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delta-rs: Native Rust Library for Delta Lake with Python Bindings
A high-performance, native Rust implementation of the Delta Lake protocol, offering robust data lake capabilities and convenient Python bindings for data engineers and analysts.
Rust
Added on 2025年5月11日2,744
Stars475
ForksRust
LanguageProject Introduction
Summary
delta-rs is the official native Rust library for interacting with Delta Lake tables. It provides a high-performance, low-level implementation of the Delta Lake protocol, along with idiomatic Python bindings, making it ideal for performance-critical data tasks and integration into native applications.
Problem Solved
Traditional Delta Lake implementations often rely on the JVM, which can introduce overhead and complexity. Accessing Delta Lake from non-JVM/Spark environments or requiring maximum performance can be challenging. delta-rs provides a native, fast alternative that enables broader language access.
Core Features
Native Rust Implementation
Interact with Delta Lake tables directly from high-performance Rust code, leveraging native execution speeds.
Python Bindings
Seamlessly read and write Delta tables within Python data workflows, integrating with libraries like pandas, polars, and pyarrow.
Protocol Compliance
Fully compliant with the Delta Lake protocol specifications, ensuring compatibility with other Delta Lake implementations.
Efficient Reading
Efficiently read data from Delta tables with support for predicates and column projection.
Data Writing Capabilities
Support for appending, overwriting, and other modifications to Delta tables.
Tech Stack
Rust
Python
Delta Lake Protocol
Apache Arrow
Parquet
使用场景 (Use Cases)
delta-rs is suitable for various scenarios where high-performance, language-native interaction with Delta Lake is required, particularly outside of traditional Spark/JVM-based ecosystems.
场景一：构建高性能ETL/ELT管道 (High-Performance ETL/ELT Pipelines)
Details
Build high-speed data ingestion, transformation, or loading processes using Rust, leveraging its performance characteristics to read from and write to Delta tables efficiently.
User Value
Significantly reduce processing time and infrastructure costs for data pipelines compared to interpreted languages for heavy data operations.
场景二：本地数据分析与处理 (Local Data Analysis and Processing)
Details
Read Delta tables directly into Python data structures (like pandas DataFrames or Polars DataFrames) for local data cleaning, analysis, or machine learning model training without needing a distributed cluster.
User Value
Simplifies local development, testing, and analysis workflows by providing direct, fast access to Delta Lake data in Python.
场景三：原生应用集成 (Native Application Integration)
Details
Develop standalone applications, command-line tools, or microservices in Rust that need to read or write data to Delta Lake tables as part of their core functionality.
User Value
Enables the creation of new types of performant applications that interact directly with Delta Lake, expanding its use beyond traditional big data frameworks.
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