Benchmark: Lodash: difference vs intersection

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/lodash/4.16.0/lodash.min.js"></script>

Script Preparation code:

var arrFull = ['past','present','future']
var arrOne = ['past']

Tests:

Difference - Full
_.difference(arrFull, arrFull)
Intersection - Full
_.intersection(arrFull, arrFull)
Difference - Partial
_.difference(arrFull, arrOne)
Intersection - Partial
_.intersection(arrOne, arrFull)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Difference - Full
Intersection - Full
Difference - Partial
Intersection - Partial

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided benchmark measures the performance of Lodash's `difference` and `intersection` functions in JavaScript, specifically when used to find differences or intersections between two arrays.

**Options Compared**

Two main options are compared:

1. **Full vs Partial Arrays**: The first option tests the difference/intersection function with both full (i.e., `arrFull`) and partial (i.e., `arrOne`) arrays. This helps identify if the library performs differently when given a complete set of data versus a smaller subset.
2. **Differences vs Intersections**: The second option compares finding differences (`_.difference`) versus intersections (`_.intersection`) between two arrays. This highlights the performance trade-off between these two common array operations.

**Pros and Cons**

*   **Full Arrays:**
    *   Pros:
        *   More representative of real-world use cases where data sets are complete.
        *   Allows for a more comprehensive understanding of the library's capabilities.
    *   Cons:
        *   May lead to slower performance due to larger datasets.
        *   Could result in over-optimization if the algorithm is heavily influenced by the full array size.
*   **Partial Arrays:**
    *   Pros:
        *   Can provide a better understanding of how the library handles partial data sets, which might be more common in certain applications (e.g., filtering or searching).
        *   Could help identify potential performance issues that may arise from handling incomplete data sets.
    *   Cons:
        *   May not accurately represent typical use cases where complete data sets are often used.
*   **Differences vs Intersections:**
    *   Pros:
        *   Highlights the trade-off between these two operations, which can be important for choosing the right library function in a given scenario.
    *   Cons:
        *   May lead to slower performance if one operation is significantly faster than the other (as observed in the benchmark).

**Library and Syntax Considerations**

In this benchmark:

*   **Lodash:** Lodash is a popular JavaScript utility library that provides a wide range of functions for tasks like array manipulation, string manipulation, and more.
    *   The `difference` function takes two arrays as input and returns a new array containing elements that are present in the first array but not in the second.
    *   The `intersection` function performs the reverse operation, returning an array with elements common to both inputs.
*   **Special JS Features/Syntax:** None mentioned.

**Alternatives**

Other alternatives for array manipulation functions might include:

*   **Array.prototype.filter()**: A more straightforward approach that filters out unwanted values from a full or partial array.
*   **Set Data Structures**: Utilizing sets can offer efficient intersection and difference operations, but may require additional setup and data type conversions.
*   **Native Array Functions**: Some browsers and platforms provide native functions for array manipulation, which might be faster than Lodash's implementations.

**Benchmarking Considerations**

When benchmarking array manipulation libraries or built-in functions, consider factors such as:

*   Input size: Test with varying array sizes to capture performance trends.
*   Algorithmic complexity: Measure the impact of different algorithms on performance.
*   Browser/platform differences: Compare results across multiple browsers and platforms to ensure applicability.

Keep in mind that benchmarks should be designed to accurately reflect real-world use cases, accounting for factors like caching, concurrent execution, or edge conditions.

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36

Browser/OS: Chrome 117 on Linux

View result in a separate tab

Test name	Executions per second
Difference - Full	5067085.0 Ops/sec
Intersection - Full	4092886.2 Ops/sec
Difference - Partial	4930303.5 Ops/sec
Intersection - Partial	4680428.5 Ops/sec

Related benchmarks:

Lodash: difference vs intersection (version: 0)

Comparing performance of: Difference - Full vs Intersection - Full vs Difference - Partial vs Intersection - Partial

Created: 7 years ago by: Guest

Jump to the latest result

Difference - Full

Intersection - Full

Difference - Partial

Intersection - Partial

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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