Benchmark: Array splice vs delete

Script Preparation code:

var array = [].fill(1,0,10000)

Tests:

delete
for(var x=0; x<array.length; x++){ delete array[x] }
splice
while(array.length - 3 > 0){ array.splice(2,1) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
delete
splice

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Android 15; Mobile; rv:136.0) Gecko/136.0 Firefox/136.0

Browser/OS: Firefox Mobile 136 on Android

View result in a separate tab

Test name	Executions per second
delete	323325600.0 Ops/sec
splice	322777952.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

LLMs can make mistakes. Check important info.

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

**Benchmark Definition and Purpose**

The provided benchmark definition is to compare the performance of two approaches: `delete` and `splice`. Both approaches are used to remove elements from an array in JavaScript. The goal of this benchmark is to determine which approach is faster.

**Options Compared**

Two options are compared:

1. **Delete**: Using the `delete` operator to remove individual elements from the array.
2. **Splice**: Using the `splice()` method to remove multiple elements from the array at once.

**Pros and Cons of Each Approach**

**Delete**

Pros:

* More intuitive and straightforward
* Only removes a single element, which can be beneficial for small arrays or specific use cases

Cons:

* Can lead to a chain of multiple `delete` operations if an array is modified frequently, leading to slower performance due to the overhead of repeated checks.
* May not be optimized by JavaScript engines for this specific operation.

**Splice**

Pros:

* More efficient in terms of memory management, as it only modifies the index range and doesn't require individual element removals.
* Can be more suitable for larger arrays or when multiple elements need to be removed at once.

Cons:

* May seem more complex or less intuitive due to its higher-order nature (i.e., it modifies a range of indices rather than individual elements).
* Can lead to slower performance if the array is modified frequently, as the engine needs to calculate the new index ranges and handle potential reallocations.

**Library and Special JS Feature**

There are no libraries or special JavaScript features mentioned in this benchmark. The focus is solely on comparing two built-in JavaScript methods: `delete` and `splice`.

**Other Considerations**

When considering performance-critical code, it's essential to think about the following:

* **Array size**: Larger arrays may favor `splice` due to its more efficient memory management.
* **Frequent array modifications**: If an array is modified frequently, `delete` might lead to slower performance due to repeated checks. In such cases, using a data structure like a linked list or a set can improve performance.
* **Engine optimizations**: JavaScript engines may optimize certain operations over others, leading to variations in performance across different browsers and versions.

**Other Alternatives**

If you're interested in exploring more microbenchmarks or alternative approaches, MeasureThat.net offers a wide range of benchmarks covering various aspects of JavaScript performance. Some examples include:

* Microbenchmarking other array methods (e.g., `push`, `pop`, `shift`, `unshift`)
* Comparing the performance of different data structures (e.g., arrays, objects, linked lists)
* Investigating the impact of CSS or DOM manipulation on JavaScript performance

Keep in mind that each benchmark has its unique characteristics and use cases. Feel free to explore MeasureThat.net to find more information and insights into JavaScript performance.

Related benchmarks:

Array splice vs delete (version: 0)

Comparing performance of: delete vs splice

Created: one year ago by: Guest

Jump to the latest result

delete

splice

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):