Open-Source Databases · cnosdb

cnosdb

CnosDB is a distributed, open-source time-series database written in Rust, designed for IoT, industrial, and operational monitoring workloads. It emphasizes high performance, data compression, and cloud-native deployment with SQL compatibility.

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Source: GitHub — github.com/cnosdb/cnosdb

1.8k

GitHub stars

314

Forks

Rust

Primary language

AGPL-3.0

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	cnosdb/cnosdb
Owner	cnosdb
Primary language	Rust
License	AGPL-3.0 — OSI-approved
Stars	1.8k
Forks	314
Open issues	82
Latest release	v2.4.3.4 (2025-09-26)
Last updated	2025-09-26
Source	https://github.com/cnosdb/cnosdb

What cnosdb is

Built in Rust with distributed architecture supporting data sharding, storage-compute separation, and Quorum consensus. Provides SQL query interface, InfluxDB line-protocol ingestion, schema-less writes, and integrations with Prometheus and Telegraf.

Quickstart

Get the cnosdb source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/cnosdb/cnosdb.git2cd cnosdb3# follow the project's README for install & configuration

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

Best use cases

IoT and Industrial Telemetry

High-volume time-series ingestion from sensors and devices with built-in compression, tag-based querying, and schema-less support for evolving data schemas.

Metrics and Observability

Multi-tenant deployment with Prometheus remote-read compatibility and historical data querying, suitable for centralized monitoring across environments.

Cloud and Kubernetes Deployments

Native distributed design with horizontal scaling, separation of compute and storage layers, and Kubernetes support for containerized cloud operations.

Implementation considerations

Requires Rust build toolchain, CMake, FlatBuffers, and Protobuf for compilation; Docker containers available for simplified deployment.
Schema-less writes supported, but schema-first table definitions recommended for query optimization and data consistency.
Out-of-order writes and historical data backfill supported; plan retention and partitioning strategy upfront for large-scale ingestion.
Distributed cluster setup demands understanding of Quorum mechanisms and node coordination; single-node mode available for development/testing.
Data compression ratios not quantified in provided data; benchmark against workload before capacity planning.

When to avoid it — and what to weigh

Commercial Proprietary Use Without Legal Review — AGPL-3.0 requires derivative works to disclose source code under same license. Closed-source commercial use demands legal assessment and likely requires enterprise licensing.
Transactional or Complex Update Workflows — Designed for append-heavy, few-delete patterns typical of time-series. Not optimized for frequent updates, complex transactions, or relational join-heavy queries.
Minimal Operational Overhead Requirement — Distributed architecture requires management of multiple nodes, Quorum coordination, and Kubernetes/orchestration expertise. Single-node deployments simpler but lose HA benefits.
Immediate Production Maturity Guarantee — Project created October 2021 with ~1,750 stars; moderate community size (82 open issues). Enterprise adoption and long-term stability not yet established.

License & commercial use

Licensed under AGPL-3.0 (GNU Affero General Public License v3.0), a copyleft license requiring source disclosure and same-license propagation for derivative works and network-accessed modifications.

AGPL-3.0 is NOT a permissive OSI license for closed-source commercial use. Internal deployment or SaaS use triggers copyleft obligations. Commercial use—proprietary or otherwise—requires explicit legal review and likely a separate commercial license agreement. Contact project maintainers or legal counsel before production deployment in revenue-generating contexts.

DEV.co evaluation signals

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

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Needs review
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	Medium

Security considerations

Project documentation does not provide security audit reports, threat model, or CVE history in given data. AGPL-3.0 code is publicly visible (source-available model). Quorum-based consensus and role-based access control mentioned; cryptographic enforcement, TLS defaults, secrets management, and compliance certifications not detailed. Requires security review before sensitive workloads.

Alternatives to consider

InfluxDB (Open Source / Cloud)

Permissive BSL/AGPL hybrid licensing (OSS tier). Mature time-series engine, broader ecosystem, commercial support available. Larger community; line-protocol native.

TimescaleDB (PostgreSQL extension)

Permissive Timescale License (business-friendly copyleft exemption) plus Apache 2.0 for hyperscaler versions. Familiar SQL/ACID semantics, strong relational query support, easier operational integration.

VictoriaMetrics

Permissive Apache 2.0 license. High compression, multi-tenant support, and Prometheus-compatible. Simpler operational footprint than distributed consensus-based systems.

Software development agency

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Confirm AGPL-3.0 compliance, benchmark against your time-series workload, and engage legal counsel on commercial use. Our team can guide licensing options and deployment architecture.

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cnosdb FAQ

Can I use CnosDB in a closed-source, proprietary product or SaaS?

Not without a commercial license agreement. AGPL-3.0 requires source disclosure and copyleft propagation. You must consult legal counsel and likely negotiate a separate commercial license with the maintainers.

What's the minimum cluster size for high availability?

Distributed Quorum mechanism requires odd-numbered nodes; 3 nodes is typical for HA (2 fail-safe tolerance). Single-node deployment available for dev/test. Full cluster operational requirements not specified in provided data.

Is CnosDB suitable for replacing my current time-series database?

Depends on workload. Optimized for append-heavy, few-delete, high-cardinality scenarios (IoT, metrics). Not designed for frequent updates or complex transactions. Benchmark with your dataset before committing; Rust implementation and distributed design differ significantly from legacy or relational systems.

How mature is the project for production use?

Created October 2021, ~4 years old, moderate community. Active development and recent releases encourage confidence, but enterprise adoption metrics are not published. Risk tolerance and SLO requirements should guide adoption; consider pilot phase or vendor support agreement if mission-critical.

Software developers & web developers for hire

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