Benchmark: Lodash omit vs Native Delete for multiple key removal

HTML Preparation code:

<script src="lodash.js"></script>

Script Preparation code:

data = {
    abf: 'asd',
    message: 'koko',
    abf2: 'asd2',
    abf3: 'asd3',
    abf4: 'asd4',
    abf5: 'asd5',
    abf6: 'asd6',
    abf7: 'asd7',
    abf8: 'asd8',
    abf9: 'asd9'
}

Tests:

Native multiple delete
const meta = Object.assign({}, data); ['abf1', 'abf2', 'abf5', 'abf7', 'abf9'].forEach(e => delete meta[e]); return { message: data, meta }
Lodash multiple omit
return { message: data, meta: _.omit(data, ['abf1', 'abf2', 'abf5', 'abf7', 'abf9']) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native multiple delete
Lodash multiple omit

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 10 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/137.0.0.0 Safari/537.36

Browser/OS: Chrome 137 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Native multiple delete	3433953.8 Ops/sec
Lodash multiple omit	1186478.8 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its test cases.

**Benchmark Purpose**

The purpose of this benchmark is to compare two approaches for removing multiple keys from an object: using the `delete` operator (native approach) and using the `omit` function from the Lodash library (Lodash approach).

**Options Compared**

Two options are being compared:

1. **Native Delete**: This approach uses the `delete` operator to remove the specified keys from the `meta` object.
2. **Lodash Multiple Omit**: This approach uses the `omit` function from the Lodash library to remove the specified keys from the `data` object.

**Pros and Cons**

**Native Delete**

Pros:

* Native implementation, so it's likely to be faster and more efficient.
* No external dependencies required.

Cons:

* Can be less readable and maintainable, as it requires using the `delete` operator in a loop.
* May not handle edge cases or errors as well as Lodash.

**Lodash Multiple Omit**

Pros:

* More readable and maintainable, as it uses a single function call to remove keys.
* Handles edge cases and errors better than native delete.

Cons:

* Requires an external dependency (Lodash library).
* May be slower due to the overhead of loading the library.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a wide range of functions for common tasks, such as array manipulation, object modification, and more. The `omit` function is used in this benchmark to remove keys from an object.

**Special JS Feature/Syntax**

None mentioned in the provided code snippet.

**Other Alternatives**

If you prefer not to use Lodash or the `delete` operator, other alternatives for removing multiple keys from an object include:

* Using a library like Underscore.js (another popular utility library)
* Implementing your own custom function for removing keys
* Using a alternative data structure, such as a Map or a Set, that doesn't require explicit key removal.

For example, you could use the following code to remove multiple keys from an object using a custom function:
```javascript
function removeKeys(obj, keys) {
  return Object.keys(obj).reduce((newObj, key) => {
    if (!keys.includes(key)) {
      newObj[key] = obj[key];
    }
    return newObj;
  }, {});
}
```
This approach is more readable and maintainable than the native delete approach, but may be slower due to the overhead of creating a new object.

Related benchmarks:

Lodash omit vs Native Delete for multiple key removal (version: 0)

Comparing performance of: Native multiple delete vs Lodash multiple omit

Created: 4 years ago by: Guest

Jump to the latest result

Native multiple delete

Lodash multiple omit

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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