Benchmark: Find vs Map (recalculated each time)

Script Preparation code:

var data = []

for (let i = 0; i < 5000; ++i) data.push({ username: 'toto', code: i })
data.push({ username: 'titi', code: 'titi' })
for (let i = 0; i < 2500; ++i) data.push({ username: 'toto', code: 5000+i })

Tests:

find
data.find(e => e.code === 'titi')
map each time
const dataIndexedByCode = data.reduce((acc, item) => { acc.set(item.code, item) return acc }, new Map()) dataIndexedByCode.get('titi')

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
find
map each time

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks and explore what's being tested in this specific benchmark.

**Benchmark Overview**

The provided JSON represents a benchmark test suite that compares the performance of two approaches to find an element in an array:

1. `data.find(e => e.code === 'titi')`
2. Creating a map of the data array using `reduce` and then indexing into it with `dataIndexedByCode.get('titi')`

**Approaches Compared**

The benchmark is comparing the performance of two approaches:

1. **`find` method**: This approach uses the `Array.prototype.find()` method to search for an element in the `data` array that matches a given condition (in this case, `e.code === 'titi'`). The `find()` method returns the first element that satisfies the condition or `undefined` if no elements match.
2. **Map-based approach**: This approach creates a map of the data array using the `Array.prototype.reduce()` method and then indexes into it with `dataIndexedByCode.get('titi')`. This approach uses a hash table (map) to store the data, allowing for O(1) lookups.

**Pros and Cons**

Here are some pros and cons of each approach:

* **`find` method**:
	+ Pros: Simple to implement, easy to read.
	+ Cons: May perform poorly if the array is large, as it needs to scan the entire array to find a match. Additionally, if no elements match, `find()` returns `undefined`, which may cause issues in some cases.
* **Map-based approach**:
	+ Pros: Can be faster for large datasets, especially when searching for an element by key, since it uses a hash table for lookups. Also eliminates the need to scan the entire array.
	+ Cons: Requires more memory and code complexity due to creating and maintaining the map.

**Library/Tool Used**

There is no specific library or tool being used in this benchmark other than JavaScript's built-in `Array.prototype.find()` and `Array.prototype.reduce()` methods.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax used in this benchmark. It uses standard JavaScript language features, such as loops, conditionals, and function calls.

**Other Alternatives**

If you're interested in exploring alternative approaches, here are a few options:

1. **Using `Array.prototype.findIndex()`**: This method is similar to `find()`, but it returns the index of the first element that satisfies the condition instead of the element itself.
2. **Using `Array.prototype.forEach()`**: Instead of using `map` or `find()`, you could use a `forEach` loop to iterate over the array and check for matches.
3. **Using Caching Libraries**: If performance is critical, consider using caching libraries like LRU Cache or Redis to cache the results of expensive computations.

Keep in mind that these alternatives may have different trade-offs in terms of complexity, memory usage, and performance.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.164 Safari/537.36

Browser/OS: Chrome 91 on Windows

View result in a separate tab

Test name	Executions per second
find	8662.6 Ops/sec
map each time	980.9 Ops/sec

Related benchmarks:

Find vs Map (recalculated each time) (version: 0)

Comparing performance of: find vs map each time

Created: 3 years ago by: Guest

Jump to the latest result

find

map each time

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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