The benchmark compares two different methods for creating a keyed collection from an array of objects, specifically assessing the performance of Lodash's keyBy function against the native JavaScript Map constructor. The tests are conducted with two different sizes of data: 10,000 and 100,000 entries.

Methods Compared

1. Lodash keyBy

   • Benchmark Definition: const keyed = _.keyBy(data, v => v.key)
   • Description: This method uses Lodash, a popular utility library, to create an object where the keys are derived from the property specified in the callback function (in this case, the key property of each object). Lodash manages iteration and data manipulation, making it cleaner and often simpler to read.

2. JavaScript Map

   • Benchmark Definition: const map = new Map(data.map((o) => [o.key, o]));
   • Description: This approach utilizes the native JavaScript Map constructor. Here, it first transforms the array into an array of key-value pairs (where the key comes from the object's key property and the value is the object itself), and then passes these pairs to the Map constructor to create a keyed collection.

Pros and Cons

Lodash keyBy

Pros:

• Conciseness: The syntax is cleaner and more readable, especially for those familiar with Lodash.
• Handles edge cases: Lodash can manage various edge cases internally, providing a level of abstraction that reduces potential errors.

Cons:

• Dependency: It requires including an external library (Lodash), which may increase the overall bundle size of your application.
• Performance Overhead: Since it is built on top of JavaScript, it may introduce slight performance overhead due to abstraction.

JavaScript Map

Pros:

• Native performance: The Map constructor, being built into the language, typically offers better performance, especially for larger datasets.
• No dependencies: Using native JavaScript features keeps the application lightweight and avoids potential issues with library versioning and size.

Cons:

• Verbose: The syntax can be less readable, and developers must manage the mapping logic themselves.
• Less feature-rich: While native features are powerful, they may not abstract away some of the complexities as nicely as Lodash does.

Considerations

• Execution Speed: In the benchmark results, for the 10,000 entries test, Lodash keyBy performed better (2310.20 executions per second) than the JS Map method (1808.89 executions per second). However, the performance gap narrows significantly as the dataset increases, with the JS Map (165.05 executions per second) outperforming Lodash's method (163.29 executions per second) at 100,000 entries. This suggests that for larger datasets, the optimized architecture of native JavaScript constructs can lead to better performance.

• Use Case Appropriateness: Choosing between these two methods can depend on the context. If performance is critical and the dataset is significantly large, native JavaScript (Map) may be the preferred choice. Conversely, for smaller datasets or situations where development speed and code clarity are prioritized, Lodash may be beneficial.

Alternatives

Other alternatives to keyBy and Map for creating keyed collections could include using plain objects, though they lack some advantages of Map, such as key ordering and object reference capabilities. There are also other libraries similar to Lodash that offer similar functionalities like Ramda, but they may also introduce their own pros and cons regarding performance and complexity.

In summary, this benchmark serves as a valuable reference for performance-critical decisions in JavaScript code dealing with keyed collections. Developers should weigh the implications of performance, readability, and dependency management when choosing between Lodash and native JavaScript options.