Benchmark: msgpackr vs. JSON Encode and Decode

HTML Preparation code:

<script src="https://unpkg.com/msgpackr@1.7.2/dist/index.min.js"></script>

AخA
 
<script src="https://unpkg.com/msgpackr@1.7.2/dist/index.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 packer = new msgpackr.Packr({
    // shouldShareStructure(keys){ return true },
    // maxSharedStructures: 1024,
    // randomAccessStructure: true,
    structures: []
});
var jsonEncodedStrings = JSON.stringify(stringData),
    jsonEncodedNumbers = JSON.stringify(numberData),
    msgpackEncodedStrings = packer.pack(stringData),
    msgpackEncodedNumbers = packer.pack(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 packer = new msgpackr.Packr({    // shouldShareStructure(keys){ return true },    // maxSharedStructures: 1024,    // randomAccessStructure: true,    structures: []});var jsonEncodedStrings = JSON.stringify(stringData),    jsonEncodedNumbers = JSON.stringify(numberData),    msgpackEncodedStrings = packer.pack(stringData),    msgpackEncodedNumbers = packer.pack(numberData);

Tests:

MsgPack Strings Encode
var result = packer.pack(stringData);
var result = packer.pack(stringData);
JSON Strings Encode
var result = JSON.stringify(stringData);
var result = JSON.stringify(stringData);
MsgPack Numbers Encode
var result = packer.pack(numberData);
var result = packer.pack(numberData);
JSON Numbers Encode
var result = JSON.stringify(numberData);
var result = JSON.stringify(numberData);
MsgPack Numbers Decode
var result = packer.unpack(msgpackEncodedNumbers);
var result = packer.unpack(msgpackEncodedNumbers);
JSON Numbers Decode
var result = JSON.parse(jsonEncodedNumbers);
var result = JSON.parse(jsonEncodedNumbers);
JSON String Decode
var result = JSON.parse(jsonEncodedStrings);
var result = JSON.parse(jsonEncodedStrings);
MsgPack String Decode
var result = packer.unpack(msgpackEncodedStrings);
var result = packer.unpack(msgpackEncodedStrings);

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
JSON Numbers Decode
JSON String Decode
MsgPack String Decode

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36

Browser/OS: Chrome 133 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
MsgPack Strings Encode	1145799.6 Ops/sec
JSON Strings Encode	2472829.5 Ops/sec
MsgPack Numbers Encode	1325743.9 Ops/sec
JSON Numbers Encode	1715999.8 Ops/sec
MsgPack Numbers Decode	1945298.2 Ops/sec
JSON Numbers Decode	1598824.6 Ops/sec
JSON String Decode	1507480.2 Ops/sec
MsgPack String Decode	1934553.1 Ops/sec

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

Let's dive into the benchmark and explain what's being tested.

Benchmark Overview

The benchmark compares the performance of two serialization libraries: JSON and msgpackr (a message packing library). The test cases are divided into four categories:

Encoding strings
Encoding numbers
Decoding numbers
Decoding strings

Each category has multiple sub-test cases, with different approaches to encode or decode data.

Encoding Strings

The first two test cases in the "Encoding strings" category compare the performance of JSON.stringify() and msgpackr.pack():

JSON.stringify(stringData) : This is a standard JavaScript method that serializes an object into a JSON string.
msgpackr.pack(stringData): This is a custom method provided by the msgpackr library, which serializes an object into a compact binary format.

The pros of using msgpackr.pack() include:

Smaller output size compared to JSON.stringify()
Faster execution times due to the reduced overhead of serialization

However, the cons include:

Non-standard format that may not be easily human-readable or machine-interpretable
Potential compatibility issues with other libraries or frameworks that rely on JSON

Encoding Numbers

The next two test cases in the "Encoding numbers" category compare the performance of JSON.stringify() and msgpackr.pack():

JSON.stringify(numbers) : This is a standard JavaScript method that serializes an array of numbers into a JSON string.
msgpackr.pack(numbers): This is a custom method provided by the msgpackr library, which serializes an array of numbers into a compact binary format.

The pros of using msgpackr.pack() include:

Smaller output size compared to JSON.stringify()
Faster execution times due to the reduced overhead of serialization

However, the cons include:

Potential compatibility issues with other libraries or frameworks that rely on JSON
May not be suitable for large datasets or complex numerical data structures

Decoding Numbers

The next two test cases in the "Decoding numbers" category compare the performance of msgpackr.decode() and JSON.parse():

msgpackr.decode(numbers): This is a custom method provided by the msgpackr library, which deserializes an array of numbers from a compact binary format.
JSON.parse(JSON.stringify(numbers)): This is a standard JavaScript method that parses a JSON string into an object.

