Open-Source Databases · fjall-rs

fjall

Fjall is an open-source, embeddable key-value storage engine written in Rust that uses a log-structured merge-tree (LSM) architecture similar to RocksDB. It provides a thread-safe API for storing and retrieving data with support for multiple keyspaces, range queries, compression, and optional transactions.

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Source: GitHub — github.com/fjall-rs/fjall

2.2k

GitHub stars

105

Forks

Rust

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	fjall-rs/fjall
Owner	fjall-rs
Primary language	Rust
License	Apache-2.0 — OSI-approved
Stars	2.2k
Forks	105
Open issues	40
Latest release	3.1.6 (2026-07-05)
Last updated	2026-07-07
Source	https://github.com/fjall-rs/fjall

What fjall is

Fjall implements an LSM-tree storage engine with MVCC-based snapshot isolation, built-in LZ4 compression, configurable durability modes, and optional serializable transactions via optimistic or single-writer implementations. It exposes a BTreeMap-like Rust API with lexicographic key ordering, range/prefix iteration, and cross-keyspace atomic batch operations.

Quickstart

Get the fjall source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/fjall-rs/fjall.git2cd fjall3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Embedded storage for time-series or log-heavy applications

Fjall's LSM structure is optimized for write-heavy workloads common in telemetry, event logging, and metrics collection. Multiple keyspaces allow logical data separation without multiple database instances.

In-process caching and state management in Rust services

Thread-safe, multi-threaded access without explicit locking, combined with range queries and durability control, suits high-performance Rust applications (CLI tools, microservices, edge agents) needing persistent local state.

Applications requiring serializable transactions on a single embedded engine

OptimisticTxDatabase and SingleWriterTxDatabase support ACID semantics for read-modify-write workflows without external coordination, reducing operational complexity vs. standalone databases.

Implementation considerations

Configure block cache to ~20–25% of available memory (or higher if dataset fits in memory) to avoid memory pressure from loaded blocks and cached values.
Use big-endian encoding for integer keys (e.g., timestamps) to maintain lexicographic ordering for range queries and iteration.
Choose durability mode per workload: default (OS buffer flush) matches RocksDB; use PersistMode::SyncAll for strict durability at the cost of write throughput.
Decide transactional requirements upfront: non-transactional Database for read-heavy or append-only workloads; OptimisticTxDatabase or SingleWriterTxDatabase for conflicting read-modify-write patterns.
Monitor memtable size (per-keyspace write buffer) to balance write latency against compaction overhead; tuning depends on access patterns and available RAM.

When to avoid it — and what to weigh

You need multi-process concurrent access — README explicitly states a single database cannot be loaded in parallel from separate processes. Use RocksDB or a dedicated server if multi-process concurrency is required.
You require a query language or relational schema — Fjall is a pure key-value store with no built-in columns, SQL, or query semantics. Applications needing structured queries should use PostgreSQL, SQLite, or equivalent.
Your data set is best managed as a standalone server — Fjall is embeddable only; it does not expose a network API. If you need a database server with multiple clients, deploy RocksDB via a wrapper service or use a standalone DBMS.
You need enterprise support or guaranteed SLAs — This is a community-driven open-source project. No mention of commercial support, SLAs, or vendor backing. Evaluate risk tolerance and internal support capacity accordingly.

License & commercial use

Licensed under Apache-2.0 (also offers MIT OR Apache-2.0 for contributions). Apache-2.0 is a permissive OSI-approved license with explicit patent grants and commercial-use allowance.

Apache-2.0 permits commercial use, modification, and distribution with minimal restrictions (must retain attribution and license copy, no trademark rights, no liability). No proprietary code paths or restrictions mentioned. However, no commercial support, indemnification, or SLA is documented; evaluate internal capacity and risk tolerance for production deployments.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Good
Assessment confidence	High

Security considerations

Written in 100% safe Rust, reducing memory-safety exploit risk. No explicit security audit results mentioned. Durability is configurable; default (OS buffer) may not meet strict integrity requirements—use PersistMode::SyncAll and test recovery if data loss is unacceptable. No mention of encryption-at-rest; data is stored plaintext on disk. Multiprocess access is explicitly unsupported to avoid concurrent corruption.

Alternatives to consider

RocksDB (C++)

More mature, wider adoption, language bindings, and proven at scale. Trade: C++ complexity, larger footprint, and less type safety than Rust. Fjall offers a pure-Rust alternative with similar LSM design.

SQLite

Embeddable, widely supported, SQL query language, and simpler schema. Trade: heavier for key-value workloads, no LSM optimization, synchronous design. Fjall suits append-heavy, range-query-heavy use cases.

LMDB / MDB.X

Ultra-lightweight, very fast reads, memory-mapped. Trade: no write-heavy optimization, smaller ecosystem, limited Rust support. Fjall is better for write-heavy LSM workloads; LMDB for read-mostly scenarios.

Software development agency

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fjall FAQ

Can I use Fjall in production?

Unknown without running benchmarks and failure tests against your workload. The project is active and uses stable Rust, but production suitability depends on your SLA requirements, data criticality, and risk tolerance for a younger codebase vs. RocksDB. No formal security audit or production deployment case studies mentioned.

Does Fjall support replication or clustering?

No. Fjall is a single-process embedded engine. Replication, sharding, or clustering must be built at the application layer using separate Fjall instances or a wrapper service.

What happens if I corrupt the disk format?

Unknown. README mentions a stable disk format with migration paths for major version breaks, but recovery or corruption detection strategies are not detailed. Ensure regular backups and test recovery procedures.

Can I use Fjall from Python, Node.js, or other languages?

Not directly. Fjall is Rust-only. Integration from other languages requires wrapping it in a Rust binary (e.g., HTTP server) or using FFI, adding complexity and maintenance burden.

Software developers & web developers for hire

Adopting fjall is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source databases software in production.

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