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iterate_many

When serializing large databases, it is often better to write out many independent JSON documents, instead of one large monolithic document containing many records. The simdjson library provides high-speed access to files or streams containing multiple small JSON documents separated by ASCII white-space characters. Given an input such as

{"text":"a"}
{"text":"b"}
{"text":"c"}
"..."

... you want to read the entries (individual JSON documents) as quickly and as conveniently as possible. Importantly, the input might span several gigabytes, but you want to use a small (fixed) amount of memory. Ideally, you'd also like the parallelize the processing (using more than one core) to speed up the process.

Contents

Motivation

The main motivation for this piece of software is to achieve maximum speed and offer a better quality of life in parsing files containing multiple small JSON documents.

The JavaScript Object Notation (JSON) RFC7159 is a handy serialization format. However, when serializing a large sequence of values as an array, or a possibly indeterminate-length or never- ending sequence of values, JSON may be inconvenient.

Consider a sequence of one million values, each possibly one kilobyte when encoded -- roughly one gigabyte. It is often desirable to process such a dataset incrementally without having to first read all of it before beginning to produce results.

How it works

Context

Before parsing anything, simdjson first preprocesses the JSON text by identifying all structural indexes (i.e. the starting position of any JSON value, as well as any important operators like ,, :, ] or }) and validating UTF8. This stage is referred to stage 1. However, during this process, simdjson has no knowledge of whether parsed a valid document, multiple documents, or even if the document is complete. Then, to iterate through the JSON text during parsing, we use what we call a JSON iterator that will navigate through the text using these structural indexes. This JSON iterator is not visible though, but it is the key component to make parsing work.

Prior to iterate_many, most people who had to parse a multiline JSON file would proceed by reading the file line by line, using a utility function like std::getline or equivalent, and would then use the parse on each of those lines. From a performance point of view, this process is highly inefficient, in that it requires a lot of unnecessary memory allocation and makes use of the getline function, which is fundamentally slow, slower than the act of parsing with simdjson (more on this here).

Unlike the popular parser RapidJson, our DOM does not require the buffer once the parsing job is completed, the DOM and the buffer are completely independent. The drawback of this architecture is that we need to allocate some additional memory to store our ParsedJson data, for every document inside a given file. Memory allocation can be slow and become a bottleneck, therefore, we want to minimize it as much as possible.

Design

To achieve a minimum amount of allocations, we opted for a design where we create only one parser object and therefore allocate its memory once, and then recycle it for every document in a given file. But, knowing that they often have largely varying size, we need to make sure that we allocate enough memory so that all the documents can fit. This value is what we call the batch size. As of right now, we need to manually specify a value for this batch size, it has to be at least as big as the biggest document in your file, but not too big so that it submerges the cached memory. The bigger the batch size, the fewer we need to make allocations. We found that 1MB is somewhat a sweet spot.

  1. When the user calls iterate_many, we return a document_stream which the user can iterate over to receive parsed documents.
  2. We call stage 1 on the first batch_size bytes of JSON in the buffer, detecting structural indexes for all documents in that batch.
  3. The document_stream owns a document instance that keeps track of the current document position in the stream using a JSON iterator. To obtain a valid document, the document_stream returns a reference to its document instance.
  4. Each time the user calls ++ to read the next document, the JSON iterator moves to the start the next document.
  5. When we reach the end of the batch, we call stage 1 on the next batch, starting from the end of the last document, and go to step 3.

Threads

But how can we make use of threads if they are available? We found a pretty cool algorithm that allows us to quickly identify the position of the last JSON document in a given batch. Knowing exactly where the end of the last document in the batch is, we can safely parse through the last document without any worries that it might be incomplete. Therefore, we can run stage 1 on the next batch concurrently while parsing the documents in the current batch. Running stage 1 in a different thread can, in best cases, remove almost entirely its cost and replaces it by the overhead of a thread, which is orders of magnitude cheaper. Ain't that awesome!

