Performance¶

Speed was never the primary goal of this library. The design goals page says so plainly: "There are certainly faster JSON libraries out there." Intuitive syntax, trivial integration, and thorough testing came first. If a hard real-time budget or the last percent of throughput matters more than convenience, a faster, more specialized library may be a better fit.

That said, how you use this library still makes a measurable difference. This page collects practical, code-verified techniques for reducing time, memory, and compile-time cost -- without repeating the detailed pages it links to.

Parsing input¶

parse accepts a string, a pair of iterators, a container, a std::istream, or a FILE* (see Parsing). Internally, every input is wrapped in an input adapter, and not all adapters are equally fast.

For inputs backed by contiguous, single-byte memory -- a std::string, a std::vector<char>, a string literal, or a pointer range -- the library uses iterator_input_adapter, wrapped in a raw pointer so the fast paths below apply on every supported standard. This adapter exposes two optimizations the lexer detects at compile time:

it can reconstruct already-consumed input on demand for error messages, instead of copying every character as it is read, and
the lexer can scan ordinary string characters directly out of the buffer, several bytes at a time, rather than one character (and one function call) at a time.

A std::istream (including std::ifstream) or FILE*, by contrast, is read through input_stream_adapter or file_input_adapter, which read one character (or one block, for binary formats) at a time and expose neither optimization -- the lexer falls back to the same byte-at-a-time path it uses for any non-contiguous, general-purpose iterator range. An iterator pair over non-contiguous but random-access storage (e.g. std::deque<char>::iterator) gets the first optimization but not the second, since the byte-scanning fast path additionally requires contiguous storage.

Practically: if the JSON text is already in memory, or small enough to read into memory, prefer passing a std::string, a std::vector<char>, or a pointer range to parse over a std::istream. For a file, that means reading it into a string first and then parsing the string, rather than passing a std::ifstream directly to parse -- the latter never benefits from either optimization:

// gets the contiguous fast paths
std::ifstream f("example.json");
std::string contents((std::istreambuf_iterator<char>(f)), std::istreambuf_iterator<char>());
json j = json::parse(contents);

// does not: input_stream_adapter has no fast path
std::ifstream f2("example.json");
json j2 = json::parse(f2);

For contiguous input with many non-ASCII characters, JSON_USE_SIMDUTF can additionally speed up UTF-8 validation by using the simdutf library instead of the built-in scalar validator; streaming inputs (files, std::istream, wide strings, user-defined adapters) always use the scalar path regardless of this macro.

Large documents¶

Parsing always produces SAX events internally; parse simply feeds them to a consumer that builds a complete basic_json value tree (a DOM) in memory. For documents too large to comfortably hold as a DOM, two alternatives avoid building it:

Implement the SAX interface directly and pass it to sax_parse; only the parts of the input you choose to keep ever become basic_json values.
Pass a parser callback to parse. This still builds a DOM, but the callback can discard finished elements as soon as they are handled, so memory usage stays bounded by one element (plus the unparsed remainder of the input) instead of the whole document -- see the recipe for streaming a large homogeneous array.

If the data is naturally record-oriented, consider JSON Lines instead of one large JSON document: reading and parsing it line by line with std::getline means only one line's value is ever in memory at a time, and a malformed line does not invalidate lines already processed.

Binary formats¶

JSON text is not a compact format. If the data is only exchanged between programs (not read by humans), the binary formats -- BJData, BON8, BSON, CBOR, MessagePack, and UBJSON -- encode the same values more compactly, which reduces both the bytes transferred and, for most of them, the work needed to parse them back. The size comparison on that page, measured against minified JSON for four reference documents, shows the effect varies a lot by document shape: CBOR and MessagePack come out at 50.5% of the minified JSON size for the numeric-array-heavy canada.json, but only around 87-88% for the string-heavy jeopardy.json, where there is less numeric data to encode more compactly. BON8 is the most compact option in that comparison for text-heavy documents (63.5%-87.5%), at the cost of an incomplete serializer (no unsigned integers above int64). Which format -- and whether it is worth the loss of human readability at all -- depends on the actual data; see the comparison tables before choosing one.

Object type: `json` vs. `ordered_json`¶

The default json type stores object keys in a std::map, giving logarithmic-time lookup, insertion, and erasure, at the cost of sorting keys alphabetically rather than preserving insertion order (see Object Order). ordered_json uses nlohmann::ordered_map instead, a std::vector-backed container with no lookup index: every key-based operation is a linear scan, so building an object of n distinct keys costs O(n²) in total -- this applies equally to inserting keys one by one and to parsing an object, since the parser inserts each key as it is read. The measurements on the ordered_map page show this is negligible at typical object sizes (2000 keys: 0.7 ms for json vs. 3.6 ms for ordered_json, a 5x factor) but grows steeply for large, machine-generated objects (16 000 keys: 3.3 ms vs. 181.6 ms, a 54x factor).

