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Model an observability estate as four related layers, not one overloaded “service” object: the logical service, its running service instances, the infrastructure entities that host or support those instances, and the people or teams accountable for operating the service. Use OpenTelemetry resource and semantic-convention attributes for identity and context, Kubernetes ownership metadata for object lifecycle relationships, and a clearly namespaced local vocabulary for dependency and human-ownership edges. OpenTelemetry’s general entity-relationship model is still in development, so local relationship edges must not be presented as an established OpenTelemetry standard.
The four-layer model
A useful ontology starts by separating concepts that are often collapsed into a single record. The separation lets you answer different operational questions without changing the meaning of an identifier.
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| Layer | What it represents | Typical examples | Identity guidance |
|---|---|---|---|
| Logical service | A stable unit that performs a business or technical function. | Checkout API, image-resizing worker, inventory database. | Use a stable service.name, optionally scoped by service.namespace. |
| Service instance | One uniquely distinguishable running instance of a logical service. | One process, pod, VM workload, or task running Checkout API. | Use service.instance.id for the individual runtime; keep the same service name across replicas. |
| Infrastructure entity | A process, container, pod, host, cluster, network endpoint, or other platform object that runs or supports the instance. | Process, container, Kubernetes pod, node, cluster, database host. | Use the identifying attributes appropriate to that entity type; retain platform-specific identity where it is operationally useful. |
| Accountability relationship | The person, team, or organizational group responsible for operating or approving changes to the service. | Payments team, data platform team, on-call rotation. | Store it as a separately sourced organizational relationship, not as a Kubernetes lifecycle owner reference. |
The resulting graph can say that a logical service has many instances, an instance runs in a pod, the pod is controlled by a workload object, and the service is accountable to a team. Each statement has a different meaning and should be queried independently.
Define service and service instance separately
Logical service identity
OpenTelemetry defines a service as a logical unit that performs a function. Horizontal replicas are still one logical service, so every replica should use the same service.name. If several related services need a common scope, set service.namespace; that namespace gives service.name a contextual boundary without changing the service’s logical identity.
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Choose names that survive deployment mechanics. A name based on a pod name, container ID, random task suffix, or node hostname describes an instance rather than the service. Conversely, a name such as checkout can remain stable while the service moves from a virtual machine to a Kubernetes deployment.
Instance identity
A service instance is a unique running instance of the logical service. Give each concurrently distinguishable instance a service.instance.id. The value should remain unique for the period in which that instance can be confused with another one; it does not need to be a human-friendly label.
Do not use service.instance.id to split one logical service into separate “services” merely because replicas scale up or down. Queries for service-level error rate should aggregate instances by service.namespace and service.name, while capacity or crash analysis can group by instance ID.
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Descriptive context is not identity
Keep stable identity attributes separate from descriptive and operational attributes. Version, environment, deployment region, criticality, language, team, and release channel can change during an entity’s lifetime. They are valuable for filtering and navigation, but changing one should not silently create a new entity identity.
At the time of the cited OpenTelemetry service semantic convention, service.name was marked stable and required, service.version recommended, and criticality marked alpha and recommended. These stability labels can change, so check the live registry before hard-coding them into a governance policy.
Choose stable, minimal identities
The OpenTelemetry entity model recommends an identity that remains unchanged over the entity’s lifetime, uses the minimally sufficient set of attributes that uniquely identifies it, and can be obtained repeatably by independent observers. This is stricter than selecting every available tag.
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Identity checklist
- Stable: the value does not change merely because a release, location, or owner changes.
- Minimal: include only what is needed to distinguish this entity from its peers.
- Observable: an agent or collector that claims the identity can reliably populate every required attribute.
- Scoped: use a namespace when the same name can legitimately occur in separate domains.
- Documented: define normalization, case, character, and lifecycle rules.
A compact ID reduces repeated context and storage, but operators may need joins to understand it. Rich resource attributes make navigation easier and correlation faster, at the cost of larger telemetry payloads and indexes. OpenTelemetry describes this balance as “telescoping identity”: carry a compact stable identity, then expand it with context when a user or system needs more detail.
When an observer cannot establish identity
A shared registry can help different collectors agree on names, but an observer should not claim an identity whose required attributes it cannot populate reliably. Defer to an observer that can provide the full identity, or represent the observation with another entity type until the missing context is available. This prevents a partial or guessed ID from contaminating the graph.
Attach telemetry to the observed workload
OpenTelemetry’s Resource specification says a resource “MUST identify the observed entity for which telemetry is being produced.” The observed entity is the workload or runtime being measured, even when a separate auto-instrumentation agent, sidecar, or collector emits the data.
For an application span, metric, or log, resource context should therefore identify the application service and its runtime environment. Depending on the deployment, that context can lead an operator from a signal to a process, container, Kubernetes pod, namespace, deployment, node, or cluster.
