Benchmark: Object vs Map lookup w/ rando integer keys

Script Preparation code:

window.m = new Map();
window.o = {};
for (let i = 0; i < 1000; ++i) {
  window.m.set(Math.random(10000) + i, i);
  window.o[Math.random(10000) + i] = i;
}

Tests:

Map Lookup
for (let i = 0; i < 10000; ++i) { if (window.m.get( Math.random(10000) + i) !== i) { } }
Object Lookup
for (let i = 0; i < 10000; ++i) { if (window.o[Math.random(10000) + i] !== i) { } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Map Lookup
Object Lookup

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Measuring the performance of JavaScript microbenchmarks is crucial to identify optimization opportunities and compare different approaches. Let's dive into the explanation.

**What is being tested?**

The provided JSON benchmark tests two approaches:

1. **Map Lookup**: The test creates a large map (a data structure that stores key-value pairs) and iterates over its entries using `window.m.get()`. It then checks if each value matches the expected integer value.
2. **Object Lookup**: The test creates a large object and iterates over its properties using `window.o[key]`. It then checks if each property value matches the expected integer value.

**Options compared**

The two approaches are compared in terms of their lookup performance, which is measured by the number of executions per second (ExecutionsPerSecond).

**Pros and Cons of different approaches:**

1. **Map Lookup**: Pros:
	* Maps provide fast lookups using a hash table (O(1) average time complexity).
	* They are suitable for large datasets.
2. **Object Lookup**: Pros:
	* Objects can be used with arbitrary keys, not just integers.
	* Objects are easier to work with in some cases.

Cons:

1. **Map Lookup**:
	* Maps can become slow if the key distribution is skewed or sparse.
	* They might require more memory for large datasets.
2. **Object Lookup**:
	* Object lookups (e.g., `window.o[key]`) have a higher average time complexity than map lookups (O(n) in the worst case, where n is the number of properties).
	* Objects can be slower due to overhead from property access.

**Library and purpose**

The `Map` object is a built-in JavaScript library that provides fast key-value pair lookups. It's similar to an associative array or hash table.

In this benchmark, `window.m = new Map()` creates a new map instance and stores integers as keys with their corresponding values.

**Special JS feature or syntax**

This benchmark doesn't use any special features or syntax specific to JavaScript versions or engines. However, it's worth noting that the benchmark uses `Math.random(10000) + i` to generate random integer keys, which can be influenced by the system clock and environment factors.

**Other alternatives**

If you want to explore alternative approaches, consider:

1. **Using a more optimized data structure**, like a `WeakMap` or an `Array` with `Object.keys()` or `Object.values()`, depending on your specific use case.
2. **Implementing a custom lookup function**, using techniques like hash tables or balanced trees.
3. **Testing with different data distributions** (e.g., random integers, strings, or objects) to see how it affects the performance of each approach.

Keep in mind that the choice of approach depends on your specific requirements and constraints.

Related benchmarks:

Object vs Map lookup w/ rando integer keys (version: 0)

Comparing the performance of lookups in large objects vs large maps when the keys are integers

Comparing performance of: Map Lookup vs Object Lookup

Created: 2 years ago by: Guest

Jump to the latest result

Map Lookup

Object Lookup

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):