Fast JSON encoder/decoder compatible with encoding/json for Go
Fast JSON encoder/decoder compatible with encoding/json for Go
go get github.com/goccy/go-json
Replace import statement from
encoding/jsonto
github.com/goccy/go-json
-import "encoding/json" +import "github.com/goccy/go-json"
| name | encoder | decoder | compatible with
encoding/json| | :----: | :------: | :-----: | :-----------------------------: | | encoding/json | yes | yes | N/A | | json-iterator/go | yes | yes | partial | | easyjson | yes | yes | no | | gojay | yes | yes | no | | segmentio/encoding/json | yes | yes | yes | | jettison | yes | no | no | | simdjson-go | no | yes | no | | goccy/go-json | yes | yes | yes |
json-iterator/goisn't compatible with
encoding/jsonin many ways, but it hasn't been supported for a long time.
$ cd benchmarks $ go test -bench .
go-jsonis very fast in both encoding and decoding compared to other libraries. It's easier to implement by using automatic code generation for performance or by using a dedicated interface, but
go-jsondares to stick to compatibility with
encoding/jsonand is the simple interface. Despite this, we are developing with the aim of being the fastest library.
Here, we explain the various speed-up techniques implemented by
go-json.
The techniques listed here are the ones used by most of the libraries listed above.
Since the only value required for the result of
json.Marshal(interface{}) ([]byte, error)is
[]byte, the only value that must be allocated during encoding is the return value
[]byte.
Also, as the number of allocations increases, the performance will be affected, so the number of allocations should be kept as low as possible when creating
[]byte.
Therefore, there is a technique to reduce the number of times a new buffer must be allocated by reusing the buffer used for the previous encoding by using
sync.Pool.
Finally, you allocate a buffer that is as long as the resulting buffer and copy the contents into it, you only need to allocate the buffer once in theory.
type buffer struct { data []byte }var bufPool = sync.Pool{ New: func() interface{} { return &buffer{data: make([]byte, 0, 1024)} }, }
buf := bufPool.Get().(*buffer) data := encode(buf.data) // reuse buf.data
newBuf := make([]byte, len(data)) copy(newBuf, buf)
buf.data = data bufPool.Put(buf)
As you know, the reflection operation is very slow.
Therefore, using the fact that the address position where the type information is stored is fixed for each binary ( we call this
typeptr), we can use the address in the type information to call a pre-built optimized process.
For example, you can get the address to the type information from
interface{}as follows and you can use that information to call a process that does not have reflection.
To process without reflection, pass a pointer (
unsafe.Pointer) to the value is stored.
type emptyInterface struct { typ unsafe.Pointer ptr unsafe.Pointer }var typeToEncoder = map[uintptr]func(unsafe.Pointer)([]byte, error){}
func Marshal(v interface{}) ([]byte, error) { iface := (*emptyInterface)(unsafe.Pointer(&v) typeptr := uintptr(iface.typ) if enc, exists := typeToEncoder[typeptr]; exists { return enc(iface.ptr) } ... }
※ In reality,
typeToEncodercan be referenced by multiple goroutines, so exclusive control is required.
Marshal
json.Marshaland
json.Unmarshalreceive
interface{}value and they perform type determination dynamically to process. In normal case, you need to use the
reflectlibrary to determine the type dynamically, but since
reflect.Typeis defined as
interface, when you call the method of
reflect.Type, The reflect's argument is escaped.
Therefore, the arguments for
Marshaland
Unmarshalare always escape to the heap. However,
go-jsoncan use the feature of
reflect.Typewhile avoiding escaping.
reflect.Typeis defined as
interface, but in reality
reflect.Typeis implemented only by the structure
rtypedefined in the
reflectpackage. For this reason, to date
reflect.Typeis the same as
*reflect.rtype.
Therefore, by directly handling
*reflect.rtype, which is an implementation of
reflect.Type, it is possible to avoid escaping because it changes from
interfaceto using
struct.
The technique for working with
*reflect.rtypedirectly from
go-jsonis implemented at https://github.com/goccy/go-json/blob/master/rtype.go.
Also, the same technique is cut out as a library ( https://github.com/goccy/go-reflect )
Initially this feature was the default behavior of
go-json. But after careful testing, I found that I passed a large value to
json.Marshal()and if the argument could not be assigned to the stack, it could not be properly escaped to the heap (a bug in the Go compiler).
Therefore, this feature will be provided as an optional until this issue is resolved.