Thread support is only active if thread supported is detected in which case the macro SIMDJSON_THREADS_ENABLED is set. You can also manually pass SIMDJSON_THREADS_ENABLED=1 flag to the library. Otherwise the library runs in single-thread mode.

You should be consistent. If you link against the simdjson library built for multithreading (i.e., with SIMDJSON_THREADS_ENABLED), then you should build your application with multithreading system (setting SIMDJSON_THREADS_ENABLED=1 and linking against a thread library).

A document_stream instance uses at most two threads: there is a main thread and a worker thread.

Support

Since we want to offer flexibility and not restrict ourselves to a specific file format, we support any file that contains any amount of valid JSON document, separated by one or more character that is considered whitespace by the JSON spec. Anything that is not whitespace will be parsed as a JSON document and could lead to failure.

Whitespace Characters:

  • Space
  • Linefeed
  • Carriage return
  • Horizontal tab

If your documents are all objects or arrays, then you may even have nothing between them. E.g., [1,2]{"32":1} is recognized as two documents.

Some official formats (non-exhaustive list):

API

Example:

//  R"( ... )" is a C++ raw string literal.
auto json = R"({ "foo": 1 } { "foo": 2 } { "foo": 3 } )"_padded;
// _padded returns an simdjson::padded_string instance
ondemand::parser parser;
ondemand::document_stream docs = parser.iterate_many(json);
for (auto doc : docs) {
  std::cout << doc["foo"] << std::endl;
}
// Prints 1 2 3

See basics.md for an overview of the API.

Streaming directly from a memory-mapped file

When your input is a large NDJSON / JSON-lines file on disk, the most efficient way to feed iterate_many is to use simdjson::padded_memory_map. It returns a padded_string_view with the right amount of trailing padding, so you can hand it straight to iterate_many without ever copying the file contents into your own buffer.

padded_memory_map is available on POSIX systems (Linux, macOS, BSD, ...) by default. On Windows it is an opt-in feature with the following requirements:

  1. Build simdjson with -DSIMDJSON_ENABLE_MEMORY_FILE_MAPPING_ON_WINDOWS=ON, or — if you consume simdjson as a pre-built library — define SIMDJSON_ENABLE_MEMORY_FILE_MAPPING_ON_WINDOWS=1, raise NTDDI_VERSION to at least NTDDI_WIN10_RS4 (0x0A000005, Windows 10 version 1803), and add onecore.lib to your link line yourself. The Windows implementation uses the modern memory APIs CreateFileMapping2 / MapViewOfFile3, which are available starting with that version of Windows and are exported by onecore.lib.
  2. #include <windows.h> before #include "simdjson.h" in every translation unit where you want to use padded_memory_map. simdjson deliberately does not pull in <windows.h> itself, so the class is only declared when the Win32 types are already visible.

If either requirement is not met on Windows, the padded_memory_map class is not declared at all and any code that references it fails to compile with an "unknown identifier" error. The availability of the class can be tested with the macro SIMDJSON_HAS_PADDED_MEMORY_MAP.

On POSIX, padded_memory_map uses mmap to map the file directly into memory with zero copies. On Windows (when enabled), it uses CreateFileMapping2 + MapViewOfFile3 for true zero-copy mapping whenever the file does not end within SIMDJSON_PADDING bytes of a page boundary; for those rare cases, it transparently falls back to reading the file into a heap-allocated padded buffer so that the returned view always has SIMDJSON_PADDING accessible zero bytes after the file content.

#ifdef _WIN32
#include <windows.h> // Must come BEFORE <simdjson.h> on Windows
#endif
#include "simdjson.h"

// ...

simdjson::padded_memory_map map("huge_stream.ndjson");
if (!map.is_valid()) { /* file missing, unreadable, too large, ... */ return; }

simdjson::ondemand::parser parser;
simdjson::ondemand::document_stream stream;
auto error = parser.iterate_many(map.view()).get(stream);
if (error) { std::cerr << error << std::endl; return; }

for (auto doc : stream) {
  // process each JSON document in the stream
  std::cout << doc << std::endl;
}

Important lifetime rule: the padded_string_view returned by map.view() is only valid while the padded_memory_map instance is alive, so keep map alive for as long as you are iterating the stream.

