Building a high performance JSON parser #json

栏目: IT技术 · 发布时间: 4天前

来源: dave.cheney.net

本文转载自:https://dave.cheney.net/high-performance-json.html,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有。

Abstract

JSON is important, damn near everything that we do as programmers or operators involves JSON at some point. JSON decoding is expensive, if your product talks JSON then performance of marshalling data in and out of JSON is important. This is a talk about designing an efficient replacement for encoding/json.Decoder .

The code for this talk is available https://github.com/pkg/json .

JSON is an important data interchange format. Damn near everything we do as programmers involves JSON in some way.

At the same time, JSON decoding is expensive. Every Go release we see improvements in the efficiency of the encoding/json package. Sometimes these are large improvements, like the move away from segmented stacks in Go 1.3, more recently these improvements have been moderate. In the 1.15 cycle I’ve seen two performance improvement that had to be rolled back because, while they made it faster, they broke a subtle implicit behaviour that people were relying on. c.f. Hyrum’s Law.

In the Go ecosystem there are a bunch of alternative JSON libraries, usually maintained by a small number of people, so this suggested to me that this is a problem with some meat on its bones, and equally, not impenetrable. I figured I’d give it a try.

At the lowest level pkg/json.Scanner can tokenize streaming JSON without allocation (provided it is supplied a few kilobytes of buffer).

BenchmarkScanner/canada-16                  1561           3524005 ns/op         638.78 MB/s           0 B/op          0 allocs/op
BenchmarkScanner/citm_catalog-16            3556           1555322 ns/op        1110.51 MB/s           0 B/op          0 allocs/op
BenchmarkScanner/twitter-16                 7068            836031 ns/op         755.37 MB/s           0 B/op          0 allocs/op
BenchmarkScanner/code-16                    1543           3640425 ns/op         533.03 MB/s           0 B/op          0 allocs/op
BenchmarkScanner/example-16               341224             16362 ns/op         796.00 MB/s           0 B/op          0 allocs/op
BenchmarkScanner/sample-16                 12771            467360 ns/op        1471.01 MB/s           0 B/op          0 allocs/op

At the next level pkg/json.Decoder.Token is 2-3x faster than encoding/json.Decoder.Token .

BenchmarkDecoderToken/pkgjson/canada-16                      267          22073095 ns/op         101.98 MB/s     4402914 B/op     222279 allocs/op
BenchmarkDecoderToken/encodingjson/canada-16                  86          67830737 ns/op          33.19 MB/s    17740387 B/op     889106 allocs/op
BenchmarkDecoderToken/pkgjson/citm_catalog-16               1114           5183226 ns/op         333.23 MB/s      965992 B/op      81995 allocs/op
BenchmarkDecoderToken/encodingjson/citm_catalog-16           288          20882018 ns/op          82.71 MB/s     5661597 B/op     324692 allocs/op
BenchmarkDecoderToken/pkgjson/twitter-16                    2356           2467042 ns/op         255.98 MB/s      768354 B/op      38992 allocs/op
BenchmarkDecoderToken/encodingjson/twitter-16                471          12606205 ns/op          50.10 MB/s     3584017 B/op     187319 allocs/op
BenchmarkDecoderToken/pkgjson/code-16                        346          16877006 ns/op         114.98 MB/s     4304233 B/op     320235 allocs/op
BenchmarkDecoderToken/encodingjson/code-16                    73          80255994 ns/op          24.18 MB/s    23355962 B/op    1319125 allocs/op
BenchmarkDecoderToken/pkgjson/example-16                  113912             53083 ns/op         245.35 MB/s       16016 B/op        914 allocs/op
BenchmarkDecoderToken/encodingjson/example-16              21734            273991 ns/op          47.53 MB/s       82416 B/op       4325 allocs/op
BenchmarkDecoderToken/pkgjson/sample-16                     6642            871796 ns/op         788.59 MB/s      213761 B/op       5081 allocs/op
BenchmarkDecoderToken/encodingjson/sample-16                1803           3287623 ns/op         209.12 MB/s      723782 B/op      26095 allocs/op

Because allocations make up a large proportion of the Decoder.Token API, pkg/json.Decoder provides an alternative API that produces significantly fewer allocations and is 8-10x faster.

BenchmarkDecoderNextToken/pkgjson/canada-16                 1197           4825232 ns/op         466.52 MB/s         136 B/op          3 allocs/op
BenchmarkDecoderNextToken/encodingjson/canada-16              90          65392440 ns/op          34.42 MB/s    17740399 B/op     889106 allocs/op
BenchmarkDecoderNextToken/pkgjson/citm_catalog-16           2709           2162849 ns/op         798.58 MB/s         136 B/op          3 allocs/op
BenchmarkDecoderNextToken/encodingjson/citm_catalog-16       301          20064314 ns/op          86.08 MB/s     5661597 B/op     324692 allocs/op
BenchmarkDecoderNextToken/pkgjson/twitter-16                5395           1068106 ns/op         591.25 MB/s         152 B/op          4 allocs/op
BenchmarkDecoderNextToken/encodingjson/twitter-16            494          12072956 ns/op          52.31 MB/s     3584013 B/op     187319 allocs/op
BenchmarkDecoderNextToken/pkgjson/code-16                   1135           5124666 ns/op         378.65 MB/s         248 B/op          6 allocs/op
BenchmarkDecoderNextToken/encodingjson/code-16                74          77579973 ns/op          25.01 MB/s    23355955 B/op    1319125 allocs/op
BenchmarkDecoderNextToken/pkgjson/example-16              269010             22323 ns/op         583.43 MB/s         152 B/op          4 allocs/op
BenchmarkDecoderNextToken/encodingjson/example-16          22707            264078 ns/op          49.32 MB/s       82416 B/op       4325 allocs/op
BenchmarkDecoderNextToken/pkgjson/sample-16                10000            510445 ns/op        1346.85 MB/s        1144 B/op          9 allocs/op
BenchmarkDecoderNextToken/encodingjson/sample-16            1836           3161804 ns/op         217.44 MB/s      723781 B/op      26095 allocs/op

At the highest level, pkg/json can unmarshal data into a Go object with the same API as encoding/json . This is very much a work in progress, but the results are promising for folks who want to use this package as a drop in replacement.

BenchmarkDecoderDecodeInterfaceAny/pkgjson/canada-16                 217          27425893 ns/op          82.08 MB/s     8747163 B/op     281408 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/canada-16            153          38347477 ns/op          58.70 MB/s    20647616 B/op     392553 allocs/op
BenchmarkDecoderDecodeInterfaceAny/pkgjson/citm_catalog-16           747           8008839 ns/op         215.66 MB/s     5197853 B/op      89673 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/citm_catalog-16      360          16607501 ns/op         104.00 MB/s     9406809 B/op      95389 allocs/op
BenchmarkDecoderDecodeInterfaceAny/pkgjson/twitter-16               1606           3714515 ns/op         170.01 MB/s     2130731 B/op      30182 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/twitter-16           862           6927998 ns/op          91.15 MB/s     4283407 B/op      31278 allocs/op
BenchmarkDecoderDecodeInterfaceAny/pkgjson/code-16                   333          17939351 ns/op         108.17 MB/s     7331643 B/op     232059 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/code-16              236          25324951 ns/op          76.62 MB/s    12332753 B/op     271292 allocs/op
BenchmarkDecoderDecodeInterfaceAny/pkgjson/example-16              76874             78079 ns/op         166.81 MB/s       50980 B/op        739 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/example-16         40886            146685 ns/op          88.79 MB/s       82855 B/op        782 allocs/op
BenchmarkDecoderDecodeInterfaceAny/pkgjson/sample-16                5240           1116081 ns/op         615.99 MB/s      399970 B/op       5542 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/sample-16           1123           5369313 ns/op         128.04 MB/s     2661872 B/op       7527 allocs/op

This is a story of how I went about building this package.

