Fjall

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Fjall is an open-source, log-structured, embeddable key-value storage engine written in Rust. It offers a thread-safe BTreeMap-like API and LSM-tree-based storage.

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At a glance

Pricing

open-source

Free tier

—

API

Yes

Skill level

Technical

About

What is fjall?

Fjall is an open-source, embeddable key-value storage engine implemented in Rust. It utilizes a log-structured design and provides a thread-safe API similar to a BTreeMap. The storage mechanism is based on an LSM-tree, which is optimized for write-heavy workloads. Key features include support for efficient range and prefix searching, the ability to manage multiple keyspaces, and built-in data compression to optimize storage usage. It is designed for developers needing a robust and performant embedded storage solution.

Best used for

Embedding a high-performance, log-structured key-value store directly into applications.

Common actions

Store data

Manage key-value pairs

Embed database

Optimize storage

Perform data searches

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Capabilities

Key features

Log-structured storage
Embeddable key-value
Rust-based
Thread-safe API
Built-in compression

Target Audience

DevelopersSoftware EngineersSystem Architects

Integrations

Not yet documented

Pricing & Plans

open-source

Free

FAQs

Given Fjall is open-source, are there any commercial support options available or is it purely community-driven?

As an open-source project, Fjall primarily relies on community support through its GitHub repository. While there are no official commercial support plans, developers can contribute or seek assistance from the active user base and maintainers.

What are the typical performance characteristics of Fjall, especially regarding its write-heavy workload optimization and LSM-tree design?

Fjall's LSM-tree design excels in write-heavy scenarios by minimizing random disk I/O. It offers high write throughput and efficient data compaction, though read performance might vary depending on the depth of the LSM-tree and cache hit rates.

How does Fjall's built-in compression impact performance and storage usage compared to uncompressed data?

The built-in compression in Fjall significantly reduces storage footprint, which can be crucial for large datasets. While compression/decompression adds a slight CPU overhead, it often improves overall performance by reducing disk I/O, especially for I/O-bound workloads.

Can Fjall be used in environments other than Rust applications, for example, through FFI or bindings to other languages?

Fjall is implemented in Rust and designed for Rust applications. While it's technically possible to create FFI bindings for other languages, this is not officially supported and would require custom development to integrate it into non-Rust projects.

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