The file must not be modified while the memory map is in use. If you need a fully independent copy of the data, use simdjson::padded_string::load(...) instead.

If you prefer single-document parsing on a memory-mapped file, the same pattern applies to parser.iterate(...):

simdjson::padded_memory_map map(myfilename);
if (!map.is_valid()) { /* handle error */ }
simdjson::padded_string_view view = map.view(); // view is usable while padded_memory_map is in scope
ondemand::document doc = parser.iterate(view); // parse the JSON

Use cases

From jsonlines.org:

  • Better than CSV

    ["Name", "Session", "Score", "Completed"]
["Gilbert", "2013", 24, true]
["Alexa", "2013", 29, true]
["May", "2012B", 14, false]
["Deloise", "2012A", 19, true]

    CSV seems so easy that many programmers have written code to generate it themselves, and almost every implementation is different. Handling broken CSV files is a common and frustrating task. CSV has no standard encoding, no standard column separator and multiple character escaping standards. String is the only type supported for cell values, so some programs attempt to guess the correct types.

    JSON Lines handles tabular data cleanly and without ambiguity. Cells may use the standard JSON types.

    The biggest missing piece is an import/export filter for popular spreadsheet programs so that non-programmers can use this format.

  • Easy Nested Data

    {"name": "Gilbert", "wins": [["straight", "7♣"], ["one pair", "10♥"]]}
{"name": "Alexa", "wins": [["two pair", "4♠"], ["two pair", "9♠"]]}
{"name": "May", "wins": []}
{"name": "Deloise", "wins": [["three of a kind", "5♣"]]}

    JSON Lines' biggest strength is in handling lots of similar nested data structures. One .jsonl file is easier to work with than a directory full of XML files.

Tracking your position

Some users would like to know where the document they parsed is in the input array of bytes. It is possible to do so by accessing directly the iterator and calling its current_index() method which reports the location (in bytes) of the current document in the input stream. You may also call the source() method to get a std::string_view instance on the document and error() to check if there were any error.

Let us illustrate the idea with code:

    auto json = R"([1,2,3]  {"1":1,"2":3,"4":4} [1,2,3]  )"_padded;
    simdjson::ondemand::parser parser;
    simdjson::ondemand::document_stream stream;
    auto error = parser.iterate_many(json).get(stream);
    if (error) { /* do something */ }
    auto i = stream.begin();
	 size_t count{0};
    for(; i != stream.end(); ++i) {
        auto doc = *i;
        if (!i.error()) {
          std::cout << "got full document at " << i.current_index() << std::endl;
          std::cout << i.source() << std::endl;
          count++;
        } else {
          std::cout << "got broken document at " << i.current_index() << std::endl;
          return false;
        }
    }

This code will print:

got full document at 0
[1,2,3]
got full document at 9
{"1":1,"2":3,"4":4}
got full document at 29
[1,2,3]

Incomplete streams

Some users may need to work with truncated streams. The simdjson may truncate documents at the very end of the stream that cannot possibly be valid JSON (e.g., they contain unclosed strings, unmatched brackets, unmatched braces). After iterating through the stream, you may query the truncated_bytes() method which tells you how many bytes were truncated. If the stream is made of full (whole) documents, then you should expect truncated_bytes() to return zero.

