alterlab-dask

Dask

Overview

Dask is a Python library for parallel and distributed computing that enables three critical capabilities:

• Larger-than-memory execution on single machines for data exceeding available RAM
• Parallel processing for improved computational speed across multiple cores
• Distributed computation supporting terabyte-scale datasets across multiple machines

Dask scales from laptops (processing ~100 GiB) to clusters (processing ~100 TiB) while maintaining familiar Python APIs.

When to Use This Skill

This skill should be used when:

• Process datasets that exceed available RAM
• Scale pandas or NumPy operations to larger datasets
• Parallelize computations for performance improvements
• Process multiple files efficiently (CSVs, Parquet, JSON, text logs)
• Build custom parallel workflows with task dependencies
• Distribute workloads across multiple cores or machines

Core Capabilities

Dask provides five main components, each suited to different use cases:

1. DataFrames - Parallel Pandas Operations

Purpose: Scale pandas operations to larger datasets through parallel processing.

When to Use:

• Tabular data exceeds available RAM
• Need to process multiple CSV/Parquet files together
• Pandas operations are slow and need parallelization
• Scaling from pandas prototype to production

Reference Documentation: For comprehensive guidance on Dask DataFrames, refer to references/dataframes.md which includes:

• Reading data (single files, multiple files, glob patterns)
• Common operations (filtering, groupby, joins, aggregations)
• Custom operations with map_partitions
• Performance optimization tips
• Common patterns (ETL, time series, multi-file processing)

Quick Example:

import dask.dataframe as dd

# Read multiple files as single DataFrame
ddf = dd.read_csv('data/2024-*.csv')

# Operations are lazy until compute()
filtered = ddf[ddf['value'] > 100]
result = filtered.groupby('category').mean().compute()