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Find vs Map (recalculated each time)
(version: 0)
Comparing performance of:
find vs map each time
Created:
4 years ago
by:
Guest
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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>
Run tests (2)
Previous results
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Test case name
Result
find
map each time
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
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.
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