I’m using a pre release version of Go 1.15 built from source. If you’re using an older version your numbers may vary. When Go 1.15 comes out, you should upgrade.

The design of this package has the following features

Reasonably compatible with the encoding/json package

This package offers the same high level json.Decoder API with higher throughput and/or reduced allocations. A success criteria for this package would be as a drop in replacement for encoding/json .

Supports streaming operations

It’s nice if you can have the entire input in memory but that’s unrealistic. Input sizes are usually unknown and potentially unbounded. Buffering in memory is a availability risk. Buffering before processing introduces latency, streaming reads lets you process data as it arrives and logically overlap with transfer or read. This package supports streaming operation via io.Reader input sources (which is also required for compatibility with encoding/json )

Allocation free, or bounded API

In addition to the encoding/json API, provide an alternative API that can operate with minimal, ideally no allocations.

We’re all familiar with profiling and tracing (go tool pprof, go tool trace) as techniques that we can use to examine the performance of a program once it is written. Are there tools that we can use to estimate the performance of a program before we write it?

Let’s talk about the time complexity of this problem.

JSON doesn’t use length markers; to know how much to read, we have to read it all. This means the lower bounds on the time to process the file is the size of the file. Specifically how long it takes to process each byte.

But reading the file isn’t enough, we have to follow the JSON state machine to figure out where the tokens start and end. Now, just reading N bytes, we need to process those N bytes, so the performance is at least read(N)+parse(N) . But there are other costs, if we have to allocate memory to read or process those bytes, then that will cost us.

There are several other factors:

  • We know that the big factor in the performance of a parse is the size of the input. Ideally we want N to be the number of bytes in the input, that is, we want to process each byte only once. If we touch the same byte more than once, that adds overhead, and complicates processing if we have to keep those bytes around to come back and look at them again.

  • Just like we don’t want to process a byte more than once, we want to avoid processing a token more than once. We want to limit the number of function calls. Ideally O(tokens) , not O(bytes) .

  • Limit function calls in the hot path inside the Scanner or Decoder . encoding/json uses one function call per byte, pkg/json does better at one call per token. If we did nothing else we’d be ahead.

  • Limit copies. If we design to limit copying of data then we limit the number we revisit a byte.

  • Limit allocations. If you limit the number of places you can copy from and too, ideally only copies within existing buffers, then you naturally limit allocations. Limiting allocations reduces runtime in two ways:

    1. Reduce the overhead in taking the allocation. The heap is a shared resource, allocating on the heap requires working with shared data structures. This means locks, cache contention, etc. c.f. Amdahl’s Law

    2. Reduce the overhead of cleaning up allocations. The less allocations you make, the less heap you consume and the less garbage you produce. Reducing these two factors reduces the overhead of background and foreground garbage collection.

JSON is a stream of tokens. To build higher level components like pretty printers and decoders we need to break the stream into tokens.

A JSON decoder has two main components 1. A scanner, or tokeniser, that converts a stream a bytes into a stream of JSON tokens. 2. An unmarshaller that applies a stream of JSON tokens to a Go object. Let’s talk about tokenization first:

JSON is regular, well defined grammar. There is a great set of charts over on json.org.

{"a": 1, "b": true, "c": [1, "two", null]}

Is a stream of,

  • { , the opening brace, signifying a collection of name/value pairs.

  • "a" , the string a .

  • : , a colon, the delimiter between the key and the value in the key/value pair.

  • 1 , the number one.

  • , , a comma, the delimiter between one key/value pair and the next.

  • "b" , the string b .

  • :

  • true , the boolean value for true.

  • ,

  • "c" , the string c .

  • :

  • [ , the opening square brace, signifying and ordered list of values.

  • 1

  • ,

  • "two"

  • ,

  • null , a null or void value (rare).

  • ] , closing square brace, terminates the list of values

  • } , closing curly brace, terminating the key/value collection.

encoding/json does this with the Decoder.Token API. You declare a json.Decoder , then call Token until err is non nil .

package main

import (
	"encoding/json"
	"fmt"
	"strings"
)

func main() {
	input := `{"a": 1, "b": true, "c": [1, "two", null]}`
	dec := json.NewDecoder(strings.NewReader(input))
	for {
		tok, err := dec.Token()
		if err != nil {
			break
		}
		fmt.Printf("%v\t(%T)\n", tok, tok)
	}

}

When we run this we get the following output

{       (json.Delim)
a       (string)
1       (float64)
b       (string)
true    (bool)
c       (string)
[       (json.Delim)
1       (float64)
two     (string)
<nil>   (<nil>)
]       (json.Delim)
}       (json.Delim)

This is rather convenient, tok is an interface{} value so it can represent both the value being returned, and also it’s type; strings are string , numbers are float64 , booleans are real true and false , even null is represented as a nil .

But there is a cost to this convenience. To see why, let’s talk about a string. When we write this statement

var b = make([]byte, 10)
var s = string(b)

The compiler makes a copy of b, because the rules of Go say the strings are immutable. If the string and the byte slice shared the same backing data, then changing b could change the contents of s . This would be bad so string(b) copies the contest of b .

Now lets look at the inputs to the json.Decoder , it’s an io.Reader .

% go doc encoding/json NewDecoder
package json // import "encoding/json"

func NewDecoder(r io.Reader) *Decoder
    NewDecoder returns a new decoder that reads from r.

    The decoder introduces its own buffering and may read data from r beyond the
    JSON values requested.

Let’s look at the io.Reader.Read method

% go doc io Reader.Read
package io // import "io"

func Read(p []byte) (n int, err error)

You give Read a []byte buffer, Read returns to you the number of bytes it read into the buffer, and possibly an error.

So, now we know the input is a stream of bytes, and the output is runes, float64, bools, and strings. At a minimum the input "hello" is going to result in a byte slice []byte{'h','e','l','l','o'} and that byte slice is going to be be copied to a string.

package main

import (
	"encoding/json"
	"fmt"
	"io"
	"strings"
)

func main() {
	input := `"hello"`
	var r io.Reader = strings.NewReader(input) // reads strings as []byte
	dec := json.NewDecoder(r)
	tok, _ := dec.Token()
	fmt.Printf("%s\t(%T)\n", tok, tok)
}

The convenience of the Decoder.Token API means one allocation per token. But it gets worse.

Values assigned to interfaces escape

A more seriously issue, from a performance point of view, is assigning a value to an interface generally causes an allocation. Because of the design of the Decoder.Token API, the concrete value assigned to each token token causes the value to escape to the heap. So not only do we have an allocation for every []byte to string conversion, but each token escapes to the heap. The number of allocations is tied to the number of tokens in the file, and the size of those allocations will be in part related to the size of the file.

This is for several reasons, which all have the same underlying cause, the garbage collector. We all know that an interface value is a two word structure. It looks something like this

type interface struct {
    type uintptr
    data uintptr
}
uintptr above is not to suggest that type and data are pointers (although in most cases they are) just that the size of those fields is large enough to hold the address of a value in memory — the size of a pointer. It’s unsigned because a signed pointer would halve the address space, and a negative pointer doesn’t make any sense.

interface values are special in that they record both the value and the type of the value. Another property of interface values is they can hold any value, reguardless of their type.

In early Go versions it was possible for an interface to store a uintptr or smaller value directly in the data field of the interface. However this changed in Go 1.6TODO checkbecause it is not possible to change two fields atomically, which caused a problem for the concurrent collector.
var x interface{} = 1
x = "one"

From the point of view of the compiler, this must change the type stored in x from int to string and the value from 1 to "one" atomically.

