Dask

Language: Python

Data Science

Dask was created by Matthew Rocklin in 2014 to enable scalable analytics in Python. It provides parallel collections that mimic the interfaces of NumPy arrays, Pandas DataFrames, and Python iterators, making it easy to transition existing code to parallel or distributed computing.

Dask is a flexible library for parallel computing in Python. It allows you to scale NumPy, Pandas, and Python functions to multi-core machines or distributed clusters for large-scale data processing.

Installation

pip: pip install dask[complete]

conda: conda install -c conda-forge dask

Usage

Dask allows you to parallelize computations on large datasets by providing data structures like Dask Array, Dask DataFrame, and Dask Bag. It integrates seamlessly with NumPy, Pandas, and Scikit-learn, and can run on a single machine or distributed cluster.

Dask Array basic operations

import dask.array as da
x = da.arange(1000, chunks=100)
y = x + 1
print(y.sum().compute())

Creates a Dask array, performs an element-wise addition, and computes the sum in parallel.

Dask DataFrame operations

import dask.dataframe as dd
df = dd.read_csv('data/*.csv')
print(df.head())
print(df['column'].mean().compute())

Reads multiple CSV files as a Dask DataFrame and computes the mean of a column in parallel.

Delayed computations

from dask import delayed

@delayed
def inc(x):
    return x + 1

@delayed
def add(x, y):
    return x + y

x = inc(10)
y = inc(20)
z = add(x, y)
print(z.compute())

Demonstrates Dask’s delayed interface for building computation graphs lazily, then executing them in parallel.

Parallel machine learning with Dask-ML

import dask_ml.linear_model as dlm
from dask.distributed import Client
client = Client()
model = dlm.LinearRegression()
# Fit model on Dask arrays or DataFrames

Shows how to scale machine learning computations using Dask-ML and distributed clients.

Distributed computing

from dask.distributed import Client
client = Client('tcp://scheduler-address:8786')
# Submit tasks to the cluster
future = client.submit(pow, 2, 10)
print(future.result())

Connects to a Dask distributed cluster and submits tasks for parallel execution.

Error Handling

ValueError: Chunk size too large: Adjust chunk sizes to fit in memory when creating Dask arrays or DataFrames.

FileNotFoundError: Ensure all file paths exist when using Dask’s read functions.

dask.distributed.core.TimeoutError: Check connectivity to the distributed cluster and adjust timeout settings.

Best Practices

Break large datasets into appropriately sized chunks to optimize parallelism.

Use Dask collections that mimic familiar NumPy/Pandas interfaces to reduce learning curve.

Leverage `compute()` only when necessary to trigger execution; avoid multiple small `compute()` calls.

Use Dask’s distributed scheduler for multi-machine setups for large-scale computation.

Monitor tasks with the Dask dashboard to detect bottlenecks and improve performance.

Official Docs Github