The pros of using msgpackr.decode() include:

Faster execution times due to the optimized decoding algorithm
Smaller output size compared to JSON.parse()

However, the cons include:

Potential compatibility issues with other libraries or frameworks that rely on JSON
May not be suitable for large datasets or complex numerical data structures

Decoding Strings

The final two test cases in the "Decoding strings" category compare the performance of msgpackr.decode() and JSON.parse():

msgpackr.decode(stringData): This is a custom method provided by the msgpackr library, which deserializes an object from a compact binary format.
JSON.parse(JSON.stringify(stringData)): This is a standard JavaScript method that parses a JSON string into an object.

The pros of using msgpackr.decode() include:

Faster execution times due to the optimized decoding algorithm
Smaller output size compared to JSON.parse()

However, the cons include:

Potential compatibility issues with other libraries or frameworks that rely on JSON
May not be suitable for large datasets or complex object structures

Conclusion

In summary, the benchmark highlights the performance benefits of using msgpackr over JSON for serializing and deserializing numbers and strings. However, it's essential to consider the potential trade-offs in terms of compatibility, readability, and maintainability.

For most use cases, JSON remains a safe and widely supported choice due to its simplicity and familiarity. However, for specific applications where compactness and speed are critical, msgpackr may offer significant advantages.

Ultimately, the choice between JSON and msgpackr depends on the specific requirements of your project, including factors such as performance, compatibility, and ease of use.

LLMs can make mistakes. Check important info.

Let's dive into the benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark compares the performance of two serialization libraries: JSON and msgpackr (a message packing library). The test cases are divided into four categories:

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

Each category has multiple sub-test cases, with different approaches to encode or decode data.

**Encoding Strings**

The first two test cases in the "Encoding strings" category compare the performance of JSON.stringify() and msgpackr.pack():

* `JSON.stringify(stringData)` : This is a standard JavaScript method that serializes an object into a JSON string.
* `msgpackr.pack(stringData)`: This is a custom method provided by the msgpackr library, which serializes an object into a compact binary format.

The pros of using `msgpackr.pack()` include:

* Smaller output size compared to JSON.stringify()
* Faster execution times due to the reduced overhead of serialization

However, the cons include:

* Non-standard format that may not be easily human-readable or machine-interpretable
* Potential compatibility issues with other libraries or frameworks that rely on JSON

**Encoding Numbers**

The next two test cases in the "Encoding numbers" category compare the performance of JSON.stringify() and msgpackr.pack():

* `JSON.stringify(numbers)` : This is a standard JavaScript method that serializes an array of numbers into a JSON string.
* `msgpackr.pack(numbers)`: This is a custom method provided by the msgpackr library, which serializes an array of numbers into a compact binary format.

The pros of using `msgpackr.pack()` include:

* Smaller output size compared to JSON.stringify()
* Faster execution times due to the reduced overhead of serialization

However, the cons include:

* Potential compatibility issues with other libraries or frameworks that rely on JSON
* May not be suitable for large datasets or complex numerical data structures

**Decoding Numbers**

The next two test cases in the "Decoding numbers" category compare the performance of msgpackr.decode() and JSON.parse():

* `msgpackr.decode(numbers)`: This is a custom method provided by the msgpackr library, which deserializes an array of numbers from a compact binary format.
* `JSON.parse(JSON.stringify(numbers))`: This is a standard JavaScript method that parses a JSON string into an object.

The pros of using `msgpackr.decode()` include:

* Faster execution times due to the optimized decoding algorithm
* Smaller output size compared to JSON.parse()

However, the cons include:

* Potential compatibility issues with other libraries or frameworks that rely on JSON
* May not be suitable for large datasets or complex numerical data structures

**Decoding Strings**

The final two test cases in the "Decoding strings" category compare the performance of msgpackr.decode() and JSON.parse():

* `msgpackr.decode(stringData)`: This is a custom method provided by the msgpackr library, which deserializes an object from a compact binary format.
* `JSON.parse(JSON.stringify(stringData))`: This is a standard JavaScript method that parses a JSON string into an object.

The pros of using `msgpackr.decode()` include:

* Faster execution times due to the optimized decoding algorithm
* Smaller output size compared to JSON.parse()

However, the cons include:

* Potential compatibility issues with other libraries or frameworks that rely on JSON
* May not be suitable for large datasets or complex object structures

**Conclusion**

Ultimately, the choice between JSON and msgpackr depends on the specific requirements of your project, including factors such as performance, compatibility, and ease of use.

Related benchmarks:

msgpackr vs. JSON Encode and Decode (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 JSON Numbers Decode vs JSON String Decode vs MsgPack String Decode

Created: 2 years ago by: Guest

Jump to the latest result

MsgPack Strings Encode

JSON Strings Encode

MsgPack Numbers Encode

JSON Numbers Encode

MsgPack Numbers Decode

JSON Numbers Decode

JSON String Decode

MsgPack String Decode

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):