Benchmark: unionby - MeasureThat.net

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

var users = [
  { 'name': 'barney',  'age': 36 },
];

var newUsers = [
  { 'name': 'fred',    'age': 40 },
  { 'name': 'barney',  'age': 36 },
  { 'name': 'pebbles', 'age': 2 }
];

for(let i = 0; i < 2000; i++) {
  var newUser = { 'name': 'user_' + i, 'age': Math.floor(Math.random() * i) };
  newUsers.push(newUser);
  users.push(newUser)
}

Tests:

loadash
const unionUsers2 = _.unionBy(newUsers, users, 'name'); console.log('ub', unionUsers2.length)
native
let obj = {} users.forEach((user) => obj[user.name] = user) newUsers.forEach((user) => obj[user.name] = user) console.log('test', Object.values(obj).length)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
loadash
native

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):

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided JSON represents a JavaScript microbenchmarking test case on MeasureThat.net. The goal is to compare two approaches for merging two arrays of objects based on a common key, "name". The benchmark measures which approach performs better in terms of execution speed.

**Script Preparation Code**

The script preparation code creates two arrays of users:

* `users`: an initial array with one user object
* `newUsers`: an array that will be modified to include new user objects

The script then adds 2000 new user objects to both arrays, using a loop. The new user objects are created randomly and have a unique name.

**Html Preparation Code**

The HTML preparation code includes a reference to the Lodash library, which is used in one of the benchmark test cases.

**Benchmark Test Cases**

There are two individual test cases:

1. **"loadash"`**
This test case uses the `lodash.unionBy` function to merge the `users` and `newUsers` arrays based on the "name" key. The result is stored in the `unionUsers2` variable.
2. **"native"`**
This test case creates an object literal that will store all users from both arrays, using a loop. The result is stored in the `obj` variable.

**Comparison of Approaches**

The two approaches differ in how they handle duplicate keys:

* In the "loadash" approach, if there are duplicate keys, only the last value associated with the key will be kept. This can lead to data loss.
* In the "native" approach, all values associated with a key are stored as an array.

**Pros and Cons of Each Approach**

**Loadash (Lodash)**

Pros:

* Efficient merging algorithm
* Handles duplicate keys by default

Cons:

* Requires Lodash library to be included in the test environment
* May lose data if there are duplicate keys

**Native JavaScript**

Pros:

* Does not require an external library
* Preserves all values associated with a key, even if they are duplicates

Cons:

* More complex implementation
* May lead to increased memory usage due to the use of object literals

**Other Considerations**

* The benchmark only tests merging arrays of objects based on a common key. Other scenarios may require different approaches.
* The use of Lodash library can add complexity to the test environment, especially for small projects or projects with limited resources.

**Alternative Approaches**

In addition to the "loadash" and "native" approaches, other methods could be considered:

* Using `Array.prototype.reduce()` method to merge arrays
* Utilizing a custom implementation using JavaScript's built-in `Map` data structure

However, these alternatives may not provide the same level of efficiency or readability as the "loadash" and "native" approaches.

Related benchmarks:

unionby (version: 0)

Comparing performance of: loadash vs native

Created: 2 years ago by: Guest

Jump to the latest result

loadash

native

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):