Benchmark: quick testasdfasdf

Script Preparation code:

var documents = []
var orderIds = []
let i = 0
let len = 1000

for (; i < len; i++) {
    orderIds.push(len - i);
    documents.push({
        orderId: i,
        documents: [{
            id: 'test'
        }]
    });
}

Tests:

reduce + map keyed access
var keyedOrdersDocuments = documents.reduce((acc, { orderId: id, documents }) => ({...acc, [id]: documents}), {}); var result = orderIds.map((id) => keyedOrdersDocuments[id]);
map + find access
var result = orderIds.map((id) => documents.find(({orderId})=> id === orderId));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce + map keyed access
map + find access

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what is being tested.

**Benchmark Definition**

The benchmark definition represents a JavaScript microbenchmark. It consists of three parts:

1. **Script Preparation Code**: This code initializes variables, sets up data structures, and performs any necessary setup for the benchmark. In this case, it creates two arrays: `documents` and `orderIds`, with 1000 elements each. The `documents` array contains objects with an `orderId` property, which is used as a key to map the `orderIds` array.
2. **Html Preparation Code**: This code is not present in this benchmark definition, suggesting that it's not required for this specific test case.
3. **Benchmark Definition String**: This string defines the actual JavaScript code being benchmarked. In this case, there are two test cases:

**Test Cases**

The first test case uses the `reduce` method to create a new object with keyed access, where the key is the `orderId` and the value is an array of documents. The second test case uses the `map` method in combination with the `find` method to achieve similar results.

**Options Compared**

These two test cases compare the performance of using `reduce` with keyed access versus using `map` followed by `find` for accessing elements in the `documents` array.

**Pros and Cons**

1. **Reduce with Keyed Access**
	* Pros:
		+ More concise code
		+ May be faster due to reduced function call overhead
	* Cons:
		+ Can be less readable if not familiar with the syntax
		+ May have performance issues with large datasets or complex key paths
2. **Map + Find Access**
	* Pros:
		+ More familiar and intuitive for some developers
		+ Can handle edge cases better due to explicit filtering
	* Cons:
		+ Longer code path, potentially slower due to more function calls

**Library**

There is no explicit library used in this benchmark definition. However, it's worth noting that the `find` method uses a similar approach as the `indexOf` method or other search algorithms in some libraries.

**Special JS Feature/Syntax**

Neither of these test cases utilize any special JavaScript features or syntax beyond standard language constructs.

**Other Alternatives**

Alternative approaches to benchmarking this specific use case might include:

* Using a library like Lodash, which provides a `findIndex` method for searching arrays
* Implementing a custom search algorithm using bitwise operations or other optimization techniques
* Measuring the performance of using an in-place data structure, such as a linked list or binary search tree, instead of an array-based approach.

Keep in mind that these alternatives would likely change the benchmark's output and interpretation, so it's essential to consider the specific requirements and constraints of the project when choosing an approach.

Related benchmarks:

quick testasdfasdf (version: 0)

Comparing performance of: reduce + map keyed access vs map + find access

Created: 3 years ago by: Guest

Jump to the latest result

reduce + map keyed access

map + find access

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):