Linear-time shuffling of large data sets
npm install big-shuffleLinear-time shuffling of large datasets for Node.js
This package uses the Rao
algorithm to shuffle data sets that are too large to fit in memory. The
algorithm is described pretty well by Chris Hardin.
The input stream is randomly scattered into "piles" which
are stored on disk. Then each pile is shuffled in-memory with
Fisher-Yates.
If your data set is extremely large, then even your piles may not fit in
memory. In that case, the algorithm could recurse until the piles are
small enough, but that feature is not yet implemented here.
Because the input elements are written to disk as part of the shuffle,big-shuffle can only take string data. If you need to shuffle other
types of times, serialize them to string first.
Support for shuffling Buffer and Uint8Array objects may be added
later if there is demand.
npm install big-shuffle
For TypeScript users:
``ts
import { shuffle } from 'big-shuffle';
import * as path from 'path';
const inArray = [];
function *asyncRange(max: number) {
for (let i = 0; i < max; i++) {
yield i.toString(10);
}
}
const shuffled = shuffle(asyncRange(1000000));
for await (const i of shuffled) {
console.log();
}
`
This will generate, shuffle, and print a million random numbers.
Should work the same for JavaScript users after a few changes.
`
function shuffle(
inStream: AsyncIterable
numPiles: number = 1000, // More piles reduces memory usage but requires more open file descriptors
pileDir: string = path.join(__dirname, 'shuffle_piles'), // Filesystem path where the files are located
): Promise
Note that the shuffled iterable will not yield any records until the
input iterable is fully consumed.
class ShuffleTransform extends stream.Transform{
constructor(
numPiles: number = 1000, // More piles reduces memory usage but requires more open file descriptors
pileDir: string = path.join(__dirname, 'shuffle_piles'), // Filesystem path where the files are located
)
}