If insertion order matters and an object routinely has many thousands of keys, ordered_json's quadratic build cost may not be acceptable. The library's ObjectType template parameter can be set to a different container instead: nlohmann::fifo_map keeps insertion order with a real lookup index (avoiding the quadratic cost), while std::unordered_map, boost::unordered_flat_map, absl::flat_hash_map, and similar hash maps trade insertion order for average-case constant-time lookup (through an adapter, since their template argument order does not match what basic_json expects) -- see Object Order for the full list.

Avoiding copies¶

Move instead of copy. Constructing a basic_json from an existing one is linear in its size for the copy constructor but constant for the move constructor. The same applies to assigning a large std::string, std::vector, or other container into a value: pass it as std::move(x) rather than x whenever x is no longer needed afterwards.
Access without copying. get<T>() returns a copy of the stored value converted to T. When a reference or pointer to the value already stored inside the basic_json is enough, get_ref() and get_ptr() access it directly: both pages state, word for word, "No copies are made." -- at the cost of that reference or pointer becoming invalid once the underlying value changes.
Iterate by reference. basic_json::iterator::operator*() returns a reference (an alias for basic_json&), but a range-based for loop with a by-value loop variable (for (auto el : j)) still copies each element, because plain auto drops the reference. Write for (const auto& el : j) (or auto& for a mutable loop), and use items() the same way when the key is needed too -- its own examples use for (auto& el : j.items()).
Construct in place. emplace_back() (arrays, amortized constant time) and emplace() (objects, logarithmic in the size of the container for json) forward their arguments directly to a basic_json constructor, rather than requiring a temporary value to be constructed and then copied or moved in. push_back() has an rvalue overload (push_back(basic_json&&)) for a value that already exists: j.push_back(std::move(value)) moves it in instead of copying it.
Skip the bounds check when it is redundant. at() and operator[] have the same complexity (constant for a valid array index, logarithmic for an object key in json) -- the difference is that at() additionally checks the key or index and throws if it is invalid, while operator[] does not (see unchecked access and checked access). Prefer operator[] when the surrounding code has already established that the access is valid.
Reserve array capacity. basic_json has no public reserve(), but when building a large array incrementally with a known final size, get_ref() exposes the underlying array_t so it can be reserved directly -- see "reserving array capacity" for the one-line recipe.

Serialization¶

dump() with the default indent = -1 selects "the most compact representation" (word for word from the page); any non-negative indent pretty-prints instead, which is more readable but produces more bytes and more work. dump() builds and returns a complete string_t containing the whole serialization. operator<< writes directly to a std::ostream instead, through the same serializer, but without ever materializing that intermediate string -- so if the destination is a stream (a file, or std::cout), os << j; avoids the allocation and copy that os << j.dump(); would incur for large values.

Diagnostics overhead¶

Two opt-in macros add diagnostic information to exceptions and to every value, at a cost that is only worth paying while it is in use:

JSON_DIAGNOSTICS adds a JSON Pointer to exception messages, pointing at the value that triggered the exception. Quoting the page directly: "enabling this macro increases the size of every JSON value by one pointer and adds some runtime overhead" -- every value gains a parent pointer that has to be kept up to date as the document is built and modified.
JSON_DIAGNOSTIC_POSITIONS adds start_pos() and end_pos(), the byte offsets a value occupied in its parsed input. Quoting the page: "enabling this macro increases the size of every JSON value by two std::size_t fields and adds slight runtime overhead to parsing, copying JSON value objects, and the generation of error messages for exceptions."

Both default to off. Enable them where better diagnostics are worth the overhead (for example, while validating untrusted input, or in a debug build), and keep them off in a release build that does not need them.

Compile time¶

<nlohmann/json_fwd.hpp> forward-declares basic_json, json, ordered_json, json_pointer, and adl_serializer, pulling in only a handful of lightweight standard headers instead of the full json.hpp. A header that only needs to name nlohmann::json -- in a function signature or a class member declaration, for instance -- can include json_fwd.hpp and leave #include <nlohmann/json.hpp> to the source files that actually parse, build, or serialize values, the same way a project would forward-declare any other heavy class to keep it out of widely-included headers:

// my_type.hpp
#include <nlohmann/json_fwd.hpp>

class my_type
{
    nlohmann::json config() const;
};

// my_type.cpp
#include <nlohmann/json.hpp>
#include "my_type.hpp"

nlohmann::json my_type::config() const { /* ... */ }

One caveat: ABI-affecting macros such as JSON_DIAGNOSTICS and JSON_DIAGNOSTIC_POSITIONS are encoded into the library's inline namespace name. Every translation unit -- whether it includes json_fwd.hpp or the full header -- must define them the same way, or linking fails with undefined references instead of a compile error.

If I/O support is not needed at all, JSON_NO_IO excludes <cstdio>, <ios>, <iosfwd>, <istream>, and <ostream> outright and drops the std::istream/FILE* parse overloads and operator<< that depend on them (dump() itself is unaffected, since it only returns a string); it exists for environments where those headers are unavailable (such as Intel SGX), and as a side effect those headers are then never processed by the compiler at all.