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Model observability as four related but separate layers: a logical service, its running service instances, the processes and infrastructure 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, then add a clearly namespaced local relationship vocabulary for dependency edges. Treat Kubernetes owner references as object-lifecycle metadata—not proof of human service ownership.
This separation keeps telemetry identities stable while still letting engineers navigate from a trace or metric to a pod, deployment, host, controller, and responsible team. It also avoids claiming that OpenTelemetry already supplies a finished, general-purpose relationship ontology: the entity model remains under development, and its documentation says relationship modeling will be refined in future specification work.
The four layers your ontology must keep separate
An observability graph becomes difficult to query when “service,” “application,” “pod,” “owner,” and “dependency” are used as interchangeable labels. Start by defining four entity classes and the links between them.
| Layer | What it represents | Typical identity | Examples |
|---|---|---|---|
| Logical service | A durable unit that performs a business or technical function, regardless of how many copies are running. | service.namespace plus service.name |
Payments API, image-resize worker, checkout database |
| Service instance | One concurrently distinguishable running instance of a logical service. | The service identity plus a unique service.instance.id |
One process, task, VM workload, or pod-backed instance |
| Runtime and infrastructure | Processes, containers, hosts, clusters, pods, namespaces, deployments, and other entities that execute or support the instance. | Stable identifiers appropriate to each platform and scope | process, container, host, k8s.pod, k8s.cluster |
| People and teams | Organizational responsibility for operating, changing, or approving work on a service. | A separately governed person, team, or group identifier | Platform team, checkout on-call group, service owner |
The logical service is the object a catalog, architecture map, or reliability review normally discusses. The instance is what emitted a particular signal. Infrastructure entities explain where and under what control plane that instance ran. A team is an accountability relationship, not a runtime object. Keeping those distinctions means a rollout can replace every instance without changing the service identity, while a team handoff can change accountability without changing either the service or its deployment.
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Define stable identity before adding relationships
OpenTelemetry’s service conventions distinguish a service from a service instance. service.name identifies the logical service and should remain the same across horizontally scaled instances. service.namespace groups related services and gives service.name a scope; two organizations can therefore use the same short name without creating an accidental collision. service.instance.id distinguishes one running instance from its peers.
As of the OpenTelemetry semantic-convention material accessed on October 11, 2026, service.name is marked stable and required, service.version is recommended, and service criticality is listed as alpha and recommended. Stability labels can change, so check the live registry when implementing or publishing a schema.
Identity attributes versus descriptive attributes
The OpenTelemetry entity model recommends that identity attributes remain unchanged for the lifetime of an entity, use the smallest set that uniquely identifies it, and be obtainable repeatably by independent observers. Attributes such as version, environment, region, criticality, deployment metadata, and team references are usually descriptive or operational context. They can change without turning the observed object into a new logical entity.
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| Attribute type | Use it for | Do not use it for |
|---|---|---|
| Identity | Stable joins and deduplication across signals and observers | Frequently changing release, health, or ownership state |
| Descriptive context | Navigation, filtering, dashboards, and incident context | Redefining the entity every time a value changes |
| Relationship metadata | Explaining how two already identified entities are connected | Embedding an entire dependency graph into one entity’s name |
A practical service identity
A minimal service record might contain:
{
"type": "service",
"service.namespace": "commerce",
"service.name": "checkout-api",
"service.version": "2026.10.3",
"environment": "production"
}Only the namespace and name are being used here as the logical identity. Version and environment help a human understand the record but do not create a different service every time a release or deployment target changes.
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When an observer cannot establish identity
A central registry can publish canonical identifiers, but an observer should not assert an identity it cannot populate reliably. If an agent sees a process but cannot obtain the attributes required to distinguish the intended entity, it should defer to an observer that can provide the full identity or represent the observation using another, more appropriate entity type. This prevents a guessed name from becoming a false join across traces, metrics, and logs.
Attach telemetry to the workload being observed
OpenTelemetry’s Resource specification states: “It MUST identify the observed entity for which telemetry is being produced.” In practice, that means resource attributes should describe the application process, service instance, container, pod, or other workload that the signal represents, even when a separate auto-instrumentation agent or collector technically emits the data.
Resource context gives an operator a path from a signal to its execution environment. A trace span can carry service identity; resource data can add process and container details; Kubernetes enrichment can add pod, namespace, deployment, and cluster context. These are navigation and correlation aids, not replacements for the stable logical-service identity.
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Model context as entities, not a flattened label pile
Use separate entity records or consistently scoped attributes for the runtime layers you need to query:
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- Process: the operating-system process that executed code.
- Container: the container boundary, when a container runtime supplies a stable identifier.
- Host: the VM, physical machine, or node providing compute.
- Kubernetes pod: the schedulable workload unit and its namespace.
- Deployment or controller: the object responsible for creating or updating a set of pods.
- Cluster: the Kubernetes control-plane domain in which those objects exist.
