Benchmark: msgpack-lite vs. JSON parse include

HTML Preparation code:

<script src="https://rawgit.com/kawanet/msgpack-lite/master/dist/msgpack.min.js"></script>

AخA
 
<script src="https://rawgit.com/kawanet/msgpack-lite/master/dist/msgpack.min.js"></script>

Script Preparation code:

var stringData = {"glossary":{"title":"example glossary","GlossDiv":{"title":"S","GlossList":{"GlossEntry":{"ID":"SGML","SortAs":"SGML","GlossTerm":"Standard Generalized Markup Language","Acronym":"SGML","Abbrev":"ISO 8879:1986","GlossDef":{"para":"A meta-markup language, used to create markup languages such as DocBook.","GlossSeeAlso":["GML","XML"]},"GlossSee":"markup"}}}}};
var numberData = [
  [1, 2, 3, 4, -1, true, null],
  [3, 6, 5, 4,  1, false, 7],
  [3, 2, 8, 1,  0, true, 0],
  [10, 11, 12, 13,  14, false, true],
  [15, 16, 17, 18, 19, true, null],
  [20, 21, 22, 23,  24, false, 7],
  [25, 26, 27, 28,  29, true, 0],
  100, 200, 300,
  [
    [1, 0, 0, 0, 0],
    [0, 1, 0, 0, 0],
    [0, 0, 1, 0, 0],
    [0, 0, 0, 1, 0],
    [0, 0, 0, 0, 1]
  ]
];
var jsonEncodedStrings = JSON.stringify(stringData),
    jsonEncodedNumbers = JSON.stringify(numberData),
    msgpackEncodedStrings = msgpack.encode(stringData),
    msgpackEncodedNumbers = msgpack.encode(numberData);

 
var stringData = {"glossary":{"title":"example glossary","GlossDiv":{"title":"S","GlossList":{"GlossEntry":{"ID":"SGML","SortAs":"SGML","GlossTerm":"Standard Generalized Markup Language","Acronym":"SGML","Abbrev":"ISO 8879:1986","GlossDef":{"para":"A meta-markup language, used to create markup languages such as DocBook.","GlossSeeAlso":["GML","XML"]},"GlossSee":"markup"}}}}};var numberData = [  [1, 2, 3, 4, -1, true, null],  [3, 6, 5, 4,  1, false, 7],  [3, 2, 8, 1,  0, true, 0],  [10, 11, 12, 13,  14, false, true],  [15, 16, 17, 18, 19, true, null],  [20, 21, 22, 23,  24, false, 7],  [25, 26, 27, 28,  29, true, 0],  100, 200, 300,  [    [1, 0, 0, 0, 0],    [0, 1, 0, 0, 0],    [0, 0, 1, 0, 0],    [0, 0, 0, 1, 0],    [0, 0, 0, 0, 1]  ]];var jsonEncodedStrings = JSON.stringify(stringData),    jsonEncodedNumbers = JSON.stringify(numberData),    msgpackEncodedStrings = msgpack.encode(stringData),    msgpackEncodedNumbers = msgpack.encode(numberData);

Tests:

MsgPack Strings Encode
var result = msgpack.encode(stringData);
var result = msgpack.encode(stringData);
JSON Strings Encode
var result = JSON.stringify(stringData);
var result = JSON.stringify(stringData);
MsgPack Numbers Encode
var result = msgpack.encode(numberData);
var result = msgpack.encode(numberData);
JSON Numbers Encode
var result = JSON.stringify(numberData);
var result = JSON.stringify(numberData);
MsgPack Numbers decode
var result = msgpack.decode(msgpackEncodedNumbers);
var result = msgpack.decode(msgpackEncodedNumbers);
Msgpack String decode
var result = msgpack.decode(msgpackEncodedStrings);
var result = msgpack.decode(msgpackEncodedStrings);
JSON parse numbers
var result = JSON.parse(jsonEncodedNumbers);
var result = JSON.parse(jsonEncodedNumbers);
JSON parse strings
var result = JSON.parse(jsonEncodedStrings);
var result = JSON.parse(jsonEncodedStrings);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

Test case name	Result
MsgPack Strings Encode
JSON Strings Encode
MsgPack Numbers Encode
JSON Numbers Encode
MsgPack Numbers decode
Msgpack String decode
JSON parse numbers
JSON parse strings

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
MsgPack Strings Encode	342990.2 Ops/sec
JSON Strings Encode	2092594.2 Ops/sec
MsgPack Numbers Encode	399532.0 Ops/sec
JSON Numbers Encode	1302370.8 Ops/sec
MsgPack Numbers decode	1130025.4 Ops/sec
Msgpack String decode	350388.0 Ops/sec
JSON parse numbers	1056902.1 Ops/sec
JSON parse strings	1232080.2 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 5 months ago):

Let's break down the provided benchmark and explain what is being tested, compared options, pros and cons, and other considerations.

