October 3, 2019

5009 words 24 mins read



Parsing gigabytes of JSON per second

repo name lemire/simdjson
repo link https://github.com/lemire/simdjson
homepage https://arxiv.org/abs/1902.08318
language C++
size (curr.) 8224 kB
stars (curr.) 9121
created 2018-03-23
license Apache License 2.0

simdjson : Parsing gigabytes of JSON per second

Build Status CircleCI Build Status Fuzzing Status

A C++ library to see how fast we can parse JSON with complete validation.

JSON documents are everywhere on the Internet. Servers spend a lot of time parsing these documents. We want to accelerate the parsing of JSON per se using commonly available SIMD instructions as much as possible while doing full validation (including character encoding). This library is part of the Awesome Modern C++ list.

Real-world usage

If you are planning to use simdjson in a product, please work from one of our releases.

Research article (VLDB Journal)

A description of the design and implementation of simdjson is in our research article:

We also have an informal blog post providing some background and context.

Some people enjoy reading our paper:


QCon San Francisco 2019 (best voted talk):

simdjson at QCon San Francisco 2019

Performance results

simdjson uses three-quarters less instructions than state-of-the-art parser RapidJSON and fifty percent less than sajson. To our knowledge, simdjson is the first fully-validating JSON parser to run at gigabytes per second on commodity processors.

On a Skylake processor, the parsing speeds (in GB/s) of various processors on the twitter.json file are as follows.

parser GB/s
simdjson 2.2
RapidJSON encoding-validation 0.51
RapidJSON encoding-validation, insitu 0.71
sajson (insitu, dynamic) 0.70
sajson (insitu, static) 0.97
dropbox 0.14
fastjson 0.26
gason 0.85
ultrajson 0.42
jsmn 0.28
cJSON 0.34
JSON for Modern C++ (nlohmann/json) 0.10


  • We support 64-bit platforms like Linux or macOS, as well as Windows through Visual Studio 2017 or later.
  • A processor with
    • AVX2 (i.e., Intel processors starting with the Haswell microarchitecture released 2013 and AMD processors starting with the Zen microarchitecture released 2017),
    • or SSE 4.2 and CLMUL (i.e., Intel processors going back to Westmere released in 2010 or AMD processors starting with the Jaguar used in the PS4 and XBox One)
    • or a 64-bit ARM processor (ARMv8-A): this covers a wide range of mobile processors, including all Apple processors currently available for sale, going as far back as the iPhone 5s (2013).
  • A recent C++ compiler (e.g., GNU GCC or LLVM CLANG or Visual Studio 2017), we assume C++17. GNU GCC 7 or better or LLVM’s clang 6 or better.
  • Some benchmark scripts assume bash and other common utilities, but they are optional.


This code is made available under the Apache License 2.0.

Under Windows, we build some tools using the windows/dirent_portable.h file (which is outside our library code): it under the liberal (business-friendly) MIT license.

Runtime dispatch

On Intel and AMD processors, we get best performance by using the hardware support for AVX2 instructions. However, simdjson also runs on older Intel and AMD processors. We require a minimum feature support of SSE 4.2 and CLMUL (2010 Intel Westmere or better). The code automatically detects the feature set of your processor and switches to the right function at runtime (a technique sometimes called runtime dispatch).

On x64 hardware, you should typically build your code by specifying the oldest/less-featureful system you want to support so that runtime dispatch may work. The minimum requirement for simdjson is the equivalent of a Westmere processor (SSE 4.2 and PCLMUL). If you build your code by asking the compiler to use more advanced instructions (e.g., -mavx2, /AVX2 or -march=haswell), then it will break runtime dispatch and your binaries will fail to run on older processors.

We also support 64-bit ARM. We assume NEON support. There is no runtime dispatch on ARM.

If you expect your code to run on older processors, you can check that the CPU is supported as follows:

if (simdjson::active_implementation->name() == "unsupported") {
  printf("unsupported CPU\n");

This check is not useful on ARM processors since all 64-bit ARM processors are supported.

It is not necessary to do this check: if you omit it, you will get back the error code UNSUPPORTED_ARCHITECTURE when trying to parse documents. However, you can call simdjson::active_implementation->name() to check which CPU configuration has been detected (e.g., haswell, westmere).

