name: dask
description: "Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows."

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

1. Test on small data with synchronous scheduler:

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

1. Validate with threads on sample:

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

1. 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.