Benchmark: Tree traverse: new vs copy

HTML Preparation code:


<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

var data = 1
var treePrototype = {
    bool: {
        filter: [{
            very: {
                long: {
                    tree: {
                        data: 2
                    }
                }
            }
        }],
        must_not: [],
        must: [],
        should: []
    }
}

Tests:

Build tree from scratch
var tree = { bool: { filter: [], must_not: [], must: [], should: [] } } tree.bool.filter.push({ very: { long: { tree: { data: data } } } })
Modify
var cloned = _.cloneDeep(treePrototype) cloned.bool.filter[0].very.long.tree.data = data

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Build tree from scratch
Modify

Fastest: N/A

Slowest: N/A

Latest run results:

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

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I'd be happy to help explain the benchmark and its results.

**Benchmark Definition**

The benchmark is designed to compare the performance of two approaches: building a tree data structure from scratch, and modifying an existing cloned tree.

**Script Preparation Code**

The script preparation code defines a prototype `treePrototype` that represents the root of the tree. The tree has several nested properties, including `bool`, which contains an array `filter`. This array is used to test the performance of building the tree from scratch.

**Html Preparation Code**

The HTML preparation code includes a reference to the Lodash library, which provides utility functions for working with data structures like trees.

**Individual Test Cases**

There are two test cases:

1. **Build tree from scratch**: This test case defines an empty `tree` object and then pushes an object onto its `filter` array. The resulting tree structure is identical to the original `treePrototype`.
2. **Modify**: This test case creates a cloned copy of the original `treePrototype` using Lodash's `cloneDeep` function, and then modifies the resulting clone by updating one of its nested properties.

**What's tested**

The benchmark tests the performance difference between two approaches:

1. Building a tree data structure from scratch.
2. Modifying an existing cloned tree (using the `cloneDeep` function).

**Options compared**

Two options are compared in this benchmark:

* **Building from scratch**: This approach creates a new tree object and populates its properties manually.
* **Cloning and modifying**: This approach uses Lodash's `cloneDeep` function to create a copy of the original tree prototype, and then modifies the clone.

**Pros and Cons**

Here are some pros and cons of each approach:

**Building from scratch:**

Pros:

* Can be more efficient for small trees.
* Allows for better control over the tree structure.

Cons:

* Requires more manual work to create the tree structure.
* May be slower for larger trees due to the overhead of creating a new object.

**Cloning and modifying:**

Pros:

* Faster than building from scratch, especially for large trees.
* Reduces the need for manual code to create the tree structure.

Cons:

* Requires an additional library call (Lodash) to clone the tree.
* May not be suitable for very small trees due to the overhead of cloning.

**Library: Lodash**

The `cloneDeep` function from Lodash creates a deep copy of the original object, which is then modified in the second test case. This allows us to easily create a cloned copy of the tree prototype and measure its performance.

**Special JS feature/syntax**

There are no special JavaScript features or syntax used in this benchmark. However, it's worth noting that Lodash is a popular utility library that provides many useful functions for working with data structures like trees.

**Other alternatives**

If you wanted to implement your own tree cloning and modification algorithms without using an external library like Lodash, you could consider the following approaches:

* Using a recursive function to clone the tree structure.
* Implementing a custom `cloneDeep` function that iterates over the properties of the original object and creates new objects with identical properties.

However, these alternatives may not be as efficient or convenient as using an existing library like Lodash.

Related benchmarks:

Tree traverse: new vs copy (version: 0)

Comparing performance of: Build tree from scratch vs Modify

Created: 5 years ago by: Registered User

Jump to the latest result

Build tree from scratch

Modify

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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