dask

Dask - Scalable Parallel Computing

Dask provides high-level collections (Arrays, DataFrames, Bags) that mimic the APIs of NumPy and pandas but operate in parallel on data sets that are larger than memory.

When to Use

• Processing datasets that don't fit in RAM (Out-of-core computing).
• Speeding up computations by using all available CPU cores.
• Parallelizing custom Python functions or complex workflows (dask.delayed).
• Scaling machine learning pipelines to large clusters.
• Handling large-scale arrays in physics, climate science, or imaging.
• Analyzing massive log files or unstructured data (dask.bag).

Reference Documentation

Official docs: https://docs.dask.org/
Dask Examples: https://examples.dask.org/
Search patterns: dask.dataframe, dask.array, dask.delayed, client.compute, dask.distributed

Core Principles

Lazy Evaluation

Dask doesn't compute results immediately. Instead, it builds a Task Graph. Actual computation only happens when you explicitly call .compute() or .persist().

Chunks and Partitions

• Dask Array: Composed of many small NumPy arrays called chunks.
• Dask DataFrame: Composed of many small pandas DataFrames called partitions.

Use Dask For

Collection	Analogy	Use Case
dask.array	NumPy	Large-scale multidimensional math.
dask.dataframe	pandas	Large CSV/Parquet/SQL tables.
dask.bag	Lists/Toolz	Unstructured data (JSON, Logs).
dask.delayed	Functions	Custom parallel logic.

Do NOT Use For

• Data that fits easily in RAM (pandas/NumPy are faster due to lower overhead).
• Simple tasks where multiprocessing or concurrent.futures suffice.
• Situations where low-latency response is required (Dask adds scheduling overhead).

Quick Reference

Installation

pip install "dask[complete]"

Standard Imports

import dask.array as da
import dask.dataframe as dd
from dask import delayed, compute
from dask.distributed import Client