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What “structured logging” means
A structured log is an event recorded with consistent, typed fields—not merely a line that happens to contain valid JSON. OpenTelemetry describes structured logs in terms of a consistent schema; JSON is one possible encoding, alongside other formats. A JSON record whose fields change shape or meaning from one service or release to another can still be hard to query and analyze. OpenTelemetry’s overview of logs
For example, a stable event might carry a timestamp, severity, service identity, message, and request or trace identifiers as separate attributes. A readable message can coexist with those fields: tools can filter on the attributes, while a person can still understand the event.
How the formats compare in a SaaS logging pipeline
| Need | Structured records, often JSON | Plain-text records |
|---|---|---|
| Filtering and queries | Supports field-oriented queries when the collector and backend preserve and index the fields. Google Cloud Logging supports JSON-path queries and field indexing for structured payloads. Google Cloud: Structured logging | Can be searched as text, but operators may need fragile text matching or parsing to isolate values. Google Cloud says textPayload content cannot be indexed like structured fields. Google Cloud: Log entry data model |
| Schema consistency | Useful when field names, types, and semantics remain stable across services and releases; JSON syntax by itself does not ensure that consistency. | Often free-form, so downstream parsing depends on predictable message patterns or explicit collector rules. |
| Correlation | Can carry service, request, trace, and span context as distinct fields, making records easier to join when conventions are consistent. OpenTelemetry Logs Data Model | Can include identifiers in the message, but extracting and correlating them generally requires parsing. |
| Human inspection | May be less pleasant to read as a raw compact line; a pretty printer or log viewer can make it easier to inspect. | Often convenient to scan directly, particularly during local development. |
| Collection and compatibility | Works well only if the logger, collector, and backend agree on parsing, timestamp and severity mapping, and nested values. | Can remain appropriate where an existing collector parses it reliably; mixed formats may need normalization. |
| Performance and cost | No general comparative cost or speed advantage is established by the sources cited here. | No general comparative cost or speed advantage is established by the sources cited here. |
Google Cloud’s documentation describes the practical query distinction: “For these logs, you can construct queries that search specific JSON paths and you can index specific fields in the log payload.” That capability depends on structured payloads being recognized by the service, not just on JSON being printed somewhere inside a text message. Google Cloud: Structured logging
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What to put in a useful log event
Start with a small shared schema. OpenTelemetry’s log model includes fields such as timestamp, severity, resource identity, body, and trace and span IDs. AWS guidance also recommends transaction and correlation identifiers across components. Adapt names to your stack, but define them once and use them consistently. OpenTelemetry Logs Data Model AWS: Centralized and structured logging
- Time and severity: emit a timestamp and a severity value the collector can interpret consistently.
- Service and environment: identify which service and deployment context produced the record.
- Event or message: keep a human-readable explanation where it helps, while retaining important searchable values as fields.
- Request and trace context: include request, transaction, trace, or span identifiers when available, using conventions that let operators connect events across components.
- Event-specific attributes: put variable context into explicit attributes or nested objects rather than changing a shared field’s type or hiding the only useful value in free-form text.
OpenTelemetry’s body can be a human-readable string or structured values. Its logging guidance is designed to work with existing logging libraries and sources as well as newly emitted structured logs, which can help teams normalize records rather than replace every logger at once. OpenTelemetry Logging OpenTelemetry Logs Data Model
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Choose output based on the application and pipeline
Production services that need operational queries
Prefer structured output when incidents require filtering by severity, service, request ID, or event attributes, or when dashboards and automated analysis depend on those dimensions. Emit it in a format your collector and backend can preserve. Google Cloud documents emitting JSON to standard output for an agent to collect, using a cloud logging client or API, and recommends an agent where available. Google Cloud: Structured logging
Local development and legacy systems
Plain-text output can be a reasonable developer-facing choice when it improves readability, and it may remain suitable for a legacy pipeline whose parser is dependable. If you use different formatters in development and production, keep the underlying event meaning and important fields consistent; production still needs records the pipeline can interpret.
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Mixed sources or an OpenTelemetry pipeline
You do not have to make every source emit the same syntax immediately. OpenTelemetry supports mapping existing log formats into its data model. A collector can help normalize sources, but verify that it preserves severity, timestamps, resource identity, nested attributes, and correlation context rather than flattening or discarding them. OpenTelemetry Logging
Protect sensitive information before it enters a log
Structured fields make data easy to attach and query; that convenience does not make sensitive data appropriate to log. AWS advises removing, masking, sanitizing, hashing, or encrypting sensitive values. Avoid directly recording access tokens, passwords, session IDs, connection strings, encryption keys, sensitive personal data, or payment information. Apply minimization at the point where the event is created, and control who can query or export retained logs. AWS: Logging best practices
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How to migrate without breaking observability
- Define the event contract. Specify field names, types, and meanings for shared data such as timestamp, severity, service, environment, event, and request or trace context.
- Choose the output and collection path. Decide whether applications emit JSON to standard output for an agent, use a logging client or API, or bridge existing libraries through OpenTelemetry. Check how each source will be normalized.
- Test representative events end to end. Send examples through local output, collection, parsing, storage, queries, dashboards, and alerts. Include multiline exceptions, escaped characters, nested objects, timestamps, severity values, and request IDs.
- Check backend-specific features. Confirm that field extraction and indexing work as expected, and test any metric extraction or other conventions tied to your current format.
- Roll out and monitor the change. Compare whether expected events and fields arrive correctly, then adjust parsers, dashboards, and alerts before retiring the old format.
Format changes can have platform-specific side effects. AWS Lambda, for example, documents that the selected format affects new logs only and notes embedded-metric compatibility issues in some configurations. Treat that as a Lambda-specific warning, not a universal rule for every SaaS logging stack; check the behavior of your own runtime, collector, and backend. AWS Lambda: Configuring JSON and plain text log formats
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Decision rule
- Choose structured, schema-stable logs for production when field-level filtering, correlation, or automated analysis matters.
- Choose plain text where direct readability or a proven legacy parser matters more, provided the operational pipeline can still extract what it needs.
- Use separate development and production formatters if useful, but keep event semantics aligned.
- Do not assume JSON is faster or cheaper; choose log levels carefully and consider sampling noisy debug output as volume grows.
- Validate the complete path through collector and backend, and prevent sensitive data from being emitted.
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