Consider the following example where a truncated document ({"key":"intentionally unclosed string ) containing 39 bytes has been left within the stream. In such cases, the first two whole documents are parsed and returned, and the truncated_bytes() method returns 39.

    auto json = R"([1,2,3]  {"1":1,"2":3,"4":4} {"key":"intentionally unclosed string  )"_padded;
    simdjson::ondemand::parser parser;
    simdjson::ondemand::document_stream stream;
    auto error = parser.iterate_many(json,json.size()).get(stream);
    if (error) { std::cerr << error << std::endl; return; }
    for(auto i = stream.begin(); i != stream.end(); ++i) {
       std::cout << i.source() << std::endl;
    }
    std::cout << stream.truncated_bytes() << " bytes "<< std::endl; // returns 39 bytes

This will print:

[1,2,3]
{"1":1,"2":3,"4":4}
39 bytes

Importantly, you should only call truncated_bytes() after iterating through all of the documents since the stream cannot tell whether there are truncated documents at the very end when it may not have accessed that part of the data yet.

Comma-separated documents

To parse comma-separated documents like {"a":1},{"b":2},{"c":3}, use the stream_format::comma_delimited parameter:

auto json = R"({"a":1},{"b":2},{"c":3})"_padded;
ondemand::parser parser;
ondemand::document_stream stream;
auto error = parser.iterate_many(json, ondemand::DEFAULT_BATCH_SIZE,
                                 simdjson::stream_format::comma_delimited).get(stream);
if (error) { std::cerr << error << std::endl; return; }
for (auto doc : stream) {
    std::cout << doc << std::endl;
}
// Prints: {"a":1}
//         {"b":2}
//         {"c":3}

Whitespace around the commas is allowed:

auto json = R"({"a":1} , {"b":2} , {"c":3})"_padded;  // Also works

Nested commas inside objects and arrays are preserved:

auto json = R"({"arr":[1,2,3]},{"obj":{"x":1,"y":2}})"_padded;
// Correctly parses as 2 documents, not 6

Mixed document types are supported:

auto json = R"(1, 2, 3, 4, "a", "b", "c", {"hello": "world"}, [1, 2, 3])"_padded;
ondemand::parser parser;
ondemand::document_stream doc_stream;
auto error = parser.iterate_many(json, ondemand::DEFAULT_BATCH_SIZE,
                                 simdjson::stream_format::comma_delimited).get(doc_stream);
if (error) { std::cerr << error << std::endl; return; }
for (auto doc : doc_stream) {
    std::cout << doc.type() << std::endl;
}
// Prints: number number number number string string string object array

Extra top-level separators are tolerated for compatibility with the legacy allow_comma_separated behavior. For example, leading commas, trailing commas, and repeated commas are treated as empty separators rather than documents.

Legacy allow_comma_separated parameter (deprecated)

The allow_comma_separated boolean parameter is deprecated. When set to true, it now internally maps to stream_format::comma_delimited.

The old single-batch limitation no longer applies - comma-delimited parsing now supports multi-batch processing and threading for optimal performance on large files.

JSON Text Sequences (RFC 7464)

RFC 7464 defines a format for streaming JSON values using ASCII Record Separator (RS, 0x1E) as a delimiter. Each JSON text is preceded by RS and optionally followed by ASCII Line Feed (LF, 0x0A).

Example input:

<RS>{"name":"doc1"}<LF>
<RS>{"name":"doc2"}<LF>
<RS>{"name":"doc3"}<LF>

To parse JSON text sequences, use the stream_format::json_sequence parameter:

// Build input with RS (0x1E) and LF (0x0A) delimiters
std::string input_str;
input_str += '\x1e'; input_str += "{\"a\":1}"; input_str += '\x0a';
input_str += '\x1e'; input_str += "{\"b\":2}"; input_str += '\x0a';
input_str += '\x1e'; input_str += "{\"c\":3}"; input_str += '\x0a';
simdjson::padded_string input(input_str);

ondemand::parser parser;
ondemand::document_stream stream;
auto error = parser.iterate_many(input, ondemand::DEFAULT_BATCH_SIZE,
                                 simdjson::stream_format::json_sequence).get(stream);
if (error) { std::cerr << error << std::endl; return; }
for (auto doc : stream) {
    std::cout << doc << std::endl;
}

The stream_format enum has the following values:

  • stream_format::whitespace_delimited (default): Standard NDJSON/JSON Lines format
  • stream_format::json_sequence: RFC 7464 format with RS delimiters
  • stream_format::comma_delimited: Comma-separated JSON documents
  • stream_format::comma_delimited_array: A single JSON array whose elements are iterated as comma-delimited documents (see below)

The trailing LF after each JSON text is optional but recommended by the RFC for robustness.