To do this the compiler stores a pointer to the value in the data slot. This means, each token escapes to the heap . But it gets worse, we know that a Go string is itself a small struct.

type string struct {
    ptr *[]byte
    len int
}

So to convert a []byte token to a string first we copy the []byte , that goes on the heap, then a string header is created, and that goes on the heap, and finally the pointer to that header goes into the data slot in the interface.

package main

import (
	"encoding/json"
	"strings"
	"testing"
)

func BenchmarkJSONDecodeHello(b *testing.B) {
	input := `"hello"`
	r := strings.NewReader(input) // reads strings as []byte
	dec := json.NewDecoder(r)
	b.ReportAllocs()
	b.SetBytes(int64(len(input)))
	b.ResetTimer()
	for i := 0; i < b.N; i++ {
		r.Seek(0, 0)
		tok, _ := dec.Token()
		if tok != "hello" {
			b.Fatal()
		}
	}
}
% go test -bench=. -memprofile=m.p json/tok_test.go
goos: darwin
goarch: amd64
BenchmarkJSONDecodeHello-4       3523440               355 ns/op          19.73 MB/s          37 B/op          3 allocs/op
PASS
ok      command-line-arguments  1.951s

API design influences allocation.

Eliminating allocations through API design.

Spoiler alert, most of the speedups of this package come from reducing allocations; specifically the time not spent in the heap allocation path, and the time not spent in GC cycles, is available for scanning.

If we want to build an API that has lower allocations than encoding/json , we have to address each of the problems I’ve discussed.

Let’s look back at the sequence of tokens; { "a" : 1 , "b" : true , "c" : [ 1 , "two" , null ] and } .

It turns out that the first character in the token tells you what the token is

  • { , } - collection start, end

  • [ , ] - array start, end

  • t - true

  • f - false

  • n - null

  • " - string

  • - , 0 - 9 - a number

This is the first improvement in the Scanner.Next , and Decoder.NextToken API’s. Rather than converting the []byte to a value, it just returns the token straight from the input—​a simple subslice.

package main

import (
	"fmt"
	"github.com/pkg/json"
	"strings"
)

func main() {
	input := `{"a": 1, "b": true, "c": [1, "two", null]}`
	dec := json.NewDecoder(strings.NewReader(input))
	for {
		tok, err := dec.NextToken()
		if err != nil {
			break
		}
		fmt.Printf("%s\t(%T)\n", tok, tok)
	}
}
{       ([]uint8)
"a"     ([]uint8)
1       ([]uint8)
"b"     ([]uint8)
true    ([]uint8)
"c"     ([]uint8)
[       ([]uint8)
1       ([]uint8)
"two"   ([]uint8)
null    ([]uint8)
]       ([]uint8)
}       ([]uint8)

There are a few subtleties with this API.

  1. Because the output is a subslice of the input, not a copy, there are restrictions on how long the output is valid for. This is similar to the bufio.Scanner API.

  2. Sometimes people want to know type of the token; collection, array, string, number, etc, sometimes they want the token value , the string , the number , in a form they can work with. Scanner.Next and Decoder.NextToken aren’t convenient for that, but they can be used to build higher level abstractions more efficiently.

Let’s talk about reading data. This can be tricky to do efficiently because JSON is not length delimited, you have to read until you find the end of the token. The traditional way to do this is with an io.Reader .

  • You can read one byte at a time, but you need a place to store store the thing your waking over, also might need to put the byte back,

  • You can read into a buffer, then look in buffer for start and end of token. If the end token isn’t in the buffer need to do a lot of bookkeeping and copying to move the data around the the buffer or grow the buffer to make room for more data.

encoding/json does a combination of these, often with a smattering of sync.pool to try to reuse small objects transparently.

The alternative is an idea inspired by Steven Schveighoffer’s iopipeand Phil Pearl.

// A byteReader implements a sliding window over an io.Reader.
type byteReader struct {
	data   []byte
	offset int
	r      io.Reader
	err    error
}

// release discards n bytes from the front of the window.
func (b *byteReader) release(n int) {
	b.offset += n
}

// window returns the current window.
// The window is invalidated by calls to release or extend.
func (b *byteReader) window() []byte {
	return b.data[b.offset:]
}

// extend extends the window with data from the underlying reader.
func (b *byteReader) extend() int {

JSON contains a mixture of tokens and whitespace. Space, tab, newline and carriage return can occur between tokens and are ignored, thus the search for a token begins with a search for the first non whitespace character.

{"a": 1, "b": true, "c": [1, "two", null]}

This is a good time to talk about optimising the search for whitespace with an example of using a byteReader .

func countWhitespace(br *byteReader) int {
	n := 0
	w := br.window()
	for {
		for _, c := range w {
			if isWhitespace(c) {
				n++
			}
		}
		br.release(len(w))
		if br.extend() == 0 {
			return n
		}
		w = br.window()
	}
}

This does the minimum, visit each character and makes one function call (which is inlined), and counts the number of whitespace characters. Any (useful) JSON decoder code cannot go faster that this.

What is the fastest way to implement isWhitespace ? Here’s the implementation that encoding/json uses (with a different name)

func isSpace(c byte) bool {
        return c <= ' ' && (c == ' ' || c == '\t' || c == '\r' || c == '\n')
}

Let’s compare this with some other implementations.

https://github.com/davecheney/whitespace

Based on this research, the manually inlined whitespace[c] version is the fastest.

name                             time/op
CountWhitespace/canada-16          1.10ms ± 2%
CountWhitespace/citm_catalog-16     838µs ± 1%
CountWhitespace/twitter-16          306µs ± 1%
CountWhitespace/code-16             937µs ± 1%
CountWhitespace/example-16         6.40µs ± 1%
CountWhitespace/sample-16           333µs ± 1%

name                             speed
CountWhitespace/canada-16        2.04GB/s ± 2%
CountWhitespace/citm_catalog-16  2.06GB/s ± 1%
CountWhitespace/twitter-16       2.06GB/s ± 1%
CountWhitespace/code-16          2.07GB/s ± 1%
CountWhitespace/example-16       2.04GB/s ± 1%
CountWhitespace/sample-16        2.06GB/s ± 1%

So this is our baseline.

Now we can tell which characters are tokens and which are simply whitespace, let’s step up a level and talk about breaking up those tokens.

// Next returns a []byte referencing the the next lexical token in the stream.
// The []byte is valid until Next is called again.
// If the stream is at its end, or an error has occured, Next returns a zero
// length []byte slice.
//
// A valid token begins with one of the following:
//
//  { Object start
//  [ Array start
//  } Object end
//  ] Array End
//  , Literal comma
//  : Literal colon
//  t JSON true
//  f JSON false
//  n JSON null
//  " A string, possibly containing backslash escaped entites.
//  -, 0-9 A number
func (s *Scanner) Next() []byte {
	// release the previous token
	s.br.release(s.pos)
	s.pos = 0

	c := s.token()
	length := 0
	switch c {
	case ObjectStart, ObjectEnd, Colon, Comma, ArrayStart, ArrayEnd:
		length = 1
		s.pos = 1
	case True:
		length = validateToken(&s.br, "true")
		s.pos = length
	case False:
		length = validateToken(&s.br, "false")
		s.pos = length
	case Null:
		length = validateToken(&s.br, "null")
		s.pos = length
	case String:
		// string
		length = parseString(&s.br)
		if length < 2 {
			return nil
		}
		s.pos = length
	case 0:
		// eof
		return nil
	default:
		// ensure the number is correct.
		length = s.parseNumber()
		if length < 0 {
			return nil
		}
	}
	return s.br.window()[:length]
}

This is the core loop of Scanner.Next . Scanner.Next skips over any intermediate whitespace, determines the token from the first character in the window, then continues to read until the token is read or we hit the end of the input.

