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For reliable error debugging, a structured log should record when an event occurred, which service emitted it, what happened, how severe it was, and which request or operation it belongs to. Use consistent field names, types, and meanings; add trace and span IDs when available, and capture useful exception details without logging secrets or unnecessary personal data.
A practical baseline for error logs
Structured logging means recording events as fields with stable names, types, and meanings—not simply placing free-form prose inside a JSON string. JSON Lines can make records easy to transport, but the format alone does not make a log queryable. OpenTelemetry’s vendor-neutral [Logs Data Model](https://opentelemetry.io/docs/specs/otel/logs/data-model/) is a useful reference for defining what a record represents.
The following example is an illustrative schema, not a universal standard. Adapt field names to your logging library, instrumentation conventions, and backend.
{
"timestamp": "2026-10-04T04:03:42.393659Z",
"severity": "ERROR",
"event_name": "payment.authorize.failed",
"message": "Payment authorization failed",
"service.name": "checkout-api",
"service.version": "1.8.2",
"environment": "production",
"trace_id": "…",
"span_id": "…",
"error.type": "AuthorizationTimeout",
"error.message": "Authorization provider timed out",
"error.stack_trace": "…",
"attributes": {
"payment_provider": "provider-name",
"retry_count": 1
}
}Keep the human-readable summary concise and use separate fields for information you need to filter, group, or correlate. The exact names and nesting may differ between systems; consistency within your own logs matters more than copying this example verbatim.
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Which fields belong in the record?
Time and severity
Record the time the event happened, in a documented, consistent format such as an ISO 8601 timestamp in UTC. If delayed delivery matters, keep the event time distinct from the time the collector observed the record. OpenTelemetry calls these Timestamp and ObservedTimestamp, respectively.
Choose a consistent severity vocabulary, such as DEBUG, INFO, WARN, ERROR, and FATAL. OpenTelemetry defines numeric severity ranges as well; use a normalized numeric value where your pipeline needs one. Do not assume that unrelated custom strings have a comparable ordering.
Event identity and message
Give recurring event classes a stable name, for example db.query.failed or payment.authorize.failed. An event name identifies the kind of event; it should not change with each occurrence. Put a short, readable explanation in a message or body field, then use attributes for values that distinguish one occurrence from another. OpenTelemetry’s EventName describes this event-class role.
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Service and source identity
Include the emitting service or application and, where useful, its version, environment, and relevant deployment or infrastructure identity. These relatively stable source details describe where the record came from. Keep them distinct from attributes about one particular failure, such as a retry count or route. OpenTelemetry distinguishes resource information from record attributes.
Trace, span, and request context
When an event is part of traced work, include its trace ID and span ID so investigators can move between the log and the relevant trace. A span ID should be accompanied by a trace ID. OpenTelemetry documents the relationship in its [log data model](https://opentelemetry.io/docs/specs/otel/logs/data-model/), and its [trace context specification](https://opentelemetry.io/docs/specs/otel/trace/api/#context-interaction) describes trace context handling.
Add a request or interaction ID, route or operation, and other non-sensitive context only when it helps locate or reproduce the failure. OWASP’s logging guidance frames useful event context in terms of when, where, who, and what, and gives interaction identifiers, actions, and objects as examples. Those examples are prompts for choosing relevant fields, not a mandate to collect every identity or address in every environment.
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Exception details and code location
Represent an exception’s type, useful message, and stack trace in fields your chosen instrumentation and backend can ingest. A stack trace is often valuable for locating a fault, but its mapping is platform-dependent. Google Cloud, for example, documents parsing a stack trace placed in the JSON message field for Error Reporting. That convention is not universal; check the target backend’s current documentation before relying on it.
File, function, or line information can also help when it is available and useful. Google Cloud documents a source-location mapping for its logging integration. Treat that as a platform mapping rather than a portable field requirement.
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Add failure-specific context selectively
Start with the baseline record, then add only attributes that help answer a concrete diagnostic question: which operation failed, what dependency was involved, or whether a retry had already occurred. For a payment authorization timeout, a provider identifier and retry count may help distinguish failure patterns. Do not attach the full request or payment payload just because it is available.
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Before adding a field, decide whether it is safe, useful, and consistently defined. Avoid access tokens, credentials, payment details, and unnecessary personal data in attributes, messages, or exception text. URLs, identities, addresses, and request content can also contain sensitive information. Redact or omit fields according to your system’s access, retention, and data-handling policies. OWASP advises selecting event data for the intended monitoring and analysis; it does not establish a universal privacy or retention policy for every jurisdiction.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make the schema consistent and queryable
Document field names, types, allowed lengths, event classifications, and date/time formats, then apply those definitions across services. OWASP recommends consistent event classification and documented syntax, lengths, data types, and time formats in its [logging cheat sheet](https://cheatsheetseries.owasp.org/cheatsheets/Logging_Cheat_Sheet.html).
- Use one meaning and type for each field. Do not emit a number as a string in one service and as a numeric value in another.
- Use stable event names for categories of failures; keep occurrence-specific details in attributes.
- Keep source identity separate from per-event context.
- Check after serialization and ingestion that fields remain structured and searchable.
- Confirm how your backend handles special keys, timestamps, trace context, and exception stacks.
OpenTelemetry provides a shared model, but a portable concept does not guarantee identical field names or behavior in every logging API and backend. Map the concepts to the conventions supported by your stack and verify the resulting records.
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Portable concepts versus platform-specific mappings
Time, severity, event identity, source identity, trace context, and diagnostic attributes are useful concepts across logging systems. Their exact representation and ingestion behavior can vary. For example, Google Cloud documents mappings for JSON severity, source location, and trace/span fields, and describes the message-field stack-trace behavior for Error Reporting in its [structured logging documentation](https://cloud.google.com/logging/docs/structured-logging).
When comparing logging implementations, check whether they preserve event and observed timestamps, correlate trace and span IDs, parse exceptions and stack traces, keep attributes queryable, and provide the access, redaction, and retention controls your system requires. Test these behaviors in the target stack instead of assuming that a key recognized by one platform will work the same way elsewhere.
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