JSON Array As A Document Stream

Sometimes an input is a single, well-formed JSON array — [{"a":1},{"b":2},{"c":3}] — but you want to iterate its elements one at a time without materializing the whole array. Use stream_format::comma_delimited_array:

auto json = R"([{"a":1},{"b":2},{"c":3}])"_padded;
ondemand::parser parser;
ondemand::document_stream stream;
auto error = parser.iterate_many(json, ondemand::DEFAULT_BATCH_SIZE,
                                 simdjson::stream_format::comma_delimited_array).get(stream);
if (error) { std::cerr << error << std::endl; return; }
for (auto doc : stream) {
    std::cout << doc << std::endl;
}
// Prints: {"a":1}
//         {"b":2}
//         {"c":3}

The parser strips the outer [ and ] plus any surrounding JSON whitespace (space, tab, LF, CR) and then behaves exactly like stream_format::comma_delimited over the remaining bytes. All comma-delimited features are inherited: multi-batch processing, threading, mixed scalar types, and nested commas preserved inside inner objects and arrays.

// All of these work:
auto a = R"([1, "x", true, null, {"k":"v"}, [1,2]])"_padded;  // mixed scalars
auto b = R"(  [ 1, 2, 3 ] )"_padded;                          // whitespace
auto c = R"([])"_padded;                                      // empty array → 0 docs

If the input is not a well-formed outer array (missing [, missing ], or empty / all-whitespace), iterate_many returns TAPE_ERROR. Content inside the array is not validated up front — individual document parse errors surface when you iterate, just like comma_delimited.

Positions reported via current_index() are relative to the stripped buffer (the bytes between [ and ]), not the original input, for consistency with the existing BOM-stripping behavior.

C++20 features

In C++20, the standard introduced the notion of customization point. A customization point is a function or function object that can be customized for different types. It allows library authors to provide default behavior while giving users the ability to override this behavior for specific types.

A tag_invoke function serves as a mechanism for customization points. It is not directly part of the C++ standard library but is often used in libraries that implement customization points. The tag_invoke function is typically a generic function that takes a tag type and additional arguments. The first argument is usually a tag type (often an empty struct) that uniquely identifies the customization point (e.g., deserialization of custom types in simdjson). Users or library providers can specialize tag_invoke for their types by defining it in the appropriate namespace, often inline namespace.

You can deserialize you own data structures conveniently if your system supports C++20. When it is the case, the macro SIMDJSON_SUPPORTS_CONCEPTS will be set to 1 by the simdjson library.

Consider a custom class Car:

struct Car {
  std::string make;
  std::string model;
  int year;
  std::vector<float> tire_pressure;
};

You may support deserializing directly from a JSON value or document to your own Car instance by defining a single tag_invoke function:

namespace simdjson {
// This tag_invoke MUST be inside simdjson namespace
template <typename simdjson_value>
auto tag_invoke(deserialize_tag, simdjson_value &val, Car& car) {
  ondemand::object obj;
  auto error = val.get_object().get(obj);
  if (error) {
    return error;
  }
  if ((error = obj["make"].get_string(car.make))) {
    return error;
  }
  if ((error = obj["model"].get_string(car.model))) {
    return error;
  }
  if ((error = obj["year"].get(car.year))) {
    return error;
  }
  if ((error = obj["tire_pressure"].get<std::vector<float>>().get(
           car.tire_pressure))) {
    return error;
  }
  return simdjson::SUCCESS;
}
} // namespace simdjson

Importantly, the tag_invoke function must be inside the simdjson namespace. Let us explain each argument of tag_invoke function.