Let’s look at how token works, then we’ll talk about some optimisations

func (s *Scanner) token() byte {
	w := s.br.window()
	for {
		for _, c := range w {
			if whitespace[c] {
				s.pos++
				continue
			}

			// release whitespace
			s.br.release(s.pos)
			s.pos = 0
			return c
		}
		if s.br.extend() == 0 {
			// eof
			return 0
		}
		w = s.br.window()[s.pos:]
	}
}

var whitespace = [256]bool{
	' ':  true,
	'\r': true,
	'\n': true,
	'\t': true,
}
  • We start by getting the current window from the byteReader . This is a []byte slice of all the data that is yet to be read.

  • We’re looking for the first non whitespace character. If the character is a whitespace we increment s.pos to ignore the character and loop around.

  • If we do find a non whitespace character, we release s.pos characters from the front of the window. Now the start of the window is properly aligned with the first character of the token.

  • It turns out that we also get the first character of the token for free, it’s in c , so we can return that as a hint to Scanner.Next .

  • If we run out of characters without hitting a token then we called extend() to grow the window.

  • If we couldn’t grow, then we’ve run out of input and haven’t got a token, so give up.

  • Otherwise update w with a new window.

This is the basic operation of byteReader , we’ll see that pattern repeated across the scanner. Some things to note:

  • Note the lack of error handling, its not part of the inner loop, it only happens when we have to read more data from the underlying reader.

  • extend hides the process of reading into, growing, refilling the buffer, it makes the loop above it-- Scanner.token simpler; if there is data in the window, process it, extend if you need too, give up if you can’t extend.

  • release is similar, it shrinks the start of the window to exclude data that we don’t care about. No need to copy or move data around, no need to

  • extend is not in the hot path, so there is no need to optimise it, its performance is a function of the buffer it is given. In practice a buffer of 8k is sufficient.

Let’s talk about the performance of this code.

name                     time/op
Scanner/canada-16         4.41ms ± 2%
Scanner/citm_catalog-16   2.55ms ± 3%
Scanner/twitter-16        1.03ms ± 1%
Scanner/code-16           4.21ms ± 1%
Scanner/example-16        21.4µs ± 1%
Scanner/sample-16          822µs ± 1%

name                     speed
Scanner/canada-16        510MB/s ± 2%
Scanner/citm_catalog-16  677MB/s ± 3%
Scanner/twitter-16       615MB/s ± 1%
Scanner/code-16          461MB/s ± 1%
Scanner/example-16       608MB/s ± 1%
Scanner/sample-16        837MB/s ± 1%

This is a benchmark you saw earlier, minus a few optimisations we’ll talk about next.

Comparing the performance of Scanner.Next to our whitespace benchmark we can see that we’re between 1/4 and 2/5ths of our baseline.

Let’s talk about the first improvement we can make to the code. Note the amount of work being spent to keep s.pos up to date. We know that s.pos is set to 0 just before Scanner.Next calls this function, and we set s.pos to zero on the way out of the function, so the changes we make to s.pos within the function are invisible—​its zero on entry, and zero on exit.

We can rewrite the function to keep a local pos value, which has an impressive effect on token .

func (s *Scanner) token() byte {
	w := s.br.window()
	pos := 0
	for {
		for _, c := range w {
			if whitespace[c] {
				pos++
				continue
			}

			// release whitespace
			s.br.release(pos)
			return c
		}
		if s.br.extend() == 0 {
			// eof
			return 0
		}
		w = s.br.window()[pos:]
	}
}

var whitespace = [256]bool{
	' ':  true,
	'\r': true,
	'\n': true,
	'\t': true,
}
name                     old time/op    new time/op    delta
Scanner/canada-16          4.39ms ± 1%    4.43ms ± 4%     ~     (p=1.000 n=5+5)
Scanner/citm_catalog-16    2.52ms ± 1%    1.80ms ± 4%  -28.46%  (p=0.008 n=5+5)
Scanner/twitter-16         1.03ms ± 2%    0.95ms ± 3%   -7.41%  (p=0.008 n=5+5)
Scanner/code-16            4.24ms ± 2%    4.18ms ± 1%     ~     (p=0.095 n=5+5)
Scanner/example-16         21.4µs ± 1%    18.9µs ± 2%  -11.68%  (p=0.008 n=5+5)
Scanner/sample-16           828µs ± 2%     528µs ± 2%  -36.24%  (p=0.008 n=5+5)

name                     old speed      new speed      delta
Scanner/canada-16         512MB/s ± 1%   509MB/s ± 4%     ~     (p=1.000 n=5+5)
Scanner/citm_catalog-16   685MB/s ± 1%   958MB/s ± 4%  +39.84%  (p=0.008 n=5+5)
Scanner/twitter-16        616MB/s ± 2%   665MB/s ± 3%   +8.01%  (p=0.008 n=5+5)
Scanner/code-16           458MB/s ± 2%   465MB/s ± 1%     ~     (p=0.095 n=5+5)
Scanner/example-16        608MB/s ± 1%   688MB/s ± 2%  +13.23%  (p=0.008 n=5+5)
Scanner/sample-16         831MB/s ± 2%  1303MB/s ± 2%  +56.84%  (p=0.008 n=5+5)

By keeping pos local the compiler avoided those temporary writes back to memory.

The question I have for you is why did this improve some inputs and not others?

The answer, I think, is different inputs have different amounts of whitespace. For example canada only has 33 whitespace characters whereas citm has 1,227,563.

There is a larger improvement we can make for the runtime of this code, and it relates to inlining. Inlining is the process of automatically (or manually) copying the body of a function into, in line with , its caller. This avoids the overhead of the function call.

Usually inlining is performed automatically by the compiler according to a set of rules it controls. The Go compiler has reasonable support for inlining, but has a number of limitations.

% go build -gcflags=-m=2 2>&1 | grep cannot | grep -v decoder
./reader.go:31:6: cannot inline (*byteReader).extend: function too complex: cost 198 exceeds budget 80
./scanner.go:99:6: cannot inline (*Scanner).token: unhandled op FOR
./scanner.go:130:6: cannot inline validateToken: unhandled op FOR
./scanner.go:153:6: cannot inline parseString: unhandled op FOR
./scanner.go:182:6: cannot inline (*Scanner).parseNumber: unhandled op FOR
./scanner.go:56:6: cannot inline (*Scanner).Next: function too complex: cost 476 exceeds budget 80

The first is the size of the function, byteReader.extend cannot be inlined because it is too complex.The second is statements within the fuction, Scanner.token cannot be inlined because it contains a for statement. Also note that Scanner.Next , the caller of Scanner.token cannot be inlined because it also too complex.

Let’s go back to the constraints. Scanner.Next is called for each token in the input. This means that Scanner.token is called for each token in the input. Scanner.token cannot be automatically inlined into its called because it is too complex. Therefore we’re paying for an extra function call for each token.

