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A print statement works when one process writes to one terminal and one developer reads the output. It breaks down when a single user request passes through several services and someone has to answer questions such as which component failed, which lines belong to the same request, and how many retries happened. Structured logging addresses this by recording each event as a timestamped record with named, consistently typed fields and shared context, so the same query works in every service. The format matters less than the discipline: JSON that uses drifting field names is still hard to query.
What makes a log “structured”
OpenTelemetry’s Logs documentation defines a structured log as “a log with a defined, consistent schema or typed fields that downstream systems can reliably parse and interpret.” The definition is about the contract between the producer and whatever reads the record, not about the syntax used to write it.
That distinction explains most of the confusion. A record can be valid JSON and still be semistructured: one service calls the error field err, another calls it error_message, and a third sometimes puts a number where a string is expected. OpenTelemetry separates three categories:
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- Unstructured: free-form messages meant mainly for people to read. OpenTelemetry notes these are often more human-readable but much harder to parse and analyze at scale, and they usually need custom parsing to extract the timestamp and the event body.
- Semistructured: key/value pairs or JSON whose shape varies from event to event or from service to service.
- Structured: records with a stable schema, where a given field name always means the same thing and has the same type.
A print statement sits in the first category. Logging JSON from a logging library usually lands in the second until someone enforces names and types.
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Why one service gets by
Inside a single service, a developer can scan the output, recognise a repeated message, and grep for a string. The reader supplies the context that the log line omits: they know which module printed it and what the code was doing. That works because the audience is small and the producer and consumer are the same person.
The weakness appears when the reader is an operator, a dashboard, or an automated alert, and when the events come from many components. Compare two ways of recording the same failure:
2026-10-09 14:02:11 ERROR payment request failed after retry, order 88213, attempt 3, timeout talking to ledger
{"timestamp":"2026-10-09T14:02:11.482Z","severity":"ERROR","service.name":"checkout-api","event.name":"payment.request.failed","error.category":"timeout","retry.count":3,"order.id":"88213","trace_id":"4bf92f3577b34da6a3ce929d0e0e4736","span_id":"00f067aa0ba902b7","downstream":"ledger"}The first line is readable, but finding every timeout against the ledger requires a regular expression that breaks the moment someone rewords the message. The second record exposes the same facts as fields, so a query such as “all payment.request.failed events with error.category equal to timeout in the last hour” needs no text matching. The field names above are illustrative, not a required schema.
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Correlation: the context that links services
Consistent fields solve the parsing problem. Shared context solves the joining problem. OpenTelemetry describes three dimensions that let a reader connect records:
| Dimension | What it contains | What it answers |
|---|---|---|
| Time | The timestamp of the event | What happened before or after this event? |
| Execution context | Trace ID and span ID | Which events belong to the same request, and which operation emitted them? |
| Resource context | Attributes describing the telemetry origin, such as the service | Which component or deployment produced this record? |
Trace and span IDs and resource attributes are different things and should not be conflated. A trace ID tells you that two records belong to one request; a resource attribute tells you where each record came from. You need both to answer “which service was slow for this request.”
Adding an ID field does not by itself create distributed tracing. The trace context has to be propagated from one service to the next, and instrumentation or the collector has to preserve it on the way out. A log line with an empty or regenerated trace ID will look structured and still fail to join.
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A minimum field set to start with
Teams often begin by agreeing on a short list that every service emits, then adding event-specific fields with consistent names and types. A practical baseline:
- Timestamp in one timezone and format, such as UTC ISO 8601.
- Severity from a fixed set of levels.
- Service name, identical across all instances of a service.
- Event name, a stable identifier that does not change when a human message is reworded.
- Trace ID and span ID when a request context exists.
- Event-specific fields, such as error category or retry count, each with one name and one type.
The sourced requirement is stable structure and meaning. Your team does not need to adopt this particular set, but it should be written down and enforced.
Migrating without a big-bang rewrite
OpenTelemetry describes several ways for existing applications to produce structured, correlated logs. They trade application changes against collection work, so choose by the constraint that binds you most.
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Keep your logging library and add a bridge
OpenTelemetry describes appenders that bridge existing logging libraries into its log model and can configure processing and export at startup. This fits services that already log through a library, because most call sites stay as they are. The cost is a one-time setup per service and a dependency on the bridge’s mapping of library fields to the common model.
Keep stdout or files and collect them
An application can keep writing to stdout or to a file while a collector tails the output and parses it. This minimises changes to the application. The remaining work is real: the collector must handle file rotation, parse the actual format, and cope with output that was never specified precisely. If the format drifts, the parser fails quietly on some records.
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Export directly over OTLP
OpenTelemetry describes OTLP export as a formal, highly structured route that removes file tailing and most parser maintenance. You give up the simplicity of a local file you can open with less, you need a destination that accepts OTLP, and you commit more deeply to the OpenTelemetry logging path.
Compare the three paths
| Path | Application change | Parsing and rotation work | Trace and resource context | Local inspection | Destination requirement |
|---|---|---|---|---|---|
| Bridge an existing logging library | Setup per service; call sites usually unchanged | Low, since records enter the common model | Depends on the bridge and propagation | Depends on the local handler you keep | Collector or backend that receives the exported logs |
| Keep stdout or files and collect | Minimal if output format is already controlled | High: tailing, rotation, and parser maintenance | Only if the emitted format includes it | Easy: the file or stream is readable as is | Collector that can read and parse the format |
| Export directly over OTLP | Exporter configuration and SDK setup | Low: no file tailing or parser | Carried in the protocol’s structure | Harder unless you also keep a local handler | Destination that accepts OTLP |
The table reflects the trade-offs OpenTelemetry describes. Your own cell values depend on your stack, so verify them against your libraries and collector version.
A sequence that works in practice
- Write down the shared service and context fields, and the names and types for each event you care about.
- Adopt the schema in one service and make the change visible in one query: for example, count one event type by service and error category.
- Check that trace and span IDs survive every hop in that service, including outbound calls and background jobs.
- Add a bridge or collection path for the next service, then repeat the query across both.
This is a practical ordering rather than an official rollout plan, but it keeps each step testable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a backend can do with structured fields
Google Cloud Logging is a concrete example of why the format matters to the query layer. It stores structured JSON payloads in jsonPayload, where queries can address JSON paths and selected payload fields can be indexed. Text in textPayload is searchable as text, but its contents cannot be indexed in the same way. This is a Cloud Logging behaviour, not a general property of every log backend, and indexing of specific fields may depend on configuration.
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If your services are in Python, the OpenTelemetry Python Contrib project documents an opt-in way to inject trace information into log records, including otelTraceID, otelSpanID, otelServiceName, and otelTraceSampled. This is Python integration behaviour and is not switched on by default in every language or library, so check the instrumentation documentation for your version before relying on it.
Sensitive values
The OpenTelemetry documentation’s structured log example masks a password value. Treat that as an illustration of redaction, not as a policy. Structured fields make it easier to query for sensitive data by accident, so decide which fields may never be emitted, mask or drop them at the source, and review what the collector forwards. Retention and access rules are separate decisions that this change does not answer.
Quick Recap
What structured logging does not fix
- It does not make an incident faster to resolve by itself. Results depend on field design, context propagation, collection, and backend support.
- It does not require OpenTelemetry. Consistent fields are useful with any pipeline, although OpenTelemetry supplies a shared vocabulary for correlation.
- It does not require one serialisation. JSON is common, but protobuf or another encoding can carry the same schema.
- It does not replace good event selection. Logging every variable still produces noise, which is expensive to store and hard to read.
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