Benchmark: quick testasdfasdf pt2

Script Preparation code:

var documents = []
var orderIds = []
let i = 0
let len = 10000

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 benchmark and explain what's being tested.

**Benchmark Definition**

The provided benchmark definition is a JSON object that describes two test cases:

1. `reduce + map keyed access`
2. `map + find access`

This definition includes two parts:

* **Script Preparation Code**: This code sets up some variables, including an array of 10,000 order IDs (`len`) and two arrays: `orderIds` and `documents`. The `documents` array is populated with objects that have an `orderId` property.
* **Html Preparation Code**: This field is empty, indicating that no HTML preparation code is needed for this benchmark.

**Test Cases**

Each test case has a unique name (`reduce + map keyed access` or `map + find access`) and a benchmark definition string. The benchmark definitions are written in JavaScript and describe how to perform the specified operations on the prepared data.

Let's analyze each test case:

1. **`reduce + map keyed access`**:
	* The benchmark definition uses the `reduce()` method to create an object that maps each order ID to its corresponding documents array.
	* Then, it uses the `map()` method to apply a transformation function to each document in the resulting object.
2. **`map + find access`**:
	* This test case uses the `map()` method to transform each order ID into an equivalent value ( likely an index into the `documents` array).
	* Then, it uses the `find()` method to search for a specific document with a matching `orderId`.

**Comparison**

The two test cases compare the performance of different approaches:

1. **`reduce + map keyed access`**: This approach uses a mapping function to create an object that maps each order ID to its documents array, and then applies a transformation function to each document using `map()`. This approach allows for efficient lookup and transformation of data.
2. **`map + find access`**: This approach directly transforms each order ID into an equivalent value using `map()`, and then searches for the corresponding document using `find()`.

**Pros and Cons**

Here are some pros and cons of each approach:

1. **`reduce + map keyed access`**:
	* Pros: Efficient lookup and transformation, reduces number of iterations.
	* Cons: Requires additional memory to store the mapping object.
2. **`map + find access`**:
	* Pros: Less memory required, no need for intermediate data structures.
	* Cons: May require more iterations to search for documents.

**Other Considerations**

When evaluating these test cases, consider factors like:

* Data size and complexity
* Performance requirements (e.g., speed, throughput)
* Memory constraints

**Alternative Approaches**

There are other approaches that might be considered, such as:

1. **`filter()` + `forEach()`**: Instead of using `map()` and `find()`, you could use `filter()` to narrow down the documents array and then apply a transformation function using `forEach()`.
2. **Array.prototype.at()** (if supported by the browser): This method would allow for direct access to elements in an array using their index, eliminating the need for `map()` and `find()`. However, not all browsers support this method.

Keep in mind that these alternative approaches may have different performance characteristics or trade-offs.

Related benchmarks:

quick testasdfasdf pt2 (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):