Benchmark: ArrayBuffer vs JSON serialization ---- v3

Tests:

JSON serialization
const arr = Array.from({ length: 5000 }).map(() => Math.random()); const serialized = JSON.stringify(arr); const deserialized = JSON.parse(serialized); const serializedAgain = JSON.stringify(deserialized);
ArrayBuffer serialization
const arr = Array.from({ length: 5000 }).map(() => Math.random()); const buffer = new ArrayBuffer(arr.length * 10); const view = new DataView(buffer); const l = arr.length * 4; for (let i = 0; i<l; i+=4){ view.setFloat32(arr[i]); } const view2 = new DataView(buffer);

Historical Performance Data aggregated from 9 community runs

Desktop Results:

Chrome 9 runs

ArrayBuffer serialization

5K/s

JSON serialization

2K/s

Fastest: ArrayBuffer serialization (2.7x faster)

View exact numbers

✓ ArrayBuffer serialization: 4,949 ops/sec
• JSON serialization: 1,860 ops/sec

Results show median performance across all community submissions. Run this benchmark yourself to verify on your device.

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
JSON serialization
ArrayBuffer serialization

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/150.0.0.0 Safari/537.36

Browser/OS: Chrome 150 on Mac OS X 10.15.7

View result in a separate tab

ArrayBuffer serialization

2K/s

JSON serialization

1K/s

View exact numbers

Test name	Executions per second
✓ ArrayBuffer serialization	2,385 Ops/sec
JSON serialization	1,093 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 provided benchmark and explain what's being tested.

**Overview**

The benchmark compares two approaches for serializing and deserializing large arrays of random numbers:

1. **ArrayBuffer serialization**: This approach uses the `ArrayBuffer` object to store the array data in a binary format, which can be efficiently stored and transmitted.
2. **JSON serialization**: This approach uses the JavaScript `JSON.stringify()` function to convert the array into a text-based JSON string, which can be easily human-readable but may not be as efficient for large datasets.

**Options being compared**

The two options are:

1. **ArrayBuffer**: A binary buffer object that can store raw data in a compact format.
2. **JSON**: A lightweight data interchange format that represents data as a text-based JSON string.

**Pros and cons of each approach:**

* **ArrayBuffer**:
	+ Pros:
		- More efficient storage and transmission of large datasets
		- Can be used for binary data, such as images or audio files
		- Supports direct memory access through `DataView`
	+ Cons:
		- Requires JavaScript engine support for binary buffers
		- May require additional code to handle serialization and deserialization
* **JSON**:
	+ Pros:
		- Human-readable format makes it easier to debug and visualize data
		- Can be easily parsed by humans or other languages (e.g., JSON libraries)
		- Supports schema validation and data normalization
	+ Cons:
		- May require additional overhead due to stringification and parsing
		- Not as efficient for large datasets

**Other considerations:**

* **JavaScript engine**: The benchmark assumes that the JavaScript engine being used can handle binary buffers and supports `DataView`. Some older engines may not support this feature.
* **Library usage**: In both test cases, no library is explicitly mentioned. However, JSON serialization relies on built-in `JSON` functions, while ArrayBuffer serialization requires manual implementation or a custom library.

**Special JS features/syntax:**

* None are mentioned in the provided benchmark definition.

**Alternatives:**

If you need to compare other approaches for serializing and deserializing large datasets, consider:

1. **Binary formats**: Other binary formats like MessagePack, Protocol Buffers, or SQLite BLOBs could be compared.
2. **Streaming algorithms**: Algorithms that process data in chunks, such as compression, encryption, or deduplication, might be of interest.
3. **Data storage technologies**: Storage solutions like relational databases (e.g., MySQL), NoSQL databases (e.g., MongoDB), or distributed file systems (e.g., Apache HDFS) could be compared.

Keep in mind that the specific alternatives will depend on your use case and requirements.

ArrayBuffer vs JSON serialization ---- v3 (version: 0) Benchmark.js

Comparing performance of: `JSON serialization vs ArrayBuffer serialization`

Created: 4 years ago by: Guest

Go to the latest result

JSON serialization

ArrayBuffer serialization

Historical Performance Data aggregated from 9 community runs

Desktop Results:

Chrome 9 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

ArrayBuffer vs JSON serialization ---- v3 (version: 0) Benchmark.js

Comparing performance of: JSON serialization vs ArrayBuffer serialization

Created: 4 years ago by: Guest

Go to the latest result

JSON serialization

ArrayBuffer serialization

Historical Performance Data aggregated from 9 community runs

Desktop Results:

Chrome 9 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Related benchmarks

Comparing performance of: `JSON serialization vs ArrayBuffer serialization`