Benchmark: Map vs Object add - lookup - delete

Script Preparation code:

var map = new Map();
var obj = {};

map.set('a', 5);
obj['a'] = 5;

var i = 0, count = 1000, a;

Tests:

Map
for (i = 0; i < count; i++) { a = map.get('a'); map.delete('a'); map.set('a', 5); }
Obj
for (i = 0; i < count; i++) { a = obj['a']; delete obj['a']; obj['a'] = 5; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Map
Obj

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

The provided JSON represents a JavaScript microbenchmark on MeasureThat.net, which compares the performance of two data structures: `Map` and `Object`. The benchmark is designed to measure the time it takes to perform lookup and delete operations on these data structures.

**What is tested?**

In this benchmark, the following operations are performed:

1. Lookup (`get`) in both `Map` and `Object`.
2. Delete (`delete`) in both `Map` and `Object.
3. Re-setting the value of a key to its initial value (5) after deletion.

**Options compared:**

The two options being compared are:

* **Map**: A JavaScript data structure that stores key-value pairs, where keys are unique strings or symbols, and values can be any type.
* **Object**: A built-in JavaScript data structure that stores key-value pairs using property names as keys and values as values.

**Pros and cons of each approach:**

**Map:**

Pros:

* Fast lookup and delete operations (average O(1) time complexity).
* Efficient use of memory, as it only stores unique keys.
* Can handle a large number of keys without significant performance degradation.

Cons:

* Requires the key to be a string or symbol, which can limit its flexibility.
* May not be suitable for large amounts of data due to memory constraints.

**Object:**

Pros:

* Flexible and widely supported, as it's built into JavaScript.
* Can handle any type of value, including complex objects.
* Suitable for large amounts of data, as it uses dynamic property names.

Cons:

* Lookup and delete operations can be slower (average O(n) time complexity).
* May lead to performance degradation with a large number of keys due to memory constraints.
* Not suitable for use in very large datasets or real-time applications.

**Library usage:**

There is no explicit library mentioned in the provided JSON. However, it's worth noting that both `Map` and `Object` are built-in JavaScript data structures.

**Special JS features or syntax:**

None mentioned in this specific benchmark.

**Benchmark preparation code explanation:**

The script preparation code initializes two variables:

* `map`: A new instance of the `Map` data structure.
* `obj`: An empty `Object`.

It then sets a key-value pair for both data structures using the `set()` method and dot notation, respectively. Finally, it loops 1000 times, performing lookup, delete, and re-setting operations on each data structure.

**Individual test cases:**

Each test case has its own JavaScript code, which performs the same set of operations (lookup, delete, and re-set) on either `Map` or `Object`.

**Alternative approaches:**

Other alternatives for this benchmark could be:

* Using other data structures like `Set`, `WeakMap`, or a custom implementation.
* Comparing performance with other languages or frameworks.
* Incorporating additional factors, such as memory allocation or garbage collection, to simulate real-world scenarios.

By using MeasureThat.net and following the provided JSON structure, users can easily create and run their own JavaScript microbenchmarks to compare different data structures, libraries, or syntax features.

Related benchmarks:

Map vs Object add - lookup - delete (version: 0)

Lookup of map vs object

Comparing performance of: Map vs Obj

Created: 6 years ago by: Guest

Jump to the latest result

Map

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):