Benchmark: msgpack vs json 2

HTML Preparation code:

<script crossorigin src="https://unpkg.com/@msgpack/msgpack"></script>

Script Preparation code:

var data =  {"L":[{"i":1,"m":1024,"l":1048576,"s":1,"c":2,"f":3,"O":[{"i":1,"k":1},{"i":2,"k":1,"p":1.25},{"i":3,"k":1,"p":2.5},{"i":4,"k":1,"p":3.75},{"i":5,"k":1,"p":5}]},{"i":2,"m":1025,"l":1048577,"s":1,"c":2,"f":3,"O":[{"i":1,"k":1},{"i":2,"k":1,"p":1.25},{"i":3,"k":1,"p":2.5},{"i":4,"k":1,"p":3.75},{"i":5,"k":1,"p":5}]},{"i":3,"m":1026,"l":1048578,"s":1,"c":2,"f":3,"O":[{"i":1,"k":1},{"i":2,"k":1,"p":1.25},{"i":3,"k":1,"p":2.5},{"i":4,"k":1,"p":3.75},{"i":5,"k":1,"p":5}]},{"i":4,"m":1027,"l":1048579,"s":1,"c":2,"f":3,"O":[{"i":1,"k":1},{"i":2,"k":1,"p":1.25},{"i":3,"k":1,"p":2.5},{"i":4,"k":1,"p":3.75},{"i":5,"k":1,"p":5}]},{"i":5,"m":1028,"l":1048580,"s":1,"c":2,"f":3,"O":[{"i":1,"k":1},{"i":2,"k":1,"p":1.25},{"i":3,"k":1,"p":2.5},{"i":4,"k":1,"p":3.75},{"i":5,"k":1,"p":5}]},{"i":6,"m":1029,"l":1048581,"s":1,"c":2,"f":3,"O":[{"i":1,"k":1},{"i":2,"k":1,"p":1.25},{"i":3,"k":1,"p":2.5},{"i":4,"k":1,"p":3.75},{"i":5,"k":1,"p":5}]},{"i":7,"m":1030,"l":1048582,"s":1,"c":2,"f":3,"O":[{"i":1,"k":1},{"i":2,"k":1,"p":1.25},{"i":3,"k":1,"p":2.5},{"i":4,"k":1,"p":3.75},{"i":5,"k":1,"p":5}]},{"i":8,"m":1031,"l":1048583,"s":1,"c":2,"f":3,"O":[{"i":1,"k":1},{"i":2,"k":1,"p":1.25},{"i":3,"k":1,"p":2.5},{"i":4,"k":1,"p":3.75},{"i":5,"k":1,"p":5}]},{"i":9,"m":1032,"l":1048584,"s":1,"c":2,"f":3,"O":[{"i":1,"k":1},{"i":2,"k":1,"p":1.25},{"i":3,"k":1,"p":2.5},{"i":4,"k":1,"p":3.75},{"i":5,"k":1,"p":5}]},{"i":10,"m":1033,"l":1048585,"s":1,"c":2,"f":3,"O":[{"i":1,"k":1},{"i":2,"k":1,"p":1.25},{"i":3,"k":1,"p":2.5},{"i":4,"k":1,"p":3.75},{"i":5,"k":1,"p":5}]}]};
var strBuf = JSON.stringify(data);
var buf = MessagePack.encode(data);

Tests:

MessagePack.encode
var result = MessagePack.decode(buf);
JSON.parse
var result = JSON.parse(strBuf);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
MessagePack.encode
JSON.parse

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 15 days ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/147.0.0.0 Safari/537.36

Browser/OS: Chrome 147 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
MessagePack.encode	142591.9 Ops/sec
JSON.parse	274072.4 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its various components.

**Benchmark Overview**

The benchmark is designed to compare the performance of two data serialization formats: MessagePack (MP) and JSON (JavaScript Object Notation). The benchmark consists of a single test case, where two different scripts are run to measure their execution time.

**Test Case 1: MessagePack.encode**

This script prepares a large data structure in MP format using the `MessagePack.encode()` function. The data structure is defined as a nested object with various properties, including integers, strings, and arrays. The prepared data is then converted to a string buffer using `JSON.stringify()`. Finally, the `MessagePack.encode()` function is called to encode the data buffer into MP format.

**Test Case 2: JSON.parse**

This script prepares a similar large data structure in JSON format using `JSON.stringify()`. The same nested object is defined as in the previous test case. After preparing the data, it is converted to a string buffer and passed directly to the `JSON.parse()` function for decoding.

**Options Compared**

The two test cases compare the following options:

* MessagePack (MP) vs. JSON
* Encoding a large data structure using MP vs. encoding using JSON

**Pros and Cons of Each Approach**

* **MessagePack**
	+ Pros:
		- Smaller binary size compared to JSON, which can lead to faster transmission and storage.
		- Can be more efficient for larger data structures due to its compact representation.
	+ Cons:
		- Less widely supported than JSON, with a smaller community and fewer libraries available.
* **JSON**
	+ Pros:
		- Widely supported by most programming languages and libraries.
		- Human-readable format makes it easier to debug and understand data structures.
	+ Cons:
		- Larger binary size compared to MP, which can lead to slower transmission and storage.

**Library: MessagePack**

The `@msgpack/msgpack` library is used in the benchmark to encode and decode the data structure. This library provides a convenient API for working with MP format, allowing developers to easily serialize and deserialize large data structures.

**Special JS Feature: None**

There are no special JavaScript features or syntaxes being tested in this benchmark.

**Other Alternatives**

If MessagePack is not an option, other alternatives for encoding and decoding data structures include:

* Protocol Buffers (protobuf): A binary format developed by Google that provides efficient serialization and deserialization.
* XML: An extensible markup language that can be used to represent data structures in a human-readable format.

Overall, the benchmark provides a simple yet informative comparison of MessagePack and JSON performance for encoding and decoding large data structures.

Related benchmarks:

msgpack vs json 2 (version: 0)

Comparing performance of: MessagePack.encode vs JSON.parse

Created: 5 years ago by: Guest

Jump to the latest result

MessagePack.encode

JSON.parse

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):