To use it, add
NoEscapelike
MarshalNoEscape()
I explained that you can use
typeptrto call a pre-built process from type information.
In other libraries, this dedicated process is processed by making it an function calling like anonymous function, but function calls are inherently slow processes and should be avoided as much as possible.
Therefore,
go-jsonadopted the Instruction-based execution processing system, which is also used to implement virtual machines for programming language.
If it is the first type to encode, create the opcode ( instruction ) sequence required for encoding. From the second time onward, use
typeptrto get the cached pre-built opcode sequence and encode it based on it. An example of the opcode sequence is shown below.
json.Marshal(struct{ X int `json:"x"` Y string `json:"y"` }{X: 1, Y: "hello"})
When encoding a structure like the one above, create a sequence of opcodes like this:
- opStructFieldHead ( `{` ) - opStructFieldInt ( `"x": 1,` ) - opStructFieldString ( `"y": "hello"` ) - opStructEnd ( `}` ) - opEnd
※ When processing each operation, write the letters on the right.
In addition, each opcode is managed by the following structure ( Pseudo code ).
type opType int const ( opStructFieldHead opType = iota opStructFieldInt opStructFieldStirng opStructEnd opEnd ) type opcode struct { op opType key []byte next *opcode }
The process of encoding using the opcode sequence is roughly implemented as follows.
func encode(code *opcode, b []byte, p unsafe.Pointer) ([]byte, error) { for { switch code.op { case opStructFieldHead: b = append(b, '{') code = code.next case opStructFieldInt: b = append(b, code.key...) b = appendInt((*int)(unsafe.Pointer(uintptr(p)+code.offset))) code = code.next case opStructFieldString: b = append(b, code.key...) b = appendString((*string)(unsafe.Pointer(uintptr(p)+code.offset))) code = code.next case opStructEnd: b = append(b, '}') code = code.next case opEnd: goto END } } END: return b, nil }
In this way, the huge
switch-caseis used to encode by manipulating the linked list opcodes to avoid unnecessary function calls.
One of the advantages of encoding using the opcode sequence is the ease of optimization. The opcode sequence mentioned above is actually converted into the following optimized operations and used.
- opStructFieldHeadInt ( `{"x": 1,` ) - opStructEndString ( `"y": "hello"}` ) - opEnd
It has been reduced from 5 opcodes to 3 opcodes ! Reducing the number of opcodees means reducing the number of branches with
switch-case. In other words, the closer the number of operations is to 1, the faster the processing can be performed.
In
go-json, optimization to reduce the number of opcodes itself like the above and it speeds up by preparing opcodes with optimized paths.
Recursive processing is required during encoding if the type is defined recursively as follows:
type T struct { X int U *U }type U struct { T *T }
b, err := json.Marshal(&T{ X: 1, U: &U{ T: &T{ X: 2, }, }, }) fmt.Println(string(b)) // {"X":1,"U":{"T":{"X":2,"U":null}}}
In
go-json, recursive processing is processed by the operation type of
opStructFieldRecursive.
In this operation, after acquiring the opcode sequence used for recursive processing, the function is not called recursively as it is, but the necessary values are saved by itself and implemented by moving to the next operation.
The technique of implementing recursive processing with the
JMPoperation while avoiding the
CALLoperation is a famous technique for implementing a high-speed virtual machine.
For more details, please refer to the article ( but Japanese only ).
When retrieving the data cached from the type information by
typeptr, we usually use map. Map requires exclusive control, so use
sync.Mapfor a naive implementation.
However, this is slow, so it's a good idea to use the
atomicpackage for exclusive control as implemented by
segmentio/encoding/json( https://github.com/segmentio/encoding/blob/master/json/codec.go#L41-L55 ).
This implementation slows down the set instead of speeding up the get, but it works well because of the nature of the library, it encodes much more for the same type.
However, as a result of profiling, I noticed that
runtime.mapaccess2accounts for a significant percentage of the execution time. So I thought if I could change the lookup from map to slice.
There is an API named
typelinksdefined in the
runtimepackage that the
reflectpackage uses internally. This allows you to get all the type information defined in the binary at runtime.
The fact that all type information can be acquired means that by constructing slices in advance with the acquired total number of type information, it is possible to look up with the value of
typeptrwithout worrying about out-of-range access.
However, if there is too much type information, it will use a lot of memory, so by default we will only use this optimization if the slice size fits within 2Mib .
If this approach is not available, it will fall back to the
atomicbased process described above.
