Benchmark: Lodash Chain vs Native (with &&) vs Native (without &&) vs Flow with random data

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>
<script src='https://cdn.jsdelivr.net/g/lodash@4(lodash.min.js+lodash.fp.min.js)'></script>

Script Preparation code:

var fp = _.noConflict();

var data = Array(10000000).fill({ a: Math.random() < 0.5 ? 'a' : 'b', b: Math.random() * 100000 });

Tests:

Chain
_.chain(data).filter(f => f.a === 'a').filter(f => f.b > 50000).filter(f => f.b + 1 === 2).map(f => f.a).filter(f => f === 'a').value()
Native (with &&)
data.filter(f => f.a === 'a' && f.b > 50000 && f.b + 1 === 2).map(f => f.a).filter(f => f === 'a')
Native (without &&)
data.filter(f => f.a === 'a').filter(f => f.b > 50000).filter(f => f.b + 1 === 2).map(f => f.a).filter(f => f === 'a')
Flow
fp.flow( fp.filter(f => f.a === 'a'), fp.filter(f => f.b > 50000), fp.filter(f => f.b + 1 === 2), fp.map(f => f.a), fp.filter(f => f === 'a') )(data)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Chain
Native (with &&)
Native (without &&)
Flow

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is tested, compared options, pros and cons, library usage, special JavaScript features or syntax, and other considerations.

**Benchmark Definition:**

The benchmark measures the performance of three different approaches to filter and map data:

1. **Lodash Chain**: Uses Lodash's `chain` method to create a pipeline of methods that are applied to the data in sequence.
2. **Native (with &&)**: Uses the logical AND operator (`&&`) to combine multiple conditions in a single line of code.
3. **Native (without &&)**: Similar to the previous point, but without using the logical AND operator.
4. **Flow**: Uses Lodash's `flow` method to create a pipeline of methods that are applied to the data in sequence.

**Script Preparation Code:**

The script preparation code creates an array of 10 million objects with two properties each (`a` and `b`). The values of these properties are randomly generated, with `a` being either 'a' or 'b', and `b` being a random number between 0 and 100000.

**Html Preparation Code:**

The HTML preparation code includes two external JavaScript files:

1. A local copy of Lodash (version 4.17.5)
2. A remote copy of Lodash with only the fp module (which provides functional programming utilities)

**Individual Test Cases:**

Each test case defines a separate benchmark that applies one of the four approaches to filter and map the data.

Here's what each approach does:

* **Lodash Chain**: Creates a pipeline of methods that are applied to the data in sequence. The `chain` method returns an object with a `value()` method, which is called to execute the final operation.
* **Native (with &&)**: Combines multiple conditions using the logical AND operator (`&&`). This approach can be more efficient than the Lodash pipeline because it avoids the overhead of function calls and object creation. However, it may be less readable and maintainable for complex operations.
* **Native (without &&)**: Similar to the previous point, but without using the logical AND operator. This approach is likely to be slower than the native `&&` approach due to the additional overhead introduced by the separate conditionals.
* **Flow**: Creates a pipeline of methods that are applied to the data in sequence, similar to Lodash Chain. However, it uses the `flow` method instead of chaining methods.

**Library Usage:**

The benchmark uses two external libraries:

1. Lodash (version 4.17.5)
2. Lodash with fp module (a subset of the full Lodash library)

Lodash is a popular utility library for JavaScript that provides functional programming utilities, including `chain` and `flow`. The fp module, which includes only functional programming utilities, is likely used to isolate these specific features from other parts of the library.

**Special JavaScript Features or Syntax:**

There are no special JavaScript features or syntax used in this benchmark. However, it does demonstrate the use of logical operators (`&&`) for conditional statements, as well as function call chains and pipelines.

**Pros and Cons of Different Approaches:**

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

* **Lodash Chain**: Pros:
	+ Easy to read and maintain
	+ Does not require explicit function calls or pipeline setup
Cons:
	+ May be slower due to overhead of Lodash library functions
* **Native (with &&)**: Pros:
	+ Can be faster than Lodash Chain due to reduced overhead
Cons:
	+ Less readable and maintainable for complex operations
* **Native (without &&)**: Pros: None notable. Cons:
	+ Likely slower than native `&&` approach due to additional overhead.
* **Flow**: Pros:
	+ Similar to Lodash Chain in readability and maintainability
Cons:
	+ May be slower due to overhead of Lodash library functions

**Other Considerations:**

1. **Data Size**: The benchmark uses a large dataset (10 million objects) to measure performance. This can impact results, as larger datasets can introduce significant overhead.
2. **Browser Performance**: The benchmark measures execution speed across different browsers and devices. This highlights the importance of considering browser-specific factors when optimizing code for performance.
3. **Lodash Version**: The benchmark uses an older version of Lodash (4.17.5). Using newer versions may result in improved performance or added features.

**Alternatives:**

1. **Other functional programming libraries**: There are other JavaScript libraries, such as Ramda or Immutable.js, that provide similar functionality to Lodash.
2. **Native implementation**: Instead of using a library like Lodash, you could implement the pipeline logic yourself in native JavaScript.
3. **Just-in-time (JIT) compilation**: Some browsers and Node.js environments support JIT compilation, which can optimize code for performance.

In conclusion, this benchmark provides a valuable comparison of four different approaches to filtering and mapping data: Lodash Chain, Native (with &&), Native (without &&), and Flow. By understanding the pros and cons of each approach, developers can choose the most suitable method for their specific use case and performance requirements.

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 11_0_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.80 Safari/537.36

Browser/OS: Chrome 86 on Mac OS X 11.0.0

View result in a separate tab

Test name	Executions per second
Chain	17.9 Ops/sec
Native (with &&)	6.8 Ops/sec
Native (without &&)	6.7 Ops/sec
Flow	8.0 Ops/sec

Related benchmarks:

Lodash Chain vs Native (with &&) vs Native (without &&) vs Flow with random data (version: 0)

Comparing performance of: Chain vs Native (with &&) vs Native (without &&) vs Flow

Created: 3 years ago by: Guest

Jump to the latest result

Chain

Native (with &&)

Native (without &&)

Flow

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):