batch-me-up
v1.0.1
Published
A utility for efficiently splitting data into batches based on available CPU resources
Downloads
142
Maintainers
Readme
batch-me-up
A utility for efficiently splitting data into batches based on available CPU resources.
Why?
- Automatic Batch Sizing: Optimizes batch size based on the number of available CPUs for efficient processing.
- Custom Batch Sizes: Allows us to specify batch sizes for specific needs.
- Flexibility: Works with arrays of any data type.
Install
npm install batch-me-up
Or yarn:
yarn add batch-me-up
Alternatively, you can also include this module directly in your HTML file from CDN:
UMD: https://cdn.jsdelivr.net/npm/batch-me-up/dist/index.umd.js
ESM: https://cdn.jsdelivr.net/npm/batch-me-up/+esm
CJS: https://cdn.jsdelivr.net/npm/batch-me-up/dist/index.cjs
Usage
import generateBatches from 'batch-me-up'
const data = [1, 2, 3, 4, 5, 6, 7, 8]
// determine batch size based on available CPUs
const batches = await generateBatches(data)
// or specify a custom batch size
const batchesWithCustomSize = await generateBatches(data, 2)
// process each batch
const results = await Promise.all(
batches.map(async batch => {
// process each item within the batch concurrently
return await Promise.all(batch.map(processItem))
})
)
// flatten the results array, if needed
const finalResults = results.flat()
console.log(finalResults) // Output: [2, 4, 6, 8, 10, 12, 14, 16]
API
generateBatches<T = any>(data: T[], batchSize?: number): Promise<T[][]>
Generates batches of data based on the number of CPUs available or a provided batch size.
data
(array): The array of data to be batched.batchSize
(number, optional): The desired size of each batch. If not provided, the function automatically determines the optimal batch size based on available CPUs.
Returns: An array of arrays, where each sub-array represents a batch of the original data.
Use cases
- Parallel Processing: Divide a large dataset into batches for parallel processing using libraries like
Promise.all
or worker threads. - Streaming Data: Process data in chunks as it is received from a stream or API.
- Machine Learning: Batch training data for efficient model training.
Contributing
We 💛 issues.
When committing, please conform to the semantic-release commit standards. Please install commitizen
and the adapter globally, if you have not already.
npm i -g commitizen cz-conventional-changelog
Now you can use git cz
or just cz
instead of git commit
when committing. You can also use git-cz
, which is an alias for cz
.
git add . && git cz
License
A project by Stilearning © 2021-2024.