OpenTelemetry logging: An introduction

OTel logging offers IT teams a unified, unambiguous mapping structure for converting log data from different sources, systems and formats into a single, language-neutral model, while preserving original semantic meanings.

With a vendor-agnostic, structured data model, observability tools can more easily and reliably parse logs, attach metadata and correlate logs with metrics and traces for deep, end-to-end visibility.

OTel logging enables enterprises to switch and mix observability backends without reinstrumenting the entire IT architecture, which is why it has become the industry standard for instrumentation in modern, cloud-native systems. These capabilities help IT teams and site reliability engineers (SREs) avoid the observability gaps and data fragmentation issues that occur when every vendor, stack and device has its own logging schema and transmission protocols.

OTel logging also facilitates automatic cross-signal correlation, which dramatically improves debugging and reduces observability vendor lock-in.

OpenTelemetry logs are a vital component of OpenTelemetry. OpenTelemetry is an open source observability framework that includes a collection of software development kits (SDKs), vendor-agnostic application programming interfaces (APIs) and other tools for application, system and device instrumentation.

Instrumentation code used to vary widely, and no single commercial provider offered a tool capable of gathering data from every app and service on a network. This functionality gap made it difficult (and often, impossible) for teams to collect data from different programming languages, formats and runtime environments.

Traditional observability approaches also made changing backend infrastructure and components a time-consuming, labor-intensive process.

If, for example, a development team wanted to switch out backend tools, they would have to completely reinstrument their code and configure new agents (software components that execute automated build and release tasks) to send telemetry data to the new servers. Fragmented approaches created data silos and confusion, making it difficult to resolve performance issues effectively.

OpenTelemetry represented a significant advancement in observability tools because it standardized the way telemetry data is gathered, analyzed and transmitted to backend platforms. It provided an open source solution—based on community-driven standards—for collecting data about system behavior and security, helping teams streamline monitoring and observability in distributed ecosystems.

OpenTelemetry logging relies on several cooperating components that create, structure, transport and deliver log records as part of a unified observability pipeline. They include:

A log record is a central data structure that represents a single log event in OpenTelemetry. It follows a standard data model that defines fields and semantics, so logs from different languages and systems can be interpreted consistently by backends.

Log records appear like structured rows in a spreadsheet describing one event and answering basic questions, such as “what happened,” “when did this happen,” “how important is it” and “where did it come from.”

The log data model is the common template that every record follows, across languages and systems. It defines which top-level fields exist and what each of them means.

A typical log data model requires that records include:

• A timestamp and an observed timestamp (when the event happened and when it was seen)

• Severity numbers and severity text (also called the log level), which describe the severity level of an event

• The body (the main log message, often a short string or JSON object)

• Attributes, which add finer-grained details about each event (such as user or HTTP details)

• Resource information that identifies the emitting service and deployment environment

• Trace IDs and span IDs that link the event to a specific request (simplifies data correlation)

The structure data models impose turns logs from different services and technologies into rich, queryable telemetry entities that look and behave the same when they enter the observability pipeline. Log data models enable IT teams to correlate logs with traces, group them by service or user, and run the same kinds of queries across the IT environment, instead of dealing with an array of incompatible log formats.

And because the log model is open and community-driven, OTel’s instrumentation and log pipelines are portable (not tied to a single software-as-a-service (SaaS) product or proprietary agent). They work across frameworks and platforms, which is particularly valuable in polyglot microservice environments.

A Logger is the object that writes log records, similar to logger instances in other logging frameworks (such as Java and Python). When an observability platform sends a “log this error” or “log this info message” command, it’s sending the message to a Logger.

Different parts of an application or system can require their own Loggers (for example, one for “checkout” and one for “payments”), but they all follow the same rules for generating logs.

Loggers are obtained from a Logger Provider, which is responsible for creating Loggers and configuring them with the correct pipeline, attributes and exporters.​ When software code asks for a Logger, the Provider provides one that is already appropriately configured.

This design enables different parts of a system to use different loggers that still share a common configuration and are all centrally managed by the Logger Provider. It also makes it easier for IT teams to swap implementations and adjust logging behavior globally without modifying every call site.

The OTel Collector is a separate service that receives, processes and exports telemetry data—including log records—from many sources. Applications send their logs (and traces and metrics) to the Collector, which can receive data in different formats, standardize it and then send it to one or more backends.

Collectors support dedicated log pipelines built around three main subcomponents: receivers, processors and Exporters.

Receivers ingest logs from various inputs, such as OpenTelemtetry Protocol (OTLP) streams and log files from file-tailing receivers. Processors perform operations such as batching, attribute modification, context enrichment (adding Kubernetes pod metadata, for example), filtering and sampling on log records. Exporters within the Collector then forward processed logs to one or more destinations.

Log record Exporters sit within an OTel Collector or an application SDK and define where and how telemetry data is delivered to backend systems.

After the Logs SDK creates and processes a log record, the Exporter encodes the log data model into a specific protocol or format and sends the record to a downstream “consumer.” Consumers can include OTel Collectors, observability solutions or open-source stores, for example.

Exporters can be push-based or pull-based, and they can be swapped and combined to simultaneously transmit the same log records to different backends without modifying the application code. The flexibility Exporters provide makes it easier for enterprises to evolve their observability architecture as requirements change.

The Logs API and Logs SDK work like an outlet on a power strip: the API is the outlet type, and the SDK is the power strip that carries the electricity.

OpenTelemetry APIs comprise a set of functions that code and logging libraries call on to create uniform log entries. Essentially, the Logs API determines the “shape” a log must take to “plug in” to OTel. The Logs API helps ensure that the same API calls will work no matter which observability backend or vendor the IT team chooses later.

