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Now we can construct customers from JSON:
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JSON can be parsed into the value container in one step using a free function. In the following snippet, a parse error is indicated by a thrown exception:
可使用自由函数将 JSON 一步解析至值容器。在以下代码片段中，解析错误通过抛出异常指示：
Error codes are also possible:
也支持错误码：
By default, the parser is strict and only accepts JSON compliant with the standard. However this behavior can be relaxed by filling out an options structure enabling one or more extensions. Here we use a static buffer and enable two non-standard extensions:
默认情况下，解析器采用严格模式，仅接受符合标准的 JSON。但可通过填充一个选项结构体来启用一个或多个扩展，从而放宽此行为。以下示例使用静态缓冲区并启用了两个非标准扩展：
The parser in this library implements a https://en.wikipedia.org/wiki/Online_algorithm[__streaming algorithm__]; it can process JSON piece-by-piece, without the requirement that the entire input is available from the start. The parser uses a temporary memory allocation to do its work. If you plan on parsing multiple JSONs, for example in a network server, keeping the same parser instance will allow re-use of this temporary storage, improving performance.
本库中的解析器实现了一种 https://en.wikipedia.org/wiki/Online_algorithm[__streaming algorithm__]（流式算法）；它可逐段处理 JSON，无需从一开始就提供完整输入。解析器在工作时会使用临时内存分配。如果计划解析多个 JSON（例如在网络服务器中），复用同一个解析器实例可以重用该临时存储，从而提升性能。
With strategic use of the right memory resources, parser instance, and calculated upper limits on buffer sizes, it is possible to parse and examine JSON without __any__ dynamic memory allocations. This is explored in more detail in a later section.
通过合理使用适当的内存资源、解析器实例及缓冲区大小的计算上限，可在不进行__任何__动态内存分配的情况下解析和检查 JSON。后续章节将对此进行更详细探讨。
Serializing
序列化
Simple free functions are provided for serializing a <<ref_value>> to a {std_string} containing JSON:
提供了简单的自由函数，用于将 &lt;<ref_value>&gt; 序列化为包含 JSON 的 {std_string}：</ref_value>
The serializer in this library implements a https://en.wikipedia.org/wiki/Online_algorithm[__streaming algorithm__]; it can output JSON a piece at a time, without the requirement that the entire output area is allocated at once:
本库中的序列化器实现了 https://en.wikipedia.org/wiki/Online_algorithm[__streaming algorithm__]（流式算法）；它可逐段输出 JSON，无需一次性分配整个输出空间：
Value Conversion
值转换
Given a user-defined type:
给定一个用户定义的类型：
We can define a conversion from the user-defined type to a <<ref_value>> by defining an overload of `tag_invoke` in the same namespace. This maps `customer` to a JSON object:
我们可在同一命名空间中定义 `tag_invoke` 的重载，来实现从用户定义类型到 &lt;<ref_value>&gt; 的转换。此操作将 `customer` 映射为 JSON 对象：</ref_value>
This allows us to use the library function <<ref_value_from>> to produce a <<ref_value>> from our type:
这允许我们可以使用库函数 &lt;<ref_value_from>&gt; 从我们的类型生成一个 &lt;<ref_value>&gt;：</ref_value></ref_value_from>
The library knows what to do with standard containers. Here we convert an array of customers to a value:
该库能够自动处理标准容器。以下我们将一个客户数组转换为一个值：
To go from JSON to a user-defined type we use <<ref_value_to>>, which uses another overload of `tag_invoke`. This converts a JSON value to a `customer`. It throws an exception if the contents of the value do not match what is expected:
为将 JSON 转换为用户定义类型，我们使用 &lt;<ref_value_to>&gt;（它会调用 `tag_invoke` 的另一个重载）。此操作将 JSON 值转换为 `customer`。若值内容不符合预期，则抛出异常：</ref_value_to>
The code above defines the convenience function `extract`, which deduces the types of the struct members. This works, but requires that the struct is {req_DefaultConstructible}. An alternative is to construct the object directly, which is a little more verbose but doesn't require default construction:
上述代码定义了便捷函数 `extract`，它能推导结构体成员的类型。这种方式可行，但要求该结构体是 {req_DefaultConstructible}。另一种方法是直接构造对象，虽然略显冗长，但无需默认构造：
Now we can construct customers from JSON:
现在我们可以从 JSON 构造客户对象（customer）：
The library's generic algorithms recognize when you are converting a <<ref_value>> to a container which resembles an array or object, so if you wanted to turn a JSON array into a vector of customers you might write:
当将 &lt;<ref_value>&gt; 转换为类似数组或对象的容器时，库的通用算法可自动识别。因此若要将 JSON 数组转换为客户向量，可编写如下代码：</ref_value>
English Chinese (Simplified Han script) Actions
When a <<ref_value>>, <<ref_array>>, or <<ref_object>> is assigned or constructed from an initializer list, the creation of the new value happens only once. This makes initializer lists equally efficient as using the other ways to create a value. The types in this library are first-class, supporting copy and move construction and assignment:
当 &lt;<ref_value>&gt;, &lt;<ref_array>&gt;, 或 &lt;<ref_object>&gt; 通过初始化列表进行赋值或构造时，新值的创建仅发生一次。这使得初始化列表在效率上与其他创建值的方式相当。本库中的类型均为一等类型，支持复制和移动构造及赋值：</ref_object></ref_array></ref_value>
To permit custom memory allocation strategies, these containers all allow construction with a <<ref_storage_ptr>> which is a smart pointer to a {ref_memory_resource}. The constructor signatures have the same ordering as their `std` equivalents which use {req_Allocator} parameters. Once a container is constructed its memory resource can never change. Here we create an array without performing any dynamic allocations:
