Benchmark: msgpack vs. JSON

HTML Preparation code:

<script src="https://unpkg.com/@msgpack/msgpack@2.7.2/dist.es5+umd/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 = MessagePack.encode(stringData),
    msgpackEncodedNumbers = MessagePack.encode(numberData);

Tests:

MsgPack Strings Encode
var result = MessagePack.encode(stringData);
JSON Strings Encode
var result = JSON.stringify(stringData);
MsgPack Numbers Encode
var result = MessagePack.encode(numberData);
JSON Numbers Encode
var result = JSON.stringify(numberData);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
MsgPack Strings Encode
JSON Strings Encode
MsgPack Numbers Encode
JSON Numbers Encode

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):

LLMs can make mistakes. Check important info.

I'll break down the explanation of what's tested, options compared, pros and cons, library usage, special JS features, and alternatives.

**What's being tested:**

The benchmark compares two data encoding formats:

1. **JSON (JavaScript Object Notation)**: a human-readable format for exchanging data between systems.
2. **MsgPack**: a binary-based format that is more compact than JSON.

Specifically, the benchmark tests how quickly each format encodes and decodes:

* Strings (`var jsonEncodedStrings` vs. `msgpackEncodedStrings`)
* Numbers (`var jsonEncodedNumbers` vs. `msgpackEncodedNumbers`)

**Options compared:**

The two options being compared are:

1. **JSON encoding**
2. **MsgPack encoding**

**Pros and Cons of each approach:**

**JSON Encoding:**

Pros:

* Wide adoption and support in many systems
* Easy to read and write for humans
* Supports most data types, including strings, numbers, booleans, arrays, objects

Cons:

* More verbose than MsgPack, resulting in larger encoded files
* May not be as efficient for large datasets or high-performance applications

**MsgPack Encoding:**

Pros:

* More compact than JSON, making it a good choice for large datasets
* Faster execution times due to binary encoding
* Supports most data types, including strings, numbers, booleans, arrays, objects

Cons:

* Less human-readable and more challenging to write for humans
* May require additional libraries or tools for decoding

**Library usage:**

The benchmark uses the `MessagePack` library to encode and decode data. MsgPack is a binary-based data encoding format that provides a compact representation of data.

**Special JS features:**

There are no special JavaScript features mentioned in the benchmark code. However, it's worth noting that JavaScript engines like Firefox have implemented various optimizations and features, such as Just-In-Time (JIT) compilation and Garbage Collection, which can impact performance.

**Alternatives:**

Other alternatives to JSON and MsgPack include:

* **Protocol Buffers**: a language-agnostic data serialization format developed by Google
* **Thrift**: a framework for building scalable and efficient services that uses protocol buffers as the data serialization format
* **Apache Avro**: a data serialization format designed for high-performance and flexibility

These formats are often used in industry applications, but may require additional setup and libraries compared to JSON or MsgPack.

I hope this explanation helps!

Latest run results:

Run details: (Test run date: 5 days ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Windows

View result in a separate tab

Test name	Executions per second
MsgPack Strings Encode	36252.1 Ops/sec
JSON Strings Encode	706793.1 Ops/sec
MsgPack Numbers Encode	159166.4 Ops/sec
JSON Numbers Encode	556989.8 Ops/sec

Related benchmarks:

msgpack vs. JSON (version: 0)

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

Created: 2 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 25 days ago):