Computed GOTOs

For best performance, we use a technique called “computed goto”, it is also sometimes described as “Labels as Values”. Though it is not part of the C++ standard, it is supported by many major compilers and it brings measurable performance benefits that are difficult to achieve otherwise. The computed gotos are automatically disabled under Visual Studio. If you wish to forcefully disable computed gotos, you can do so by compiling the code with the macro SIMDJSON_NO_COMPUTED_GOTO defined. It is not recommended to disable computed gotos if your compiler supports it. In fact, you should almost never need to be concerned with computed gotos.

Thread safety

The simdjson library is mostly single-threaded. Thread safety is the responsability of the caller: it is unsafe to reuse a document::parser object between different threads.

If you are on an x64 processor, the runtime dispatching assigns the right code path the first time that parsing is attempted. The runtime dispatching is thread-safe.

The json stream parser is threaded, using exactly two threads.

Large files

If you are processing large files (e.g., 100 MB), it is likely that the performance of simdjson will be limited by page misses and/or page allocation. On some systems, memory allocation runs far slower than we can parse (e.g., 1.4GB/s).

You will get best performance with large or huge pages. Under Linux, you can enable transparent huge pages with a command like echo always > /sys/kernel/mm/transparent_hugepage/enabled (root access may be required). We recommend that you report performance numbers with and without huge pages.

Another strategy is to reuse pre-allocated buffers. That is, you avoid reallocating memory. You just allocate memory once and reuse the blocks of memory.

Including simdjson

Code usage and example

The main API involves allocating a document::parser, and calling parser.parse() to create a fully navigable document-object-model (DOM) view of a JSON document. The DOM can be accessed via JSON Pointer paths, or as an iterator (document::iterator(doc)). See ‘Navigating the parsed document’ for more.

All examples below use use #include "simdjson.h", #include "simdjson.cpp" and using namespace simdjson;.

The simplest API to get started is document::parse(), which allocates a new parser, parses a string, and returns the DOM. This is less efficient if you’re going to read multiple documents, but as long as you’re only parsing a single document, this will do just fine.

auto [doc, error] = document::parse(string("[ 1, 2, 3 ]"));
if (error) { cerr << "Error: " << error_message(error) << endl; exit(1); }
cout << doc;

If you’re using exceptions, it gets even simpler (simdjson won’t use exceptions internally, so you’ll only pay the performance cost of exceptions in your own calling code):

document doc = document::parse(string("[ 1, 2, 3 ]"));
cout << doc;

The simdjson library requires SIMDJSON_PADDING extra bytes at the end of a string (it doesn’t matter if the bytes are initialized). The padded_string class is an easy way to ensure this is accomplished up front and prevent the extra allocation:

document doc = document::parse(padded_string(string("[ 1, 2, 3 ]")));
cout << doc;

You can also load from a file with parser.load():

document::parser parser;
cout << parser.load(filename);

Reusing the parser for maximum efficiency

If you’re using simdjson to parse multiple documents, or in a loop, you should make a parser once and reuse it. simdjson will allocate and retain internal buffers between parses, keeping buffers hot in cache and keeping allocation to a minimum.

document::parser parser;
for (padded_string json : { string("[1, 2, 3]"), string("true"), string("[ true, false ]") }) {
  document& doc = parser.parse(json);
  cout << doc;

If you are running a server loop and want to limit the document size to keep server memory constant, you can set a maximum capacity:

document::parser parser(1024*1024); // Set max capacity to 1MB
for (int i=0;i<argc;i++) {
  auto [doc, error] = parser.parse(get_corpus(argv[i]));
  if (error == CAPACITY) { cerr << "JSON files larger than 1MB are not supported!" << endl; exit(1); }
  if (error) { cerr << error << endl; exit(1); }
  cout << doc;

If you want absolutely constant memory usage, you can even allocate the capacity yourself at the beginning:

document::parser parser(0); // This parser is not allowed to auto-allocate
auto alloc_error = parser.set_capacity(1024*1024); // Set initial capacity to 1MB
if (alloc_error) { exit(1); };

for (int i=0;i<argc;i++) {
  auto [doc, error] = parser.parse(get_corpus(argv[i]));
  if (error == CAPACITY) { cerr << "JSON files larger than 1MB are not supported!" << endl; exit(1); }
  if (error) { cerr << error << endl; exit(1); }
  cout << doc;

Newline-Delimited JSON (ndjson) and JSON lines

The simdjson library also support multithreaded JSON streaming through a large file containing many smaller JSON documents in either ndjson or JSON lines format. If your JSON documents all contain arrays or objects, we even support direct file concatenation without whitespace. The concatenated file has no size restrictions (including larger than 4GB), though each individual document must be less than 4GB.