Benchmark Definition

The benchmark is designed to compare the performance of two serialization formats: JSON (JavaScript Object Notation) and MessagePack. The test cases are divided into four categories:

Encoding strings
Encoding numbers
Decoding encoded strings
Decoding encoded numbers

Comparison Options

The comparison options are:

JSON Strings Encode: Using JSON.stringify() to encode a string object.
JSON Numbers Encode: Using JSON.stringify() to encode an array of numbers.
MsgPack Strings Encode: Using the msgpack.encode() function to encode a string object.
MsgPack Numbers Encode: Using the msgpack.encode() function to encode an array of numbers.
JSON Parse Strings: Using JSON.parse() to decode a string encoded using JSON.stringify().
JSON Parse Numbers: Using JSON.parse() to decode an array of numbers encoded using JSON.stringify().
MsgPack String Decode: Using the msgpack.decode() function to decode a string encoded using msgpack.encode().
MsgPack Numbers Decode: Using the msgpack.decode() function to decode an array of numbers encoded using msgpack.encode().

Pros and Cons

Here are some pros and cons of each option:

JSON Strings Encode:
- Pros: Wide browser support, easy to implement.
- Cons: Can be slower than MsgPack for large datasets.
MsgPack Numbers Encode:
- Pros: Faster than JSON for encoding numbers, smaller payload size.
- Cons: Less widely supported by browsers.
JSON Parse Strings:
- Pros: Wide browser support, easy to implement.
- Cons: Can be slower than MsgPack for large datasets.
MsgPack String Decode:
- Pros: Faster than JSON for decoding strings, smaller payload size.
- Cons: Requires additional libraries or code to parse MsgPack.

Other Considerations

When choosing between JSON and MsgPack, consider the following factors:

Performance: MsgPack is generally faster than JSON for encoding and decoding large datasets. However, this may not be significant in all use cases.
Browser Support: JSON is widely supported by browsers, while MsgPack requires additional libraries or code to parse.
Payload Size: MsgPack produces smaller payload sizes than JSON for encoded data.
Complexity: JSON can handle more complex data structures (e.g., nested objects), while MsgPack is optimized for simple key-value pairs.

Overall, the choice between JSON and MsgPack depends on your specific use case, performance requirements, and compatibility constraints.

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is being tested, compared options, pros and cons, and other considerations.

**Benchmark Definition**

The benchmark is designed to compare the performance of two serialization formats: JSON (JavaScript Object Notation) and MessagePack. The test cases are divided into four categories:

1. Encoding strings
2. Encoding numbers
3. Decoding encoded strings
4. Decoding encoded numbers

**Comparison Options**

The comparison options are:

* **JSON Strings Encode**: Using `JSON.stringify()` to encode a string object.
* **JSON Numbers Encode**: Using `JSON.stringify()` to encode an array of numbers.
* **MsgPack Strings Encode**: Using the `msgpack.encode()` function to encode a string object.
* **MsgPack Numbers Encode**: Using the `msgpack.encode()` function to encode an array of numbers.
* **JSON Parse Strings**: Using `JSON.parse()` to decode a string encoded using `JSON.stringify()`.
* **JSON Parse Numbers**: Using `JSON.parse()` to decode an array of numbers encoded using `JSON.stringify()`.
* **MsgPack String Decode**: Using the `msgpack.decode()` function to decode a string encoded using `msgpack.encode()`.
* **MsgPack Numbers Decode**: Using the `msgpack.decode()` function to decode an array of numbers encoded using `msgpack.encode()`.

**Pros and Cons**

Here are some pros and cons of each option:

* **JSON Strings Encode**:
	+ Pros: Wide browser support, easy to implement.
	+ Cons: Can be slower than MsgPack for large datasets.
* **MsgPack Numbers Encode**:
	+ Pros: Faster than JSON for encoding numbers, smaller payload size.
	+ Cons: Less widely supported by browsers.
* **JSON Parse Strings**:
	+ Pros: Wide browser support, easy to implement.
	+ Cons: Can be slower than MsgPack for large datasets.
* **MsgPack String Decode**:
	+ Pros: Faster than JSON for decoding strings, smaller payload size.
	+ Cons: Requires additional libraries or code to parse MsgPack.

**Other Considerations**

When choosing between JSON and MsgPack, consider the following factors:

* **Performance**: MsgPack is generally faster than JSON for encoding and decoding large datasets. However, this may not be significant in all use cases.
* **Browser Support**: JSON is widely supported by browsers, while MsgPack requires additional libraries or code to parse.
* **Payload Size**: MsgPack produces smaller payload sizes than JSON for encoded data.
* **Complexity**: JSON can handle more complex data structures (e.g., nested objects), while MsgPack is optimized for simple key-value pairs.

Overall, the choice between JSON and MsgPack depends on your specific use case, performance requirements, and compatibility constraints.

Related benchmarks:

msgpack-lite vs. JSON parse include (version: 0)

Comparing performance of: MsgPack Strings Encode vs JSON Strings Encode vs MsgPack Numbers Encode vs JSON Numbers Encode vs MsgPack Numbers decode vs Msgpack String decode vs JSON parse numbers vs JSON parse strings

Created: 4 years ago by: Guest

Jump to the latest result

MsgPack Strings Encode

JSON Strings Encode

MsgPack Numbers Encode

JSON Numbers Encode

MsgPack Numbers decode

Msgpack String decode

JSON parse numbers

JSON parse strings

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 5 months ago):