b9f4fcd681
Possible options 1. The compatibility keys maps (the approach already in the code). `backward_compat_keys.go` makes an alias key at metadata time. We rejected this option because of evidence. The alias key resolves, but it reads the wrong data. It prepares to `attributes_string['<alias>']`, and that physical key does not hold the data. 2. The flat multi-key. `GetKeys` returns multiple keys in order, and the downstream code uses the list. The option fails on semantics. It removes one piece of information that the downstream must have. The downstream must know the difference between two cases: - Two keys are the same field with two spellings so we can merge them into one expression. - Two keys are different fields with the same name. The condition builder must make one condition for each key. The operator connects the conditions. Three failures show the problem: - Negative operators connect with OR across the keys. A row that has only one spelling then always matches. Example: `env != 'prod'` matches each row that does not have one of the two keys. - A row that has both spellings with different values gets no clear result. - A value position (group-by, select) needs exactly one expression for one field. A flat list cannot point to that expression. The information must live somewhere. 3. Annotations on `TelemetryFieldKey` Maintain the `SemconvMembers` and `SemconvMaterializedColumns` fields on the keys. The information is the same as in option 4. But it's awkward because "these N keys are one family, in this order" lives in N copies, one copy on each key. 4. introduce `LogicalField` The information is the same as in option 3, but the structure holds it: - The slice is the ambiguity. - The group is the family. - The member order is the precedence. The code sorts the members one time, by family rank, at construction. - The members point to the metadata entries. The code copies nothing and changes nothing. - The identity (signal, context, data type) is on the group. A merge across contexts or data types is not possible. The design does not avoid that merge; the design cannot express it. Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
SigNoz is an open-source observability platform built on OpenTelemetry. We’re building an enterprise-grade alternative to fragmented monitoring stacks, with logs, metrics, traces, alerts, and dashboards in one place.
Choose how to run SigNoz
SigNoz Cloud (Recommended)
Fully managed SigNoz with a 30-day free trial, no credit card required, usage-based pricing that starts at $49, and regional data hosting.
Enterprise
Enterprise Cloud, BYOC, or Enterprise Self-Hosted with compliance, support, custom retention, RBAC, ingestion controls, data residency, and region selection.
Community
Free open-source SigNoz that runs in your own infrastructure. Deploy with Docker, Kubernetes, or Linux and keep full control of your data plane.
What can you monitor?
SigNoz helps teams debug production issues faster by connecting logs, metrics, traces, alerts, dashboards, exceptions, and agent-native workflows in one place.
APM Overview
Monitor service latency, error rate, throughput, Apdex, top endpoints, database calls, and external calls.
Learn more: APM documentation
Log Management
Ingest, search, aggregate, and correlate logs with traces and metrics using a visual query builder.
Learn more: Log management documentation
Metrics and Dashboards
Build dashboards for application, infrastructure, and custom metrics using Query Builder, PromQL, or ClickHouse SQL.
Learn more: Metrics documentation
Infrastructure Monitoring
Monitor Kubernetes clusters, pods, nodes, workloads, and host-level CPU, memory, disk, network, logs, and traces.
Learn more: Infrastructure monitoring documentation
LLM and AI Observability
Trace LLM apps, RAG pipelines, prompts, tool calls, tokens, latency, and costs alongside application and infrastructure telemetry.
Learn more: LLM observability documentation
Agent-Native Observability and MCP
Use the SigNoz MCP server to bring telemetry into coding agents, or use Noz inside SigNoz to investigate incidents, tune alerts, and build dashboards with production context. Noz is available only on SigNoz Cloud.
Learn more: SigNoz MCP server docs · Agent skills docs
Distributed Tracing
Follow requests across services with flamegraphs, waterfalls, span events, filters, and trace analytics.
Learn more: Distributed tracing documentation
Trace Funnels
Create funnels from traces to understand request-flow drop-offs, failed transitions, and systemic workflow issues.
Learn more: Trace funnels documentation
Also monitor: exceptions, alerts, external APIs, and integrations for OpenTelemetry, Prometheus, Kubernetes, cloud providers, language SDKs, application frameworks, databases, and LLM tools.
Why teams use SigNoz
- OpenTelemetry-native
Instrument once with open standards and keep ownership of your telemetry. - Correlated signals
Move from service charts to traces, logs, infra metrics, and exceptions without switching tools. - Single columnar database
Built for high-cardinality, high-volume observability workloads. - Predictable pricing
No per-host pricing, no user-seat pricing, and no special pricing for custom metrics. - Enterprise ready
SOC 2 Type II and HIPAA compliance, RBAC, ingestion controls, custom retention, support, BYOC, and self-hosting.
Getting started
Start on Cloud
Create a managed SigNoz workspace and get your first dashboard without running observability infrastructure.
Self-host SigNoz
Run SigNoz in your own infrastructure with Foundry, Docker, Kubernetes, or Linux.
Foundry · Docker · Kubernetes · Linux
Send data
Instrument applications and infrastructure with OpenTelemetry, Prometheus, language SDKs, and integrations.
Instrumentation · Integrations
Comparisons to familiar tools
SigNoz is often adopted by teams moving from a stack of single-purpose tools or commercial platforms with unpredictable pricing.
Prometheus
Good if you just need metrics. SigNoz keeps metrics, logs, traces, dashboards, and alerts together so teams can debug with correlated context.
Jaeger
Jaeger only does distributed tracing. SigNoz adds metrics, logs, trace analytics, dashboards, alerts, exceptions, and trace-to-log workflows.
Elastic
SigNoz uses columnar database for efficient observability analytics and high-cardinality log workloads, with 50% lower resource requirement compared to Elastic during ingestion. Check the detailed study.
Loki
In the linked benchmark, SigNoz indexed all keys in the test setup, while Loki hit max stream errors when more labels were added. Check the detailed study.
Contributing
We ❤️ contributions big or small. Please read CONTRIBUTING.md to get started with making contributions to SigNoz.
Not sure how to get started? Just ping us on #contributing in our slack community.
As always, thanks to our amazing contributors!