We can remove this overhead by manually inlining Scanner.token into its caller.

func (s *Scanner) Next() []byte {
	// release the previous token
	s.br.release(s.pos)
	w := s.br.window()
	pos := 0
	for {
		for _, c := range w {
			if whitespace[c] {
				pos++
				continue
			}

			// release whitespace
			s.br.release(pos)

			length := 0
			switch c {
			case ObjectStart, ObjectEnd, Colon, Comma, ArrayStart, ArrayEnd:
				length = 1
				s.pos = 1
			case True:
				length = validateToken(&s.br, "true")
				s.pos = length
			case False:
				length = validateToken(&s.br, "false")
				s.pos = length
			case Null:
				length = validateToken(&s.br, "null")
				s.pos = length
			case String:
				// string
				length = parseString(&s.br)
				if length < 2 {
					return nil
				}
				s.pos = length
			default:
				// ensure the number is correct.
				length = s.parseNumber()
				if length < 0 {
					return nil
				}
			}
			return s.br.window()[:length]
		}
		if s.br.extend() == 0 {
			// eof
			return nil
		}
		w = s.br.window()[pos:]
	}
}

The results support our thesis:

name                     old time/op    new time/op    delta
Scanner/canada-16          4.36ms ± 1%    3.50ms ± 0%  -19.68%  (p=0.008 n=5+5)
Scanner/citm_catalog-16    1.80ms ± 1%    1.56ms ± 2%  -13.16%  (p=0.008 n=5+5)
Scanner/twitter-16          965µs ± 2%     833µs ± 2%  -13.75%  (p=0.008 n=5+5)
Scanner/code-16            4.15ms ± 1%    3.61ms ± 1%  -12.82%  (p=0.008 n=5+5)
Scanner/example-16         18.9µs ± 2%    16.6µs ± 1%  -12.42%  (p=0.008 n=5+5)
Scanner/sample-16           515µs ± 1%     472µs ± 2%   -8.34%  (p=0.008 n=5+5)

name                     old speed      new speed      delta
Scanner/canada-16         516MB/s ± 1%   642MB/s ± 0%  +24.50%  (p=0.008 n=5+5)
Scanner/citm_catalog-16   960MB/s ± 1%  1105MB/s ± 2%  +15.16%  (p=0.008 n=5+5)
Scanner/twitter-16        654MB/s ± 2%   759MB/s ± 1%  +15.94%  (p=0.008 n=5+5)
Scanner/code-16           468MB/s ± 1%   537MB/s ± 1%  +14.69%  (p=0.008 n=5+5)
Scanner/example-16        689MB/s ± 2%   787MB/s ± 1%  +14.17%  (p=0.008 n=5+5)
Scanner/sample-16        1.33GB/s ± 1%  1.46GB/s ± 1%   +9.11%  (p=0.008 n=5+5)

By saving the function call we’ve improved throughput by 9-24%. The largest improvement comes from canada , which basically contained no whitespace, so the call to Scanner.token almost always returned immediately having done no work, while also paying for all the s.pos and release overhead.

This is where the inner loop of the Scanner stands today. Note that citm is over 50% of the baseline, sample is nearly 75%. To recap the major optimisations were:

  • whitespace[c]

  • Avoiding s.pos updates. They cannot be registerised, CPU has to do a write on every iteration. s.pos updates reduced from one per byte to one per token.

  • Scanner.Next and Scanner.token were effectively one function spread over two. Each are too large to be inlined, so we’re paying for an extra function call per token. Manually inlining them increased the indentation depth of the function, but delivered substantial speedups.

  • Most JSON contains some whitespace, it’s moderately optimised for human readability. It turns out, the more whitespace, the faster pkg/json decodes.

So far we have a Scanner which tokenises input at 50-75% of the baseline speed we established before. But there’s a few more things we need to make the scanner fully functional.

The first part of that is validation.

JSON is a state machine. Depending on the current token you’re in, some may be valid some may not be. For example, if you’ve read these tokens { , "username" , then the only valid token is : . To track this we need to layer some logic on top of Scanner.Next to assert that the token sequence is valid. This is the role of Decoder.NextToken :

const (
	stateValue        = 0
	stateObjectString = iota
	stateObjectColon
	stateObjectValue
	stateObjectComma
	stateArrayValue
	stateArrayComma
	stateEnd
)

func (d *Decoder) NextToken() ([]byte, error) {
	tok := d.scanner.Next()
	if len(tok) < 1 {
		return nil, io.EOF
	}
	switch d.state {
	case stateValue:
		return d.stateValue(tok)
	case stateObjectString:
		return d.stateObjectString(tok)
	case stateObjectColon:
		return d.stateObjectColon(tok)
	case stateObjectValue:
		return d.stateObjectValue(tok)
	case stateObjectComma:
		return d.stateObjectComma(tok)
	case stateArrayValue:
		return d.stateArrayValue(tok)
	case stateArrayComma:
		return d.stateArrayComma(tok)
	case stateEnd:
		fallthrough
	default:
		return nil, io.EOF
	}
}

This is pretty straight for stuff. We take track the current state in d.state and based on its value we dispatch to the various state methods.

Click to see the source for the various state methods.
func (d *Decoder) stateObjectString(tok []byte) ([]byte, error) {
	switch tok[0] {
	case '}':
		inObj := d.pop()
		switch {
		case d.len() == 0:
			d.state = stateEnd
		case inObj:
			d.state = stateObjectComma
		case !inObj:
			d.state = stateArrayComma
		}
		return tok, nil
	case '"':
		d.state = stateObjectColon
		return tok, nil
	default:
		return nil, fmt.Errorf("stateObjectString: missing string key")
	}
}

func (d *Decoder) stateObjectColon(tok []byte) ([]byte, error) {
	switch tok[0] {
	case Colon:
		d.state = stateObjectValue
		return d.NextToken()
	default:
		return tok, fmt.Errorf("stateObjectColon: expecting colon")
	}
}

func (d *Decoder) stateObjectValue(tok []byte) ([]byte, error) {
	switch tok[0] {
	case '{':
		d.state = stateObjectString
		d.push(true)
		return tok, nil
	case '[':
		d.state = stateArrayValue
		d.push(false)
		return tok, nil
	default:
		d.state = stateObjectComma
		return tok, nil
	}
}

func (d *Decoder) stateObjectComma(tok []byte) ([]byte, error) {
	switch tok[0] {
	case '}':
		inObj := d.pop()
		switch {
		case d.len() == 0:
			d.state = stateEnd
		case inObj:
			d.state = stateObjectComma
		case !inObj:
			d.state = stateArrayComma
		}
		return tok, nil
	case Comma:
		d.state = stateObjectString
		return d.NextToken()
	default:
		return tok, fmt.Errorf("stateObjectComma: expecting comma")
	}
}

func (d *Decoder) stateArrayValue(tok []byte) ([]byte, error) {
	switch tok[0] {
	case '{':
		d.state = stateObjectString
		d.push(true)
		return tok, nil
	case '[':
		d.state = stateArrayValue
		d.push(false)
		return tok, nil
	case ']':
		inObj := d.pop()
		switch {
		case d.len() == 0:
			d.state = stateEnd
		case inObj:
			d.state = stateObjectComma
		case !inObj:
			d.state = stateArrayComma
		}
		return tok, nil
	case ',':
		return nil, fmt.Errorf("stateArrayValue: unexpected comma")
	default:
		d.state = stateArrayComma
		return tok, nil
	}
}

func (d *Decoder) stateArrayComma(tok []byte) ([]byte, error) {
	switch tok[0] {
	case ']':
		inObj := d.pop()
		switch {
		case d.len() == 0:
			d.state = stateEnd
		case inObj:
			d.state = stateObjectComma
		case !inObj:
			d.state = stateArrayComma
		}
		return tok, nil
	case Comma:
		d.state = stateArrayValue
		return d.NextToken()
	default:
		return nil, fmt.Errorf("stateArrayComma: expected comma, %v", d.stack)
	}
}

func (d *Decoder) stateValue(tok []byte) ([]byte, error) {
	switch tok[0] {
	case '{':
		d.state = stateObjectString
		d.push(true)
		return tok, nil
	case '[':
		d.state = stateArrayValue
		d.push(false)
		return tok, nil
	case ',':
		return nil, fmt.Errorf("stateValue: unexpected comma")
	default:
		d.state = stateEnd
		return tok, nil
	}
}

