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a| *Classes* + <<ref_array>> + <<ref_basic_parser>> + <<ref_key_value_pair>> + <<ref_monotonic_resource>> + <<ref_object>> + <<ref_parser>> + <<ref_parse_options>> + <<ref_serializer>> + <<ref_serialize_options>> + <<ref_set_pointer_options>> + <<ref_static_resource>> + <<ref_storage_ptr>> + <<ref_stream_parser>> + <<ref_string>> + <<ref_value>> + <<ref_value_ref>> + <<ref_value_stack>>
a| *Classes* + <<ref_array>> + <类* + &lt;<ref_array>&gt; + &lt;<ref_basic_parser>> + <&gt; + &lt;<ref_key_value_pair>> + <&gt; + &lt;<ref_monotonic_resource>> + <&gt; + &lt;<ref_object>> + <&gt; + &lt;<ref_parser>> + <&gt; + &lt;<ref_parse_options>> + <&gt; + &lt;<ref_serializer>> + <&gt; + &lt;<ref_serialize_options>> + <&gt; + &lt;<ref_set_pointer_options>> + <&gt; + &lt;<ref_static_resource>> + <&gt; + &lt;<ref_storage_ptr>> + <&gt; + &lt;<ref_stream_parser>> + <&gt; + &lt;<ref_string>> + <<ref_value>> + <<ref_value_ref>> + <<ref_value_stack>&gt; + &lt;<ref_value>&gt; + &lt;<ref_value_ref>&gt; + &lt;<ref_value_stack>&gt;</ref_value_stack></ref_value_ref></ref_value></ref_string></ref_stream_parser></ref_storage_ptr></ref_static_resource></ref_set_pointer_options></ref_serialize_options></ref_serializer></ref_parse_options></ref_parser></ref_object></ref_monotonic_resource></ref_key_value_pair></ref_basic_parser></ref_array>
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4+|JSON
4+|JSON
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= Reference
= Reference参考
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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:
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）：
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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:
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：
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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:
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>
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The library knows what to do with standard containers. Here we convert an array of customers to a value:
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：
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This allows us to use the library function <<ref_value_from>> to produce a <<ref_value>> from our type:
This allows us to use the library function <<ref_value_from>> to produce a <<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 our type:>
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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:
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` to a映射为 JSON object:对象：</ref_value>
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Given a user-defined type:
Given a user-defined type:给定一个用户定义的类型：
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Value Conversion
Value Conversion值转换
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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:
The serializer in this library implements a本库中的序列化器实现了 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:（流式算法）；它可逐段输出 JSON，无需一次性分配整个输出空间：
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Simple free functions are provided for serializing a <<ref_value>> to a {std_string} containing JSON:
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>
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Serializing
Serializing序列化
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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.
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。
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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.
The parser in this library implements a本库中的解析器实现了一种 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.（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。
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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:
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：
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Error codes are also possible:
Error codes are also possible:也支持错误码：
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