Benchmark: sort vs find and index - 10

Script Preparation code:

var target = [];
for(var i = 0;i < 24; i++){
  target.push(i)
}

throttle = 4;

var updates = [];
for(var i =0; i < 100; i++){
  updates.push(i)
}

Tests:

sort
result = target.slice(); for(var i = 0; i < 100; i++){ c = result.slice(); u = updates[i] result.push(u) if(i % throttle == 0){ result.slice().sort((a, b) => a[0] - b[0]) } //result = result.slice(25) }
insertat
result = target.slice(); for(var i = 0; i < 100; i++){ result = result.slice(); u = updates[i] insertAt = result.findIndex((a) => a >= u) || result.length; result.splice(insertAt, 0, u) //result = result.slice(25) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
sort
insertat

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's dive into the world of JavaScript microbenchmarks!

**Benchmark Definition**
The benchmark definition is provided in JSON format, which describes a test case that compares two approaches: sorting at each update and inserting.

**What is tested?**

In this benchmark, we have two main operations:

1. Sorting at each update: This involves creating an array `target` with 24 elements, then repeatedly pushing new values onto it while also sorting the array every time the length of `updates` exceeds a threshold (set to 4). The goal is to measure the performance of this approach.
2. Insertion: This involves creating an array `target` with 24 elements, then repeatedly finding the index at which to insert new values from `updates`, and inserting them into `target`. The goal is to measure the performance of this approach.

**Options compared**

The two options are compared in terms of their performance, measured in executions per second.

**Pros and Cons**

1. **Sorting at each update**:
	* Pros: Can be useful for certain data structures or algorithms that require frequent updates.
	* Cons: Can be slow for large datasets, as sorting involves reordering the entire array.
2. **Insertion**:
	* Pros: Generally faster than sorting, especially for large datasets, as it only requires updating a specific index.
	* Cons: May not be suitable for data structures that require frequent updates or reordering.

**Library and its purpose**

The `throttle` variable is used to control when the array is sorted. However, there is no explicit library mentioned in this benchmark definition. The `slice()` method is used, which is a built-in JavaScript method.

**Special JS feature or syntax**

There are no special features or syntax used in this benchmark that require deep knowledge of JavaScript. The code uses standard JavaScript syntax and built-in methods.

**Other considerations**

In addition to performance, other factors like memory usage, cache efficiency, and robustness could also be considered when evaluating these two approaches.

**Alternatives**

If you're interested in exploring alternative approaches or optimizing the performance of this benchmark, here are some potential avenues:

* Using a data structure that allows for efficient insertion and updating, such as a balanced binary search tree or a heap.
* Optimizing the sorting algorithm used (e.g., using a more efficient sorting algorithm like mergesort or heapsort).
* Using parallel processing or multi-threading to take advantage of multiple CPU cores.
* Considering caching mechanisms to reduce the number of times the array needs to be sorted.

Keep in mind that these are just general suggestions, and the best approach will depend on the specific requirements and constraints of your use case.

Related benchmarks:

sort vs find and index - 10 (version: 2)

What is faster, sorting at each update ot inserting

Comparing performance of: sort vs insertat

Created: 4 years ago by: Registered User

Jump to the latest result

sort

insertat

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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