Quick reference

• The official daft docker image.

• Maintained by: openEuler CloudNative SIG.

• Where to get help: openEuler CloudNative SIG, openEuler.

daft | openEuler

Daft is a high-performance distributed query engine for large-scale data processing using Python or SQL, implemented in Rust. It is designed from the ground up to handle both traditional structured data and multimodal data (images, audio, video, embeddings) in a single unified framework. Daft provides a Python-native DataFrame API backed by a Rust-powered vectorized execution engine with SIMD acceleration, high-performance async I/O, cost-based query optimization, and native integration with Ray for distributed scaling across CPU/GPU clusters. Built on Apache Arrow, Daft supports universal connectivity to S3, GCS, Iceberg, Delta Lake, Hugging Face, Unity Catalog, and more.

Supported tags and respective Dockerfile links

The tag of each daft docker image is consist of the version of daft and the version of basic image. The details are as follows

Tags	Currently	Architectures
0.7.11-oe2403sp3	daft 0.7.11 on openEuler 24.03-lts-sp3	amd64, arm64

Usage

Pull the image and start an interactive container:

docker pull openeuler/daft:{Tag}
docker run -it --name daft-container openeuler/daft:{Tag}

daft is pre-installed in the image. Inside the container, you can use it directly with python3:

import daft

# Read parquet files lazily
df = daft.read_parquet("/path/to/data/*.parquet")

# Apply transformations
df = df.where(df["column_name"] > 100)
df = df.with_column("new_col", df["existing_col"] * 2)

# Display the query plan
df.explain(show_all=True)

# Materialize results
result = df.collect()
result.show(10)

You can also run a Python script directly:

docker run --rm -v $(pwd):/workspace openeuler/daft:{Tag} python3 /workspace/your_script.py

Question and answering

If you have any questions or want to use some special features, please submit an issue or a pull request on openeuler-docker-images.