Benchmark: Simple string compare vs MurmurHash on a large string

Script Preparation code:

let s10MB = "0123456789".repeat(1000*1000);
var strings10MB = Array.from(Array(20)).map(o=>s10MB + String.fromCharCode(32+~~(Math.random()*96)))

Tests:

===
const s1 = strings10MB[~~(strings10MB.length*Math.random())]; const s2 = strings10MB[~~(strings10MB.length*Math.random())]; const b = s1 === s2;
murmurhash3_32_gc
function murmurhash3_32_gc(str) { var h1 = 0xdeadbeef; var k1 = 0; for (var i = 0; i < str.length; ++i) { k1 = str.charCodeAt(i); k1 = (k1 & 0x0000ffff) | ((k1 & 0xffff0000) >>> 16); k1 *= 0xcc9e2d51; k1 = (k1 << 15) | (k1 >>> 17); h1 ^= k1; h1 = (h1 << 13) | (h1 >>> 19); h1 = h1 * 5 + 0xe6546b64; } h1 ^= str.length; h1 ^= h1 >>> 16; h1 *= 0x85ebca6b; h1 ^= h1 >>> 13; h1 *= 0xc2b2ae35; h1 ^= h1 >>> 16; return h1 >>> 0; // Convert to unsigned 32-bit integer } const s1 = strings10MB[~~(strings10MB.length*Math.random())]; const s2 = strings10MB[~~(strings10MB.length*Math.random())]; const hash1 = murmurhash3_32_gc(s1); const hash2 = murmurhash3_32_gc(s2); const b = hash1 === hash2;

Historical Performance Data aggregated from 5 community runs

Desktop Results:

Chrome 5 runs

===

1K/s

murmurhash3_32_gc

9/s

Fastest: === (125.1x faster)

View exact numbers

✓ ===: 1,086 ops/sec
• murmurhash3_32_gc: 9 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
===
murmurhash3_32_gc

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36 OPR/117.0.0.0

Browser/OS: Opera 117 on Windows

View result in a separate tab

===

817/s

murmurhash3_32_gc

10/s

View exact numbers

Test name	Executions per second
✓ ===	817 Ops/sec
murmurhash3_32_gc	10 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 is being tested.

**Benchmark Definition**

The benchmark consists of two test cases:

1. `==` (Simple string compare)
2. `murmurhash3_32_gc` (MurmurHash algorithm)

**Test Case 1: Simple String Compare (`==`)**

In this test case, a large string (`s10MB`) is generated and split into two random strings (`s1` and `s2`). The comparison of these two strings using the `===` operator is measured.

The pros of this approach are:

* Easy to understand and implement.
* Fast and simple implementation.

However, there are some cons:

* May not accurately represent real-world string comparison scenarios (e.g., case-insensitive comparison, ignoring whitespace).
* Can be affected by caching or optimization techniques in the JavaScript engine.

**Test Case 2: MurmurHash Algorithm (`murmurhash3_32_gc`)**

This test case uses the MurmurHash algorithm to generate a 32-bit hash value from a given string. The same large string (`s10MB`) is used for both input strings, and the resulting hash values are compared.

The pros of this approach are:

* Provides a deterministic and high-quality hash function.
* Can be useful for data integrity or uniqueness checks.

However, there are some cons:

* The implementation of the MurmurHash algorithm can be complex and error-prone.
* May not provide meaningful results for very large input strings (e.g., 10MB).

**Library Used:**

In both test cases, the `strings10MB` array is generated using a repeating string (`"0123456789"`) concatenated with random whitespace characters. This library is used to generate the large input string.

Other Considerations:

* The benchmark uses a relatively small input size (10MB) for the strings.
* The comparison operator (`===`) in Test Case 1 may not accurately represent real-world string comparison scenarios, such as case-insensitive comparison or ignoring whitespace.
* The MurmurHash algorithm is designed to produce deterministic and high-quality hash values, but its performance can be affected by large input sizes.

Alternatives:

* Other hashing algorithms, such as SHA-256 or BLAKE2, could be used instead of MurmurHash.
* Different string comparison operators (e.g., `==`, `===`, `>=`) could be used in Test Case 1 to evaluate their performance.
* Larger input sizes (e.g., 100MB) could be used to evaluate the performance of these algorithms on very large strings.

Overall, this benchmark provides a good starting point for evaluating the performance of simple string comparison and hashing algorithms in JavaScript.

Simple string compare vs MurmurHash on a large string (version: 0)

Worst case large string comparison.

Comparing performance of: `=== vs murmurhash3_32_gc`

Created: 2 years ago by: Guest

Go to the latest result

===

murmurhash3_32_gc

Historical Performance Data aggregated from 5 community runs

Desktop Results:

Chrome 5 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Simple string compare vs MurmurHash on a large string (version: 0)

Worst case large string comparison.

Comparing performance of: === vs murmurhash3_32_gc

Created: 2 years ago by: Guest

Go to the latest result

===

murmurhash3_32_gc

Historical Performance Data aggregated from 5 community runs

Desktop Results:

Chrome 5 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: `=== vs murmurhash3_32_gc`