Exit Zen
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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:
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floating-point coordinate pairs.
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The default algorithm that the library uses to parse numbers is fast, but may result in slight precision loss. This may not be suitable for some applications, so there is an option to enable an alternative algorithm that doesn't have that flaw, but is somewhat slower. To do this, you also need to use <<ref_parse_options>> structure.
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Note that full precision number parsing requires the algorithm to see the full number. This means, that when used with <<ref_stream_parser>>, additional memory allocations may be necessary to store the number parts which were so far accepted by the parser. The library does try its best to avoid such allocations.
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Type satisfying <<ref_is_map_like>>.
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Instances of <<ref_parser>> and <<ref_stream_parser>> offer functionality beyond what is available when using the <<ref_parse>> free functions:
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The result is an <<ref_object>>.
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Type satisfying <<ref_is_sequence_like>>.
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Visit https://boost.org/libs/json for complete documentation.
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The default memory resource uses the global `operator new` and `operator delete` to allocate memory. This resource is not reference counted and has a non-trivial deallocate function. All default-constructed <<ref_storage_ptr>> objects reference the same memory resource:
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The parser implementation uses temporary storage space to accumulate values during parsing. When using the <<ref_parse>> free functions, this storage is allocated and freed in each call. However, by declaring an instance of <<ref_parser>> or <<ref_stream_parser>>, this temporary storage can be reused when parsing more than one JSON text, reducing the total number of dynamic memory allocations.
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The result is an <<ref_array>>.
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To use the <<ref_parser>>, declare an instance. Then call <<ref_parser_write>> once with the buffer containing representing the input JSON. Finally, call <<ref_parser_release>> to take ownership of the resulting <<ref_value>> upon success. This example persists the parser instance in a class member to reuse across calls:
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Type satisfying <<ref_is_tuple_like>>.
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Sometimes a protocol may have a JSON text followed by data that is in a different format or specification. The JSON portion can still be parsed by using the function <<ref_parser_write_some>>. Upon success, the return value will indicate the number of characters consumed from the input, which will exclude the non-JSON characters:
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The <<ref_monotonic_resource>> optimizes this memory allocation pattern by allocating increasingly large blocks of global memory internally and parceling those blocks out in smaller pieces to fulfill allocation requests. It has a trivial deallocate function. The monotonic resource does not actually deallocate memory until the resource is destroyed. Thus, it is ideally suited for the use-case where JSON is parsed, and the resulting value is then inspected but not modified.
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The <<ref_stream_parser>> implements a https://en.wikipedia.org/wiki/Online_algorithm[__streaming algorithm__]; it allows incremental processing of large JSON inputs using one or more contiguous character buffers. The entire input JSON does not need to be loaded into memory at once. A network server can use the streaming interface to process incoming JSON in fixed-size amounts, providing these benefits:
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The result is an <<ref_object>> with described members' names as keys.
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The resource to use when constructing values may be specified in calls to <<ref_parse>> as shown here:
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The result is default-constructed and described members are assigned
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