Benchmark: includes vs some vs every

Script Preparation code:

var array1000 = [...Array(1000).keys()]

Tests:

includes
array1000.includes(999)
some
array1000.some(x => x === 999)
every
array1000.every(x => x!==999)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
includes
some
every

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (Linux; Android 10; K) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36

Browser/OS: Chrome 123 on Android

View result in a separate tab

Test name	Executions per second
includes	11305319.0 Ops/sec
some	876815.0 Ops/sec
every	876546.9 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Definition**

The provided JSON represents a benchmark definition, which outlines the parameters and rules for testing various JavaScript operations. The "Name" field indicates that this benchmark compares different approaches to checking for an element in an array: `includes`, `some`, and `every`.

**Options Compared**

The three options compared are:

1. **includes**: Checks if a specific value (in this case, 999) is present in the array using the `includes()` method.
2. **some**: Returns true if at least one element in the array satisfies the condition specified in the callback function (`x => x === 999`).
3. **every**: Returns true if all elements in the array satisfy the condition specified in the callback function (`x => x !== 999`).

**Pros and Cons**

Here's a brief summary of each approach:

* **includes**:
	+ Pros: Simple, efficient, and widely supported.
	+ Cons: May have performance issues with large arrays or complex values, as it uses a linear search algorithm.
* **some**:
	+ Pros: More flexible than `includes`, allowing for custom conditions. Can be faster for larger arrays when using a well-optimized implementation.
	+ Cons: May not be as straightforward to understand and use, especially for those familiar with `includes`.
* **every**:
	+ Pros: Provides a clear and concise way to check all elements in an array against a condition.
	+ Cons: Can be slower than `some` due to its more exhaustive search algorithm.

**Library Use**

None of the provided test cases explicitly use any external libraries. However, it's worth noting that some JavaScript implementations might have optimizations or specializations for specific library functions (e.g., `Array.prototype.includes()`).

**Special JS Features/Syntax**

None of the individual test cases demonstrate any special JavaScript features or syntax.

**Other Alternatives**

If you're looking to benchmark other approaches to array operations, consider exploring:

1. **Map-based approach**: Iterate over an array and use a map to count occurrences of the target value.
2. **Reduce-based approach**: Use `reduce()` to iterate over an array and accumulate results.
3. **Custom implementation using indexing**: Write a custom function that uses array indexing to directly access elements.

Keep in mind that each alternative approach may have its own trade-offs and optimization opportunities.

**Benchmarking Best Practices**

When creating your own benchmarks on MeasureThat.net or elsewhere:

1. Use relevant and representative test cases.
2. Ensure accurate measurements by minimizing overhead and avoiding unnecessary operations.
3. Optimize the benchmark code to eliminate bottlenecks.
4. Compare results across different browsers, devices, and platforms to ensure generality.

By following these guidelines and exploring various approaches, you can gain a deeper understanding of JavaScript's strengths and weaknesses in performing array operations.

Related benchmarks:

includes vs some vs every (version: 0)

Comparing performance of: includes vs some vs every

Created: 8 months ago by: Guest

Jump to the latest result

includes

some

every

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):