Alternative JSON parser for Go (so far fastest)

go get


Alternative JSON parser for Go (so far fastest)

It does not require you to know structure of payload (eg. create structs), and allows accessing fields by providing path to them. So far it is the fastest JSON parser for Go, and it is 3-9 times faster then standard encoding/json package (depending on payload size and usage), and almost do not allocate any memory, see benchmarks below.


Originally i made it for the project which rely to lot of 3-rd party API, sometimes unpredictable and complex. I love simplicity and prefer to avoid external dependecies. encoding/json require you to exactly know your data structures, or if you prefer use map[string]interface{} instead, it will be very slow and hard to manage. I investigated what's on the marked and found that most of libraries are just wrappers around encoding/json, the only package that had own parser is ffjson (and it is awesome), but it still require you to create data structures. Let's be honest JSON is not the hardest format to parse, so i wrote one, which focus on simplicity, performance.


For given JSON our goal is to extract user full name, github followers count and avatar.

import ""


data := []byte(`{
  "person": {
    "name": {
      "first": "Leonid",
      "last": "Bugaev",
      "fullName": "Leonid Bugaev"
    "github": {
      "handle": "buger",
      "followers": 109
    "avatars": [
      { "url": "", "type": "thumbnail" } 
  "company": {
    "name": "Acme"

// Extracting person variable for caching reasons
// Since we have to fetch more keys from it, and do not want parser to analyze whole record each time
person, _, _, _ := jsonparser.Get(data, "person")

// You can specify key path by providing arguments to Get function
jsonparser.Get(data, "name", "fullName")

// There is `GetNumber` and `GetBoolean` helpers if you exactly know key data type
jsonparser.GetNumber(person, "github", "followers")

// When you try to get object, it will return you []byte slice pointer to data containing it
// In `company` it will be `{"name": "Acme"}`
jsonparser.Get(data, "company")

// If key not exists it will throw error
size := 0
if value, _, err := jsonparser.GetNumber(data, "company", "size"); err != nil {
  size = value

// Get always return byte sequence containing key value, if it is array, object or simple value
// You can use `ArrayEach` helper to iterate items
// Underneeth it just calls `Get` until it can't find next item
arr, _, _, _ := jsonparser.Get(person, "gravatar", "avatars")
jsonparser.ArrayEach(arr, func(value []byte, dataType int, offset int, err error) {
    fmt.Println(jsonparser.Get(value, "url"))


Library api is really simple, you need only Get method to perform any operation. Rest it just helpers around it.

You also can view API at


func Get(data []byte, keys ...string) (value []byte, dataType int, offset int, err error)

Receives data structure, and key path to extract value from.


  • value - Pointer to original data structure containing key value, or just empty slice if nothing found or error
  • offset - Offset from provided data structure where key value ends. Used mostly internally, for example for ArrayEach helper.
  • err - If key not found or any other parsing issue it should return error. If key not found it also sets dataType to NOT_EXISTS

Accept multiple keys to specify path to JSON value (in case of quering nested structures). If no keys provided it will try to extract closest JSON value (simple ones or object/array), useful for reading streams or arrays, see ArrayEach implementation.

GetBoolean and GetNumber

func GetBoolean(data []byte, keys ...string) (val bool, offset int, err error)

func GetNumber(data []byte, keys ...string) (val float64, offset int, err error)

If you know key type, you can use helpers above. Returns same arguments as Get except dataType. If key data type do not match, it will return error.


func ArrayEach(data []byte, cb func(value []byte, dataType int, offset int, err error))

Needed for iterating arrays, accepts callback function with same return arguments as Get. Expects to receive array data structure (you need to Get it first). See example above. Underneeth it just calls Get without arguments until it can't find next item.

What makes it so fast?

  • It does not rely on encoding/json, reflection or interface{}, the only real package dependency is bytes.
  • Operates with JSON payload on byte level, providing you pointers to the original data structure: no memory allocation.
  • No automatic type conversions, by default everything is a []byte, but it provide you value type, so you can convert by yourself (there is few helpers included).


There is 3 benchmark types, trying to simulate real-life usage for small, medium and large JSON payloads. For each metric, the lower value is better. Values better then standard encoding/json marked as bold text.

Compared libraries:


If you want to skip next sections, winner is jsonparser (obviously benchmarks are biased 😏). It is 3-9 times faster then standard encoding/json package (depending on payload size and usage), and almost infinitely (literally) better in memory consumption because it operate with data on byte level, and provide direct slice pointers. Few allocations you see in benchmarks happen because type conversions.

ffjson goes next and looks really amazing considering that it is almost drop-in replacement for encoding/json.

Small payload

Each test should process 190 byte http log like json record. It should read multiple fields.

Library time/op bytes/op allocs/op
encoding/json struct 6173 880 18
encoding/json interface{} 7901 1521 38
Jeffail/gabs 7836 1649 46
bitly/go-simplejson 8273 2241 36
antonholmquist/jason 20941 7237 101
pquerna/ffjson 3163 624 15
buger/jsonparser 714 4 2

Winners are ffjson and jsonparser, where jsonparser is 8.6x faster then encoding/json and 4.4x faster then ffjson. If you look at memory allocation, jsonparser have no rivals, as it makes no data copy and operate with raw []byte structures and pointers to it.

Medium payload

Each test should process 2.4kb json record (based on Clearbit API). It should read multiple nested fields and 1 array.

Library time/op bytes/op allocs/op
encoding/json struct 53251 1336 29
encoding/json interface{} 60781 10627 215
Jeffail/gabs 71547 11202 235
bitly/go-simplejson 67865 17187 220
antonholmquist/jason 70964 19013 247
pquerna/ffjson 19634 856 20
buger/jsonparser 11442 18 2

Pattern is clear, difference between ffjson and jsonparser in CPU is smaller, but memory consumption difference only grows. gabs, go-simplejson and jason are based on encoding/json and map[string]interface{} and actually only helpers for unstructured JSON, their performance correlate with encoding/json interface{}, and they will skip next round.

Large payload

Each test should process 24kb json record (based on Discourse API) It should read 2 arrays, and for each item in array get few fields. Basically it means processing full JSON file.

Library time/op bytes/op allocs/op
encoding/json struct 602245 8273 307
encoding/json interface{} 941123 215433 3395
pquerna/ffjson 287151 7792 298
buger/jsonparser 193601 120 32

Same patterns as at medium test. Both ffjson and jsonparser have own parsing code, and not depend on encoding/json or interface{}, thats one of the reasons why it so fast.

Questions and support

All bug-reports and suggestions should go though Github Issues. If you have some private questions you can send them directly to me:


  1. Fork it
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Added some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create new Pull Request


All my development happens using Docker, and repo include some Make tasks to simplify development.

  • make build - builds docker image, usually can be called only once
  • make test - run tests
  • make fmt - run go fmt
  • make bench - run benchmarks (if you need to run only single benchmark modify BENCHMARK variable in make file)
  • make profile - runs benchmark and generate 3 files- cpu.out, mem.mprof and benchmark.test binary, which can be used for go tool pprof
  • make bash - enter container (i use it for running go tool pprof above)