  • simdjson::deserialize_tag: it is the tag for Customization Point Object (CPO). You may often ignore this parameter. It is used to indicate that you mean to provide a deserialization function for simdjson.
  • var: It receives automatically a simdjson value type (document, value, document_reference).
  • The third parameter is an instance of the type that you want to support.

Please see our main documentation (basics.md) under "Use tag_invoke for custom types (C++20)" for details about tag_invoke functions.

Given a stream of JSON documents, you can add them to a data structure such as a std::vector<Car> like so if you support exceptions:

  padded_string json =
      R"( { "make": "Toyota", "model": "Camry",  "year": 2018,
       "tire_pressure": [ 40.1, 39.9 ] }
  { "make": "Kia",    "model": "Soul",   "year": 2012,
       "tire_pressure": [ 30.1, 31.0 ] }
  { "make": "Toyota", "model": "Tercel", "year": 1999,
       "tire_pressure": [ 29.8, 30.0 ] }
)"_padded;
  ondemand::parser parser;
  ondemand::document_stream stream;
  [[maybe_unused]] auto error = parser.iterate_many(json).get(stream);
  std::vector<Car> cars;
  for(auto doc : stream) {
    cars.push_back((Car)doc); // an exception may be thrown
  }

Otherwise you may use this longer version for explicit handling of errors:

  std::vector<Car> cars;
  for(auto doc : stream) {
    Car c;
    if ((error = doc.get<Car>().get(c))) {
      std::cerr << simdjson::error_message(error); << std::endl;
      return EXIT_FAILURE;
    }
    cars.push_back(c);
  }

Performance tip: You will get better performance if you order the attributes (make, model) in the order they appear in the JSON document.

C++26 features (static reflection)

If you have a C++26 compatible compiler with P2996 static reflection support, you can compile the simdjson library with the SIMDJSON_STATIC_REFLECTION macro set to 1. When this is the case, simdjson can deserialize a stream of JSON documents directly into your own structures without writing any tag_invoke function. The library inspects the non-static public members of your type at compile time and produces the parsing code automatically.

#define SIMDJSON_STATIC_REFLECTION 1
#include "simdjson.h"

Consider the same Car structure used in the C++20 example, but without any tag_invoke glue:

struct Car {
  std::string make;
  std::string model;
  int year;
  std::vector<double> tire_pressure;
};

With C++26 static reflection enabled, you can iterate a stream of cars and push them into a std::vector<Car> directly:

auto json = R"( { "make": "Toyota", "model": "Camry",  "year": 2018,
                  "tire_pressure": [ 40.1, 39.9 ] }
                { "make": "Kia",    "model": "Soul",   "year": 2012,
                  "tire_pressure": [ 30.1, 31.0 ] }
                { "make": "Toyota", "model": "Tercel", "year": 1999,
                  "tire_pressure": [ 29.8, 30.0 ] } )"_padded;
ondemand::parser parser;
ondemand::document_stream stream;
auto error = parser.iterate_many(json).get(stream);
if (error) { /* handle error */ }
std::vector<Car> cars;
for (auto doc : stream) {
  Car c;
  if ((error = doc.get<Car>().get(c))) { /* handle error */ }
  cars.push_back(c);
}

This works for every stream_format value supported by iterate_many. The following examples each parse the same three cars, but laid out using a different streaming convention.

