Benchmark: Comparison solution A

Script Preparation code:

var arr = [
  {
    uuid:'abcd-efgh',
    total:2
  }, {
    uuid:'abcd-efgh',
    total:4
  }, {
    uuid:'ijkl-mnop',
    total:5
  },
]

Tests:

User A
const expectedOutput = [...new Set(arr.map(data => data.uuid))].map(uniqueUUID=>{ return { uuid: uniqueUUID, total: (arr.filter(data=>data.uuid===uniqueUUID)).reduce((a,b)=>a+b.total,0) } })
Map()
const mapArr = new Map(); arr.forEach((dtArr)=>{ const target = mapArr.get(dtArr.uuid) mapArr.set(dtArr.uuid,{ uuid: dtArr.uuid, total: dtArr.total+(target?.total??0) }) }) const res = [...mapArr].map((dtMap)=>dtMap[1])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
User A
Map()

Fastest: N/A

Slowest: N/A

Latest 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 JSON and explain what is being tested, compared, and their pros and cons.

**Benchmark Definition**

The benchmark definition represents two different approaches to solving the problem of aggregating data by UUID while removing duplicates. The two approaches are:

1. **Approach 1: "User A"**
This approach uses JavaScript's `map()` function in combination with a `Set` object to remove duplicates. The script first creates an array of unique UUIDs using the `map()` function, and then maps each unique UUID to an object containing the UUID and the aggregated total value.

Pros:

* Simple and concise code
* Easy to understand and maintain

Cons:

* May be less efficient than other approaches due to the overhead of creating a `Set` object and mapping over the array multiple times.

2. **Approach 2: "Map()"**
This approach uses JavaScript's built-in `Map` data structure to store and retrieve aggregated values for each UUID. The script first iterates over the input array, updating the map with the aggregated value for each UUID. Then, it converts the map back into an array of objects using the spread operator (`[...mapArr]`) and maps over this array to extract the relevant data.

Pros:

* Can be more efficient than Approach 1 due to the use of a `Map` object, which can provide faster lookup times.
* Allows for easy extension or modification of the aggregated values without having to rewrite the entire script.

Cons:

* May have higher memory usage compared to Approach 1 due to the need to store additional data in the map.
* Requires more complex code to understand and maintain.

**Libraries and Special JS Features**

In this benchmark, there are no libraries used that require special JavaScript features. The `Set` object and `Map` data structure are built-in JavaScript APIs that do not require any external libraries or special syntax.

However, it's worth noting that the use of arrow functions (`=>`) in the `map()` function calls is a modern JavaScript feature that was introduced in ECMAScript 2015 (ES6). This syntax allows for concise and readable code, but may not be compatible with older versions of JavaScript.

**Other Alternatives**

There are several alternative approaches to solving this problem that could have been used instead of the two approaches presented here. Some examples include:

* Using a custom function or class to aggregate data
* Utilizing a third-party library such as Lodash or Ramda
* Implementing a more complex algorithm using recursion or iteration

However, these alternatives would likely result in more complex code and may not provide a significant performance advantage over the two approaches presented here.

**Benchmark Results**

The latest benchmark results show that the "Map()" approach is slightly faster than the "User A" approach, with an average execution rate of 834321.4375 per second compared to 979851.625 per second for the "User A" approach. However, the performance difference between the two approaches may not be significant enough to justify using one approach over the other in all cases.

Overall, this benchmark provides a useful comparison of different approaches to solving a common problem, and highlights the importance of considering factors such as performance, memory usage, and code complexity when choosing an approach for a particular use case.

Related benchmarks:

Comparison solution A (version: 0)

Comparing performance of: User A vs Map()

Created: 3 years ago by: Guest

Jump to the latest result

User A

Map()

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):