Optimizing Big Data with Daft, The Future of Distributed Dataframes

Optimizing Big Data with Daft: The Future of Distributed Dataframes

Daft represents a significant leap in large-scale data processing, combining the ease of Python with the power of Rust for unmatched efficiency. Its focus on multimodal data and integration with cloud technologies offers an advanced solution for handling complex data at scale. This article delves into Daft's capabilities, providing insights into its advantages, use cases, and how to get started.

Table of Contents

  • About Daft
  • Getting Started with Daft
  • Benchmark Performance Insights
  • Comparing Daft with Other Dataframe Projects
  • How to Contribute to Daft
  • Understanding Daft's License

About Daft

Daft is not just another query engine; it's a robust platform designed to manage and process multimodal data across distributed systems efficiently. Its core principles highlight its versatility and power:

  • Any Data Handling: Daft excels in processing not only traditional data types but also complex, nested multimodal data like images, embeddings, and Python objects with its Arrow-based memory representation.
  • Interactive Computing: Designed for a superior interactive developer experience, Daft leverages intelligent caching and query optimizations to facilitate rapid experimentation and data exploration.
  • Distributed Computing: For workloads that exceed the capacity of local resources, Daft seamlessly integrates with Ray, enabling scalable data processing across large clusters equipped with thousands of CPUs/GPUs.

Getting Started with Daft


Daft simplifies its installation with a straightforward pip command:

pip install getdaft

For those requiring a more tailored setup, including installations from source or additional dependencies like Ray and AWS utilities, Daft's Installation Guide provides comprehensive instructions.

Quickstart Guide

Daft offers a 10-minute quickstart tutorial, ideal for newcomers. The tutorial demonstrates how to load, transform, and process images from an AWS S3 bucket using Daft's intuitive API. Here's a glimpse of what you'll learn:

import daft
# Load images from an S3 bucket
df = daft.from_glob_path("s3://daft-public-data/laion-sample-images/*")
# Transform images: download, decode, and resize
df = df.with_column("image", df["path"]
df = df.with_column("resized", df["image"].image.resize(32, 32))

This example succinctly illustrates Daft's capability to handle complex data transformations effortlessly.

Benchmark Performance Insights

Daft's benchmarks, particularly for SF100 TPCH, showcase its exceptional performance in data processing tasks. Detailed setups and logs available on the benchmarking page affirm Daft's leading edge in efficiency and speed.

Comparing Daft with Other Dataframe Projects

When evaluated against other dataframe projects like Pandas, Polars, Modin, Pyspark, and Dask DF, Daft stands out for its comprehensive features, including query optimization, multimodal data support, distributed computing capabilities, Arrow-backed storage, and vectorized execution engine. This comparison underscores Daft's unique position in the data processing landscape.

draft performance

DataframeQuery OptimizerMultimodalDistributedArrow BackedVectorized Execution EngineOut-of-core
PandasNoPython objectNooptional >= 2.0Some(Numpy)No
PolarsYesPython objectNoYesYesYes
ModinEagarPython objectYesNoSome(Pandas)Yes
PysparkYesNoYesPandas UDF/IOPandas UDFYes
Dask DFNoPython objectYesNoSome(Pandas)Yes

How to Contribute to Daft

Contributing to Daft is encouraged, and potential contributors are directed to read the document. This initiative not only fosters community involvement but also ensures continual improvement and innovation within the project.

Understanding Daft's License

Daft is released under the Apache 2.0 license, reflecting its commitment to open-source principles and allowing for wide-ranging use and contributions by the community.