Here is a simple example, using single header simdjson:

#include "simdjson.h"
#include "simdjson.cpp"

int parse_file(const char *filename) {
    simdjson::document::parser parser;
    for (const document &doc : parser.load_many(filename)) {
      // do something with the document ...

Usage: easy single-header version

See the singleheader directory for a single header version. See the included file “amalgamation_demo.cpp” for usage. This requires no specific build system: just copy the files in your project in your include path. You can then include them quite simply:

#include <iostream>
#include "simdjson.h"
#include "simdjson.cpp"
using namespace simdjson;
int main(int argc, char *argv[]) {
  document::parser parser;
  auto [doc, error] = parser.load(argv[1]);
  if(error) {
    std::cout << "not valid: " << error << std::endl;
  } else {
    std::cout << "valid" << std::endl;
  return EXIT_SUCCESS;

Note: In some settings, it might be desirable to precompile simdjson.cpp instead of including it.

Usage (old-school Makefile on platforms like Linux or macOS)

Requirements: recent clang or gcc, and make. We recommend at least GNU GCC/G++ 7 or LLVM clang 6. A 64-bit system like Linux or macOS is expected.

To test:

make test

To run benchmarks:

make parse
./parse jsonexamples/twitter.json

Under Linux, the parse command gives a detailed analysis of the performance counters.

To run comparative benchmarks (with other parsers):

make benchmark

Usage (CMake on 64-bit platforms like Linux or macOS)

Requirements: We require a recent version of cmake. On macOS, the easiest way to install cmake might be to use brew and then type

brew install cmake

There is an equivalent brew on Linux which works the same way as well.

You need a recent compiler like clang or gcc. We recommend at least GNU GCC/G++ 7 or LLVM clang 6. For example, you can install a recent compiler with brew:

brew install gcc@8

Optional: You need to tell cmake which compiler you wish to use by setting the CC and CXX variables. Under bash, you can do so with commands such as export CC=gcc-7 and export CXX=g++-7.

Building: While in the project repository, do the following:

mkdir build
cd build
cmake ..
make test

CMake will build a library. By default, it builds a shared library (e.g., libsimdjson.so on Linux).

You can build a static library:

mkdir buildstatic
cd buildstatic
make test

In some cases, you may want to specify your compiler, especially if the default compiler on your system is too old. You may proceed as follows:

brew install gcc@8
mkdir build
cd build
export CXX=g++-8 CC=gcc-8
cmake ..
make test

Usage (CMake on 64-bit Windows using Visual Studio)

We assume you have a common 64-bit Windows PC with at least Visual Studio 2017 and an x64 processor with AVX2 support (2013 Intel Haswell or later) or SSE 4.2 + CLMUL (2010 Westmere or later).

  • Grab the simdjson code from GitHub, e.g., by cloning it using GitHub Desktop.
  • Install CMake. When you install it, make sure to ask that cmake be made available from the command line. Please choose a recent version of cmake.
  • Create a subdirectory within simdjson, such as VisualStudio.
  • Using a shell, go to this newly created directory.
  • Type cmake -DCMAKE_GENERATOR_PLATFORM=x64 .. in the shell while in the VisualStudio repository. (Alternatively, if you want to build a DLL, you may use the command line cmake -DCMAKE_GENERATOR_PLATFORM=x64 -DSIMDJSON_BUILD_STATIC=OFF ...)
  • This last command (cmake ...) created a Visual Studio solution file in the newly created directory (e.g., simdjson.sln). Open this file in Visual Studio. You should now be able to build the project and run the tests. For example, in the Solution Explorer window (available from the View menu), right-click ALL_BUILD and select Build. To test the code, still in the Solution Explorer window, select RUN_TESTS and select Build.

Usage (Using vcpkg on 64-bit Windows, Linux and macOS)

vcpkg users on Windows, Linux and macOS can download and install simdjson with one single command from their favorite shell.

On 64-bit Linux and macOS:

$ ./vcpkg install simdjson

will build and install simdjson as a static library.

On Windows (64-bit):

.\vcpkg.exe install simdjson:x64-windows

will build and install simdjson as a shared library.

