Benchmark: Ramda (/w transducer) vs. Lodash v3

Ramda (/w transducer) vs. Lodash v3 (version: 0)

Compares performance on the same task using Lodash vs two styles of Ramda vs two styles of "native" Javascript. In all cases the task is pulling "counter" property from each item in an array, filtering out odd items, squaring them, then returning those squared values that have less than two digits.

Comparing performance of: Ramda without relying on currying or composition vs Ramda with transducer vs Ramda with currying and composition vs Lodash

Created: one year ago by: Registered User

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HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.28.0/ramda.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.15/lodash.min.js"></script>

AخA
 
<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.28.0/ramda.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.15/lodash.min.js"></script>

Script Preparation code:

var data = _.range(100000).map(function(i) {
  return {
    counter: i
  }
});

function isOdd(num) {
  return num % 2 === 1;
}

function square(num) {
  return num * num;
}

function lessThanThreeDigits(num) {
  return num.toString().length < 3;
}

​x
 
var data = _.range(100000).map(function(i) {  return {    counter: i  }});​function isOdd(num) {  return num % 2 === 1;}​function square(num) {  return num * num;}​function lessThanThreeDigits(num) {  return num.toString().length < 3;}

Tests:

Ramda without relying on currying or composition
var result = R.filter(lessThanThreeDigits, R.map(square, R.filter(isOdd, R.pluck('counter', data))));
var result = R.filter(lessThanThreeDigits,
R.map(square,
R.filter(isOdd,
R.pluck('counter', data))));

Ramda with transducer

var transducer = R.compose (
  R.map(R.prop('counter')),
  R.filter(isOdd),
  R.map(square),
  R.filter(lessThanThreeDigits),
);

var result = R.transduce (
  transducer,
  (acc, val) => {
    acc.push(val);
    return acc;
  },
  [],
  data,
);

 
var transducer = R.compose (  R.map(R.prop('counter')),  R.filter(isOdd),  R.map(square),  R.filter(lessThanThreeDigits),);​var result = R.transduce (  transducer,  (acc, val) => {    acc.push(val);    return acc;  },  [],  data,);

Ramda with currying and composition
var result = R.pipe( R.pluck('counter'), R.filter(isOdd), R.map(square), R.filter(lessThanThreeDigits) )(data);
var result = R.pipe(
R.pluck('counter'),
R.filter(isOdd),
R.map(square),
R.filter(lessThanThreeDigits)
)(data);

Lodash

var result = _.chain(data)
  .pick('counter')
  .filter(isOdd)
  .map(square)
  .filter(lessThanThreeDigits)
  .value();

 
var result = _.chain(data)  .pick('counter')  .filter(isOdd)  .map(square)  .filter(lessThanThreeDigits)  .value();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

Test case name	Result
Ramda without relying on currying or composition
Ramda with transducer
Ramda with currying and composition
Lodash

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36

Browser/OS: Chrome 121 on Windows

View result in a separate tab

Test name	Executions per second
Ramda without relying on currying or composition	76.0 Ops/sec
Ramda with transducer	79.5 Ops/sec
Ramda with currying and composition	74.0 Ops/sec
Lodash	554448.7 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

Let's break down the benchmark and explain what's being tested.

What is being tested?

The benchmark compares the performance of four different approaches to perform a similar task:

Using Lodash (a popular JavaScript utility library)
Ramda (a functional programming library) with two styles:
- "without relying on currying or composition" (using filter, map, and compose functions directly)
- "with transducer" (using the transduce function to create a pipeline of operations)
Ramda with currying and composition (using pipe function to chain operations)

Options compared

The benchmark compares the performance of these four approaches:

Lodash: uses its built-in functions for filtering, mapping, and reducing.
Ramda "without relying on currying or composition": uses filter, map, and compose functions directly from Ramda.
Ramda "with transducer": uses the transduce function to create a pipeline of operations that can be composed together.
Ramda with currying and composition: uses the pipe function to chain operations together.

