Benchmark: Imperative vs declarative vs FP, part 1

HTML Preparation code:

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

Script Preparation code:

var data = Array(1000000).map((_, index) => index);

Tests:

Imperative
var result = []; for (let i in data) result.push(i * 5);
Declarative
data.map(i => i * 5);
FP
_.map( _.multiply(5) )(data);

Historical Performance Data aggregated from 6 community runs

Desktop Results:

Chrome 6 runs

Imperative

1K/s

Declarative

351/s

19/s

Fastest: Imperative (70.7x faster)

View exact numbers

✓ Imperative: 1,341 ops/sec
• Declarative: 351 ops/sec
• FP: 19 ops/sec

Results show median performance across all community submissions. Run this benchmark yourself to verify on your device.

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Imperative
Declarative
FP

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Mac OS X 10.15.7

View result in a separate tab

Imperative

1K/s

Declarative

477/s

19/s

View exact numbers

Test name	Executions per second
✓ Imperative	1,327 Ops/sec
Declarative	477 Ops/sec
FP	19 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation of what is being tested in this JavaScript microbenchmark.

The benchmark compares three different approaches to perform an operation on a large array of numbers:

1. **Imperative**: This approach uses a traditional loop to iterate over the array and perform the desired operation (in this case, multiplying each element by 5) using imperative programming style.
2. **Declarative**: This approach uses a functional programming style, where the focus is on specifying what needs to be done with the data (i.e., multiplying each element by 5), rather than how it's done. The `map()` method is used to apply this operation to the array.
3. **FP (Functional Programming)**: This approach uses a library-specific function (`_.multiply(5)` in this case) to perform the desired operation, which is a more concise and declarative way of expressing the same logic as the imperative and declarative approaches.

**Pros and Cons:**

* **Imperative**: Pros - widely understood and implemented, easy to debug. Cons - can be verbose, error-prone, and less scalable.
* **Declarative**: Pros - concise, readable, and easier to maintain. Cons - may require additional setup or library support, and can be slower in some cases due to the overhead of creating intermediate arrays.
* **FP**: Pros - concise and declarative, often faster than imperative approaches (due to caching and optimization). Cons - may require additional setup or library support, and can be less understood by developers without experience with functional programming.

The benchmark's results show that:

* The FP approach is the fastest, followed closely by the Imperative approach. This suggests that the concise and declarative nature of functional programming styles can lead to performance gains.
* The Declarative approach is slower than both the Imperative and FP approaches, but still competitive with a well-optimized implementation.

**Library:**

The benchmark uses Lodash.js, a popular JavaScript utility library that provides various functions for functional programming, including `_.map()` and `_multiply()`. These functions are used to demonstrate the declarative and FP styles of programming.

**Other Considerations:**

* The benchmark only measures the performance difference between these three approaches, without considering other factors such as code readability, maintainability, or scalability.
* The results may not be representative of all scenarios, as they depend on the specific implementation and hardware used to run the benchmarks.
* Alternative approaches, such as using `Array.prototype.map()` or a custom iterative loop, are not included in this benchmark.

**Alternatives:**

Other alternatives for performing this operation include:

* Using `Array.prototype.reduce()` instead of `map()`
* Implementing a custom iterative loop without using any libraries
* Using a parallel processing library to take advantage of multiple CPU cores

Imperative vs declarative vs FP, part 1 (version: 0) Benchmark.js

Comparing performance of: `Imperative vs Declarative vs FP`

Created: 6 years ago by: Guest

Go to the latest result

Imperative

Declarative

FP

Historical Performance Data aggregated from 6 community runs

Desktop Results:

Chrome 6 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Imperative vs declarative vs FP, part 1 (version: 0) Benchmark.js

Comparing performance of: Imperative vs Declarative vs FP

Created: 6 years ago by: Guest

Go to the latest result

Imperative

Declarative

FP

Historical Performance Data aggregated from 6 community runs

Desktop Results:

Chrome 6 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

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Comparing performance of: `Imperative vs Declarative vs FP`