Benchmark Overview

This benchmark compares the performance of the Lodash library's _.pick function against a native JavaScript implementation of a similar functionality. The purpose is to evaluate which approach is faster for picking specific properties from an object when applied on a large number of objects.

Options Compared

1. Lodash _.pick:

   • Implementation: Utilizes Lodash, a popular utility library, to select specific properties from an object.
   • Test Case:

     arr.map(el => _.pick(el, ['type', 'subtype', 'card_last4', 'card_type', 'card_exp_month', 'card_exp_year', 'card_country']));
     

   • Purpose: Lodash's _.pick simplifies the process of creating a new object composed of selected properties from the original object.

2. Native JavaScript Implementation:

   • Implementation: A custom pickBy function that manually iterates through the object's properties and builds a new object based on specified keys.
   • Test Case:

     function pickBy(object) { /* implementation */ }
     arr.map(el => pickBy(el, ['type', 'subtype', 'card_last4', 'card_type', 'card_exp_month', 'card_exp_year', 'card_country']));
     

   • Purpose: Provides a utility for property selection without relying on an external library.

Performance Results

• Lodash _.pick: Approximately 10.67 executions per second.
• Native Implementation: Approximately 90.35 executions per second.

Pros and Cons

Lodash _.pick

• Pros:

  • Readability: Code is more concise and expressive, making it easier to understand at a glance.
  • Functionality: Lodash provides many other utility functions that can complement _.pick, enhancing overall development efficiency.

• Cons:

  • Performance: Slower in this benchmark compared to the native implementation.
  • Dependency: Requires the inclusion of the Lodash library, increasing the bundle size for web applications.

Native JavaScript Implementation

• Pros:

  • Performance: Faster execution as shown in the benchmark results.
  • No External Dependencies: This approach doesn't require any libraries, making it lean and reducing the overall code base size.

• Cons:

  • Code Complexity: Without the syntactical sugar of a utility library, the implementation can become verbose and harder to read, especially as functionality expands.
  • Limited Functionality: The custom function may not include additional features (e.g., deep property access, cloning) found in more robust libraries like Lodash.

Other Considerations

• Use Case: If performance is a critical concern and the operation is performed frequently in performance-sensitive applications, the native implementation is more favorable. However, for projects where code readability and maintenance might be more crucial, Lodash is a good choice despite the performance trade-off.

• Alternatives: Other alternatives could include using modern JavaScript features like destructuring combined with object spreading, or implementing libraries like Ramda or Immutable.js, which offer functional programming utilities similar to Lodash. However, these might also come with their own performance implications and trade-offs.

• Library Context: Lodash is widely used in JavaScript projects for handling data manipulation efficiently, often showcasing a balance between ease of use and performance across various tasks.

In conclusion, the decision between using Lodash or a native solution should be based on project requirements, expected data volume, and specific performance demands.