OpenTelemetry gives teams a vendor-neutral way to instrument services and move traces, metrics, and logs into observability systems. To investigate a request across services, use traces to follow its path, logs to inspect detailed events, and metrics to see how behavior changes over time. Correlation depends on more than choosing a log format: services must propagate trace context, record it where possible, and use consistent resource attributes.
What OpenTelemetry does—and what it does not
OpenTelemetry (OTel) is an open-source framework for instrumenting software, generating telemetry, collecting it, and exporting it. It includes APIs and SDKs, language support, specifications, and the OpenTelemetry Collector. The project’s documentation index, last modified August 29, 2025, says the framework is supported by more than 90 observability vendors. That is the project’s own documentation claim, not an independent market survey.
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OpenTelemetry is instrumentation and collection infrastructure, not a complete observability backend. You still need a destination that stores and lets you query the telemetry. You also do not have to deploy the Collector in every architecture: applications can send data through direct integrations when that suits the destination and operational requirements.
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Observability is the ability to ask questions about a system using the behavior data it emits, including questions not anticipated when the software was instrumented. Good instrumentation aims to provide enough context to troubleshoot without repeatedly adding new instrumentation; it cannot guarantee that every future question will be answerable.
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What is the difference between logs and distributed tracing?
A trace follows one request as it crosses components. It is made up of spans, each describing an operation with a name, timing, and attributes. A log is a timestamped message. A metric aggregates numeric observations over time. These signals answer different questions, so one does not replace the others.
| Signal | What it represents | Useful question |
|---|---|---|
| Trace | A request’s connected path through operations and services, represented by spans. | Where did this request spend time, and which operation failed? |
| Metric | Numeric application or infrastructure observations aggregated over time. | Did request rate, error rate, or CPU use change, and when? |
| Log | A timestamped event or message, optionally carrying request and source context. | What detailed event or error was recorded at this point? |
For example, a browser request might pass through a gateway, an application service, and a database. A trace can connect the operations across that path; spans show their timing and attributes. Metrics can reveal a rising error rate, while logs can supply the detailed error message for a particular operation. OpenTelemetry’s primer describes a trace as the path taken by a single request as it propagates through multiple services.
That connection relies on context propagation. If a request crosses an asynchronous boundary, such as a message queue, the producer and consumer need to carry and extract the appropriate context for spans to remain linked. Without it, the systems may still emit telemetry, but the trace may not show a continuous request path.
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For application logs, record the trace ID and span ID when the logging context has them. Those identifiers let a telemetry backend associate a log with the relevant trace or span, provided the backend supports that correlation. Also keep resource attributes—information about the source, such as service, host, container, or pod—consistent across logs, spans, and metrics.
- Time: Use timestamps so events can be examined alongside spans and metrics. Time helps locate related activity but does not establish that two events belong to the same request.
- Trace context: Include TraceId and SpanId where available. Baggage may carry additional context across participating components when the application and its propagation setup use it appropriately.
- Resource context: Attribute telemetry to its originating service and runtime environment. This helps group data from the same source, but resource attributes alone do not reconstruct request causality.
System and infrastructure logs often have no request context. In those cases, resource attribution and timestamps may be the practical way to investigate them alongside application telemetry; do not imply they can be attached to a particular request when they lack its trace context.
Structured JSON is a log encoding choice; the OpenTelemetry log data model is a normalized representation used to process and transport log records. JSON by itself does not create trace correlation. The records still need stable fields, consistent resource attribution, propagated context, and a destination that can use those fields.
How should teams handle structured logging and existing formats?
OpenTelemetry is designed to work with the existing logging ecosystem rather than requiring every team to replace its logging library. Logging integrations, appenders, or bridges can connect a library to the OpenTelemetry log data model. Existing formats can also be mapped into that model.
When teams control application code, structured records make attributes easier to parse and map consistently than ambiguous free-form text. Where a third-party component or legacy application cannot change its output, a Collector or another agent can read and parse logs, subject to the quality and consistency of the format. Parsing cannot reliably recover fields that a message never recorded or distinguish values that appear ambiguously in free text.
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How does the OpenTelemetry Collector fit into a pipeline?
The Collector is a vendor-agnostic layer for receiving, processing, and exporting telemetry. It can centralize processing or enrichment and give operators a place to route data, but it is not mandatory. The right path depends on how logs are produced, what the destination accepts, whether local files are needed, and how much collection infrastructure the team wants to operate.
| Collection path | When it can fit | Trade-offs to check |
|---|---|---|
| Application logging integration sending OTLP | First-party applications can adopt an integration and emit log records directly to a Collector or backend. | Confirm library support, context and resource attributes, destination protocol support, and where processing will occur. |
| Collector or another agent reading files | Useful when existing applications write local files or their logging output cannot be changed. | Account for parsing quality, rotation-aware tailing, checkpoints, agent deployment, and network delivery. |
| Direct integration to a destination | May suit a deployment whose destination and application integration meet its needs without a separate collection layer. | Check protocol compatibility, required processing, portability, and how consistently services can be configured. |
The OpenTelemetry log specification describes both file collection, including parsing and rotation-aware tailing, and direct OTLP emission through logging integrations. A separate agent may also be used for file collection. These are alternatives, not a single required architecture.
How do I standardize telemetry across microservices?
Use OpenTelemetry semantic conventions as shared guidance for attribute names, types, meanings, and valid values. Conventions cover signals and resources as well as domains such as HTTP, databases, messaging, RPC, and cloud providers. Using shared conventions makes telemetry easier to interpret across languages, libraries, and services than inventing near-duplicate keys in each codebase.
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Standardization does not mean every convention has identical maturity or stability. Check the current status of the conventions relevant to your signals and domains before making them a long-lived internal contract; the published conventions and their versions can change.
- Choose the questions to answer. Identify the request paths and operational problems that matter, such as locating latency across a service boundary or diagnosing an error spike.
- Agree on context and resource attributes. Decide which trace context should cross synchronous and asynchronous boundaries and which service and runtime attributes should identify telemetry sources.
- Adopt shared attribute conventions. Use applicable OpenTelemetry semantic conventions, and document any organization-specific attributes rather than letting each service invent its own names.
- Select a log path per application. Use structured output or a logging integration where code is under your control; choose file collection and parsing where existing output must remain.
- Choose the collection route. Decide whether a Collector adds useful central processing, routing, or export control, or whether a direct integration is sufficient. Verify the protocol and processing capabilities supported by the destination.
- Validate correlation end to end. Follow a request across a service boundary and check that spans connect, application logs carry trace identifiers where available, and resource attributes identify the same services consistently across signals.
Choosing a practical design
There is no universally best pipeline. A team with controllable application code and a destination that accepts its chosen protocol may favor direct telemetry integrations. A team with fixed log formats may need a file-reading Collector or another agent. A central Collector can be useful when shared receiving, processing, enrichment, or exporting simplifies operations; it also introduces components to deploy and maintain.
Quick Recap
- Prefer formally structured records when you can change the application; expect less reliable mapping from inconsistent free-form messages.
- For file collection, plan for rotation, checkpoints, parsing behavior, agent lifecycle, and delivery failures rather than treating file tailing as a one-time configuration.
- For correlation, verify both propagated trace context and matching resource attribution; neither JSON nor a shared service name alone is enough to connect request spans.
- For portability, check the telemetry protocols accepted by each destination and which transformations must happen before export.
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