Open-Source Databases · rosedblabs

rosedb

RoseDB is a lightweight, fast key-value storage engine written in Go, based on the Bitcask design model. It uses append-only log files for writes and keeps all keys in-memory for O(1) lookups, making it suitable for embedded and high-throughput applications.

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Source: GitHub — github.com/rosedblabs/rosedb

4.9k

GitHub stars

645

Forks

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	rosedblabs/rosedb
Owner	rosedblabs
Primary language	Go
License	Apache-2.0 — OSI-approved
Stars	4.9k
Forks	645
Open issues	7
Latest release	v2.4.0 (2025-02-21)
Last updated	2026-02-10
Source	https://github.com/rosedblabs/rosedb

What rosedb is

RoseDB implements the Bitcask log-structured storage model with WAL (Write-Ahead Log) backend, in-memory hash index for keys, and single-seek value retrieval. It supports batch transactions, key expiration, watches, forward/backward iteration, and crash recovery via CRC verification.

Quickstart

Get the rosedb source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/rosedblabs/rosedb.git2cd rosedb3# follow the project's README for install & configuration

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

Best use cases

High-throughput embedded key-value store

Ideal for Go applications requiring fast, predictable read/write latency with append-only semantics and datasets that fit in memory. Common in cache layers, logging systems, and real-time analytics pipelines.

Backup-friendly local persistence layer

RoseDB's write-once, append-only format simplifies backup and recovery. Suitable for applications needing simple file-based durability without complex transaction coordination.

Time-series or event log storage

Log-structured design saturates I/O bandwidth during writes. Supports batch operations, key expiration, and sequential iteration, fitting stream processing and time-series use cases with moderate key cardinality.

Implementation considerations

Keys must fit entirely in RAM; pre-calculate worst-case keyspace memory footprint before deployment.
Single-seek read model assumes key exists in index; handle not-found paths efficiently to avoid surprise disk I/O.
Batch operations are atomic but require explicit Commit() call; failed commits must be handled to prevent partial state.
Append-only design means deleted/updated keys leave tombstones on disk; periodically run compaction/merging to reclaim space.
Crash recovery scans log tails and verifies CRC; plan recovery time for large datasets to avoid extended startup windows.

When to avoid it — and what to weigh

Keyspace larger than available RAM — RoseDB keeps all keys in-memory as a mandatory constraint. Systems with millions of keys or unbounded keyspace will exhaust heap memory.
Complex querying or multi-table joins required — RoseDB is a simple key-value store with no query language, secondary indexes, or relational semantics. Use a SQL or document database if you need structured queries.
Need distributed replication or clustering — RoseDB is single-node only. No built-in support for replication, clustering, or consensus. Choose a distributed store if multi-node resilience is mandatory.
Very large individual values (multi-GB) — Bitcask model and in-memory indexing assume reasonably-sized values. Extremely large blobs may impact performance and recovery time.

License & commercial use

Licensed under Apache License 2.0 (SPDX: Apache-2.0), a permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and liability disclaimer.

Apache 2.0 clearly permits commercial use. No restrictions on closed-source applications or proprietary modifications. Standard Apache terms apply: provide attribution, note material changes, and include a copy of the license. No warranty or support guarantees from RoseDB authors.

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

RoseDB is single-threaded at the data layer and does not itself implement encryption, authentication, or authorization. Append-only format and CRC verification reduce corruption risk from partial writes, but not from adversarial tampering. File permissions and filesystem isolation must be relied upon to prevent unauthorized access. No security audit details provided in repository.

Alternatives to consider

BadgerDB

Similar embedded Go KV store with LSM-tree instead of Bitcask; supports larger keyspaces via on-disk indexes and offers more tuning knobs. Heavier memory footprint but avoids all-keys-in-RAM constraint.

BoltDB / Etcd's bbolt

B+tree-based embedded KV store with ACID transactions; slower sequential write throughput than Bitcask model but better for mixed read-write workloads and range queries.

RocksDB

Battle-tested LSM-tree KV engine (C++, Go binding available); higher operational overhead and complexity but greater flexibility, larger dataset support, and tuning surface for extreme performance.

Software development agency

Build on rosedb with DEV.co software developers

Evaluate RoseDB for your keyspace and throughput requirements. If keys exceed available RAM or you need distributed replication, consider BadgerDB or RocksDB. Contact our team to architect a solution.

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rosedb FAQ

Does RoseDB support transactions across multiple keys?

Yes, via batch operations. Batch.Put(), Batch.Delete() are buffered in-memory and committed atomically with Batch.Commit(). Failure or no-commit leaves the batch discarded.

What happens if my keyspace grows beyond RAM?

RoseDB will hit out-of-memory errors or crash. The all-keys-in-RAM design is a hard constraint; there is no overflow to disk for the index. Plan keyspace size carefully or migrate to BadgerDB/RocksDB.

Can I use RoseDB in a distributed system?

Not natively. RoseDB is single-node only. For multi-node setups, you must implement replication, consensus, or failover logic externally, or choose a distributed store.

How does RoseDB handle disk full or I/O errors?

Not clearly stated in provided data. Requires review of error handling code and operational runbooks. Append-only design reduces some classes of corruption, but full-disk and I/O failure scenarios should be tested.

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