Benchmark: Map vs Object - More items3

Script Preparation code:

var map = new Map();
var obj = {};
for (let i = 0; i < 50; i++) {
  map.set('a' + i, i);
  obj['a' + i] = i;
}
Object.freeze(obj)

var i = 0, count = 1000, a;

Tests:

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

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Map lookup
Obj lookup

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36

Browser/OS: Chrome 126 on Windows

View result in a separate tab

Test name	Executions per second
Map lookup	5314.6 Ops/sec
Obj lookup	4343.4 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the benchmark for you.

**What is being tested?**

The provided JSON represents a JavaScript microbenchmark that compares the performance of two data structures: `Map` and plain objects (`Object`). The test creates a large dataset with 50 key-value pairs and then performs lookups on both data structures to retrieve values.

**Options compared**

Two options are compared:

1. **Map lookup**: The benchmark measures the time it takes to perform lookups using the `Map` data structure.
2. **Plain object lookup**: The benchmark measures the time it takes to perform lookups using plain objects (`Object`).

**Pros and cons of each approach:**

* **Map lookup**:
	+ Pros: Maps are designed for fast lookups, with an average time complexity of O(1). This can lead to better performance in scenarios where data is frequently accessed by key.
	+ Cons: Maps require more memory than plain objects, as they store additional metadata (e.g., the number of entries).
* **Plain object lookup**:
	+ Pros: Plain objects are lightweight and easy to use. They also provide a familiar syntax for accessing values.
	+ Cons: Plain objects rely on string-based keys, which can lead to slower lookups compared to maps. The average time complexity is O(n), where n is the number of key-value pairs.

**Library and its purpose**

The `Map` data structure is a built-in JavaScript object that provides fast lookups and other benefits like efficient insertion and deletion of entries. It's particularly useful when working with large datasets or when performance is critical.

**Special JS feature or syntax**

There are no special features or syntaxes mentioned in the benchmark definition or test cases. The focus is on comparing the performance of two basic data structures.

**Other alternatives**

If you were to design an alternative benchmark, you might consider other data structures like:

* **Array**: Arrays provide O(1) access time for elements at a specific index but are limited to sequential indexing.
* **Set**: Sets provide fast membership testing and union/intersection operations but may be less suitable for lookups.
* **WeakMap**: WeakMaps are similar to Maps but with some key differences, such as automatic garbage collection of keys.

These alternatives would require adjustments to the benchmark definition and test cases to accurately reflect their performance characteristics.

Related benchmarks:

Map vs Object - More items3 (version: 0)

Lookup of map vs object

Comparing performance of: Map lookup vs Obj lookup

Created: one year ago by: Guest

Jump to the latest result

Map lookup

Obj lookup

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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