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OpenTelemetry gives teams a vendor-neutral way to instrument services and generate, collect, and export traces, metrics, and logs. To follow a production issue across service boundaries, instrument the request path, propagate trace context, record useful context in structured logs, and use consistent resource attributes across signals. A Collector can centralize receiving, processing, and exporting, but it is one pipeline option—not a requirement.
What does full-stack observability mean in practice?
Observability is the ability to ask questions about a system from the data it emits, including questions the team did not anticipate when it added instrumentation. Proper instrumentation aims to provide enough detail to troubleshoot without repeatedly changing application code, but it cannot guarantee that every incident will be diagnosable.
OpenTelemetry (OTel) is the instrumentation and telemetry infrastructure for that work, not the monitoring or observability backend itself. Its APIs and SDKs help applications create telemetry; compatible libraries and integrations can contribute it; and data can be sent to a Collector or another compatible destination. The OpenTelemetry documentation index, last modified August 29, 2025, says the project is supported by more than 90 observability vendors. That is the project’s own statement, not an independent market survey.
What is the difference between traces, metrics, and logs?
Each signal answers a different operational question. A metric summarizes numeric observations over time; a log is a timestamped message; and a span describes one timed unit of work. A trace links spans to show the path of one request across participating components.
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| Signal | What it represents | Useful question |
|---|---|---|
| Metrics | Aggregated numeric observations, such as request rate, error rate, or CPU use. | Is a problem widespread, increasing, or limited to a particular period? |
| Logs | Timestamped messages, often carrying event details or an error explanation. | What did this component report when something happened? |
| Traces and spans | A trace links spans for a request; each span records an operation and its timing and attributes. | Which part of this request path was slow or failed? |
For example, a browser request might pass through a gateway, an application service, and a database. A trace can show the time spent in each represented operation. A metric can show whether request latency or errors rose across many requests. A log can preserve the specific error detail emitted by a service. Together, they help move from “the service is failing” to “this request reached this operation, which reported this error.”
How do you correlate logs and traces with OpenTelemetry?
Correlation requires more than putting logs in JSON. Structured JSON is a way to encode a log record; the OpenTelemetry log data model is a normalized representation used to process and transport log data. JSON by itself does not connect a message to a request. For useful correlation, make the relevant fields stable, propagate context across participating components, and ensure the destination can use those fields.
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When a log is emitted during a traced operation, include its TraceId and SpanId where possible. TraceId identifies the request’s trace; SpanId identifies the associated operation. If those identifiers are present and preserved through collection, a backend can use them to locate related trace and log records. Baggage may carry additional context when the application deliberately propagates it; it is not a substitute for span or resource data.
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Check the three correlation dimensions
- Time: Preserve event timestamps so records can be examined alongside the trace’s timing.
- Trace context: Propagate context across service boundaries and record TraceId and SpanId in application logs when available.
- Resource context: Apply consistent source attributes across signals so records from the same service or runtime can be grouped.
Asynchronous work needs particular care. A queue or background task is a boundary where the application must propagate the appropriate context if later spans are to remain linked to the originating work. If context is lost, downstream activity may appear as a separate trace rather than as part of the original request.
How should teams standardize telemetry across microservices?
OpenTelemetry semantic conventions define common attribute names, types, meanings, and valid values for telemetry. They cover shared concepts and domain areas such as HTTP, databases, messaging, RPC, and cloud providers. Using the same conventions across languages and services makes queries and dashboards more coherent than inventing a different key for each component.
Standardization should cover both meaning and ownership. Decide which attributes identify a service or runtime, which describe an operation, and which carry request-specific information. Keep resource attributes stable for the source of telemetry, and use span or log attributes for details about an operation or event. Avoid putting sensitive or high-cardinality values into widely emitted telemetry without considering the storage and query consequences.
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The official semantic-conventions page displayed version 1.44.0 on October 7, 2026. That is a dated version identifier, not a guarantee that every convention is stable: the page identifies some signal areas as mixed or development. Check the current official conventions and the stability status of the specific conventions you adopt before making them an organization-wide contract.
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Where does the OpenTelemetry Collector fit?
The Collector is a vendor-agnostic layer that can receive, process, and export telemetry. It can centralize processing and routing, which is useful when teams need a shared collection point or want to keep application instrumentation less coupled to a destination. An application can also use a direct integration where that better fits the deployment and destination. The Collector is not mandatory.
For logs, the OpenTelemetry specification describes collecting from files, including rotation-aware tailing and parsing, as well as direct OTLP emission through logging integrations. A separate agent such as Fluent Bit may also be appropriate for file collection. The right route depends on the application’s control over its logs, how reliably those logs can be parsed, the destination’s supported protocols, and the operational cost of maintaining collection agents and file handling.
| Approach | Fits when | Trade-offs to assess |
|---|---|---|
| Application logging integration that emits OTLP | First-party applications can adopt an integration and send log data directly to a Collector or compatible backend. | Check library compatibility, context capture, destination support, and whether the team wants a Collector processing layer. |
| Collector or agent reads log files | Applications or third-party components already write files, or their logging setup cannot readily be changed. | File tailing, rotation, checkpoints, parsing, and delivery become operational concerns; ambiguous free-form text may not map reliably. |
| Another suitable log collection agent | An existing deployment already uses an agent suited to file collection, such as Fluent Bit. | Confirm how records are enriched, what protocol is sent onward, and whether trace context and resource attributes survive the route. |
How do you choose a logging and collection path?
Choose based on the system you have and the correlation outcome you need, rather than assuming every service should use the same logging pipeline immediately.
Quick Recap
- Inventory log sources. Separate first-party applications whose logging can change from legacy or third-party components with fixed output.
- Choose a record format and mapping. For code you control, structured records make fields easier to parse consistently. For existing formats, determine whether a Collector or another agent can map the records into the OpenTelemetry log data model reliably.
- Verify context propagation. Confirm that trace context crosses synchronous and asynchronous boundaries and that application logs can capture TraceId and SpanId for the associated operation.
- Align resource attribution. Define source attributes consistently so logs, spans, and metrics from the same service or runtime can be grouped.
- Match collection to operations. Account for file rotation, checkpoints, parsing, agent deployment, and network delivery if collecting files; account for integration compatibility and delivery behavior if emitting directly.
- Confirm destination capabilities. Check that the destination accepts the selected protocol and that required processing can occur in the Collector or another agent.
- Validate with a real request path. Follow one request across the services it touches, then check whether its spans, application logs, and relevant metrics can be found with the intended identifiers and resource attributes.
What commonly breaks correlation?
- Logs contain only free-form text. Parsing may be ambiguous, and extracting stable fields after the fact is less reliable than emitting structured records where possible.
- JSON is mistaken for correlation. A structured format helps expose fields, but the application still needs to propagate context and record it.
- Service names or resource attributes differ. Inconsistent attribution makes it harder to group telemetry from the same source across signals.
- Context stops at an asynchronous boundary. A downstream operation can become detached from the original trace if the application does not propagate the needed context.
- File collection loses records or fields. Rotation handling, parsing, and delivery configuration need validation against the actual log format and deployment.
- Infrastructure events are expected to have a request ID. Many system-level records are not caused by one request; use time and resource context where request-level trace context does not exist.
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