Benchmark: Object vs Array lookups

Script Preparation code:

var reportsArr = [{
    id: `john`,
    text: `one`
}, {
    id: `jane`,
    text: `two`
}, {
    id: `bob`,
    text: `three`
}]
var reportsObj = {
    ['john']: {
        text: `one`
    },
    [`jane`]: {
        text: `two`
    },
    [`bob`]: {
        text: `three`
    }
}

var i = 0,
    count = 1000,
    jane;

Tests:

Array lookup
for (i = 0; i < count; i++) { janeIndex = reportsArr.findIndex(el => el.id === 'jane'); }
Obj lookuo
for (i = 0; i < count; i++) { janeIndex = reportsObj['jane']; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array lookup
Obj lookuo

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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Let's break down the provided JSON benchmark and explain what is being tested, compared, and other considerations.

**Benchmark Definition**

The benchmark definition is represented by two test cases:

1. **Array Lookup**: This test case uses a `for` loop to iterate `count` number of times (1000). Inside the loop, it finds the index of an element in the `reportsArr` array that matches the string `'jane'`. The code `janeIndex = reportsArr.findIndex(el => el.id === 'jane');` is used for this purpose.
2. **Object Lookup**: This test case also uses a `for` loop to iterate `count` number of times (1000). Inside the loop, it finds the value associated with the key `'jane'` in the `reportsObj` object using the syntax `reportsObj['jane']`.

**Comparison**

The two test cases are compared in terms of execution time. The one that executes faster will be reported as the winner.

**Pros and Cons**

* **Array Lookup**: This approach is generally faster for large arrays because JavaScript's array methods, such as `findIndex`, have optimized implementations.
	+ Pros: Fast lookup, especially for large datasets.
	+ Cons: May require more memory to store the entire array, and the indexing operation can be slower if the array is too large.
* **Object Lookup**: This approach uses a dictionary-like data structure (object) and relies on property access syntax (`reportsObj['jane']`).
	+ Pros: Can be faster for small to medium-sized datasets, as object lookups are often more cache-friendly than array lookups.
	+ Cons: May not scale well for very large datasets, and the lookup can be slower if the object is too large.

**Library/Function**

There is no specific library mentioned in the benchmark definition. However, `findIndex` is a built-in JavaScript method used in both test cases.

**Special JS Feature/Syntax**

The benchmark uses the following special syntax:

* `template literals`: The string concatenation in the `Script Preparation Code`, specifically: `"var reportsArr = [...]; var reportsObj = {...};"`. This syntax allows for more readable and efficient string construction.
* **Arrow functions**: Although not explicitly used, arrow functions are implied by the syntax `el => el.id === 'jane'` in the array lookup test case. Arrow functions provide a concise way to define small, single-expression functions.

**Alternatives**

Other alternatives could include:

* Using other data structures like `Map` or `Set`, which might offer different performance characteristics.
* Implementing custom lookup algorithms or data structures.
* Using libraries like Lodash, which provides various utility functions for working with arrays and objects.

Keep in mind that the optimal approach depends on the specific use case, dataset size, and performance requirements.

Related benchmarks:

Object vs Array lookups (version: 0)

Comparing performance of: Array lookup vs Obj lookuo

Created: 4 years ago by: Guest

Jump to the latest result

Array lookup

Obj lookuo

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):