Pros and cons

Here's a brief summary of the pros and cons of each approach:

Lodash:
- Pros: widely used, well-maintained, and easy to use.
- Cons: may be slower than optimized libraries like Ramda due to its overhead.
Ramda "without relying on currying or composition":
- Pros: uses native JavaScript functions for filtering, mapping, and reducing, which might be faster.
- Cons: can lead to less readable code if not used carefully.
Ramda "with transducer":
- Pros: allows for more flexible and composable pipeline of operations.
- Cons: may have a learning curve due to the use of transduce function.
Ramda with currying and composition:
- Pros: uses a concise and readable syntax, making it easier to chain operations together.
- Cons: may be slower than other approaches due to the overhead of function creation.

Libraries used

The benchmark uses two popular JavaScript libraries:

Ramda (functional programming library)
Lodash (utility library)

Both libraries provide a set of functions for common tasks like filtering, mapping, and reducing arrays.

Special JS features or syntax

There are no special JS features or syntax used in this benchmark. The code is written using standard JavaScript syntax, with the exception of Ramda's currying and composition features.

Alternatives

If you're looking for alternatives to these libraries, here are a few options:

Immutable.js: another functional programming library that provides similar functionality to Ramda.
Underscore.js: an older utility library that provides similar functionality to Lodash.
Papaparse: a library specifically designed for parsing and manipulating arrays.

Keep in mind that the choice of library or alternative depends on your specific use case, personal preference, and performance requirements.

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what's being tested.

**What is being tested?**

The benchmark compares the performance of four different approaches to perform a similar task:

1. Using Lodash (a popular JavaScript utility library)
2. Ramda (a functional programming library) with two styles:
	* "without relying on currying or composition" (using `filter`, `map`, and `compose` functions directly)
	* "with transducer" (using the `transduce` function to create a pipeline of operations)
3. Ramda with currying and composition (using `pipe` function to chain operations)

**Options compared**

The benchmark compares the performance of these four approaches:

1. Lodash: uses its built-in functions for filtering, mapping, and reducing.
2. Ramda "without relying on currying or composition": uses `filter`, `map`, and `compose` functions directly from Ramda.
3. Ramda "with transducer": uses the `transduce` function to create a pipeline of operations that can be composed together.
4. Ramda with currying and composition: uses the `pipe` function to chain operations together.

**Pros and cons**

Here's a brief summary of the pros and cons of each approach:

1. **Lodash**:
	* Pros: widely used, well-maintained, and easy to use.
	* Cons: may be slower than optimized libraries like Ramda due to its overhead.
2. **Ramda "without relying on currying or composition"**:
	* Pros: uses native JavaScript functions for filtering, mapping, and reducing, which might be faster.
	* Cons: can lead to less readable code if not used carefully.
3. **Ramda "with transducer"**:
	* Pros: allows for more flexible and composable pipeline of operations.
	* Cons: may have a learning curve due to the use of `transduce` function.
4. **Ramda with currying and composition**:
	* Pros: uses a concise and readable syntax, making it easier to chain operations together.
	* Cons: may be slower than other approaches due to the overhead of function creation.

**Libraries used**

The benchmark uses two popular JavaScript libraries:

1. Ramda (functional programming library)
2. Lodash (utility library)

Both libraries provide a set of functions for common tasks like filtering, mapping, and reducing arrays.

**Special JS features or syntax**

There are no special JS features or syntax used in this benchmark. The code is written using standard JavaScript syntax, with the exception of Ramda's currying and composition features.

**Alternatives**

If you're looking for alternatives to these libraries, here are a few options:

1. **Immutable.js**: another functional programming library that provides similar functionality to Ramda.
2. **Underscore.js**: an older utility library that provides similar functionality to Lodash.
3. **Papaparse**: a library specifically designed for parsing and manipulating arrays.

Keep in mind that the choice of library or alternative depends on your specific use case, personal preference, and performance requirements.

Related benchmarks: