Benchmark: Testing unread id search

HTML Preparation code:

<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.11/lodash.js"></script>

Script Preparation code:

var list = [ ...Array(100000).keys() ].map((index) => ({ id: index }));
var fromIndex = 100000;

Tests:

for loop
const nextUnreadIds = { [0]: null, [1]: null, } const populateNextUnreadIds = (nextUnreadIds, fromIndex, list) => { for (let i = fromIndex; i >= 0; i--) { [0, 1].forEach((id) => { if (nextUnreadIds[id] === null && list[i] === id) { nextUnreadIds[id] = list[i].id; } }); if (nextUnreadIds[0] !== null && nextUnreadIds[1] !== null) { break; } } }; populateNextUnreadIds(nextUnreadIds, fromIndex, list);
_.findLastIndex
const nextUnreadIds = { [0]: null, [1]: null, } const populateNextUnreadIds = (nextUnreadIds, fromIndex, list) => { const firstIndex = _.findLastIndex( list, (id) => [0, 1].includes(id), fromIndex ); const firstId = list[firstIndex]; nextUnreadIds[firstId] = firstIndex; const secondIdToFind = firstId === 0 ? 1 : 0; const secondIndex = _.findLastIndex( list, (id) => secondIdToFind === id, firstIndex - 1 ); const secondId = list[secondIndex]; nextUnreadIds[secondId] = secondIndex; }; populateNextUnreadIds(nextUnreadIds, fromIndex, list);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for loop
_.findLastIndex

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

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Let's break down the provided benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Overview**

The benchmark is designed to test two different approaches for populating an object with unread IDs from a list:

1. **For Loop**: Using a traditional `for` loop to iterate through the list and populate the object.
2. **Lodash's findLastIndex**: Utilizing Lodash's `findLastIndex` function to achieve the same result.

**What's being tested**

The benchmark is comparing the performance of these two approaches:

* The first test case uses a `for` loop to iterate through the list and populate the object.
* The second test case uses Lodash's `findLastIndex` function to find the last index in the list where a specific condition is met (in this case, finding the last occurrence of an ID that exists in the `[0, 1]` array).

**Options Compared**

The two options being compared are:

* **Traditional For Loop**: A simple, iterative approach using a `for` loop to iterate through the list.
* **Lodash's findLastIndex**: A more concise and potentially faster approach utilizing Lodash's optimized function.

**Pros and Cons of Each Approach**

**For Loop:**

Pros:

* Easy to understand and implement
* No additional dependencies required

Cons:

* Can be slower due to the overhead of loop iterations
* May not be as efficient for large datasets

**Lodash's findLastIndex:**

Pros:

* More concise and expressive code
* Potential performance benefits due to Lodash's optimized implementation

Cons:

* Requires an external dependency (Lodash)
* May have additional overhead due to function calls and object lookups

**Other Considerations**

In addition to the two tested approaches, other considerations include:

* **List Size**: The benchmark is run with a list of 100,000 elements. This may affect the performance of both approaches.
* **Device Platform**: The benchmark is run on a Mac OS X 10.15.7 machine with Chrome 106. This may impact the results due to differences in browser and operating system implementations.

**Library and Special JS Features**

Lodash is used as a library in the second test case, specifically for its `findLastIndex` function. There are no special JavaScript features mentioned in this benchmark.

Related benchmarks:

Testing unread id search (version: 0)

Comparing performance of: for loop vs _.findLastIndex

Created: 3 years ago by: Guest

Jump to the latest result

for loop

_.findLastIndex

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):