.\vcpkg.exe install simdjson:x64-windows-static  

will build and install simdjson as a static library.

These commands will also print out instructions on how to use the library from MSBuild or CMake-based projects.

If you find the version of simdjson shipped with vcpkg is out-of-date, feel free to report it to vcpkg community either by submitting an issue or by creating a PR.


  • json2json mydoc.json parses the document, constructs a model and then dumps back the result to standard output.
  • json2json -d mydoc.json parses the document, constructs a model and then dumps model (as a tape) to standard output. The tape format is described in the accompanying file tape.md.
  • minify mydoc.json minifies the JSON document, outputting the result to standard output. Minifying means to remove the unneeded white space characters.
  • jsonpointer mydoc.json <jsonpath> <jsonpath> ... <jsonpath> parses the document, constructs a model and then processes a series of JSON Pointer paths. The result is itself a JSON document.


We provide a fast parser, that fully validates an input according to various specifications. The parser builds a useful immutable (read-only) DOM (document-object model) which can be later accessed.

To simplify the engineering, we make some assumptions.

  • We support UTF-8 (and thus ASCII), nothing else (no Latin, no UTF-16). We do not believe this is a genuine limitation, because we do not think there is any serious application that needs to process JSON data without an ASCII or UTF-8 encoding. If the UTF-8 contains a leading BOM, it should be omitted: the user is responsible for detecting and skipping the BOM; UTF-8 BOMs are discouraged.
  • All strings in the JSON document may have up to 4294967295 bytes in UTF-8 (4GB). To enforce this constraint, we refuse to parse a document that contains more than 4294967295 bytes (4GB). This should accommodate most JSON documents.
  • As allowed by the specification, we allow repeated keys within an object (other parsers like sajson do the same).
  • The simdjson library is fast for JSON documents spanning a few bytes up to many megabytes.

We do not aim to provide a general-purpose JSON library. A library like RapidJSON offers much more than just parsing, it helps you generate JSON and offers various other convenient functions. We merely parse the document.


  • The input string is unmodified. (Parsers like sajson and RapidJSON use the input string as a buffer.)
  • We parse integers and floating-point numbers as separate types which allows us to support large signed 64-bit integers in [-9223372036854775808,9223372036854775808), like a Java long or a C/C++ long long and large unsigned integers up to the value 18446744073709551615. Among the parsers that differentiate between integers and floating-point numbers, not all support 64-bit integers. (For example, sajson rejects JSON files with integers larger than or equal to 2147483648. RapidJSON will parse a file containing an overly long integer like 18446744073709551616 as a floating-point number.) When we cannot represent exactly an integer as a signed or unsigned 64-bit value, we reject the JSON document.
  • We support the full range of 64-bit floating-point numbers (binary64). The values range from std::numeric_limits<double>::lowest() to std::numeric_limits<double>::max(), so from -1.7976e308 all the way to 1.7975e308. Extreme values (less or equal to -1e308, greater or equal to 1e308) are rejected: we refuse to parse the input document.
  • We test for accurate float parsing with a perfect (ULP 0) accuracy. Many parsers offer only approximate floating parsing. RapidJSON also offers the option of accurate float parsing (kParseFullPrecisionFlag) but it comes at a significant performance penalty compared to the default settings.
  • We do full UTF-8 validation as part of the parsing. (Parsers like fastjson, gason and dropbox json11 do not do UTF-8 validation. The sajson parser does incomplete UTF-8 validation, accepting code point sequences like 0xb1 0x87.)
  • We fully validate the numbers. (Parsers like gason and ultranjson will accept [0e+] as valid JSON.)
  • We validate string content for unescaped characters. (Parsers like fastjson and ultrajson accept unescaped line breaks and tabs in strings.)
  • We fully validate the white-space characters outside of the strings. Parsers like RapidJSON will accept JSON documents with null characters outside of strings.


The parser works in two stages:

  • Stage 1. (Find marks) Identifies quickly structure elements, strings, and so forth. We validate UTF-8 encoding at that stage.
  • Stage 2. (Structure building) Involves constructing a “tree” of sort (materialized as a tape) to navigate through the data. Strings and numbers are parsed at this stage.

JSON Pointer

We can navigate the parsed JSON using JSON Pointers as per the RFC6901 standard.