Let’s look at the results.

name                                           time/op
DecoderNextToken/pkgjson/canada-16               5.64ms ± 1%
DecoderNextToken/encodingjson/canada-16          65.1ms ± 1%
DecoderNextToken/pkgjson/citm_catalog-16         2.42ms ± 2%
DecoderNextToken/encodingjson/citm_catalog-16    19.8ms ± 1%
DecoderNextToken/pkgjson/twitter-16              1.18ms ± 1%
DecoderNextToken/encodingjson/twitter-16         12.1ms ± 0%
DecoderNextToken/pkgjson/code-16                 6.10ms ± 2%
DecoderNextToken/encodingjson/code-16            77.5ms ± 1%
DecoderNextToken/pkgjson/example-16              25.2µs ± 1%
DecoderNextToken/encodingjson/example-16          266µs ± 1%
DecoderNextToken/pkgjson/sample-16                559µs ± 0%
DecoderNextToken/encodingjson/sample-16          3.18ms ± 2%

name                                           speed
DecoderNextToken/pkgjson/canada-16              399MB/s ± 1%
DecoderNextToken/encodingjson/canada-16        34.6MB/s ± 1%
DecoderNextToken/pkgjson/citm_catalog-16        713MB/s ± 2%
DecoderNextToken/encodingjson/citm_catalog-16  87.1MB/s ± 1%
DecoderNextToken/pkgjson/twitter-16             537MB/s ± 1%
DecoderNextToken/encodingjson/twitter-16       52.2MB/s ± 0%
DecoderNextToken/pkgjson/code-16                318MB/s ± 2%
DecoderNextToken/encodingjson/code-16          25.0MB/s ± 1%
DecoderNextToken/pkgjson/example-16             517MB/s ± 1%
DecoderNextToken/encodingjson/example-16       49.0MB/s ± 1%
DecoderNextToken/pkgjson/sample-16             1.23GB/s ± 0%
DecoderNextToken/encodingjson/sample-16         216MB/s ± 2%

Compared to encoding/json we’re 8-10x faster. But there are some things we can do to improve:

Central to the operation of Decoder.NextToken is the switch statement. As we saw earlier in the whitespace benchmark switch was the second worse performer. This is because switch , in the general case at least, is implemented as a set of if {} else if {} .. operations. Effectively what the compiler sees is

func (d *Decoder) NextToken() ([]byte, error) {
	tok := d.scanner.Next()
	if len(tok) < 1 {
		return nil, io.EOF
	}
	if d.state == stateValue {
		return d.stateValue(tok)
	}
	if d.state == stateObjectString {
		return d.stateObjectString(tok)
	}
	if d.state == stateObjectColon {
		return d.stateObjectColon(tok)
	}
	if d.state == stateObjectValue {
		return d.stateObjectValue(tok)
	}
	if d.state == stateObjectComma {
		return d.stateObjectComma(tok)
	}
	if d.state == stateArrayValue {
		return d.stateArrayValue(tok)
	}
	if d.state == stateArrayComma {
		return d.stateArrayComma(tok)
	}
	return nil, io.EOF
}

Benchmarking this explicit if {} else {} …​ version confirms this is close to what the compiler is seeing:

DecoderNextToken/pkgjson/canada-16               5.60ms ± 0%    5.65ms ± 0%  +0.96%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/canada-16          64.9ms ± 1%    65.6ms ± 1%    ~     (p=0.151 n=5+5)
DecoderNextToken/pkgjson/citm_catalog-16         2.41ms ± 1%    2.45ms ± 1%  +1.42%  (p=0.016 n=5+5)
DecoderNextToken/encodingjson/citm_catalog-16    19.8ms ± 1%    19.7ms ± 0%    ~     (p=0.690 n=5+5)
DecoderNextToken/pkgjson/twitter-16              1.17ms ± 1%    1.18ms ± 2%    ~     (p=1.000 n=5+5)
DecoderNextToken/encodingjson/twitter-16         12.2ms ± 1%    12.1ms ± 1%    ~     (p=0.310 n=5+5)
DecoderNextToken/pkgjson/code-16                 6.11ms ± 3%    6.09ms ± 1%    ~     (p=1.000 n=5+5)
DecoderNextToken/encodingjson/code-16            77.7ms ± 2%    77.2ms ± 1%    ~     (p=0.548 n=5+5)
DecoderNextToken/pkgjson/example-16              25.0µs ± 0%    25.0µs ± 1%    ~     (p=0.841 n=5+5)
DecoderNextToken/encodingjson/example-16          263µs ± 1%     264µs ± 1%    ~     (p=0.222 n=5+5)
DecoderNextToken/pkgjson/sample-16                560µs ± 1%     558µs ± 1%    ~     (p=0.841 n=5+5)
DecoderNextToken/encodingjson/sample-16          3.16ms ± 1%    3.19ms ± 1%    ~     (p=0.095 n=5+5)

name                                           old speed      new speed      delta
DecoderNextToken/pkgjson/canada-16              402MB/s ± 0%   398MB/s ± 0%  -0.95%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/canada-16        34.7MB/s ± 1%  34.3MB/s ± 1%    ~     (p=0.151 n=5+5)
DecoderNextToken/pkgjson/citm_catalog-16        716MB/s ± 1%   706MB/s ± 1%  -1.39%  (p=0.016 n=5+5)
DecoderNextToken/encodingjson/citm_catalog-16  87.3MB/s ± 1%  87.6MB/s ± 0%    ~     (p=0.690 n=5+5)
DecoderNextToken/pkgjson/twitter-16             539MB/s ± 1%   537MB/s ± 2%    ~     (p=1.000 n=5+5)
DecoderNextToken/encodingjson/twitter-16       51.9MB/s ± 1%  52.2MB/s ± 1%    ~     (p=0.310 n=5+5)
DecoderNextToken/pkgjson/code-16                318MB/s ± 2%   319MB/s ± 1%    ~     (p=1.000 n=5+5)
DecoderNextToken/encodingjson/code-16          25.0MB/s ± 2%  25.1MB/s ± 1%    ~     (p=0.548 n=5+5)
DecoderNextToken/pkgjson/example-16             521MB/s ± 0%   520MB/s ± 1%    ~     (p=0.841 n=5+5)
DecoderNextToken/encodingjson/example-16       49.5MB/s ± 1%  49.3MB/s ± 1%    ~     (p=0.167 n=5+5)
DecoderNextToken/pkgjson/sample-16             1.23GB/s ± 1%  1.23GB/s ± 1%    ~     (p=0.841 n=5+5)
DecoderNextToken/encodingjson/sample-16         218MB/s ± 1%   216MB/s ± 1%    ~     (p=0.095 n=5+5)

switch is convenient, but not optimal in the hot path. This problem turns up in many places; bytecode interpreters are a classic example. One of the optimisations compilers can make (although the Go compiler does not implement this currently) is to turn this set of if …​ else clauses into a table. This can be space efficient if the state space is small and dense (often it is not), and the result might be something like this

var stateTable = [...]func(*Decoder, []byte) ([]byte, error){
	stateValue:        (*Decoder).stateValue,
	stateObjectString: (*Decoder).stateObjectString,
	stateObjectColon:  (*Decoder).stateObjectColon,
	stateObjectValue:  (*Decoder).stateObjectValue,
	stateObjectComma:  (*Decoder).stateObjectComma,
	stateArrayValue:   (*Decoder).stateArrayValue,
	stateArrayComma:   (*Decoder).stateArrayComma,
	stateEnd:          (*Decoder).stateEnd,
}

func (d *Decoder) NextToken() ([]byte, error) {
	tok := d.scanner.Next()
	if len(tok) < 1 {
		return nil, io.EOF
	}
	return stateTable[d.state](d, tok)
}
Unfortunately this won’t compile because there is an initalisation loop.
./decoder.go:115:5: initialization loop:
        /Users/davecheney/devel/json/decoder.go:115:5: stateTable refers to
        /Users/davecheney/devel/json/decoder.go:155:19: (*Decoder).stateObjectColon refers to
        /Users/davecheney/devel/json/decoder.go:126:19: (*Decoder).NextToken refers to
        /Users/davecheney/devel/json/decoder.go:115:5: stateTable
FAIL    github.com/pkg/json [build failed\]

But there is a better trick that we can use that is more space efficient than this table. It’s one that encoding/json uses and is sometimes called a computed goto .