However, the API is just an interface. It can’t decide where the logs go or how they’re sent. That’s where the OpenTelemetry SDK—or Logs SDK—enters the picture.

The Logs SDK takes logs from the API endpoint and does the real work with them. It receives log records, enriches them (by adding service name or environment, for example), batches them and prepares them to be shipped. Then, it uses Exporters to send the processed logs to a Collector or a log platform.

Log appenders—also called log bridges—connect existing logging libraries (such as Log4j, SLF4J or similar frameworks) to the OpenTelemetry log data model.

Instead of software developers calling the Logs API directly, they can configure their existing logging framework to use an appender that transforms each traditional log event into an OTel log record.

Log bridges can also inject span and trace context into log records, enabling correlation between logs and traces even when the application code itself is unaware of tracing details.

Context propagation and correlation mechanisms are essential components of OpenTelemetry logging. When a trace span is active, a logging bridge or SDK can automatically attach the current trace ID and span ID to each emitted log record.

Correlation helps observability backends connect logs with distributed traces that represent the same request or workflow. These connections enable teams to navigate from a problematic log entry directly to a trace that shows the full path of the request. It also facilitates cross-signal analysis for metrics, traces and logs that share common resource and context fields.

OTel talks about log “types” in a few different ways: by structure, by source and by encoding or transmission protocol.

Structured logs have a consistent schema—the same fields, with stable names and data types—so observability tools can reliably parse and query them. The logs might be encoded with JSON or another format, but what makes them structured is the predictable set of data fields.

Semi-structured logs contain some recognizable structure (key-value pairs or JSON-like blobs, for example), but the schema is not consistent over time. They’re more machine readable than free-form text, but they require additional parsing and normalization before teams can analyze them effectively.

Unstructured logs are freeform text messages without a stable schema (such as ad hoc print statements or plain text server logs). They’re easy for humans to write and read but harder for automated systems to search, correlate and aggregate without considerable extra parsing. 

System and infrastructure logs are produced by operating systems, network devices, cloud infrastructure services and other platform components, instead of by application code. They capture events—such as OS errors, network issues and infrastructure status—to help teams understand the health and performance of the underlying environment

First‑party application logs come from internal services and applications, typically through an OTel SDK, logging library, sidecar (a second app container running alongside the primary container and using the same resources) or agent. They describe business events, request handling, user actions and internal errors. They can be enriched with resource attributes and instrumentation scope (which describes the log origin within the entity that generated it) to provide a more complete picture of application behavior.

Third-party logs are emitted by the external services, managed components and SaaS products that many enterprise IT environments depend on (external APIs and databases, for instance). OTel can ingest these logs alongside internal logs so that IT teams can see how external dependencies affect the ecosystem.

OTLP logs are represented in the native OTLP data model and sent over OTLP gRPC or HTTP. Each log is standardized for vendor-neutrality and quick correlation with traces and metrics.

File-based logs are written to files (application log files or web server access logs), and syslog-style logs are messages emitted using the syslog protocol. OTel typically collects these logs by using receivers that tail files or listen for syslog messages and then converts them into OTel log records for unified processing.

HTTP and JSON logs are delivered over HTTP in JSON. OpenTelemetry supports CSV, Common Log Format (CLF), LTSV and key-value pairs, as well. These formats are parsed by the collector into the common OTel log data model, so they can be queried and correlated in the same way as native OTLP logs.

Imagine SREs see an increase in “checkout failed” errors on observability dashboards for a checkout microservice. They might start the troubleshooting process by examining OTel logs, which are already standardized and include structured attributes (including order amount, item count and tracking ID) for every “order placed” log record.

Engineers can quickly check whether the failures correlate with high order volumes, a specific shipping method or a particular region, just by querying the log attributes. They can also align the time window across logs, traces and metrics, because OTel uses consistent timestamps and resource metadata across all telemetry signals.

Engineers confirm that only orders routed to a specific payment provider are failing. They follow a trace back to all logs that share that provider attribute, where they find repeated session timeouts. With that evidence, the IT team can quickly route the incident to the right owner and implement a mitigation strategy (failing over to another provider, for instance).

OTel logging differs significantly from traditional logging in its collection, standardization, correlation and log transmission procedures.  

Traditional logging systems focus on log aggregation, indexing, search and alerting based on log patterns. They are powerful tools for text search and historical analysis, but they usually examine logs in isolation from other telemetry data.

Traditional logging approaches are primarily about recording text messages locally, so developers can debug issues in a single application or host. They assume that users will read files, filter them and perhaps send them to a log aggregator at a later point. They also typically use ad hoc formats, such as plain text lines, loosely structured JSON or framework‑specific patterns with no cross‑service standard.

And because each development team can pick its own fields and naming, cross‑system analysis is a cumbersome process.

Traditional logging tools don’t often include built-in correlation or migration features, so data correlation and integration with observability backends requires manual processes and extra steps. IT teams must create custom conventions and parsing rules to stitch events together across services, and integrating log data into the observability stack generally requires custom shippers or format adapters.

OpenTelemetry logging is part of a unified observability framework that treats logs, metrics and traces as first‑class, interoperable signals governed by shared semantic conventions. From the outset, it designs logs to be correlated and analyzed across distributed systems, because the goal is to understand system behavior end-to-end.

Where traditional logging is stack- and backend-specific, OTel logging is deliberately agnostic. It provides bridges and appenders so that existing logging frameworks can emit data in the OTel format without having to rewrite every log call.

This unified scope and approach enables IT teams to create holistic troubleshooting workflows, where they can move seamlessly between dashboards and data types to understand complex, distributed failures.

OTel logging provides standardized, context-rich logs that integrate tightly with traces and metrics, making troubleshooting and analysis far more effective than traditional approaches. The benefits include: