Benchmark: Map vs POJO insertion

Tests:

map insert
var map = new Map(); for (var i = 0; i < 300; i++) { var k = Math.round(Math.random() * 10); var val = Math.round(Math.random() * 10); map.set(`${k}`, val); }
POJO insert
var pojo = {}; for (var i = 0; i < 300; i++) { var k = Math.round(Math.random() * 10); var val = Math.round(Math.random() * 10); pojo[`${k}`] = val; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map insert
POJO insert

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:146.0) Gecko/20100101 Firefox/146.0

Browser/OS: Firefox 146 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
map insert	189182.4 Ops/sec
POJO insert	455752.6 Ops/sec

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

LLMs can make mistakes. Check important info.

Measuring JavaScript performance is a crucial aspect of ensuring the efficiency and reliability of web applications.

**What's being tested?**

The provided JSON represents two individual test cases, which compare the insertion performance of a `Map` data structure (a built-in JavaScript object) versus a plain Object- Oriented Programming (POJO) implementation. The tests insert 300 key-value pairs into each data structure using random keys and values.

**Options compared:**

1. **Map**: A built-in JavaScript object that stores mappings between keys and values.
2. **POJO**: A plain JavaScript object with custom methods for inserting data.

**Pros and Cons of different approaches:**

* **Map**:
	+ Pros: Efficient insertion, fast lookup, and iteration times due to its hash table implementation.
	+ Cons: Requires the creation of a new `Map` instance for each test case, which can lead to additional memory allocation and garbage collection overhead.
* **POJO**:
	+ Pros: Simple to implement, easy to understand, and doesn't require any external libraries or dependencies.
	+ Cons: Insertion times can be slower compared to the `Map` implementation due to the overhead of accessing and modifying individual properties.

**Library/ Framework consideration:**

None mentioned in the provided JSON.

**Special JS feature/syntax: None mentioned.**

Now, let's talk about alternatives:

1. **Using a third-party library**: Libraries like Lodash or Ramda provide optimized implementations for data structures like maps, which can offer better performance and more features than the built-in `Map` implementation.
2. **Native Web Workers**: Running tests in separate Web Workers can help isolate memory allocation and garbage collection overhead, allowing for more accurate comparisons between different implementations.
3. **Just-In-Time (JIT) compilation**: Some JavaScript engines, like V8 in Chrome, use JIT compilation to optimize performance-critical code paths. Using a JIT compiler can provide better performance than plain JavaScript execution.

In conclusion, the `Map` vs POJO insertion test case is a common scenario used to compare the performance of different data structure implementations in JavaScript. By understanding the pros and cons of each approach, developers can make informed decisions about which implementation to use in their own projects, depending on specific requirements and constraints.

Related benchmarks:

Map vs POJO insertion (version: 0)

Test insertion perf for map vs pojo

Comparing performance of: map insert vs POJO insert

Created: 3 years ago by: Guest

Jump to the latest result

map insert

POJO insert

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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