Benchmark: is find arrays cached

Script Preparation code:

var data = Array(1000).fill().map((_, id) => ({
    id,
    name: `${id}_name`
}));
var data2 = Array(1000).fill().map((_, id) => ({
    id,
    name: `${id}_name`
}));

Tests:

single find (500)
const found = data.find((item) => item.id === 500); const result = { id: found?.id, name: found?.name, };
double same find (500)
const result = { id: data.find((item) => item.id === 500)?.id, name: data.find((item) => item.id === 500)?.name, };
double different find (500)
const result = { id: data.find((item) => item.id === 500)?.id, name: data2.find((item) => item.id === 500)?.name, };
single find (100)
const found = data.find((item) => item.id === 100); const result = { id: found?.id, name: found?.name, }
double same find (100)
const result = { id: data.find((item) => item.id === 100)?.id, name: data.find((item) => item.id === 100)?.name, };
double different find (100)
const result = { id: data.find((item) => item.id === 100)?.id, name: data2.find((item) => item.id === 100)?.name, };

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
single find (500)
double same find (500)
double different find (500)
single find (100)
double same find (100)
double different find (100)

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:94.0) Gecko/20100101 Firefox/94.0

Browser/OS: Firefox 94 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
single find (500)	281695.9 Ops/sec
double same find (500)	103856.5 Ops/sec
double different find (500)	103414.5 Ops/sec
single find (100)	1599733.8 Ops/sec
double same find (100)	494500.4 Ops/sec
double different find (100)	500015.2 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the provided JSON benchmark definition and explain what's being tested, compared options, pros and cons of each approach, and other considerations.

**Benchmark Definition**

The benchmark is designed to measure the performance of JavaScript array find() method with different variations. The script preparation code creates two arrays, `data` and `data2`, both containing 1000 objects with unique IDs and names.

**Test Cases**

There are four test cases:

1. **Single Find (500)**: Finds an object in the `data` array where the ID is 500.
2. **Double Same Find (500)**: Finds an object in both `data` and `data2` arrays where the ID is 500.
3. **Double Different Find (100)**: Finds objects in both `data` and `data2` arrays where the ID is either 100 or 500.
4. **Single Find (100)**: Finds an object in the `data` array where the ID is 100.

**Comparison of Options**

The benchmark tests different approaches to improve performance:

1. **Early Returns**: Some test cases use early returns, which can short-circuit the search if a match is found.
	* Pros: Can lead to faster execution times for small arrays or when a single element is sought.
	* Cons: May not be effective for large arrays or when multiple elements are searched.
2. **Looping and Destructuring**: Some test cases use looping and destructuring to iterate over the array and extract the desired properties.
	* Pros: Can be more flexible and suitable for larger arrays or complex data structures.
	* Cons: May lead to slower execution times due to the overhead of loops and destructuring.
3. **Native Array Find() Method**: The benchmark tests the native JavaScript `find()` method, which is implemented in the browser's engine.
	* Pros: Typically the fastest option for finding a single element in an array.
	* Cons: May not be as efficient for large arrays or complex data structures.

**Library and Purpose**

In this benchmark, no specific library is used. However, some test cases use the `?` operator (also known as the optional chaining operator) to access properties on objects that may be null or undefined.

**Special JS Features**

The benchmark uses:

1. **Arrow functions**: Used in the script preparation code to create arrays and objects.
2. **Destructuring assignment**: Used in some test cases to extract properties from objects.
3. **Early returns**: Used in some test cases to short-circuit the search if a match is found.

**Other Considerations**

1. **Browser Variability**: The benchmark runs on different browsers (Firefox 94) and platforms, which can lead to variations in execution times due to differences in engine optimizations or implementation details.
2. **Device Platform and Operating System**: The benchmark runs on desktop devices with Mac OS X 10.15 operating systems, which may affect performance due to the specific hardware and software configuration.

**Alternatives**

Other approaches to improve performance could include:

1. **Using a Binary Search Algorithm**: Instead of linear search, a binary search algorithm can be used to find an element in the array more efficiently.
2. **Caching Results**: If the same elements are searched multiple times, caching results can reduce the number of iterations and improve performance.
3. **Parallelizing Searches**: For large arrays, parallelizing searches using multi-threading or concurrent execution can improve performance on modern CPUs.

Keep in mind that these alternatives may have additional complexity and overhead, and may not always lead to improved performance. The benchmark's goal is to test the native JavaScript `find()` method and provide a baseline for comparison with other approaches.

Related benchmarks:

is find arrays cached (version: 0)

Comparing performance of: single find (500) vs double same find (500) vs double different find (500) vs single find (100) vs double same find (100) vs double different find (100)

Created: 3 years ago by: Guest

Jump to the latest result

single find (500)

double same find (500)

double different find (500)

single find (100)

double same find (100)

double different find (100)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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