Benchmark: object record find 2

Script Preparation code:

var data = {};

var findId='data-id-999999';

// Generate more data
for (let i = 1; i <= 1000000; i++) {
  var id = `data-id-${i}`;
  data[id] = { id: id };
}

Tests:

for...in loop
let result; for (const key in data) { if (data[key].id === findId) { result = data[key]; break; } }
Array.find
Object.values(data).find(item => item.id === findId);
for loop
const keys = Object.keys(data); for (let i = 0; i < keys.length; i++) { if (data[keys[i]].id === findId) { return data[keys[i]]; } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for...in loop
Array.find
for loop

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its various components.

**Benchmark Definition**

The benchmark is defined by two JSON objects: one for the overall benchmark definition and another for individual test cases.

* The **Overall Benchmark Definition** represents a set of options that will be compared. It defines a script preparation code, which generates a large dataset (`data`) with 1 million records, each containing an `id` property. A specific `findId` is then set to "data-id-999999".
* The **Individual Test Cases** represent the different approaches to find the record with the matching `findId`. There are three test cases:
	+ "for...in loop"
	+ "Array.find"
	+ "for loop"

Let's examine each approach:

**Approaches:**

### 1. for...in loop

* This approach uses a traditional `for...in` loop to iterate over the properties of the `data` object.
* The loop checks if the current property value (`key`) has an `id` property equal to `findId`.
* If found, the corresponding record is stored in the `result` variable and the loop breaks.

Pros:

* No additional library or module dependencies required.
* Can be easily optimized by using a more efficient data structure (e.g., a hash table) for fast lookups.

Cons:

* Performance may degrade due to the overhead of iterating over all properties, even if the desired record is found early in the iteration.
* May not perform well with large datasets or complex data structures.

### 2. Array.find

* This approach uses the `Array.prototype.find()` method to search for the first element in the array that satisfies a given condition (i.e., `item.id === findId`).
* The function returns the found record if it exists, otherwise returns `undefined`.

Pros:

* More concise and expressive than traditional loops.
* Can be optimized by using more efficient data structures or algorithms.

Cons:

* May have performance overhead due to the creation of an intermediate array with all elements, even if the desired element is found early in the search.
* Not supported in older JavaScript versions (ES5).

### 3. for loop

* This approach uses a traditional `for` loop to iterate over the indices of the `data` object's keys.
* The loop checks if the current key value (`keys[i]`) has an `id` property equal to `findId`.
* If found, the corresponding record is returned.

Pros:

* Can be optimized by using more efficient data structures or algorithms.
* No overhead from iterating over properties like in the for...in loop approach.

Cons:

* May have performance overhead due to the need to access and compare the key values with the `findId` value.

**Library/Module Considerations**

There is no explicit library or module dependency mentioned in the benchmark definition. However, it's worth noting that the `Array.prototype.find()` method uses the `indexOf()` method internally, which may have performance implications if the array is very large.

**Special JS Features/Syntax**

None of the individual test cases use any special JavaScript features or syntax.

**Other Alternatives**

If the benchmark results are not satisfactory, alternative approaches can be explored, such as:

* Using a more efficient data structure like a hash table (e.g., `Map` in modern JavaScript) for fast lookups.
* Implementing an index-based search algorithm to reduce iterations over the entire dataset.
* Utilizing hardware-accelerated vectorization or parallel processing techniques to take advantage of multi-core processors.

Please note that these alternatives may require additional development effort and optimization to ensure optimal performance.

Latest run results:

Run details: (Test run date: 3 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36

Browser/OS: Chrome 127 on Windows

View result in a separate tab

Test name	Executions per second
for...in loop	0.6 Ops/sec
Array.find	0.4 Ops/sec
for loop	0.6 Ops/sec

Related benchmarks:

object record find 2 (version: 1)

Comparing performance of: for...in loop vs Array.find vs for loop

Created: 3 months ago by: Registered User

Jump to the latest result

for...in loop

Array.find

for loop

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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