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🧠 DEEP DIVE USE CASE
How OpenTelemetry Powers Kubernetes Observability Pipelines
Kubernetes observability focuses on collecting and processing runtime data from applications, infrastructure, and the cluster itself. It enables you to monitor system health, debug issues, and understand how workloads behave in a distributed environment.
Kubernetes Observability Core Signals
1. Logs are event records generated by containers, nodes, and control plane components. In Kubernetes, most logs come from container stdout and stderr.
Log levels like INFO, WARN, ERROR, and DEBUG help indicate severity. Logs are used to understand what happened at a specific point in time and to debug failures.
2. Metrics are time series measurements of system and application behavior. In Kubernetes, they include CPU, memory, request rate, and latency collected from pods and nodes. Metrics are used to monitor health, detect anomalies, and drive alerting.
3. Traces represent the flow of a request across multiple services. In Kubernetes, they are generated by instrumented applications running in pods and show how requests move between services and where time is spent. Traces help identify latency issues and service dependencies.
Sample trace depiction
What is OpenTelemetry
It is an open standard and set of tools used to generate, collect, and process telemetry data from applications and infrastructure. It provides consistent instrumentation and a unified way to handle logs, metrics, and traces across different environments, including Kubernetes.
How a Telemetry Pipeline Works
A telemetry pipeline begins when applications and systems emit data using instrumentation libraries or agents. This data is sent to a collector over protocols like OTLP, where it becomes the central point for ingestion and processing.
Inside the pipeline, data is not just forwarded as is. It is processed through defined stages that clean, structure, enrich, and control how telemetry flows before reaching storage or analysis systems.
A telemetry pipeline begins when applications and systems emit data using instrumentation libraries or agents. This data is sent to a collector over protocols like OTLP, where it becomes the central point for ingestion and processing.
Inside the pipeline, data is not just forwarded as is. It is processed through defined stages that clean, structure, enrich, and control how telemetry flows before reaching storage or analysis systems.
Pipeline Stages:
Receivers: Accept incoming telemetry from applications, agents, or external systems
Processors: Filter unwanted data, batch for efficiency, sample high volume traces, and add metadata
Transform: Normalize formats, rename fields, and standardize structure across signals
Enrich: Attach context like service name, environment, region, or custom attributes
Exporters: Send processed data to external backends
Routing: Direct different data streams to different destinations based on rules
Drop/Discard: Remove unnecessary or noisy telemetry to reduce cost and volume
With this basic understanding, let us dig deep into how Kubernetes observability works behind the scenes using eBPF and OpenTelemetry.
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