If you look at the table above there is a pattern. Each state enumeration is matched with exactly one method. We talk about state values as a proxy for the method we want to call. What if we could just store the method directly and call it directly. And infact, we can do just that.

// A Decoder decodes JSON values from an input stream.
type Decoder struct {
	scanner *Scanner
	state   func(*Decoder, []byte) ([]byte, error)
	stack
}

func (d *Decoder) NextToken() ([]byte, error) {
	tok := d.scanner.Next()
	if len(tok) < 1 {
		return nil, io.EOF
	}
	return d.state(d, tok)
}

The the d.state(d.tok) form is known as a method expression . It’s rare to see this in most Go code, but in effect it lets you store a method as a value, then later call that method by supplying your own receiver.

Method expressions aren’t that common because in Go 1.1 the ability to capture the receiver of a method was added.

package main

type T struct{}

func (T) Foo()

func main() {
	x := (T).Foo // method expression
	var t T
	y := t.Foo // regular function value
}

The results aren’t that promising

name                                           old time/op    new time/op    delta
DecoderNextToken/pkgjson/canada-16               5.60ms ± 0%    5.61ms ± 0%     ~     (p=0.841 n=5+5)
DecoderNextToken/encodingjson/canada-16          64.9ms ± 1%    64.4ms ± 0%     ~     (p=0.222 n=5+5)
DecoderNextToken/pkgjson/citm_catalog-16         2.41ms ± 1%    2.42ms ± 1%     ~     (p=0.310 n=5+5)
DecoderNextToken/encodingjson/citm_catalog-16    19.8ms ± 1%    19.7ms ± 1%     ~     (p=0.548 n=5+5)
DecoderNextToken/pkgjson/twitter-16              1.17ms ± 1%    1.20ms ± 2%   +2.70%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/twitter-16         12.2ms ± 1%    12.1ms ± 1%     ~     (p=1.000 n=5+5)
DecoderNextToken/pkgjson/code-16                 6.11ms ± 3%    6.37ms ± 4%   +4.26%  (p=0.016 n=5+5)
DecoderNextToken/encodingjson/code-16            77.7ms ± 2%    78.6ms ± 2%     ~     (p=0.310 n=5+5)
DecoderNextToken/pkgjson/example-16              25.0µs ± 0%    25.6µs ± 1%   +2.25%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/example-16          263µs ± 1%     265µs ± 2%     ~     (p=0.222 n=5+5)
DecoderNextToken/pkgjson/sample-16                560µs ± 1%     553µs ± 0%   -1.15%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/sample-16          3.16ms ± 1%    3.17ms ± 1%     ~     (p=0.690 n=5+5)

name                                           old speed      new speed      delta
DecoderNextToken/pkgjson/canada-16              402MB/s ± 0%   401MB/s ± 0%     ~     (p=0.841 n=5+5)
DecoderNextToken/encodingjson/canada-16        34.7MB/s ± 1%  35.0MB/s ± 0%     ~     (p=0.222 n=5+5)
DecoderNextToken/pkgjson/citm_catalog-16        716MB/s ± 1%   713MB/s ± 1%     ~     (p=0.310 n=5+5)
DecoderNextToken/encodingjson/citm_catalog-16  87.3MB/s ± 1%  87.6MB/s ± 1%     ~     (p=0.548 n=5+5)
DecoderNextToken/pkgjson/twitter-16             539MB/s ± 1%   524MB/s ± 2%   -2.62%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/twitter-16       51.9MB/s ± 1%  52.0MB/s ± 1%     ~     (p=1.000 n=5+5)
DecoderNextToken/pkgjson/code-16                318MB/s ± 2%   305MB/s ± 3%   -4.07%  (p=0.016 n=5+5)
DecoderNextToken/encodingjson/code-16          25.0MB/s ± 2%  24.7MB/s ± 2%     ~     (p=0.310 n=5+5)
DecoderNextToken/pkgjson/example-16             521MB/s ± 0%   509MB/s ± 1%   -2.19%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/example-16       49.5MB/s ± 1%  49.1MB/s ± 2%     ~     (p=0.222 n=5+5)
DecoderNextToken/pkgjson/sample-16             1.23GB/s ± 1%  1.24GB/s ± 0%   +1.16%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/sample-16         218MB/s ± 1%   217MB/s ± 1%     ~     (p=0.690 n=5+5)

But it unlocks several optimisations.

func (d *Decoder) NextToken() ([]byte, error) {
	return d.state(d)
}

func (d *Decoder) stateObjectColon() ([]byte, error) {
	tok := d.scanner.Next()
	if len(tok) < 1 {
		return nil, io.ErrUnexpectedEOF
	}
	switch tok[0] {
	case Colon:
		d.state = (*Decoder).stateObjectValue
		return d.NextToken()
	default:
		return tok, fmt.Errorf("stateObjectColon: expecting colon")
	}
}

Moving the tok := d.scanner.Next() call into each state method might seem like a step backwards, but it has several positive effects.

  • The first is not passing tok into each state method. This saves 3 words on the call stack.

  • The second is, by moving the if len(tok) < 1 into the same function as the switch , it enables bounds check elimination. Previously, when the len(tok) check happened in Decoder.NextToken , Decoder.stateObjectColon doesn’t know anything about the length of tok . When the compiler encounters switch tok[0] , it needs to put a bounds check to make sure tok is at least 1 element long.

    When the if check is moved into the same function, the compiler knows that if we get further than the check then tok is at least 1 element long, so the bounds check is not needed.

