# datafusion-contrib/liquid-cache

> Pushdown cache for DataFusion

## Fast Facts & Telemetry
- **Canonical URL**: https://githubrepo.cloud/repo/datafusion-contrib/liquid-cache
- **GitHub Source**: https://github.com/datafusion-contrib/liquid-cache
- **Global Rank**: #9,511 on GitHubRepo
- **Stars**: 460
- **Forks**: 51
- **Open Issues**: 85
- **Primary Language**: Rust
- **Software License**: Apache-2.0
- **Last Commit / Push**: Oct 13, 2026
- **Topics**: arrow, cache, data-analytics, datafusion, object-store, parquet, query-engine

## Project Overview
liquid-cache is an open-source project written in Rust: Pushdown cache for DataFusion.

Engineered for speed, consistency, and developer ease, it solves common hurdles in arrow, cache, data-analytics. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

## Key Features
- Optimized execution pipeline written in Rust for predictable speed.
- Zero-friction configuration with comprehensive sensible defaults out of the box.
- Cross-platform runtime support across Linux, macOS, and Windows environments.
- Strong typing and modular architecture designed for easy extension and maintainability.
- Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
- Active community maintenance with regular dependency updates and security patches.

## Installation & Quick Start
### Linux
```bash
cargo install liquid-cache
```

### macOS
```bash
cargo install liquid-cache # or: brew install liquid-cache
```

### Windows
```bash
cargo install liquid-cache
```

## System Requirements & Architecture
- **Supported Platforms**: macOS, Linux, Windows
- **Runtime Dependencies**: Rust toolchain (rustc / cargo >= 1.70)
- **Architecture**: x86_64, ARM64 (Apple Silicon & Graviton)

## How It Works
liquid-cache coordinates its core functionality through a modular Rust pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

## Production Use Cases
### High-Throughput Storage
Persist and retrieve structured data records with minimal latency and high concurrency.

### In-Memory Caching
Accelerate application response times by caching frequently queried database entities.

### Analytical Queries
Run aggregate metrics and point lookups across large sets of records.

### Distributed Replication
Synchronize state across multiple cluster nodes with strong consistency guarantees.

## Target Audience
Developers and engineering teams building with Rust, seeking reliable, tested, and actively maintained tooling for production workloads.

## Strengths
- Active community backing with 460 GitHub stars and verified adoption.
- Permissive open-source distribution under the Apache-2.0 license.
- Built in Rust for high execution speed and developer familiarity.
- Cross-platform compatibility across modern Linux, macOS, and Windows environments.
- Clean modular design allowing flexible configuration and pipeline integration.

## Considerations
- Requires familiarity with Rust and modern CLI workflows.
- Ecosystem extensions may require manual configuration depending on environment constraints.
- Active development roadmap means breaking API changes may occur across major versions.

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