为支持自定义内存分配策略，这些容器均允许通过一个 &lt;<ref_storage_ptr>&gt;（指向 {ref_memory_resource} 的智能指针）进行构造。其构造函数签名的参数顺序与使用 {req_Allocator} 参数的标准库等价类型一致。容器一旦构造完成，其内存资源便不可更改。以下代码创建了一个未执行任何动态分配的数组：</ref_storage_ptr>
The containers in this library enforce the invariant that every element of the container will use the same memory resource:
本库中的容器强制保持一个不变式：容器的每个元素都使用相同的内存资源：
When a library type is used as the element type of a PMR container; that is, a container which uses a {ref_polymorphic_allocator}, the memory resource will automatically propagate to the type and all of its children:
当库类型用作 PMR 容器（即使用 {ref_polymorphic_allocator} 的容器）的元素类型时，内存资源将自动传播至该类型及其所有子元素：
Up until now we have shown how values may be constructed from a memory resource pointer, where ownership is not transferred. In this case the caller is responsible for ensuring that the lifetime of the resource is extended for the life of the container. Sometimes you want the container to acquire shared ownership of the resource. This is accomplished with <<ref_make_shared_resource>>:
截至目前，我们已展示如何通过内存资源指针（不转移所有权）构造值。此时调用方需确保资源生命周期覆盖容器的生命周期。有时您希望容器获取资源的共享所有权。这可通过 &lt;<ref_make_shared_resource>&gt; 实现：</ref_make_shared_resource>
A counted memory resource will not be destroyed until every container with shared ownership of the resource is destroyed.
计数内存资源在拥有其共享所有权的每个容器均被销毁之前，是不会被销毁的。
Parsing
解析
JSON can be parsed into the value container in one step using a free function. In the following snippet, a parse error is indicated by a thrown exception:
可使用自由函数将 JSON 一步解析至值容器。在以下代码片段中，解析错误通过抛出异常指示：
The parser in this library implements a https://en.wikipedia.org/wiki/Online_algorithm[__streaming algorithm__]; it can process JSON piece-by-piece, without the requirement that the entire input is available from the start. The parser uses a temporary memory allocation to do its work. If you plan on parsing multiple JSONs, for example in a network server, keeping the same parser instance will allow re-use of this temporary storage, improving performance.
本库中的解析器实现了一种 https://en.wikipedia.org/wiki/Online_algorithm[__streaming algorithm__]（流式算法）；它可逐段处理 JSON，无需从一开始就提供完整输入。解析器在工作时会使用临时内存分配。如果计划解析多个 JSON（例如在网络服务器中），复用同一个解析器实例可以重用该临时存储，从而提升性能。
With strategic use of the right memory resources, parser instance, and calculated upper limits on buffer sizes, it is possible to parse and examine JSON without __any__ dynamic memory allocations. This is explored in more detail in a later section.
通过合理使用适当的内存资源、解析器实例及缓冲区大小的计算上限，可在不进行__任何__动态内存分配的情况下解析和检查 JSON。后续章节将对此进行更详细探讨。
Simple free functions are provided for serializing a <<ref_value>> to a {std_string} containing JSON:
提供了简单的自由函数，用于将 &lt;<ref_value>&gt; 序列化为包含 JSON 的 {std_string}：</ref_value>
Given a user-defined type:
给定一个用户定义的类型：
We can define a conversion from the user-defined type to a <<ref_value>> by defining an overload of `tag_invoke` in the same namespace. This maps `customer` to a JSON object:
我们可在同一命名空间中定义 `tag_invoke` 的重载，来实现从用户定义类型到 &lt;<ref_value>&gt; 的转换。此操作将 `customer` 映射为 JSON 对象：</ref_value>
To go from JSON to a user-defined type we use <<ref_value_to>>, which uses another overload of `tag_invoke`. This converts a JSON value to a `customer`. It throws an exception if the contents of the value do not match what is expected:
为将 JSON 转换为用户定义类型，我们使用 &lt;<ref_value_to>&gt;（它会调用 `tag_invoke` 的另一个重载）。此操作将 JSON 值转换为 `customer`。若值内容不符合预期，则抛出异常：</ref_value_to>
The code above defines the convenience function `extract`, which deduces the types of the struct members. This works, but requires that the struct is {req_DefaultConstructible}. An alternative is to construct the object directly, which is a little more verbose but doesn't require default construction:
上述代码定义了便捷函数 `extract`，它能推导结构体成员的类型。这种方式可行，但要求该结构体是 {req_DefaultConstructible}。另一种方法是直接构造对象，虽然略显冗长，但无需默认构造：
Now we can construct customers from JSON:
现在我们可以从 JSON 构造客户对象（customer）：
The library's generic algorithms recognize when you are converting a <<ref_value>> to a container which resembles an array or object, so if you wanted to turn a JSON array into a vector of customers you might write:
当将 &lt;<ref_value>&gt; 转换为类似数组或对象的容器时，库的通用算法可自动识别。因此若要将 JSON 数组转换为客户向量，可编写如下代码：</ref_value>

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Now we can construct customers from JSON:
Now we can construct customers from JSON:现在我们可以从 JSON 构造客户对象（customer）：
06/05/2026
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Now we can construct customers from JSON:
Now we can construct customers from JSON:
06/05/2026
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safe-html, strict-same
Unit identifier
42902
Source string location
:263
String age
06/05/2026
Last updated
06/06/2026
Source string age
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doc/pages/quick_look_zh_Hans.adoc, string 30