If you want to know more, please refer to the implementation here
Like the encoder, the decoder also uses typeptr to call the dedicated process.
In order to decode, you have to traverse the input buffer character by position. At that time, if you check whether the buffer has reached the end, it will be very slow.
buf:
[]bytetype variable. holds the string passed to the decoder
cursor:
int64type variable. holds the current read position
buflen := len(buf) for ; cursor < buflen; cursor++ { // compare cursor and buflen at all times, it is so slow. switch buf[cursor] { case ' ', '\n', '\r', '\t': } }
Therefore, by adding the
NUL(
\000) character to the end of the read buffer as shown below, it is possible to check the termination character at the same time as other characters.
for { switch buf[cursor] { case ' ', '\n', '\r', '\t': case '\000': return nil } cursor++ }
Due to the
NULcharacter optimization, the Go compiler does a boundary check every time, even though
buf[cursor]does not cause out-of-range access.
Therefore,
go-jsoneliminates boundary check by fetching characters for hotspot by pointer operation. For example, the following code.
func char(ptr unsafe.Pointer, offset int64) byte { return *(*byte)(unsafe.Pointer(uintptr(ptr) + uintptr(offset))) }p := (*sliceHeader)(&unsafe.Pointer(buf)).data for { switch char(p, cursor) { case ' ', '\n', '\r', '\t': case '\000': return nil } cursor++ }
I found by the profiling result, in the struct decode, lookup process for field was taking a long time.
For example, consider decoding a string like
{"a":1,"b":2,"c":3}into the following structure:
type T struct { A int `json:"a"` B int `json:"b"` C int `json:"c"` }
At this time, it was found that it takes a lot of time to acquire the decoding process corresponding to the field from the field name as shown below during the decoding process.
fieldName := decodeKey(buf, cursor) // "a" or "b" or "c" decoder, exists := fieldToDecoderMap[fieldName] // so slow if exists { decoder(buf, cursor) } else { skipValue(buf, cursor) }
To improve this process,
json-iterator/gois optimized so that it can be branched by switch-case when the number of fields in the structure is 10 or less (switch-case is faster than map). However, there is a risk of hash collision because the value hashed by the FNV algorithm is used for conditional branching. Also,
gojayprocesses this part at high speed by letting the library user yourself write
switch-case.
go-jsonconsiders and implements a new approach that is different from these. I call this bitmap field optimization.
The range of values per character can be represented by
[256]byte. Also, if the number of fields in the structure is 8 or less,
int8type can represent the state of each field. In other words, it has the following structure.
00000000
00000001( assign key "a" to the first bit )
00000010( assign key "b" to the second bit )
00000100( assign key "c" to the third bit )
Bitmap structure is the following
| key index(0) | ------------------------ 0 | 00000000 | 1 | 00000000 | ~~ | | 97 (a) | 00000001 | 98 (b) | 00000010 | 99 (c) | 00000100 | ~~ | | 255 | 00000000 |
You can think of this as a Bitmap with a height of
256and a width of the maximum string length in the field name. In other words, it can be represented by the following type .
[maxFieldKeyLength][256]int8`
When decoding a field character, check whether the corresponding character exists by referring to the pre-built bitmap like the following.
var curBit int8 = math.MaxInt8 // 11111111c := char(buf, cursor) bit := bitmap[keyIdx][c] curBit &= bit if curBit == 0 { // not found field }
If
curBitis not
0until the end of the field string, then the string is You may have hit one of the fields. But the possibility is that if the decoded string is shorter than the field string, you will get a false hit.
{"a":1}
go type T struct { X int `json:"abc"` }※ Since
ais shorter than
abc, it can decode to the end of the field character without
curBitbeing 0.
Rest assured. In this case, it doesn't matter because you can tell if you hit by comparing the string length of
awith the string length of
abc.
Finally, calculate the position of the bit where
1is set and get the corresponding value, and you're done.
Using this technique, field lookups are possible with only bitwise operations and access to slices.
go-jsonuses a similar technique for fields with 9 or more and 16 or less fields. At this time, Bitmap is constructed as
[maxKeyLen][256]int16type.
Currently, this optimization is not performed when the maximum length of the field name is long (specifically, 64 bytes or more) in addition to the limitation of the number of fields from the viewpoint of saving memory usage.
I have done a lot of other optimizations. I will find time to write about them. If you have any questions about what's written here or other optimizations, please visit the
#go-jsonchannel on
gophers.slack.com.
Regarding the story of go-json, there are the following articles in Japanese only.
MIT