Platform-specific entities should remain visibly platform-specific. A k8s.pod is not identical to every abstract “workload” concept. OpenTelemetry’s entity guidance describes scoped “is-a” relationships as a way for ecosystems to extend general concepts without erasing the original scope.
Represent dependencies as typed, sourced edges
Dependency graphs answer different questions depending on how an edge was obtained. A span showing an HTTP request is evidence of observed runtime communication. A service catalog entry may declare an intended dependency. A database access policy may show an administrative relationship. Do not collapse these into one unqualified “depends on” edge.
OpenTelemetry’s entity data model gives conceptual examples such as a service related to a process and a process related to a host, but the model is still marked Development and says relationship modeling will be refined in future specification work. The semantic-conventions authoring guidance also says declaring entity relationships is not yet supported there. Therefore, a production graph that needs dependency edges today should use a local, namespaced vocabulary and label it as local rather than official OpenTelemetry.
Example local relationship vocabulary
The following names are an implementation example, not an OpenTelemetry standard:
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| Edge | Meaning | Typical evidence |
|---|---|---|
calls |
One service or instance sends a request to another. | Traces, network telemetry, or request logs |
reads_from |
An application reads data from a datastore or stream. | Database spans, client instrumentation, query logs |
writes_to |
An application writes data to a datastore or stream. | Mutation spans, producer telemetry, audit logs |
runs_as |
A logical service is represented by a particular service instance or process. | Resource identity and runtime inventory |
hosted_on |
A runtime entity executes on a host, node, cluster, or platform boundary. | Container runtime or orchestration metadata |
managed_by_controller |
A platform controller creates or manages another platform object. | Kubernetes owner reference or equivalent API metadata |
owned_by_team |
An organizational group is accountable for operating or changing a service. | Service catalog, on-call registry, or approved ownership record |
Give every edge its own provenance
Store relationship metadata alongside the source and target identifiers. At minimum, use fields equivalent to:
- Relationship type: the namespaced edge name.
- Source and target IDs: references to already identified entities.
- Evidence source: for example, trace-derived, declared architecture, inventory, policy, or administrative record.
- Observed or effective time: when the connection was seen, or when an ownership assignment took effect.
- Confidence: a locally defined value or category that distinguishes direct evidence from inference.
- Scope: environment, region, cluster, namespace, or tenant where the edge applies.
Runtime discovery and declared architecture can disagree without either being corrupt. A service may call a datastore that is absent from an old architecture document, while a declared failover dependency may not appear during a quiet observation window. Keeping provenance and time makes those differences queryable instead of forcing a single, misleading truth.
Example edge record
{
"type": "relationship",
"relationship.type": "commerce:calls",
"source": "service:commerce/checkout-api",
"target": "service:payments/authorization-api",
"evidence.source": "trace-derived",
"observed_at": "2026-10-11T14:22:08Z",
"scope.environment": "production",
"confidence": "direct"
}The namespace in commerce:calls makes it clear that the edge belongs to your local model. If a future OpenTelemetry specification defines a compatible relationship, you can map or migrate the local term without pretending it was standardized earlier.
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Kubernetes owner references express which Kubernetes object owns a dependent object and can drive lifecycle behavior such as garbage collection. Kubernetes documentation explicitly distinguishes owner references from labels and selectors: “Ownership is different from the labels and selectors mechanism that some resources also use.” A label may select a set of objects; it does not automatically identify the team responsible for the service, and it is not interchangeable with an owner reference.
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What an owner reference can tell you
- Which controller or Kubernetes object is the parent of another object.
- Which lifecycle relationship the API server should honor.
- How to navigate from a pod to a ReplicaSet, Deployment, Job, or another controller object.
What it cannot prove
- Which human team is on call.
- Who approves production changes.
- Whether a platform team or product team operates the service.
- Whether responsibility changed after a reorganization or handoff.
Represent team accountability as a separate organizational relationship or service-catalog attribute. Include an explicit owner type such as team, individual, or vendor when your governance model requires it. If handoffs matter, record an effective start and end time, plus the source that authorized the assignment. This is an implementation recommendation, not a relationship mandated by Kubernetes or OpenTelemetry.
Example of separate platform and organizational edges
{
"platform_edge": {
"type": "k8s:managed_by_controller",
"source": "k8s.pod:cluster-a/prod/checkout-7d9f",
"target": "k8s.deployment:cluster-a/prod/checkout"
},
"accountability_edge": {
"type": "org:owned_by_team",
"source": "service:commerce/checkout-api",
"target": "team:commerce-checkout",
"owner_type": "team",
"effective_from": "2026-09-01",
"source_record": "service-catalog"
}
}These two edges can coexist without implying that the Kubernetes Deployment itself is a human owner. The first describes platform lifecycle; the second describes responsibility.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A step-by-step implementation plan
- List the entity types you actually operate. Start with
service,service.instance,process,host,container, and the platform entities needed for navigation, such ask8s.podandk8s.cluster. - Choose the minimal identity for each type. Document which attributes are immutable for the entity’s lifetime and which observer can populate them. Avoid using version, health, or team fields as identity unless you have a specific, governed reason.
