使用 limit_rows 参数分批处理，避免内存溢出。
索引化常用路径：如果频繁访问相同的 JSON 路径，考虑将提取后的结果物化到普通表中。
使用 json_table 代替循环展开：json_table 是集合返回函数，比逐行处理 JSON 数组更高效。
内存调优：在处理超大 JSON 文件时，调整 DuckDB 的 memory_limit 配置参数。

终端输出截图

图：DuckDB JSON 处理的 SQL 执行示例终端输出

总结

本文通过四个实际业务场景，系统讲解了 DuckDB 中 JSON 数据处理的核心理念和方法：

read_json / read_json_auto：直接从 JSON 文件加载数据，无需预处理
json_extract：提取任意 JSON 路径的值，返回 JSON 对象
json_extract_scalar：提取标量值，返回 TEXT，适合简单查询
json_table：将 JSON 数组或对象展开为关系表，是最强大的工具
json_array_length / json_array_elements：获取数组长度和遍历数组元素
嵌套结构处理：多层嵌套 JSON 可以通过组合上述函数逐步展开

掌握这些技能后，你可以轻松应对各种复杂 JSON 数据结构，让 DuckDB 成为你处理非结构化数据的强大武器。

更多 DuckDB 实战技巧，请关注 DuckDB Lab（duckdblab.org）

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