dask

Dask

Overview

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

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

Current upstream: dask 2026.3.0 (PyPI, March 2026). Docs: docs.dask.org. Since 2025.1.0, the expression-based DataFrame API with query planning is the only implementation — do not install dask-expr separately or set dataframe.query-planning: False.

Quick Start

Installation

uv pip install "dask>=2025.1"

For a typical pandas/NumPy workflow with the distributed scheduler and dashboard:

uv pip install "dask[complete]"

Remote object storage (S3, GCS, Azure):

uv pip install s3fs    # s3:// paths
uv pip install gcsfs   # gs:// paths

Requires Python 3.10+ (3.9 support dropped in 2024.12). DataFrame I/O requires PyArrow 16+ (as of dask 2026.1.2).

When to Use This Skill

This skill should be used when:

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:

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

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()

Key Points:

2. Arrays - Parallel NumPy Operations

Purpose: Extend NumPy capabilities to datasets larger than memory using blocked algorithms.

When to Use:

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

Quick Example:

import dask.array as da

# Create large array with chunks
x = da.random.random((100000, 100000), chunks=(10000, 10000))

# Operations are lazy
y = x + 100
z = y.mean(axis=0)

# Compute result
result = z.compute()

Key Points:

3. Bags - Parallel Processing of Unstructured Data

Purpose: Process unstructured or semi-structured data (text, JSON, logs) with functional operations.

When to Use:

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

Quick Example:

import dask.bag as db
import json

# Read and parse JSON files
bag = db.read_text('logs/*.json').map(json.loads)

# Filter and transform
valid = bag.filter(lambda x: x['status'] == 'valid')
processed = valid.map(lambda x: {'id': x['id'], 'value': x['value']})

# Convert to DataFrame for analysis
ddf = processed.to_dataframe()

Key Points:

4. Futures - Task-Based Parallelization

Purpose: Build custom parallel workflows with fine-grained control over task execution and dependencies.

When to Use:

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

Quick Example:

from dask.distributed import Client

client = Client()  # Create local cluster

# Submit tasks (executes immediately)
def process(x):
    return x ** 2

futures = client.map(process, range(100))

# Gather results
results = client.gather(futures)

client.close()

Key Points:

5. Schedulers - Execution Backends

Purpose: Control how and where Dask tasks execute (threads, processes, distributed).

When to Choose Scheduler:

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

Quick Example:

import dask
import dask.dataframe as dd

# Use threads for DataFrame (default, good for numeric)
ddf = dd.read_csv('data.csv')
result1 = ddf.mean().compute()  # Uses threads

# Use processes for Python-heavy work
import dask.bag as db
bag = db.read_text('logs/*.txt')
result2 = bag.map(python_function).compute(scheduler='processes')

# Use synchronous for debugging
dask.config.set(scheduler='synchronous')
result3 = problematic_computation.compute()  # Can use pdb

# Use distributed for monitoring and scaling
from dask.distributed import Client
client = Client()
result4 = computation.compute()  # Uses distributed with dashboard

Key Points:

Best Practices

For comprehensive performance optimization guidance, memory management strategies, and common pitfalls to avoid, refer to references/best-practices.md. Key principles include:

Start with Simpler Solutions

Critical Performance Rules

1. Don't Load Data Locally Then Hand to Dask

# Wrong: Loads all data in memory first
import pandas as pd
df = pd.read_csv('large.csv')
ddf = dd.from_pandas(df, npartitions=10)

# Correct: Let Dask handle loading
import dask.dataframe as dd
ddf = dd.read_csv('large.csv')

2. Avoid Repeated compute() Calls

# Wrong: Each compute is separate
for item in items:
    result = dask_computation(item).compute()

# Correct: Single compute for all
computations = [dask_computation(item) for item in items]
results = dask.compute(*computations)

3. Don't Build Excessively Large Task Graphs

4. Choose Appropriate Chunk Sizes

5. Use the Dashboard

from dask.distributed import Client
client = Client()
print(client.dashboard_link)  # Monitor performance, identify bottlenecks

Common Workflow Patterns

ETL Pipeline

import dask.dataframe as dd

# Extract: Read data
ddf = dd.read_csv('raw_data/*.csv')

# Transform: Clean and process
ddf = ddf[ddf['status'] == 'valid']
ddf['amount'] = ddf['amount'].astype('float64')
ddf = ddf.dropna(subset=['important_col'])

# Load: Aggregate and save
summary = ddf.groupby('category').agg({'amount': ['sum', 'mean']})
summary.to_parquet('output/summary.parquet')

Unstructured to Structured Pipeline

import dask.bag as db
import json

# Start with Bag for unstructured data
bag = db.read_text('logs/*.json').map(json.loads)
bag = bag.filter(lambda x: x['status'] == 'valid')

# Convert to DataFrame for structured analysis
ddf = bag.to_dataframe()
result = ddf.groupby('category').mean().compute()

Large-Scale Array Computation

import dask.array as da

# Load or create large array
x = da.from_zarr('large_dataset.zarr')

# Process in chunks
normalized = (x - x.mean()) / x.std()

# Save result (use mode= for overwrite; zarr_array_kwargs for compression)
da.to_zarr(normalized, 'normalized.zarr', mode='w')

Custom Parallel Workflow

from dask.distributed import Client

client = Client()

# Scatter large dataset once
data = client.scatter(large_dataset)

# Process in parallel with dependencies
futures = []
for param in parameters:
    future = client.submit(process, data, param)
    futures.append(future)

# Gather results
results = client.gather(futures)

Selecting the Right Component

Use this decision guide to choose the appropriate Dask component:

Data Type:

Operation Type:

Control Level:

Workflow Type:

Integration Considerations

File Formats

Conversion Between Collections

# Bag → DataFrame
ddf = bag.to_dataframe()

# DataFrame → Array (for numeric data)
arr = ddf.to_dask_array(lengths=True)

# Array → DataFrame
ddf = dd.from_dask_array(arr, columns=['col1', 'col2'])

With Other Libraries

Debugging and Development

Iterative Development Workflow

dask.config.set(scheduler='synchronous')
result = computation.compute()  # Can use pdb, easy debugging

sample = ddf.head(1000)  # Small sample
# Test logic, then scale to full dataset

from dask.distributed import Client
client = Client()
print(client.dashboard_link)  # Monitor performance
result = computation.compute()

Common Issues

Memory Errors:

Slow Start:

Poor Parallelization:

Reference Files

All reference documentation files can be read as needed for detailed information:

Load these files when users need detailed information about specific Dask components, operations, or patterns beyond the quick guidance provided here.