    We can see this in the debug information. ` % go build -gcflags=-d=ssa/prove/debug=2 2>&1 | grep 135\: ./decoder.go:135:13: Proved IsInBounds (v31) `

  • The final optimisation occurs because Decoder.NextToken , which was previously too complex to inline

    ./decoder.go:107:6: cannot inline (*Decoder).NextToken: function too complex: cost 145 exceeds budget 80

    is now inlinable

./bench_test.go:217:29: inlining call to (*Decoder).NextToken method(*Decoder) func() ([]byte, error) { return d.state(d) }

Which means, calls to dec.NextToken() in

for {
    _, err := dec.NextToken()
    if err == io.EOF {
        break
    }
    check(b, err)
    n++
}

becomes a direct call to the current state method.

for {
    _, err := dec.state(dec) // this is what the compiler sees
    if err == io.EOF {
        break
    }
    check(b, err)
    n++
}

Let’s look at the results

DecoderNextToken/pkgjson/canada-16               5.60ms ± 0%    4.73ms ± 1%  -15.54%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/canada-16          64.9ms ± 1%    64.2ms ± 1%     ~     (p=0.056 n=5+5)
DecoderNextToken/pkgjson/citm_catalog-16         2.41ms ± 1%    2.17ms ± 2%   -9.94%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/citm_catalog-16    19.8ms ± 1%    19.6ms ± 1%     ~     (p=0.095 n=5+5)
DecoderNextToken/pkgjson/twitter-16              1.17ms ± 1%    1.05ms ± 1%  -10.84%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/twitter-16         12.2ms ± 1%    11.9ms ± 1%   -2.02%  (p=0.008 n=5+5)
DecoderNextToken/pkgjson/code-16                 6.11ms ± 3%    5.15ms ± 1%  -15.77%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/code-16            77.7ms ± 2%    77.0ms ± 2%     ~     (p=0.095 n=5+5)
DecoderNextToken/pkgjson/example-16              25.0µs ± 0%    22.0µs ± 0%  -12.15%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/example-16          263µs ± 1%     263µs ± 0%     ~     (p=0.690 n=5+5)
DecoderNextToken/pkgjson/sample-16                560µs ± 1%     510µs ± 1%   -8.92%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/sample-16          3.16ms ± 1%    3.16ms ± 1%     ~     (p=0.841 n=5+5)

name                                           old speed      new speed      delta
DecoderNextToken/pkgjson/canada-16              402MB/s ± 0%   476MB/s ± 1%  +18.39%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/canada-16        34.7MB/s ± 1%  35.1MB/s ± 1%     ~     (p=0.056 n=5+5)
DecoderNextToken/pkgjson/citm_catalog-16        716MB/s ± 1%   795MB/s ± 2%  +11.05%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/citm_catalog-16  87.3MB/s ± 1%  88.3MB/s ± 1%     ~     (p=0.095 n=5+5)
DecoderNextToken/pkgjson/twitter-16             539MB/s ± 1%   604MB/s ± 1%  +12.16%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/twitter-16       51.9MB/s ± 1%  53.0MB/s ± 1%   +2.05%  (p=0.008 n=5+5)
DecoderNextToken/pkgjson/code-16                318MB/s ± 2%   377MB/s ± 1%  +18.71%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/code-16          25.0MB/s ± 2%  25.2MB/s ± 2%     ~     (p=0.095 n=5+5)
DecoderNextToken/pkgjson/example-16             521MB/s ± 0%   593MB/s ± 0%  +13.82%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/example-16       49.5MB/s ± 1%  49.6MB/s ± 0%     ~     (p=0.651 n=5+5)
DecoderNextToken/pkgjson/sample-16             1.23GB/s ± 1%  1.35GB/s ± 1%   +9.80%  (p=0.008 n=5+5)
DecoderNextToken/encodingjson/sample-16         218MB/s ± 1%   218MB/s ± 1%     ~     (p=0.841 n=5+5)

9-18% improvement over the previous version.

The second part of decoding is conversion from JSON tokens to Go object. In Go this is called unmarshalling .

This part of the package is very much a work in progress. Unmarshalling is the most expensive part of the package because it combines reflect, which is expensive, with unavoidable allocations.

BenchmarkDecoderDecodeInterfaceAny/pkgjson/canada-16                 217          27425893 ns/op          82.08 MB/s     8747163 B/op     281408 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/canada-16            153          38347477 ns/op          58.70 MB/s    20647616 B/op     392553 allocs/op
BenchmarkDecoderDecodeInterfaceAny/pkgjson/citm_catalog-16           747           8008839 ns/op         215.66 MB/s     5197853 B/op      89673 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/citm_catalog-16      360          16607501 ns/op         104.00 MB/s     9406809 B/op      95389 allocs/op
BenchmarkDecoderDecodeInterfaceAny/pkgjson/twitter-16               1606           3714515 ns/op         170.01 MB/s     2130731 B/op      30182 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/twitter-16           862           6927998 ns/op          91.15 MB/s     4283407 B/op      31278 allocs/op
BenchmarkDecoderDecodeInterfaceAny/pkgjson/code-16                   333          17939351 ns/op         108.17 MB/s     7331643 B/op     232059 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/code-16              236          25324951 ns/op          76.62 MB/s    12332753 B/op     271292 allocs/op
BenchmarkDecoderDecodeInterfaceAny/pkgjson/example-16              76874             78079 ns/op         166.81 MB/s       50980 B/op        739 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/example-16         40886            146685 ns/op          88.79 MB/s       82855 B/op        782 allocs/op
BenchmarkDecoderDecodeInterfaceAny/pkgjson/sample-16                5240           1116081 ns/op         615.99 MB/s      399970 B/op       5542 allocs/op
BenchmarkDecoderDecodeInterfaceAny/encodingjson/sample-16           1123           5369313 ns/op         128.04 MB/s     2661872 B/op       7527 allocs/op
  • Can use unsafe here

  • Allocations affect performance. The garbage collector might be fast to allocate and efficient to collect, but not allocating will always be faster.

  • When dealing with data , allocations, can be the biggest performance cost of an algorithm. O(n) allocations—​Per token or per byte—​can be the limiting constraint on an algo, you can never go below that floor.

  • API design influences performance, the encoding/json.Decoder API requires allocations as returning a primitive value via an interface causes it to escape to the heap — it effectively becomes a pointer to the value. This includes strings .

  • Careful attention to the per byte and per token overheads delivered a JSON decoded that performs 2-3x faster than enconding/json.Decoder.Token and 8-10 faster with the alternative NextToken API.

  • O(n) functions per bytes of input are the second limit. Optimising the hot path to convert per byte into per token/line/message/etc batches is key to avoiding function call overhead. Note how I predicted the outcome of encoding/json.Decoder.Token without looking at the code.

  • Beyond this is the domain of micro optimisations. Things like moving statements across function call boundaries to play inlining tricks. Don’t reach for the tricks I showed here without addressing the big O effects in your api. It won’t make any difference.

Performance matters, sometimes

I started this project because I wanted to apply some ideas that I saw to Go. I believed that I could implement an efficient JSON parser based on my assumption that encoding/json was slower than it could be because of its API.

It turned out that I was right, it looks like there is 2-3x performance in some unmarshalling paths and between 8x and 10x performance in tokenisation, if you’re prepared to accept a different API.

In this presentation I made some statements about performance, but these are because I specifically constructed the question such that the run time of the algorithm was the most important feature. Don’t extrapolate my words to mean that all code should optimise for runtime and allocations above all else.

Sometimes performance matters, most times it doesn’t. It probably matters more when writing library code. It probably matters when dealing with input data of an unknown size and quality. It certainly doesn’t matter unless you have metrics that show a code path is too slow, and profiling to identify the cause.

The basics are done. Now what?

  • Better compatibility with the encoding/json package.

  • Reduce allocations in Decoder.NextToken , probably by replacing the []bool state stack with a bit vector.

  • Decode encoded strings in Scanner.parseString , I think this can be done without allocation because the unencoded form is always smaller.

  • Scanner.parseNumber is slow because it visits its input twice; once at the scanner and a second time when it is converted to a float. I did an experiment and the first parse can be faster if we just look to find the termination of the number without validation, canada.json went from 650mb/s to 820mb/sec.

  • The readBuffer model are generally applicable to other kinds of parsing, xml decoding, http requests, source code …​

Benchmarking is hard, especially on laptops.

  • OSX seems to be very poor at process isolation.

  • Downloading a file while running a benchmark cost 10%.

  • Storing my source on iCloud cost 10% (randomly).

  • OSX seems to scan the binary on first run which can slow the first benchmark in a run. Workaround: use go test -c and pause before running the test binary.

  • Workaround: shut down everything on the machine, don’t touch it, don’t let it go to sleep.


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