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@ -57,6 +57,425 @@ $ go test -bench .
<img width="700px" src="https://user-images.githubusercontent.com/209884/107126754-054ada80-68f5-11eb-9f93-199f2d75bec7.png"></img>
<img width="700px" src="https://user-images.githubusercontent.com/209884/107126752-024fea00-68f5-11eb-8f63-f32844de2c99.png"></img>
# How it works
`go-json` is 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-json` dares to stick to compatibility with `encoding/json` and 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`.
## Basic technique
The techniques listed here are the ones used by most of the libraries listed above.
### Buffer reuse
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.
```go
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)
```
### Elimination of reflection
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.
```go
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, `typeToEncoder` can be referenced by multiple goroutines, so exclusive control is required.
## Unique speed-up technique
## Encoder
### Do not escape arguments of `Marshal`
`json.Marshal` and `json.Unmarshal` receive `interface{}` value and they perform type determination dynamically to process.
In normal case, you need to use the `reflect` library to determine the type dynamically, but since `reflect.Type` is defined as `interface`, when you call the method of `reflect.Type`, The reflect's argument is escaped.
Therefore, the arguments for `Marshal` and `Unmarshal` are always escape to the heap.
However, `go-json` can use the feature of `reflect.Type` while avoiding escaping.
`reflect.Type` is defined as `interface`, but in reality `reflect.Type` is implemented only by the structure `rtype` defined in the `reflect` package.
For this reason, to date `reflect.Type` is 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 `interface` to using `struct`.
The technique for working with `*reflect.rtype` directly from `go-json` is 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 `NoEscape` like `MarshalNoEscape()`
### Encoding using opcode sequence
I explained that you can use `typeptr` to 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-json` adopted 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 `typeptr` to get the cached pre-built opcode sequence and encode it based on it. An example of the opcode sequence is shown below.
```go
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 ).
```go
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.
```go
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-case` is used to encode by manipulating the linked list opcodes to avoid unnecessary function calls.
### Opcode sequence optimization
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.
### Change recursive call from CALL to JMP
Recursive processing is required during encoding if the type is defined recursively as follows:
```go
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 `JMP` operation while avoiding the `CALL` operation is a famous technique for implementing a high-speed virtual machine.
For more details, please refer to [the article](https://engineering.mercari.com/blog/entry/1599563768-081104c850) ( but Japanese only ).
### Dispatch by typeptr from map to slice
When retrieving the data cached from the type information by `typeptr`, we usually use map.
Map requires exclusive control, so use `sync.Map` for a naive implementation.
However, this is slow, so it's a good idea to use the `atomic` package 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.mapaccess2` accounts 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 `typelinks` defined in the `runtime` package that the `reflect` package 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 `typeptr` without 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 `atomic` based process described above.
If you want to know more, please refer to the implementation [here](https://github.com/goccy/go-json/blob/master/codec.go#L24-L76 )
## Decoder
### Dispatch by typeptr from map to slice
Like the encoder, the decoder also uses typeptr to call the dedicated process.
### Faster termination character inspection using NUL character
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` : `[]byte` type variable. holds the string passed to the decoder
`cursor` : `int64` type variable. holds the current read position
```go
buflen := len(buf)
for ; i < buflen; cursor++ { // compare i 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.
```go
for {
switch buf[cursor] {
case ' ', '\n', '\r', '\t':
case '\000':
return nil
}
cursor++
}
```
### Use Boundary Check Elimination
Due to the `NUL` character optimization, the Go compiler does a boundary check every time, even though `buf[cursor]` does not cause out-of-range access.
Therefore, `go-json` eliminates boundary check by fetching characters for hotspot by pointer operation. For example, the following code.
```go
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++
}
```
### Checking the existence of fields of struct using Bitmaps
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:
```go
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.
```go
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/go` is 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, `gojay` processes this part at high speed by letting the library user yourself write `switch-case`.
`go-json` considers 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, `int8` type can represent the state of each field.
In other words, it has the following structure.
- Base ( 8bit ): `00000000`
- Key "a": `00000001` ( assign key "a" to the first bit )
- Key "b": `00000010` ( assign key "b" to the second bit )
- Key "c": `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 `256` and a width of the maximum string length in the field name.
In other words, it can be represented by the following type .
```go
[maxFieldKeyLength][256]int8`
```
When decoding a field character, check whether the corresponding character exists by referring to the pre-built bitmap like the following.
```go
var curBit int8 = math.MaxInt8 // 11111111
c := char(buf, cursor)
bit := bitmap[keyIdx][c]
curBit &= bit
if curBit == 0 {
// not found field
}
```
If `curBit` is not `0` until 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.
- input: `{"a":1}`
```go
type T struct {
X int `json:"abc"`
}
```
※ Since `a` is shorter than `abc`, it can decode to the end of the field character without `curBit` being 0.
Rest assured. In this case, it doesn't matter because you can tell if you hit by comparing the string length of `a` with the string length of `abc`.
Finally, calculate the position of the bit where `1` is 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-json` uses a similar technique for fields with 9 or more and 16 or less fields. At this time, Bitmap is constructed as `[maxKeyLen][256]int16` type.
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.
### Others
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-json` channel on `gophers.slack.com` .
## Reference
Regarding the story of go-json, there are the following articles in Japanese only.
- https://speakerdeck.com/goccy/zui-su-falsejsonraiburariwoqiu-mete
- https://engineering.mercari.com/blog/entry/1599563768-081104c850/
# License
MIT