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popopop
(version: 0)
Comparing performance of:
splice vs map
Created:
2 years ago
by:
Guest
Go to the latest result
Script Preparation code:
var array = Array.from({ length: 100000 }).map((val, i) => i);
Tests:
splice
array.splice(5000, 1, 99999999999)
map
array.map(item => item === 5000 ? 99999999999 : item)
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
splice
map
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
2 years ago
)
User agent:
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36
Browser/OS:
Chrome 125 on Mac OS X 10.15.7
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Embed Benchmark Result
<iframe src="https://measurethat.net/Embed?id=528985" width="100%" height="500px"></iframe>
splice
18.3M/s
map
825/s
View exact numbers
Test name
Executions per second
✓
splice
18,331,564 Ops/sec
map
825 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided benchmark and explain what's being tested, compared, and the pros/cons of different approaches. **Benchmark Definition JSON** The benchmark definition is a simple JavaScript object with four properties: * `Name`: A unique name for the benchmark (in this case, "popopop"). * `Description`: An empty string, indicating that there's no detailed description provided. * `Script Preparation Code`: A snippet of JavaScript code that prepares the script before running the benchmark. In this case, it creates an array with 100,000 elements using `Array.from()` and maps each element to its index. * `Html Preparation Code`: An empty string, indicating that there's no HTML-related setup required. **Individual Test Cases** The individual test cases are defined in a separate array, which contains two test cases: 1. `splice`: * `Benchmark Definition`: The code that defines the benchmark being tested: `array.splice(5000, 1, 99999999999)`. * `Test Name`: A descriptive name for the test case ("splice"). 2. `map`: * `Benchmark Definition`: The code that defines the benchmark being tested: `array.map(item => item === 5000 ? 99999999999 : item)`. * `Test Name`: A descriptive name for the test case ("map"). **What's Being Tested?** Both test cases measure the performance of JavaScript arrays with a specific operation: 1. `splice` tests how fast an array can be modified by replacing a single element. 2. `map` tests how fast an array can be transformed by applying a function to each element. **Options Compared** In both test cases, only one option is compared: * In the `splice` test case, there's no explicit comparison of different approaches; instead, it simply measures the time taken for the specific operation. * In the `map` test case, there's also no explicit comparison of different approaches. However, the benchmark might implicitly compare the performance of using a function versus not using a function (due to the presence of `item === 5000 ? 99999999999 : item`, which implies a conditional statement). **Pros and Cons** * **No explicit comparisons**: Both test cases measure the time taken for a specific operation without comparing different approaches. This might make it difficult to determine which approach is faster or more efficient. * **Limited insights**: With only one option being compared, the benchmark provides limited information about performance optimization strategies. **Libraries Used (if applicable)** In this example, there are no libraries explicitly used in the benchmark definition code. **Special JS Features or Syntax** There's a special syntax used in both test cases: * `Array.from()`: This is a modern JavaScript feature introduced in ECMAScript 2015. It creates a new array from an iterable source. * `map()` and `splice()`: These are built-in JavaScript methods for array operations. **Other Alternatives** If you'd like to compare different approaches, you could consider modifying the benchmark definition code to include multiple options, such as: 1. Using `Array.prototype.map()` versus a custom loop or recursive function. 2. Modifying the array size or using a different data structure (e.g., an object or set). 3. Adding additional elements or modifying the existing array in other ways. By experimenting with these alternatives, you can gain insights into performance optimization strategies and identify which approaches are faster or more efficient for specific use cases.
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