Benchmark: Encode vs Blob v2.1

Script Preparation code:

iterations = 1000;
function randomStringGen(n) {
    let strings = [];
    for (let i = 0; i < n; i++) {
      let s = Math.random().toString(36).substring(2, 15) + Math.random().toString(36).substring(2, 15);
      strings[i] = s;
    }
    return strings;
}
var randomStrings = randomStringGen(iterations);
var encoder = new TextEncoder();

Tests:

TextEncoder
var j = 0; for (let i = 0; i < iterations; i++) { var str = randomStrings[i % iterations]; new TextEncoder().encode(str).length }
Blob
var j = 0; for (let i = 0; i < iterations; i++) { var str = randomStrings[i % iterations]; new Blob([str]).size }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
TextEncoder
Blob

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks!

The provided JSON represents a benchmark for comparing two approaches: encoding strings using `TextEncoder` and creating a blob from a string.

**TextEncoder Approach**

In this approach, a `for` loop is used to iterate over an array of random strings generated by the `randomStringGen` function. For each iteration, a new `TextEncoder` object is created, and its `encode` method is called with one of the random strings as an argument. The length of the resulting encoded string is measured.

**Blob Approach**

In this approach, a similar `for` loop is used to iterate over the same array of random strings. For each iteration, a new blob is created from the current random string using the `Blob` constructor with a single-element array containing the string. The size of the resulting blob is then measured.

**Comparison and Considerations**

The main difference between these two approaches lies in how they handle string encoding and conversion to a binary format.

*   **TextEncoder**: This approach uses the `TextEncoder` API, which is designed for efficient encoding of text data into a binary format. The encoded string can be used directly as needed, without requiring additional conversions.
*   **Blob**: In this approach, a blob is created from each string using the `Blob` constructor. While this works, it requires an extra step to measure the size of the resulting blob.

Pros and Cons:

*   **TextEncoder Approach**:
    *   Pros: More efficient encoding process, as `TextEncoder` uses optimized algorithms for string encoding.
    *   Cons: May require additional conversions or processing steps when using the encoded string directly.
*   **Blob Approach**:
    *   Pros: Provides direct access to the binary representation of the string without requiring additional conversions.
    *   Cons: More overhead due to creating a blob, which may impact performance.

**Library and Purpose**

In this benchmark, two libraries are used:

1.  `TextEncoder`: This is a built-in JavaScript library that provides an efficient way to encode text data into a binary format. Its primary purpose is to handle text encoding for various use cases.
2.  `Blob`: This is another built-in JavaScript library that represents a binary file-like object. While not primarily designed for string encoding, it can be used to convert strings to a binary format.

**Special JS Features or Syntax**

There are no specific JavaScript features or syntax mentioned in the benchmark.

**Alternative Approaches**

Other alternatives could include:

1.  **Buffer**: Instead of using `TextEncoder`, you could use the `Buffer` class to encode and manipulate binary data.
2.  **TypedArray**: You could also consider using typed arrays (e.g., `Uint8Array`) for more efficient storage and manipulation of binary data.

Keep in mind that these alternatives would likely introduce additional complexity and may not be as straightforward to implement as the `TextEncoder` approach.

Latest run results:

Run details: (Test run date: 14 days ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36

Browser/OS: Chrome 129 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
TextEncoder	728.8 Ops/sec
Blob	20.4 Ops/sec

Related benchmarks:

Encode vs Blob v2.1 (version: 0)

Comparing performance of: TextEncoder vs Blob

Created: one year ago by: Guest

Jump to the latest result

TextEncoder

Blob

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):