- Set logical service identity consistently. Use the same
service.namespaceandservice.namefor all horizontally scaled instances of one service. Generate a distinctservice.instance.idfor each concurrently distinguishable instance. - Configure resource attribution at the source. Ensure telemetry resources identify the workload being observed, not merely the collector or auto-instrumentation process that exported the signal.
- Normalize platform context. Add process, container, host, pod, namespace, deployment, and cluster context using stable platform identifiers and explicit scopes.
- Declare a local relationship namespace. Publish the edge names, allowed source and target types, cardinality rules, and versioning policy. Mark the vocabulary as local until a relevant OpenTelemetry standard exists.
- Capture provenance and time. Require an evidence source and observed or effective timestamp for every dependency and accountability edge. Add confidence when your system infers relationships.
- Import Kubernetes ownership separately. Ingest owner references as platform lifecycle edges. Keep labels and selectors in their own category, and do not map either one directly to a team owner without an additional organizational record.
- Attach organizational accountability. Link services—not merely pods—to teams or individuals through your service catalog, on-call system, or approved ownership registry. Record owner type and handoff dates if required.
- Test joins and lifecycle changes. Verify that a deployment rollout preserves service identity, that a restarted instance receives a new instance identity when appropriate, that a pod replacement does not erase the logical service, and that a team handoff changes accountability without rewriting historical runtime edges.
Validation rules that catch common modeling failures
- Duplicate service identities: two unrelated workloads share the same unscoped
service.name. Fix by applying a meaningful namespace. - Instance churn disguised as service churn: every pod name becomes a new service. Fix by keeping pod or instance identifiers below the stable logical-service node.
- Mutable identity: a release version or environment change creates a new logical service. Keep those values descriptive unless your local governance explicitly defines a different entity boundary.
- Collector identity leakage: telemetry is attributed to the agent that emitted it rather than the workload it observed. Set resource identity for the observed entity.
- Unqualified dependency edges: “depends on” hides whether the edge came from traces, a design declaration, or an administrative record. Split edge types and retain provenance.
- Controller-as-team error: a Deployment, owner reference, or label is shown as the human owner. Add a separate organizational relationship.
- Observer overclaim: an agent fills in a guessed identifier. Let an observer without reliable identity defer or use a less specific entity type.
- Time-blind ownership: historical incidents display today’s team as the owner. Store effective periods when accountability changes matter.
- Unscoped platform IDs: a pod name or namespace is assumed globally unique. Include the cluster or other required scope in the identifier.
Trade-offs to decide explicitly
| Decision | Benefit | Cost or risk | Practical choice |
|---|---|---|---|
| Compact identity versus rich resource data | Small stable IDs reduce repeated payload and make joins predictable. | Operators need additional lookups to understand an entity. | Use minimal identity plus descriptive context for navigation. |
| Central registry versus observer-derived identity | A registry can improve agreement among systems. | Observers may claim IDs they cannot reliably populate. | Only assert registry identity when the observer can provide the required attributes. |
| Runtime discovery versus declared architecture | Observed traffic reflects what actually happened; declarations capture intended or rare paths. | They can disagree and produce apparent contradictions. | Store both as distinct evidence sources with time and confidence. |
| Kubernetes ownership versus service ownership | Owner references accurately model object lifecycle. | They say nothing by themselves about human accountability. | Keep platform edges and organizational ownership in separate domains. |
| OpenTelemetry alignment versus local completeness | Standard resource and semantic conventions improve interoperability. | The current relationship model does not cover every graph your organization needs. | Reuse standard attributes and clearly namespace local relationship extensions. |
What OpenTelemetry does—and does not—standardize today
OpenTelemetry gives you a strong vocabulary for service identity, resource context, and telemetry correlation. It does not, as of the documentation accessed October 11, 2026, provide a finished vendor-neutral ontology for every dependency edge or for human team ownership. The entity data model is marked Development, and relationship work is explicitly expected to evolve. The semantic-conventions modeling guidance does not currently support declaring entity relationships there.
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That limitation is not a reason to flatten your data. It is a reason to draw a precise boundary: use OpenTelemetry-compatible attributes where they exist, preserve platform-specific scope, and publish your relationship layer as a versioned local model. When the standard evolves, you can map local edges to official concepts while retaining provenance and historical compatibility.
A durable graph shape
A useful query path should be possible without guessing:
- Start at a trace, metric, or log resource.
- Resolve the observed service instance.
- Walk to its logical service through the stable service identity.
- Navigate to the process, container, pod, deployment, node, or cluster that supplied runtime context.
- Follow typed dependency edges to downstream services and datastores, filtering by evidence source and time.
- Follow the separate organizational edge to the team accountable during the incident’s effective period.
This shape lets one graph answer operationally different questions: “Which instance handled this request?”, “What infrastructure hosted it?”, “What did it call?”, and “Which team was responsible then?” No single name, label, or owner field has to carry all four meanings.
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