Benchmark: Map vs Object vs Reduce

Script Preparation code:

var numberSet = new Array(10000).fill(0).map(() => Math.floor(Math.random() * 20))
var stringSet = new Array(10000).fill(0).map(() => Math.random().toString(16).slice(2, 7))

var numberMap = new Map();
var stringMap = new Map();

var numberObject = {};
var stringObject = {};

Tests:

Numbers Map
numberSet.forEach((num) => numberMap.set((numberMap.get(num) || 0) + 1))
Numbers Object
numberSet.forEach((num) => numberObject[num] = (numberObject[num] || 0) + 1)
Strings Map
stringSet.forEach((num) => stringMap.set((stringMap.get(num) || 0) + 1))
Strings Object
stringSet.forEach((num) => stringObject[num] = (stringObject[num] || 0) + 1)
Numbers Object Reduce
numberSet.reduce((obj, num) => { obj[num] = (obj[num] || 0) + 1; return obj; }, {})
Strings Object Reduce
stringSet.reduce((obj, string) => { obj[string] = (obj[string] || 0) + 1; return obj; }, {})

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Numbers Map
Numbers Object
Strings Map
Strings Object
Numbers Object Reduce
Strings Object Reduce

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):

LLMs can make mistakes. Check important info.

Let's break down the benchmark test case.

**What is being tested?**

The test case compares the performance of four different approaches to iterate over an array and update a collection (either a Map or an Object) with counts for each element:

1. **Numbers Map**: Updating a Map with counts for numbers using `Map.set()` and `Map.get()`.
2. **Numbers Object**: Updating an Object with counts for numbers using object literal syntax.
3. **Strings Map**: Updating a Map with counts for strings using `Map.set()` and `Map.get()`.
4. **Strings Object Reduce**: Using the `reduce()` method to update an Object with counts for strings.

**Options compared**

The test case compares two main options: updating a Map vs updating an Object. Within each option, there are two sub-options:

* For Maps:
	+ Numbers Map: Updating a Map with counts for numbers.
	+ Strings Map: Updating a Map with counts for strings.
* For Objects:
	+ Numbers Object: Updating an Object with counts for numbers using object literal syntax.
	+ Strings Object Reduce: Using the `reduce()` method to update an Object with counts for strings.

**Pros and cons of each approach**

Here's a brief summary:

* **Map**:
	+ Pros: Efficient iteration, fast lookups, and updates.
	+ Cons: Requires creating and managing a separate Map object.
* **Object**:
	+ Pros: Simple to use, no need to create a separate object.
	+ Cons: Iteration can be slow, especially for large objects.

The `reduce()` method is also being compared in the Strings Object Reduce test case. While not directly comparable to the other options, it's worth noting that `reduce()` is generally slower than using `Map` or Object literals due to its iterative nature.

**Libraries and features**

No external libraries are used in this test case.

**Special JS feature or syntax**

The `reduce()` method is a built-in JavaScript function that takes an array and returns a single value (in this case, an updated Object). No special JS features or syntax are being used in this test case.

**Alternatives**

Other alternatives to update counts for numbers and strings could be:

* Using an Array with indices as keys (e.g., `array[num] = count`).
* Creating a custom data structure, such as a hash table.
* Utilizing external libraries like Lodash or Underscore.js that provide similar functionality.

Keep in mind that these alternatives may have their own performance characteristics and use cases.

Related benchmarks:

Map vs Object vs Reduce (version: 3)

Comparing performance of: Numbers Map vs Numbers Object vs Strings Map vs Strings Object vs Numbers Object Reduce vs Strings Object Reduce

Created: 5 years ago by: Registered User

Jump to the latest result

Numbers Map

Numbers Object

Strings Map

Strings Object

Numbers Object Reduce

Strings Object Reduce

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):