dask

SKILL.md

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()

Key Points:

Operations are lazy (build task graph) until .compute() called

Use map_partitions for efficient custom operations

Convert to DataFrame early when working with structured data from other sources

2. Arrays - Parallel NumPy Operations

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

When to Use:

Arrays exceed available RAM

NumPy operations need parallelization

Working with scientific datasets (HDF5, Zarr, NetCDF)

Need parallel linear algebra or array operations

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

Creating arrays (from NumPy, random, from disk)

Chunking strategies and optimization

Common operations (arithmetic, reductions, linear algebra)

Custom operations with map_blocks

Integration with HDF5, Zarr, and XArray

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:

Chunk size is critical (aim for ~100 MB per chunk)

Operations work on chunks in parallel

Rechunk data when needed for efficient operations

Use map_blocks for operations not available in Dask

3. Bags - Parallel Processing of Unstructured Data

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

When to Use:

Processing text files, logs, or JSON records

Data cleaning and ETL before structured analysis

Working with Python objects that don't fit array/dataframe formats

Need memory-efficient streaming processing

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

Reading text and JSON files

Functional operations (map, filter, fold, groupby)

Converting to DataFrames

Common patterns (log analysis, JSON processing, text processing)

Performance considerations

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:

Use for initial data cleaning, then convert to DataFrame/Array

Use foldby instead of groupby for better performance

Operations are streaming and memory-efficient

Convert to structured formats (DataFrame) for complex operations

4. Futures - Task-Based Parallelization

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

When to Use:

Building dynamic, evolving workflows

Need immediate task execution (not lazy)

Computations depend on runtime conditions

Implementing custom parallel algorithms

Need stateful computations

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

Setting up distributed client

Submitting tasks and working with futures

Task dependencies and data movement

Advanced coordination (queues, locks, events, actors)

Common patterns (parameter sweeps, dynamic tasks, iterative algorithms)

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:

Requires distributed client (even for single machine)

Tasks execute immediately when submitted

Pre-scatter large data to avoid repeated transfers

~1ms overhead per task (not suitable for millions of tiny tasks)

Use actors for stateful workflows

5. Schedulers - Execution Backends

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

When to Choose Scheduler:

Threads (default): NumPy/Pandas operations, GIL-releasing libraries, shared memory benefit

Processes: Pure Python code, text processing, GIL-bound operations

Synchronous: Debugging with pdb, profiling, understanding errors

Distributed: Need dashboard, multi-machine clusters, advanced features

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

Detailed scheduler descriptions and characteristics

Configuration methods (global, context manager, per-compute)

Performance considerations and overhead

Common patterns and troubleshooting

Thread configuration for optimal performance

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:

Threads: Lowest overhead (~10 µs/task), best for numeric work

Processes: Avoids GIL (~10 ms/task), best for Python work

Distributed: Monitoring dashboard (~1 ms/task), scales to clusters

Can switch schedulers per computation or globally

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

Before using Dask, explore:

Better algorithms

Efficient file formats (Parquet instead of CSV)

Compiled code (Numba, Cython)

Data sampling

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

Increase chunk sizes if millions of tasks

Use map_partitions/map_blocks to fuse operations

Check task graph size: len(ddf.__dask_graph__())

4. Choose Appropriate Chunk Sizes

Target: ~100 MB per chunk (or 10 chunks per core in worker memory)

Too large: Memory overflow

Too small: Scheduling overhead

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
da.to_zarr(normalized, 'normalized.zarr')

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:

Tabular data → DataFrames

Numeric arrays → Arrays

Text/JSON/logs → Bags (then convert to DataFrame)

Custom Python objects → Bags or Futures

Operation Type:

Standard pandas operations → DataFrames

Standard NumPy operations → Arrays

Custom parallel tasks → Futures

Text processing/ETL → Bags

Control Level:

High-level, automatic → DataFrames/Arrays

Low-level, manual → Futures

Workflow Type:

Static computation graph → DataFrames/Arrays/Bags

Dynamic, evolving → Futures

Integration Considerations

File Formats

Efficient: Parquet, HDF5, Zarr (columnar, compressed, parallel-friendly)

Compatible but slower: CSV (use for initial ingestion only)

For Arrays: HDF5, Zarr, NetCDF

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

XArray: Wraps Dask arrays with labeled dimensions (geospatial, imaging)

Dask-ML: Machine learning with scikit-learn compatible APIs

Distributed: Advanced cluster management and monitoring

Debugging and Development

Iterative Development Workflow

Test on small data with synchronous scheduler:

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

Validate with threads on sample:

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

Scale with distributed for monitoring:

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

Common Issues

Memory Errors:

Decrease chunk sizes

Use persist() strategically and delete when done

Check for memory leaks in custom functions

Slow Start:

Task graph too large (increase chunk sizes)

Use map_partitions or map_blocks to reduce tasks

Poor Parallelization:

Chunks too large (increase number of partitions)

Using threads with Python code (switch to processes)

Data dependencies preventing parallelism

Reference Files

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

references/dataframes.md - Complete Dask DataFrame guide

references/arrays.md - Complete Dask Array guide

references/bags.md - Complete Dask Bag guide

references/futures.md - Complete Dask Futures and distributed computing guide

references/schedulers.md - Complete scheduler selection and configuration guide

references/best-practices.md - Comprehensive performance optimization and troubleshooting

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