Benchmark: Map vs Object2 - MeasureThat.net

Script Preparation code:

var map = new Map();
var obj = {};

var i = 0, count = 10000, a;

Tests:

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

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: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36

Browser/OS: Chrome 123 on Windows

View result in a separate tab

Test name	Executions per second
Map lookup	745.4 Ops/sec
Obj lookup	783.2 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**What is tested?**

The provided JSON represents a benchmark test case that compares two approaches to achieve a specific result: looking up values in an object versus using a `Map` data structure.

In the first test case, "Obj lookup", we have a simple object `obj` with a key-value pair `'a'`. We then access the value associated with the key by indexing into the object, i.e., `obj['a']`.

In the second test case, "Map lookup", we create an empty `Map` instance and add a single entry to it with key `'a'` mapped to the value `5`. We then retrieve the value associated with the key using the `get()` method of the map.

**Options compared**

The two approaches differ in their underlying data structure and access methods:

1. **Object lookup**: Using an object (`obj`) as a simple key-value store. Accessing values is done by indexing into the object, which means looking up the value associated with the given key.
2. **Map lookup**: Using a `Map` instance to store key-value pairs. The `get()` method of the map is used to retrieve the value associated with a specific key.

**Pros and cons**

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

* **Object lookup**:
	+ Pros: Simple, easy to implement, and works well for small datasets.
	+ Cons: Slow for large datasets due to the need to iterate through all keys during lookups.
* **Map lookup**:
	+ Pros: Faster than object lookup for large datasets since map operations are optimized for direct access.
	+ Cons: May require additional memory for the `Map` instance and can be less intuitive for some developers.

**Other considerations**

When deciding between these two approaches, consider the following factors:

* **Dataset size**: For small datasets, object lookup might be sufficient. However, as the dataset grows larger, map lookup becomes a better choice.
* **Performance requirements**: If high-performance is critical, using a `Map` instance can provide significant speedups.
* **Memory constraints**: In cases where memory is limited, object lookup might be more suitable due to its lower memory footprint.

**Library and special JS features**

In the provided benchmark test case, no specific libraries are used. However, if we were to extend this test case, we might consider using a library like `lodash` for utility functions or `console.table()` for displaying results in a more readable format.

No special JavaScript features are explicitly mentioned in the provided JSON.

**Alternatives**

Other alternatives for similar benchmarks could include:

1. Using a `Set` instance to store unique values.
2. Implementing a custom hash table data structure.
3. Comparing performance using other data structures, such as arrays or linked lists.

Keep in mind that each alternative may introduce additional complexity and considerations not present in the original benchmark test case.

Related benchmarks:

Map vs Object2 (version: 0)

Lookup of map vs object

Comparing performance of: Map lookup vs Obj lookup

Created: 5 years 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):