You can build a tool (jsonpointer) to parse a JSON document and then issue an array of JSON Pointer queries:

make jsonpointer
 ./jsonpointer jsonexamples/small/demo.json /Image/Width /Image/Height /Image/IDs/2
 ./jsonpointer jsonexamples/twitter.json /statuses/0/id /statuses/1/id /statuses/2/id /statuses/3/id /statuses/4/id /statuses/5/id

In C++, given a document::parser, we can move to a node with the move_to method, passing a std::string representing the JSON Pointer query.

From a simdjson::document::parser instance, you can create an iterator (of type simdjson::document::parser::Iterator which is in fact simdjson::document::parser::BasicIterator<DEFAULT_MAX_DEPTH> ) via a constructor:

document::parser::Iterator pjh(parser); // parser is a ParsedJSON

You then have access to the following methods on the resulting simdjson::document::parser::Iterator instance:

  • bool is_ok() const: whether you have a valid iterator, will be false if your parent parsed document::parser is not a valid JSON.
  • size_t get_depth() const: returns the current depth (start at 1 with 0 reserved for the fictitious root node)
  • int8_t get_scope_type() const: a scope is a series of nodes at the same depth, typically it is either an object ({) or an array ([). The root node has type ‘r’.
  • bool move_forward(): move forward in document order
  • uint8_t get_type() const: retrieve the character code of what we’re looking at: [{"slutfn are the possibilities
  • int64_t get_integer() const: get the int64_t value at this node; valid only if get_type() is “l”
  • uint64_t get_unsigned_integer() const: get the value as uint64; valid only if get_type() is “u”
  • const char *get_string() const: get the string value at this node (NULL ended); valid only if get_type() is “, note that tabs, and line endings are escaped in the returned value, return value is valid UTF-8, it may contain NULL chars, get_string_length() determines the true string length.
  • uint32_t get_string_length() const: return the length of the string in bytes
  • double get_double() const: get the double value at this node; valid only if gettype() is “d”
  • bool is_object_or_array() const: self-explanatory
  • bool is_object() const: self-explanatory
  • bool is_array() const: self-explanatory
  • bool is_string() const: self-explanatory
  • bool is_integer() const: self-explanatory
  • bool is_unsigned_integer() const: Returns true if the current type of node is an unsigned integer. You can get its value with get_unsigned_integer(). Only a large value, which is out of range of a 64-bit signed integer, is represented internally as an unsigned node. On the other hand, a typical positive integer, such as 1, 42, or 1000000, is as a signed node. Be aware this function returns false for a signed node.
  • bool is_double() const: self-explanatory
  • bool is_number() const: self-explanatory
  • bool is_true() const: self-explanatory
  • bool is_false() const: self-explanatory
  • bool is_null() const: self-explanatory
  • bool is_number() const: self-explanatory
  • bool move_to_key(const char *key): when at {, go one level deep, looking for a given key, if successful, we are left pointing at the value, if not, we are still pointing at the object ({) (in case of repeated keys, this only finds the first one). We seek the key using C’s strcmp so if your JSON strings contain NULL chars, this would trigger a false positive: if you expect that to be the case, take extra precautions. Furthermore, we do the comparison character-by-character without taking into account Unicode equivalence.
  • bool move_to_key_insensitive(const char *key): as above, but case insensitive lookup
  • bool move_to_key(const char *key, uint32_t length): as above except that the target can contain NULL characters
  • void move_to_value(): when at a key location within an object, this moves to the accompanying, value (located next to it). This is equivalent but much faster than calling next().
  • bool move_to_index(uint32_t index): when at [, go one level deep, and advance to the given index, if successful, we are left pointing at the value,i f not, we are still pointing at the array
  • bool move_to(const char *pointer, uint32_t length): Moves the iterator to the value correspoding to the json pointer. Always search from the root of the document. If successful, we are left pointing at the value, if not, we are still pointing the same value we were pointing before the call. The json pointer follows the rfc6901 standard’s syntax: https://tools.ietf.org/html/rfc6901
  • bool move_to(const std::string &pointer) : same as above but with a std::string parameter
  • bool next(): Within a given scope (series of nodes at the same depth within either an array or an object), we move forward. Thus, given [true, null, {“a”:1}, [1,2]], we would visit true, null, { and [. At the object ({) or at the array ([), you can issue a “down” to visit their content. valid if we’re not at the end of a scope (returns true).
  • bool prev(): Within a given scope (series of nodes at the same depth within either an array or an object), we move backward.
  • bool up(): moves back to either the containing array or object (type { or [) from within a contained scope.
  • bool down(): moves us to start of that deeper scope if it not empty. Thus, given [true, null, {“a”:1}, [1,2]], if we are at the { node, we would move to the “a” node.
  • void to_start_scope(): move us to the start of our current scope, a scope is a series of nodes at the same level
  • void rewind(): repeatedly calls up until we are at the root of the document
  • bool print(std::ostream &os, bool escape_strings = true) const: print the node we are currently pointing at