Keep emitter and subject distinct
An instrumentation agent is a telemetry producer; it is not automatically the service represented by the telemetry. If an agent runs as a sidecar, do not overwrite the application’s service.name with the sidecar name. Record the agent as its own entity only when you are observing or managing the agent itself.
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Use resource attributes for deployment and runtime context that operators routinely need: service namespace and name, instance ID, version, environment, process identity, container identity, and Kubernetes resource identity where available. Treat these as navigation and correlation data unless your local identity policy explicitly makes one of them part of a stable key.
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Represent dependencies as typed, local relationships
Dependency graphs need more precision than an unqualified “depends on” edge. A request call, a database read, a message publication, a hosting relationship, and an administrative responsibility have different direction, evidence, and failure implications.
The OpenTelemetry entity data model gives conceptual examples of a service related to a process and a process related to a host, but it states that entity relationship modeling will be refined in future specification work. The semantic-conventions authoring guidance also says declaring entity relationships is not currently supported in that conventions modeling guide. Consequently, an implementation that needs dependency edges today should define a local, namespaced vocabulary and label it as local.
A practical edge vocabulary
| Local edge | Meaning | Typical evidence |
|---|---|---|
calls |
One service or instance invokes another over a protocol. | Trace spans, service-mesh telemetry, or access logs. |
reads_from |
A workload reads data from a datastore or stream. | Database spans, client metrics, query logs. |
writes_to |
A workload writes data to a datastore, queue, or stream. | Producer spans, write logs, broker telemetry. |
runs_as |
A logical service is realized by a service instance. | Deployment inventory or resource correlation. |
hosted_on |
An instance or infrastructure object runs on another infrastructure entity. | Container runtime and platform metadata. |
managed_by_controller |
A platform controller owns the lifecycle of a dependent object. | Kubernetes owner references. |
owned_by_team |
An organizational group is accountable for operating the service. | Service catalog, on-call roster, or signed ownership record. |
These names are an editorial implementation pattern, not an official OpenTelemetry vocabulary. Prefix them with your organization’s namespace, define direction and cardinality, and version the schema like any other API.
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Store the source of an edge, the time it was observed, and—when useful—a confidence or evidence type. Distinguish at least three cases:
- Runtime-discovered: inferred from traces, network flows, queries, or message traffic.
- Declared architecture: asserted in configuration, deployment manifests, or an architecture registry.
- Administrative: assigned by a service catalog, ownership record, or operating agreement.
An observed call from service A to service B during a time window is not proof that every code path depends on B, and a declared dependency is not proof that traffic occurred recently. Preserve the distinction so users can ask whether an edge is currently active, designed, or merely accountable.
Use validity intervals
Dependency and ownership edges change. Add an observed-at timestamp for runtime discovery and, where handoffs or planned changes matter, effective-from and effective-until fields. Keep historical edges rather than overwriting them if incident analysis requires reconstructing what was true at an earlier time.
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Model Kubernetes ownership without confusing it with team accountability
Kubernetes owner references identify which Kubernetes object owns a dependent object for API and lifecycle purposes. They support relationships such as a ReplicaSet controlling Pods or a Deployment controlling a ReplicaSet. Kubernetes documentation explicitly distinguishes ownership from the labels-and-selectors mechanism that resources also use.
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- Which Kubernetes object is the lifecycle owner of another object.
- Which controller relationship can affect garbage collection and reconciliation.
- How to navigate from a pod to its controller hierarchy.
What it does not tell you
- Which human team operates the application.
- Who approves production changes.
- Which on-call rotation receives an alert.
- Whether two workloads share a business capability or data contract.
Labels and selectors have a different role: they select sets of objects for services, controllers, and queries. Do not treat a matching label as equivalent to an owner reference, and do not infer team accountability from either mechanism without an explicit organizational mapping.
Store accountability as a separate relationship
Represent human or team responsibility with a sourced organizational edge such as owned_by_team. Include an explicit owner type (team, individual, group, or rotation), a stable organizational identifier, the source system, and an effective period when handoffs matter. This is an implementation recommendation rather than a relationship mandated by OpenTelemetry or Kubernetes.
A step-by-step implementation procedure
- List the entity types. Start with
service,service.instance,process,container,host, and the platform entities you actually operate, such ask8s.pod,k8s.deployment, andk8s.cluster. - Assign stable identity keys. Define the minimal attributes for each type. For services, establish the rules for
service.namespaceandservice.name; for instances, define howservice.instance.idis generated and when it expires. - Separate context fields. Put version, environment, region, criticality, and release information in descriptive attributes unless a documented local policy justifies identity use.
- Map telemetry resources. Ensure every signal identifies the workload being observed, not merely the agent that emitted it. Test traces, metrics, and logs from the same workload for consistent resource values.
