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Riemann is an open-source event stream processing system written in Clojure that aggregates and processes events from distributed systems. It provides a domain-specific language for defining complex event workflows, making it suitable for real-time monitoring and metrics collection across infrastructure.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | riemann/riemann |
| Owner | riemann |
| Primary language | Clojure |
| License | EPL-1.0 — Requires review (not clearly OSI) |
| Stars | 4.3k |
| Forks | 502 |
| Open issues | 29 |
| Latest release | 0.3.12 (2025-05-26) |
| Last updated | 2026-04-05 |
| Source | https://github.com/riemann/riemann |
What riemann is
Riemann ingests events via multiple protocols, applies stream transformations and aggregations using a Clojure-based DSL, and routes results to outputs (alerting, dashboards, databases). It handles event correlation, windowing, and stateful processing natively within a functional programming model.
Get the riemann source
Clone the repository and explore it locally.
git clone https://github.com/riemann/riemann.gitcd riemann# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Clojure runtime dependency (JVM); operationalize Java environment, GC tuning, and memory management for sustained production workloads.
- Event schema design and DSL familiarity required; plan for Clojure training or hire experienced developers to define stream logic safely.
- State management and windowing configuration must be carefully sized; unbounded aggregations can exhaust memory on long-running streams.
- Input protocol and output sink integrations (TCP, UDP, HTTP, databases) must be vetted and configured for your topology.
- Monitoring Riemann itself (recursive monitoring) is possible but must be explicitly configured; avoid circular dependencies or feedback loops.
When to avoid it — and what to weigh
- Team lacks Clojure expertise — Configuration and extension require writing Clojure code. Teams unfamiliar with functional programming or Clojure will face a steep learning curve and slower iteration.
- Need visual rule builder or no-code configuration — Riemann requires programmatic DSL; there is no web UI for rule definition. Operators without development skills cannot configure monitoring independently.
- Require multi-tenant SaaS platform — Riemann is self-hosted infrastructure software. It does not provide managed hosting, isolation, or SaaS deployment models out of the box.
- Enterprise support and SLA guarantees are mandatory — Unknown whether commercial support agreements or SLAs are available. The project is community-driven; enterprise support terms are not documented here.
License & commercial use
Licensed under Eclipse Public License 1.0 (EPL-1.0), a reciprocal open-source license. EPL-1.0 is OSI-approved and allows modification and distribution; derivative works must remain open-source under EPL-1.0 or compatible licenses.
EPL-1.0 permits commercial use of Riemann as-is or with modifications, provided modifications remain under EPL-1.0 or compatible open license. Proprietary extensions or internal-only forks are allowed, but any distributed derivative must be open-sourced. Verify your legal and compliance team's interpretation of reciprocal clauses for your specific use case.
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 |
EPL-1.0 licensed code is publicly auditable. Riemann ingests event streams over network protocols; validate input source authentication, encryption in transit (TLS for HTTP), and access controls on the monitoring interface. No mention of built-in encryption, authentication mechanisms, or security audit history in provided data; review threat model and penetration testing maturity independently. Running untrusted Clojure in the DSL carries code-execution risk; sandbox DSL evaluation if accepting user-defined rules.
Alternatives to consider
Logstash (ELK Stack)
Log-centric event pipeline with larger ecosystem; visual rule builders available via Kibana. Heavier resource footprint and less focus on real-time metrics aggregation.
Prometheus + Alertmanager
Dominant open-source metrics-driven monitoring platform with pull-based scraping, PromQL DSL, and wide integrations. Simpler operational model for infrastructure metrics; less flexible for custom stream processing.
Kafka Streams / Apache Flink
General-purpose stream processing frameworks with higher throughput and more mature distributed processing. Steeper learning curve and more heavyweight for small-to-medium monitoring use cases.
Build on riemann with DEV.co software developers
If your team has Clojure expertise and needs a flexible, self-hosted stream processor for complex event workflows, Riemann is worth a proof-of-concept. Otherwise, compare Prometheus, Logstash, or managed platforms first.
Talk to DEV.coRelated open-source tools
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riemann FAQ
Can I run Riemann in Kubernetes?
Does Riemann replace Prometheus or Grafana?
What is the learning curve for Riemann's DSL?
Is Riemann suitable for small teams or startups?
Custom software development, end to end
DEV.co helps companies turn open-source tools like riemann into production software. Our software development services cover the full lifecycle — architecture, integration, delivery, and maintenance. Engage us to implement or customize it for your open-source observability stack.
Evaluate Riemann for your monitoring pipeline
If your team has Clojure expertise and needs a flexible, self-hosted stream processor for complex event workflows, Riemann is worth a proof-of-concept. Otherwise, compare Prometheus, Logstash, or managed platforms first.