Here is a code sample to dump back the parsed JSON to a string:

    document::parser::Iterator pjh(parser);
    if (!pjh.is_ok()) {
      std::cerr << " Could not iterate parsed result. " << std::endl;
      return EXIT_FAILURE;
    // where compute_dump is :

void compute_dump(document::parser::Iterator &pjh) {
  if (pjh.is_object()) {
    std::cout << "{";
    if (pjh.down()) {
      pjh.print(std::cout); // must be a string
      std::cout << ":";
      compute_dump(pjh); // let us recurse
      while (pjh.next()) {
        std::cout << ",";
        std::cout << ":";
        compute_dump(pjh); // let us recurse
    std::cout << "}";
  } else if (pjh.is_array()) {
    std::cout << "[";
    if (pjh.down()) {
      compute_dump(pjh); // let us recurse
      while (pjh.next()) {
        std::cout << ",";
        compute_dump(pjh); // let us recurse
    std::cout << "]";
  } else {
    pjh.print(std::cout); // just print the lone value

The following function will find all user.id integers:

void simdjson_scan(std::vector<int64_t> &answer, document::parser::Iterator &i) {
   while(i.move_forward()) {
     if(i.get_scope_type() == '{') {
       bool found_user = (i.get_string_length() == 4) && (memcmp(i.get_string(), "user", 4) == 0);
       if(found_user) {
          if(i.is_object() && i.move_to_key("id",2)) {
            if (i.is_integer()) {

In-depth comparisons

If you want to see how a wide range of parsers validate a given JSON file:

make allparserscheckfile
./allparserscheckfile myfile.json

For performance comparisons:

make parsingcompetition
./parsingcompetition myfile.json

For broader comparisons:

make allparsingcompetition
./allparsingcompetition myfile.json

Both the parsingcompetition and allparsingcompetition tools take a -t flag which produces a table-oriented output that can be conventiently parsed by other tools.


One can run tests and benchmarks using docker. It especially makes sense under Linux. A privileged access may be needed to get performance counters.

git clone https://github.com/lemire/simdjson.git
cd simdjson
docker build -t simdjson .
docker run --privileged -t simdjson

Other programming languages

We distinguish between “bindings” (which just wrap the C++ code) and a port to another programming language (which reimplements everything).

Various References

Inspiring links:

Validating UTF-8 takes no more than 0.7 cycles per byte:

Remarks on JSON parsing

  • The JSON spec defines what a JSON parser is:

    A JSON parser transforms a JSON text into another representation. A JSON parser MUST accept all texts that conform to the JSON grammar. A JSON parser MAY accept non-JSON forms or extensions. An implementation may set limits on the size of texts that it accepts. An implementation may set limits on the maximum depth of nesting. An implementation may set limits on the range and precision of numbers. An implementation may set limits on the length and character contents of strings.

  • JSON is not JavaScript:

    All JSON is Javascript but NOT all Javascript is JSON. So {property:1} is invalid because property does not have double quotes around it. {‘property’:1} is also invalid, because it’s single quoted while the only thing that can placate the JSON specification is double quoting. JSON is even fussy enough that {“property”:.1} is invalid too, because you should have of course written {“property”:0.1}. Also, don’t even think about having comments or semicolons, you guessed it: they’re invalid. (credit:https://github.com/elzr/vim-json)

  • The structural characters are:

    begin-array     =  [ left square bracket
    begin-object    =  { left curly bracket
    end-array       =  ] right square bracket
    end-object      =  } right curly bracket
    name-separator  = : colon
    value-separator = , comma

Pseudo-structural elements

A character is pseudo-structural if and only if:

  1. Not enclosed in quotes, AND
  2. Is a non-whitespace character, AND
  3. Its preceding character is either: (a) a structural character, OR (b) whitespace.