- Define local edge types. Select names such as
calls,reads_from,writes_to,runs_as,hosted_on,managed_by_controller, andowned_by_teamonly after documenting their semantics. - Attach evidence metadata. Require source, observed time, and—where decisions depend on it—confidence, scope, and validity interval on every relationship.
- Import platform lifecycle data. Translate Kubernetes owner references into controller edges while preserving the referenced API group, kind, namespace, and name. Keep labels and selectors in their own fields.
- Join organizational data. Resolve service identifiers to team or rotation records from a maintained catalog. Record the source and effective dates rather than deriving accountability from infrastructure metadata.
- Validate observer completeness. Reject or quarantine records that claim an identity without the attributes required to establish it. Prefer a less-specific entity type over a guessed service identity.
- Version the ontology. Publish changes to entity keys and edge semantics, provide migration rules, and retain historical records when changing ownership or dependency interpretation.
Example record design
The following illustrative record keeps service identity, runtime context, platform ownership, and team accountability in separate fields. The edge names are local and should be replaced or namespaced according to your governance process.
{
"entity": {
"type": "service",
"identity": {
"service.namespace": "commerce",
"service.name": "checkout"
},
"attributes": {
"service.version": "2026.10.3",
"deployment.environment": "production",
"criticality": "high"
}
},
"relationships": [
{
"type": "runs_as",
"target": {
"type": "service.instance",
"service.instance.id": "checkout-7f6d9c8b9f-p4k2"
},
"source": "deployment-inventory",
"observed_at": "2026-10-11T12:00:00Z"
},
{
"type": "hosted_on",
"target": {
"type": "k8s.pod",
"namespace": "commerce-prod",
"name": "checkout-7f6d9c8b9f-p4k2"
},
"source": "kubernetes-api",
"observed_at": "2026-10-11T12:00:00Z"
},
{
"type": "owned_by_team",
"target": {
"type": "team",
"id": "payments-platform"
},
"source": "service-catalog",
"effective_from": "2026-09-01T00:00:00Z"
}
]
}In this example, the version and environment help operators filter telemetry but do not redefine the service. The pod relationship describes runtime placement, while the team relationship describes accountability. Neither is substituted for the other.
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Trade-offs and design decisions
Compact identity versus rich navigation
A minimal identity is easier to compare across collectors and cheaper to index. Rich context reduces the number of joins an operator needs during an incident. Use the compact identity as the key and attach context at the resource or relationship level so you can expand or update descriptive data without renaming the entity.
Central registry versus observer-derived identity
A central registry can standardize names, ownership, and documentation. It can also become stale or unreachable. Observer-derived identity reflects what a running workload can prove, but different observers may disagree if normalization rules are weak. Use registry data to enrich and reconcile observations; do not claim an identity that the observing system cannot reliably establish.
OpenTelemetry alignment versus local completeness
Reuse OpenTelemetry semantic conventions and resource concepts wherever they fit. Keep the relationship layer explicitly local until OpenTelemetry publishes more complete, generally applicable relationship guidance. This preserves interoperability without pretending that organization-specific edge semantics are standardized.
Current graph versus historical truth
A live graph is useful for routing alerts and assessing blast radius. Historical intervals are essential for post-incident analysis, audits, and ownership handoffs. Decide whether each consumer needs the latest edge, all observed edges, or a time-qualified answer, and retain enough provenance to support that query.
Validation and failure handling
- Replicas appear as separate services: check that all instances share the same
service.nameand differ only byservice.instance.idor runtime attributes. - Telemetry is attributed to an agent: inspect resource population in the instrumentation pipeline and restore the observed workload’s identity.
- One service has many names: normalize namespace and name at deployment and collector boundaries; document aliases during migration.
- Dependencies are contradictory: retain both evidence records, label their source and time, and define precedence rules instead of silently merging them.
- Team ownership is missing: mark accountability unknown or stale; never infer it from a pod label, selector, or controller owner.
- An observer lacks identifying attributes: defer identity assignment or use a less-specific entity type until a trustworthy observer supplies the missing context.
- Controller relationships are mistaken for business dependencies: keep Kubernetes
managed_by_controlleredges in the infrastructure subgraph and do not promote them to service-to-service calls.
What this model can and cannot standardize
This approach gives an organization a disciplined way to model identity, runtime context, infrastructure placement, observed dependencies, and accountability. It aligns service and resource concepts with OpenTelemetry and uses Kubernetes owner references for the lifecycle relationship they actually represent.
It is not a finished vendor-neutral ontology for every runtime edge or every form of human ownership. OpenTelemetry’s entity model remains marked Development in the cited material, and its documentation says relationship modeling will be refined in future specification work. Treat local edges as versioned implementation contracts, monitor changes in the live specifications, and preserve source and time so the graph remains explainable.
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