Benchmark: Restoring actual data

Tests:

forEach + find + push
const responseData = new Array(100).fill(null).map((_, index) => ({ contract: `contract_${index}`, token_id: index, hash: `hash_${index}`, })); const cachedData = new Array(100).fill(null).map((_, index) => ({ contract: `contract_${index % 2}`, token_id: index, hash: `hash_${index}`, nft: index % 2 === 0 ? { data: `NFT data for ${index}` } : null, })); const readyForRender = []; const toUpdate = []; responseData.forEach(item => { const actualCachedData = cachedData.find( cache => cache.contract === item.contract && cache.token_id === item.token_id && cache.hash === item.hash ); if (actualCachedData && actualCachedData.nft) { readyForRender.push(actualCachedData); } else { toUpdate.push(item); } });
Reduce + find
const responseData = new Array(100).fill(null).map((_, index) => ({ contract: `contract_${index}`, token_id: index, hash: `hash_${index}`, })); const cachedData = new Array(100).fill(null).map((_, index) => ({ contract: `contract_${index % 2}`, token_id: index, hash: `hash_${index}`, nft: index % 2 === 0 ? { data: `NFT data for ${index}` } : null, })); const { readyForRender, toUpdate } = responseData.reduce((accumulator, item) => { const actualCachedData = cachedData.find(cache => cache.contract === item.contract && cache.token_id === item.token_id && cache.hash === item.hash ); if (actualCachedData && actualCachedData.nft) { return { ...accumulator, readyForRender: [...accumulator.readyForRender, actualCachedData], }; } else { return { ...accumulator, toUpdate: [...accumulator.toUpdate, item], }; } }, { readyForRender: [], toUpdate: [] });
map + find + filter
const responseData = new Array(100).fill(null).map((_, index) => ({ contract: `contract_${index}`, token_id: index, hash: `hash_${index}`, })); const cachedData = new Array(100).fill(null).map((_, index) => ({ contract: `contract_${index % 2}`, token_id: index, hash: `hash_${index}`, nft: index % 2 === 0 ? { data: `NFT data for ${index}` } : null, })); const loadedIndices = responseData.map((item, index) => { const actualCachedData = cachedData.find(cache => cache.contract === item.contract && cache.token_id === item.token_id && cache.hash === item.hash ); return actualCachedData && actualCachedData.nft ? index : null; }).filter(index => index !== null); const readyForRender = loadedIndices.map(index => cachedData[index]); const toUpdate = responseData.filter((_, index) => !loadedIndices.includes(index));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
forEach + find + push
Reduce + find
map + find + filter

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):

LLMs can make mistakes. Check important info.

The provided benchmark measures the performance of three different approaches for filtering and processing data in JavaScript:

1. **`map`, `find`, and `filter`**: This approach uses the `map` method to create a new array with the filtered elements, then uses the `find` method to filter out elements that don't meet the condition, and finally uses the `filter` method to remove duplicates.
2. **`forEach`, `find`, and `push`**: This approach uses the `forEach` method to iterate over the data, finds the matching cached data using `find`, and then pushes it into a new array.
3. **`Reduce`**: This approach uses the `reduce` method to process each element in the data, finding the matched cached data along the way.

Now, let's discuss the pros and cons of each approach:

**1. `map`, `find`, and `filter`**:

Pros:

* Clear and concise code
* Easy to read and understand

Cons:

* May have higher overhead due to creating new arrays and iterating over the original data twice.
* Can be slower for very large datasets.

**2. `forEach`, `find`, and `push`**:

Pros:

* More intuitive and natural flow of execution, as it iterates over the data once.
* Less overhead compared to creating new arrays.

Cons:

* Code can become less readable and harder to understand due to using a loop and pushing elements into an array.
* May require additional checks for edge cases or empty arrays.

**3. `Reduce`**:

Pros:

* Can be more efficient, as it only iterates over the data once and accumulates the results.
* Can be easier to implement for some use cases, as it's a natural fit for reducing an array.

Cons:

* Code can become less readable and harder to understand due to using a complex reduction operation.
* May require additional checks for edge cases or empty arrays.

Other considerations:

* The `map`, `find`, and `filter` approach is generally considered the most JavaScript-like way of solving this problem, making it easier for developers familiar with this syntax to understand and implement.
* The `forEach`, `find`, and `push` approach can be a good choice when you need to process data in a more sequential manner or when working with older browsers that don't support `map`.
* The `Reduce` approach is suitable for problems that require accumulating results from an array, such as calculating the sum of elements.

Alternatives:

* If you're dealing with large datasets and need optimal performance, consider using a more specialized library like Lodash or Ramda, which provide optimized functions for common use cases.
* For even better performance, consider using Web Workers or parallel processing to take advantage of multiple CPU cores.
* When working with older browsers or environments that don't support modern JavaScript features, consider using polyfills or transpilers to ensure compatibility.

In conclusion, the choice of approach depends on your specific requirements, such as readability, performance, and compatibility.

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36

Browser/OS: Chrome 122 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
forEach + find + push	33438.6 Ops/sec
Reduce + find	33113.2 Ops/sec
map + find + filter	41510.7 Ops/sec

Related benchmarks:

Restoring actual data (version: 0)

Comparing performance of: forEach + find + push vs Reduce + find vs map + find + filter

Created: 8 months ago by: Registered User

Jump to the latest result

forEach + find + push

Reduce + find

map + find + filter

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):