This helps as we redefine some new characters as pseudo-structural such as the characters 1, G, n in the following:

{ “foo” : 1.5, “bar” : 1.5 GEOFF_IS_A_DUMMY bla bla , “baz”, null }

Academic References

  • T.Mühlbauer, W.Rödiger, R.Seilbeck, A.Reiser, A.Kemper, and T.Neumann. Instant loading for main memory databases. PVLDB, 6(14):1702–1713, 2013. (SIMD-based CSV parsing)
  • Mytkowicz, Todd, Madanlal Musuvathi, and Wolfram Schulte. “Data-parallel finite-state machines.” ACM SIGARCH Computer Architecture News. Vol. 42. No. 1. ACM, 2014.
  • Lu, Yifan, et al. “Tree structured data processing on GPUs.” Cloud Computing, Data Science & Engineering-Confluence, 2017 7th International Conference on. IEEE, 2017.
  • Sidhu, Reetinder. “High throughput, tree automata based XML processing using FPGAs.” Field-Programmable Technology (FPT), 2013 International Conference on. IEEE, 2013.
  • Dai, Zefu, Nick Ni, and Jianwen Zhu. “A 1 cycle-per-byte XML parsing accelerator.” Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays. ACM, 2010.
  • Lin, Dan, et al. “Parabix: Boosting the efficiency of text processing on commodity processors.” High Performance Computer Architecture (HPCA), 2012 IEEE 18th International Symposium on. IEEE, 2012. http://parabix.costar.sfu.ca/export/1783/docs/HPCA2012/final_ieee/final.pdf
  • Deshmukh, V. M., and G. R. Bamnote. “An empirical evaluation of optimization parameters in XML parsing for performance enhancement.” Computer, Communication and Control (IC4), 2015 International Conference on. IEEE, 2015.
  • Moussalli, Roger, et al. “Efficient XML Path Filtering Using GPUs.” ADMS@ VLDB. 2011.
  • Jianliang, Ma, et al. “Parallel speculative dom-based XML parser.” High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on. IEEE, 2012.
  • Li, Y., Katsipoulakis, N.R., Chandramouli, B., Goldstein, J. and Kossmann, D., 2017. Mison: a fast JSON parser for data analytics. Proceedings of the VLDB Endowment, 10(10), pp.1118-1129. http://www.vldb.org/pvldb/vol10/p1118-li.pdf
  • Cameron, Robert D., et al. “Parallel scanning with bitstream addition: An xml case study.” European Conference on Parallel Processing. Springer, Berlin, Heidelberg, 2011.
  • Cameron, Robert D., Kenneth S. Herdy, and Dan Lin. “High performance XML parsing using parallel bit stream technology.” Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds. ACM, 2008.
  • Shah, Bhavik, et al. “A data parallel algorithm for XML DOM parsing.” International XML Database Symposium. Springer, Berlin, Heidelberg, 2009.
  • Cameron, Robert D., and Dan Lin. “Architectural support for SWAR text processing with parallel bit streams: the inductive doubling principle.” ACM Sigplan Notices. Vol. 44. No. 3. ACM, 2009.
  • Amagasa, Toshiyuki, Mana Seino, and Hiroyuki Kitagawa. “Energy-Efficient XML Stream Processing through Element-Skipping Parsing.” Database and Expert Systems Applications (DEXA), 2013 24th International Workshop on. IEEE, 2013.
  • Medforth, Nigel Woodland. “icXML: Accelerating Xerces-C 3.1. 1 using the Parabix Framework.” (2013).
  • Zhang, Qiang Scott. Embedding Parallel Bit Stream Technology Into Expat. Diss. Simon Fraser University, 2010.
  • Cameron, Robert D., et al. “Fast Regular Expression Matching with Bit-parallel Data Streams.”
  • Lin, Dan. Bits filter: a high-performance multiple string pattern matching algorithm for malware detection. Diss. School of Computing Science-Simon Fraser University, 2010.
  • Yang, Shiyang. Validation of XML Document Based on Parallel Bit Stream Technology. Diss. Applied Sciences: School of Computing Science, 2013.
  • N. Nakasato, “Implementation of a parallel tree method on a GPU”, Journal of Computational Science, vol. 3, no. 3, pp. 132-141, 2012.


The work is supported by the Natural Sciences and Engineering Research Council of Canada under grant number RGPIN-2017-03910.

comments powered by Disqus