The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →To add structured JSON logging, keep your app’s existing logger where practical, define a stable event schema, add request and trace context, redact sensitive data before logging, and emit records to a runtime or collector that forwards them to a searchable backend. JSON is the format; consistent field names, types, and meanings are what make the records structured and useful.
1. Find your logger and decide where records go
Start by locating the logger already used by your web framework and identifying its current output destination. Retaining that logger is usually simpler than replacing it: OpenTelemetry is designed to work with existing logging solutions through appenders or bridges that connect their records to its log model. The relevant integration is typically configured at application startup, though the exact setup depends on the language and library. See OpenTelemetry’s log specification.
Also find out what currently collects the output. A container or managed runtime may capture JSON written to stdout or stderr, while a virtual machine may require an installed agent. Do not assume that printing JSON automatically delivers it to a backend; confirm the collection route for your hosting platform.
2. Define a stable schema
Choose one JSON object per event and document each field’s name, type, and meaning before changing call sites. OpenTelemetry cautions that a JSON-encoded log is not automatically structured: without a stable schema, it may be only semistructured. Its log model distinguishes the event body from typed fields and attributes, and also represents timestamps, severity, trace and span IDs, resource context, and instrumentation scope. See the OpenTelemetry log data model and its logs concepts.
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A small SaaS app might begin with a record like this:
{
"timestamp": "2026-10-04T01:38:17.300559Z",
"severity": "INFO",
"message": "subscription.updated",
"service.name": "billing-api",
"deployment.environment": "production",
"request.id": "req_…",
"http.request.method": "POST",
"http.response.status_code": 200
}This is an illustrative starting point, not a required convention. Align names and types with your logger, telemetry conventions, and backend; document what each field means so that services do not use the same name for different things. Keep values machine-readable: for example, use a numeric status code rather than embedding it only in prose.
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Useful common fields
For a useful baseline, include event time, severity, a concise message or event body, service identity, event type, and an interaction or request identifier. OWASP describes the purpose as recording “when, where, who and what” for each event. The precise fields depend on what the event is meant to help you investigate; do not add a field merely because the request makes it available.
3. Log events that answer operational questions
Prefer a stable event name such as subscription.updated over relying on a sentence whose wording may change between releases. Add context that explains the action and outcome. For a web request, useful candidates include a request identifier, route or operation, HTTP method, response status, and duration. For an application event, record what action occurred and whether it succeeded.
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OWASP recommends enough information for later monitoring and analysis, but also warns against indiscriminate collection. Choose fields based on the operational question you need to answer, rather than serializing the entire request or response. See the OWASP Logging Cheat Sheet.
4. Add request IDs and trace context
At the application boundary, generate a request or interaction ID, or accept one only when it comes from a trusted source. Carry it through the request lifecycle and include it on related log events. This gives you a practical way to find records belonging to the same operation.
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If distributed tracing is enabled, include trace and span context using the supported OpenTelemetry instrumentation or logging bridge for your stack. A trace ID links records to a distributed request; a span ID identifies a particular operation within that trace. Resource context identifies the service or other origin that produced a record. Together, these dimensions help you move from a log to related work across services. Which libraries and configuration are appropriate depends on your language and current implementation; check the official documentation for that stack before selecting packages.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Redact secrets before records leave the app
Apply an allowlist or redaction policy at the logging boundary, before emitting a record to stdout, stderr, or a collector. Do not log passwords, access tokens, encryption keys, database connection strings, payment-card or bank data, or sensitive personal information directly. OWASP recommends removing, masking, sanitizing, hashing, or encrypting sensitive values where appropriate.
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Treat headers and request or response bodies as sensitive until they have been reviewed. Logging selected fields is safer and easier to reason about than logging an entire object and trying to remove every risky value afterward. The policy should cover exceptions and debug-level events as well as routine application logs.
6. Emit records and confirm the backend parses them
For a containerized or managed deployment, writing serialized JSON to stdout or stderr can be a simple handoff to the runtime. Whether those streams are collected, how they are parsed, and where they appear depends on the platform. Google Cloud documents JSON structured payloads and JSON-path query and index access; its guidance distinguishes services that ingest stdout or stderr from VM setups that use the Ops Agent. See Google Cloud’s structured logging documentation, last updated 2026-09-30 UTC.
Before relying on the new format in production, inspect a representative record in the actual destination. Check that the backend parses the JSON, interprets the timestamp and severity as intended, exposes fields for search, handles multiline exceptions, and preserves redaction and request or trace correlation. This check catches problems that valid JSON at the application boundary cannot reveal.
7. Choose a backend separately from the log format
JSON logging does not require a particular vendor. Compare the options against your deployment and operating needs:
- Collection path: Does the hosting platform capture stdout and stderr automatically, or does it need an agent or collector?
- Existing stack: Can your current logger emit the fields you need and connect to OpenTelemetry without a disruptive rewrite?
- Correlation: Can you search logs alongside trace IDs and service or resource metadata?
- Operations and governance: Evaluate retention, access controls, data residency, ingestion costs, and the effort required to operate collectors and indexes against your own requirements.
Google Cloud Logging and Datadog are examples, not universal recommendations. Datadog documents JSON logging and OpenTelemetry integrations for log and trace correlation with supported libraries; consult its log and trace correlation guidance if you are evaluating that ecosystem. Exact setup and library support vary by language and integration.
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