newline-tools 0.1.1

Tools for working with large datasets

Newline Tools

File processing utilities for working with massive datasets.

Installation

pip install newline-tools

CLI Usage

The newline command provides several subcommands:

newline <command> [options]

shuffle

Shuffle lines in a file:

newline shuffle <input_file> <output_file> [-b BUFFER_SIZE] [--progress] [--include_empty] [-r ROUNDS] [--seed SEED]

Options:

• -b, --buffer_size: Buffer size in bytes (default: 1GB)
• --progress: Show progress bars during shuffling
• --include_empty: Include empty lines during shuffling (default: ignore empty lines)
• -r, --rounds: Number of shuffling rounds (default: 1)
• --seed: Seed for random number generator (for reproducibility)

dedupe

Remove duplicate lines:

newline dedupe <input_file> <output_file> [--progress] [--error_ratio ERROR_RATIO]

Options:

• --progress: Show progress bar during deduplication
• --error_ratio: Error ratio for the Bloom filter (default: 1e-5)

split

Split a file into parts:

newline split <input_file> <output_prefix> (-n NUM_PARTS | -s SIZE | -p PROPORTIONS) [--progress]

Options:

• -n, --num_parts: Number of parts to split into
• -s, --size: Size of each part (e.g., '100MB', '1GB')
• -p, --proportions: Split by proportions (must sum to 1)
• --progress: Show progress bar during splitting

Examples:

newline split input.txt output_prefix -n 5
newline split input.txt output_prefix -s 100MB
newline split input.txt output_prefix -p 0.3 0.3 0.4

sample

Sample lines from a file:

newline sample <input_file> <output_file> (-n NUM_LINES | -p PERCENTAGE) [--progress] [--seed SEED]

Options:

• -n, --num_lines: Number of lines to sample
• -p, --percentage: Percentage of lines to sample
• --progress: Show progress bar during sampling
• --seed: Seed for random number generator (for reproducibility)

Python Usage

from newline_tools import Shuffle, Dedupe, Split, Sample

# Shuffle
shuffler = Shuffle('input.txt', buffer_size=2**24, progress=True, ignore_empty=True, rounds=2, seed=42)
shuffler.shuffle('output.txt')

# Dedupe
deduper = Dedupe('input.txt', progress=True)
deduper.dedupe('output.txt', error_ratio=1e-5)

# Split
splitter = Split('input.txt', progress=True)
splitter.split_by_parts('output_prefix', 5)
# or
splitter.split_by_size('output_prefix', '100MB')
# or
splitter.split_by_proportion('output_prefix', [0.3, 0.3, 0.4])

# Sample
sampler = Sample('input.txt', 'output.txt', sample_size=10000, progress=True, seed=42)
sampler.sample(method='reservoir')  # or 'index'

License

Dedicated to the public domain (CC0). Use as you wish.