Benchmark: map vs obj vs array

Script Preparation code:

var i = 0, count = 100000, a;
var map = new Map();
var array = [];
var obj = {};

for (i = 0; i < count; i++) {
  const randomValue = ~~(Math.random() * 100);
  map.set(i, randomValue);
  array.push({id: i, randomValue});
  obj[i] = randomValue;
}

Tests:

map
for (i = 0; i < count; i++) { a = map.get(i); }
array
for (i = 0; i < count; i++) { a = array.find(el => el.id = i); }
obj
for (i = 0; i < count; i++) { a = obj[i] }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map
array
obj

Fastest: N/A

Slowest: N/A

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

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Let's dive into the provided benchmark JSON and explore what's being tested.

**Benchmark Definition**

The benchmark is designed to compare the performance of three data structures: `Map`, `Array`, and `Object`. The script preparation code creates:

1. A new `Map` instance (`map`) with 100,000 entries.
2. An empty `Array` instance (`array`) with 100,000 elements.
3. An empty `Object` instance (`obj`) with 100,000 properties.

The loop iterates 100,000 times, and in each iteration:

* For the "map" test case, it retrieves a value from the map using the `get()` method.
* For the "array" test case, it finds an element in the array using the `find()` method with a callback function that checks if the element's `id` property matches the current index (`i`).
* For the "obj" test case, it accesses a value from the object using the bracket notation (`obj[i]`).

**Options being compared**

The three options being compared are:

1. **Map**: A data structure that stores key-value pairs in an ordered collection of key-value pairs.
2. **Array**: A linear collection of values that can be accessed by index.
3. **Object**: An unordered collection of key-value pairs.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

1. **Map**:
	* Pros: Efficient lookups, fast iteration, and good memory usage for large datasets.
	* Cons: May have higher overhead due to the use of a hash table, and not as intuitive to work with as other data structures.
2. **Array**:
	* Pros: Fast iteration, simple indexing, and widely supported.
	* Cons: Less efficient lookups compared to `Map`, especially for large datasets or sparse data.
3. **Object**:
	* Pros: Simple to access properties using bracket notation, and intuitive to work with.
	* Cons: Less efficient than `Map` for large datasets, as it uses a hash table under the hood.

**Library usage**

None of the benchmark cases use any external libraries or dependencies.

**Special JS features or syntax**

The benchmark makes use of some JavaScript features:

1. **Array.prototype.find()**: A method introduced in ECMAScript 2015 (ES6) that finds the first element in an array that satisfies a provided testing function.
2. **Map.prototype.get()**: A method introduced in ECMAScript 2015 (ES6) that retrieves the value associated with a given key from a map.

**Other alternatives**

If you'd like to explore alternative data structures or approaches, here are some options:

1. **Set**: Similar to `Map`, but without keys.
2. **SortedArray**: A custom implementation of an array that maintains its elements in sorted order.
3. **HashTable**: An external library or a built-in object (e.g., in some languages) that provides fast lookups and insertion/deletion operations.

Keep in mind that each data structure has its strengths and weaknesses, and the best choice depends on the specific use case and performance requirements.

Related benchmarks:

map vs obj vs array (version: 0)

Comparing performance of: map vs array vs obj

Created: 3 years ago by: Guest

Jump to the latest result

map

array

obj

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):