Whitespace-delimited (default, NDJSON / JSON Lines)

auto json = R"( { "make": "Toyota", "model": "Camry",  "year": 2018,
                  "tire_pressure": [ 40.1, 39.9 ] }
                { "make": "Kia",    "model": "Soul",   "year": 2012,
                  "tire_pressure": [ 30.1, 31.0 ] }
                { "make": "Toyota", "model": "Tercel", "year": 1999,
                  "tire_pressure": [ 29.8, 30.0 ] } )"_padded;
ondemand::parser parser;
ondemand::document_stream stream;
auto error = parser.iterate_many(json, ondemand::DEFAULT_BATCH_SIZE,
                                 simdjson::stream_format::whitespace_delimited).get(stream);
if (error) { /* handle error */ }
std::vector<Car> cars;
for (auto doc : stream) {
  cars.push_back((Car)doc); // throws on error
}

Comma-delimited documents

auto json = R"( { "make": "Toyota", "model": "Camry",  "year": 2018,
                  "tire_pressure": [ 40.1, 39.9 ] },
                { "make": "Kia",    "model": "Soul",   "year": 2012,
                  "tire_pressure": [ 30.1, 31.0 ] },
                { "make": "Toyota", "model": "Tercel", "year": 1999,
                  "tire_pressure": [ 29.8, 30.0 ] } )"_padded;
ondemand::parser parser;
ondemand::document_stream stream;
auto error = parser.iterate_many(json, ondemand::DEFAULT_BATCH_SIZE,
                                 simdjson::stream_format::comma_delimited).get(stream);
if (error) { /* handle error */ }
std::vector<Car> cars;
for (auto doc : stream) {
  Car c;
  if ((error = doc.get<Car>().get(c))) { /* handle error */ }
  cars.push_back(c);
}

A single JSON array as a stream of documents

When the input is a single JSON array, you can stream its elements one at a time without materializing the entire array as a std::vector upfront:

auto json = R"( [ { "make": "Toyota", "model": "Camry",  "year": 2018,
                    "tire_pressure": [ 40.1, 39.9 ] },
                  { "make": "Kia",    "model": "Soul",   "year": 2012,
                    "tire_pressure": [ 30.1, 31.0 ] },
                  { "make": "Toyota", "model": "Tercel", "year": 1999,
                    "tire_pressure": [ 29.8, 30.0 ] } ] )"_padded;
ondemand::parser parser;
ondemand::document_stream stream;
auto error = parser.iterate_many(json, ondemand::DEFAULT_BATCH_SIZE,
                                 simdjson::stream_format::comma_delimited_array).get(stream);
if (error) { /* handle error */ }
std::vector<Car> cars;
for (auto doc : stream) {
  Car c;
  if ((error = doc.get<Car>().get(c))) { /* handle error */ }
  cars.push_back(c);
}

JSON Text Sequences (RFC 7464)

// Build input with RS (0x1E) and LF (0x0A) delimiters
std::string input_str;
auto append = [&](std::string_view doc) {
  input_str += '\x1e'; input_str += doc; input_str += '\x0a';
};
append(R"({ "make": "Toyota", "model": "Camry",  "year": 2018, "tire_pressure": [ 40.1, 39.9 ] })");
append(R"({ "make": "Kia",    "model": "Soul",   "year": 2012, "tire_pressure": [ 30.1, 31.0 ] })");
append(R"({ "make": "Toyota", "model": "Tercel", "year": 1999, "tire_pressure": [ 29.8, 30.0 ] })");
simdjson::padded_string input(input_str);

ondemand::parser parser;
ondemand::document_stream stream;
auto error = parser.iterate_many(input, ondemand::DEFAULT_BATCH_SIZE,
                                 simdjson::stream_format::json_sequence).get(stream);
if (error) { /* handle error */ }
std::vector<Car> cars;
for (auto doc : stream) {
  Car c;
  if ((error = doc.get<Car>().get(c))) { /* handle error */ }
  cars.push_back(c);
}

In every case, the user-defined type (Car here) does not need a hand-written tag_invoke overload: the library generates the deserialization code from the type's public data members at compile time.

Performance tip: You will get better performance if you order the attributes (make, model) in the order they appear in the JSON document.