datalevin
Datalevin is a durable Datalog database that combines SQL-like query performance with a more composable query language. It supports multiple deployment modes (embedded, client-server, or as a pod) and can function as a document store, vector database, or full-text search engine.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | datalevin/datalevin |
| Owner | datalevin |
| Primary language | Clojure |
| License | EPL-2.0 — OSI-approved |
| Stars | 1.4k |
| Forks | 82 |
| Open issues | 26 |
| Latest release | 0.10.18 (2026-05-28) |
| Last updated | 2026-07-08 |
| Source | https://github.com/datalevin/datalevin |
What datalevin is
Built on LMDB for high-read performance with ACID semantics and WAL support, Datalevin implements a cost-based query optimizer and Datalog query language compatible with Datomic. It offers native bindings for Java, Python, Node.js, and Clojure, with features including vector indexing, full-text search, embedded document indexing, and an MCP server for AI integrations.
Get the datalevin source
Clone the repository and explore it locally.
git clone https://github.com/datalevin/datalevin.gitcd datalevin# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Embedded use requires understanding Datalog query semantics and schema design patterns; Datomic learning resources apply but Datalevin differences must be reviewed (cardinality definitions, schema structure, deletion semantics).
- Client-server mode requires operational setup and maintenance of Raft cluster, RBAC, and WAL management; not a simple single-file database like SQLite when clustering is enabled.
- Large documents (approaching 2 GiB) and vector indexing add memory and CPU overhead; benchmark transaction throughput and query latency against your specific workload before production use.
- Java/Python/Node.js bindings exist but are maintained alongside the Clojure core; monitor binding stability and versioning alignment, especially for less common language bindings.
- Vector search and full-text search features are built-in but performance against specialized engines (e.g., Elasticsearch, Vespa) is data-dependent; prototype with realistic datasets.
When to avoid it — and what to weigh
- Heavy SQL Ecosystem Dependency — If your team and tooling are heavily invested in SQL, ORMs, and SQL-specific workflows, the learning curve for Datalog and schema differences from Datomic may incur friction.
- Requires Temporal/Bitemporal Auditing — Datalevin explicitly rejects Datomic's temporal model—deleted data is gone. If you need built-in historical data tracking or point-in-time queries, use a different database.
- Multi-Tenant SaaS at Massive Scale — While the README mentions Raft-based HA clustering, cluster design, operational complexity, and multi-tenant isolation strategies are not detailed. Large-scale multi-tenant deployments require careful review.
- Existing Codebase with Strong Database Lock-In — Migrating from PostgreSQL, MongoDB, or other mature systems to Datalevin is non-trivial; lack of standardized migration tooling and smaller ecosystem may complicate adoption in established teams.
License & commercial use
EPL-2.0 (Eclipse Public License 2.0) is a copyleft open-source license that requires derivative works to be licensed under the same terms and makes source code available.
EPL-2.0 permits commercial use and distribution, including as embedded libraries in proprietary applications, provided modifications to Datalevin itself are published under EPL-2.0. Closed-source dependent applications are allowed. Review with legal counsel if significant modifications to Datalevin core are planned or if license compliance reporting is mandatory in your environment.
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 | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Server mode includes role-based access control (RBAC), but specific controls (authentication methods, encryption at rest, TLS in transit) are not described in the README. ACID semantics on LMDB fork provide transaction integrity. No security audit, CVE history, or penetration test results are cited. Evaluate threat model, compliance requirements, and security posture before handling sensitive data; request security documentation from maintainers.
Alternatives to consider
Datomic / Datomic Cloud
Mature, commercial Datalog database with extensive temporal features and cloud-native scaling, but proprietary, higher cost, and introduces the temporal semantics Datalevin deliberately avoids.
PostgreSQL (SQL + JSONB + Full-Text Search)
Established open-source RDBMS with SQL familiarity, mature ecosystem, and advanced features (JSONB, FTS, window functions). Lacks Datalog composability and recursive rules, but proven at scale.
MongoDB + Vector Search (Atlas)
Document-oriented, mature ecosystem, built-in vector search (Atlas Search). No Datalog; requires schema-on-read management and operational complexity for sharding; higher operational overhead than embedded solutions.
Build on datalevin with DEV.co software developers
Download the source, run the examples, and prototype with your data. For clustering, HA, and production security posture, engage the maintainers or conduct a vendor security review. Consider your team's Datalog familiarity and migration effort before committing.
Talk to DEV.coRelated open-source tools
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datalevin FAQ
Can I use Datalevin as a drop-in replacement for SQLite?
What is the storage overhead and disk size limit?
How does clustering and high availability work?
Is Datalevin suitable for mobile or edge deployment?
Software development & web development with DEV.co
From first prototype to production, DEV.co delivers software development services around tools like datalevin. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across vector databases and beyond.
Ready to Evaluate Datalevin?
Download the source, run the examples, and prototype with your data. For clustering, HA, and production security posture, engage the maintainers or conduct a vendor security review. Consider your team's Datalog familiarity and migration effort before committing.