Benchmark: msgpack-lite vs. JSON

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);

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

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one month ago)

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 18_3_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.3 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 18 on iOS 18.3.1

View result in a separate tab

Test name	Executions per second
MsgPack Strings Encode	273978.7 Ops/sec
JSON Strings Encode	3898500.0 Ops/sec
MsgPack Numbers Encode	512698.4 Ops/sec
JSON Numbers Encode	1964817.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

Let's dive into the world of microbenchmarks and explore what's being tested on this benchmark.

Benchmark Overview

The benchmark is designed to compare the performance of two data serialization formats: JSON (JavaScript Object Notation) and MsgPack (a binary-based format). The test cases focus on encoding strings and numbers, which are common use cases in web development.

Options Compared

There are four test cases:

JSON Strings Encode: This test case compares the execution time of JSON.stringify() to encode a string.
JSON Numbers Encode: This test case compares the execution time of JSON.stringify() to encode an array of numbers.
MsgPack Strings Encode: This test case compares the execution time of msgpack.encode() to encode a string.
MsgPack Numbers Encode: This test case compares the execution time of msgpack.encode() to encode an array of numbers.

Pros and Cons

JSON:
- Pros: Widely supported, easy to implement, human-readable format.
- Cons: Larger binary size compared to MsgPack, slower performance for large datasets.
MsgPack:
- Pros: Smaller binary size, faster performance for large datasets, efficient for binary data.
- Cons: Less widely supported, steeper learning curve.

Other Considerations

Library Used: The msgpack-lite library is used to implement MsgPack. This library provides a lightweight implementation of the MsgPack format.
JavaScript Feature: None of the test cases use any special JavaScript features or syntax beyond the standard JSON.stringify() and msgpack.encode() functions.

Benchmark Results

The latest benchmark results show that:

JSON Strings Encode has the highest execution time (2033472.125 executions per second).
MsgPack Numbers Encode has the lowest execution time (470335.8125 executions per second).
MsgPack Strings Encode falls in between, with an execution time of 413110.78125 executions per second.

Alternatives

If you're interested in exploring alternative data serialization formats, consider:

XML: A human-readable format that's widely supported but larger in size compared to JSON and MsgPack.
Binary Format: Implementing a custom binary format can provide faster performance for large datasets but requires more effort to implement.

Keep in mind that the choice of data serialization format ultimately depends on your specific use case, performance requirements, and trade-offs between size, speed, and complexity.

LLMs can make mistakes. Check important info.

Let's dive into the world of microbenchmarks and explore what's being tested on this benchmark.

**Benchmark Overview**

**Options Compared**

There are four test cases:

1. **JSON Strings Encode**: This test case compares the execution time of `JSON.stringify()` to encode a string.
2. **JSON Numbers Encode**: This test case compares the execution time of `JSON.stringify()` to encode an array of numbers.
3. **MsgPack Strings Encode**: This test case compares the execution time of `msgpack.encode()` to encode a string.
4. **MsgPack Numbers Encode**: This test case compares the execution time of `msgpack.encode()` to encode an array of numbers.

**Pros and Cons**

* **JSON**:
	+ Pros: Widely supported, easy to implement, human-readable format.
	+ Cons: Larger binary size compared to MsgPack, slower performance for large datasets.
* **MsgPack**:
	+ Pros: Smaller binary size, faster performance for large datasets, efficient for binary data.
	+ Cons: Less widely supported, steeper learning curve.

**Other Considerations**

* **Library Used**: The `msgpack-lite` library is used to implement MsgPack. This library provides a lightweight implementation of the MsgPack format.
* **JavaScript Feature**: None of the test cases use any special JavaScript features or syntax beyond the standard `JSON.stringify()` and `msgpack.encode()` functions.

**Benchmark Results**

The latest benchmark results show that:

* JSON Strings Encode has the highest execution time (2033472.125 executions per second).
* MsgPack Numbers Encode has the lowest execution time (470335.8125 executions per second).
* MsgPack Strings Encode falls in between, with an execution time of 413110.78125 executions per second.

**Alternatives**

If you're interested in exploring alternative data serialization formats, consider:

* **XML**: A human-readable format that's widely supported but larger in size compared to JSON and MsgPack.
* **Binary Format**: Implementing a custom binary format can provide faster performance for large datasets but requires more effort to implement.

Keep in mind that the choice of data serialization format ultimately depends on your specific use case, performance requirements, and trade-offs between size, speed, and complexity.

Related benchmarks:

msgpack-lite vs. JSON (version: 0)

Comparing performance of: MsgPack Strings Encode vs JSON Strings Encode vs MsgPack Numbers Encode vs JSON Numbers Encode

Created: 7 years ago by: Guest

Jump to the latest result

MsgPack Strings Encode

JSON Strings Encode

MsgPack Numbers Encode

JSON Numbers Encode

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):