OpenTelemetry Best Practices: A Guide to Implementation and Scaling

Why Observability Matters More Than Ever

Modern software applications are increasingly distributed, making it harder to understand system performance and diagnose issues. Observability—the ability to measure a system’s internal state by analyzing its outputs—has become a crucial part of maintaining reliable, performant applications.

OpenTelemetry (Otel) has emerged as the leading open-source observability framework, standardizing how traces, metrics, and logs are collected, processed, and exported. But while its flexibility and extensibility are powerful, implementing and scaling OpenTelemetry effectively can be challenging.

This guide will walk you through best practices for deploying OpenTelemetry, optimizing resource consumption, and troubleshooting common issues. Whether you’re an SRE ensuring system reliability, a platform engineer integrating observability into your infrastructure, or a developer seeking better debugging insights, these strategies will help you make the most of OpenTelemetry.

Learn more about why a strong observability strategy is essential for modern software teams.

Getting Started: Implementing OpenTelemetry

Understanding OpenTelemetry’s Core Components

Before implementing OpenTelemetry, it's essential to understand its key components. Instrumentation libraries are available for multiple languages, collecting telemetry data either automatically or through manual instrumentation. SDKs allow for configuration and customization of data collection, while exporters forward data to observability backends such as OpenSearch, Elasticsearch, Grafana, and Prometheus. The OpenTelemetry Collector acts as a vendor-agnostic agent that receives, processes, and exports telemetry data, ensuring a centralized and scalable observability pipeline.

Instrumenting Your Application

Ensuring that your telemetry data is consistent and meaningful is crucial. Each span should include attributes relevant to the operation it represents. For example, when tracing an HTTP request, attributes such as the request method, URL, and response status code provide valuable insights. Avoid collecting unnecessary attributes, as they can clutter your data and make analysis more complex. Regularly reviewing and refining the attributes you collect helps maintain clarity and relevance.

Instrumentation involves adding OpenTelemetry capabilities to your application. Auto-instrumentation simplifies adoption by covering common frameworks and libraries without modifying application code, while manual instrumentation provides deeper insights by capturing custom traces and spans specific to business logic.

For example, in a Python application, OpenTelemetry can be installed and configured with:

pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlpfrom opentelemetry import trace from opentelemetry.sdk.trace import TracerProvider trace.set_tracer_provider(TracerProvider()) tracer = trace.get_tracer(__name__)

Auto-instrumentation is available for popular frameworks like Flask and Django, allowing developers to collect telemetry data with minimal effort.

Deploying the OpenTelemetry Collector

The OpenTelemetry Collector standardizes how telemetry data is processed before being sent to an observability backend. It helps decouple data collection from backend observability platforms, reducing application overhead and increasing flexibility. A typical Collector configuration defines receivers, processors, and exporters to handle telemetry efficiently:

receivers: otlp: protocols: grpc: http: processors: batch: exporters: otlphttp: endpoint: "https://otel-collector.example.com" service: pipelines: traces: receivers: [otlp] processors: [batch] exporters: [otlphttp]

The Collector can be deployed as a standalone service or as a sidecar within a Kubernetes cluster to ensure observability at scale.

Adopting OpenTelemetry in Enterprise Environments

Adopting OpenTelemetry in an enterprise setting requires a structured approach. Many organizations already use observability tools like OpenSearch, Elasticsearch, and Grafana, making a smooth integration essential. The first step is assessing your existing telemetry pipeline and identifying where OpenTelemetry fits in. This includes mapping out data sources, defining trace and metric collection strategies, and determining how OpenTelemetry will integrate with your existing backends.

Migration is another crucial aspect. If you're transitioning from proprietary APM solutions, consider running OpenTelemetry in parallel to validate data consistency before fully switching over. Ensuring that OpenTelemetry supports your logging and tracing needs across microservices and distributed systems will help maintain observability without disruption.

Enterprise adoption also involves aligning stakeholders. SREs, platform engineers, and security teams should collaborate on defining data retention policies, security controls, and performance optimization strategies. Standardizing OpenTelemetry configurations across services helps maintain consistency, while automated deployment pipelines streamline adoption at scale.

Scaling OpenTelemetry for Production

Adhering to Semantic Conventions

Following established semantic conventions ensures uniformity in your telemetry data, making it easier to integrate with various observability tools. OpenTelemetry provides a set of semantic conventions that standardize how to represent common operations and attributes. Adopting these conventions enhances compatibility and streamlines analysis across distributed systems.

Managing Resource Usage

Batching telemetry data can significantly improve performance by reducing the overhead associated with sending individual data points. Configuring OpenTelemetry to batch data before exporting reduces network strain and lowers resource consumption. Adjust batch sizes and time intervals based on your application's needs to strike the right balance between data granularity and performance efficiency.

As telemetry data volume increases, efficient resource management becomes crucial. Sampling strategies like tail-based sampling retain only valuable traces, reducing data volume while maintaining observability. Batch processing improves performance by aggregating telemetry data before exporting, and applying compression helps reduce storage and bandwidth costs. Filtering unnecessary spans can further optimize performance, ensuring only relevant data is stored and analyzed.

Deploying OpenTelemetry in Kubernetes

For Kubernetes-based environments, the OpenTelemetry Operator simplifies deployment by automating resource provisioning, configuration management, and scaling. The Collector can be deployed as a DaemonSet to ensure efficient telemetry collection across all nodes. Using Helm, the OpenTelemetry Operator can be installed with:

helm install opentelemetry-operator open-telemetry/opentelemetry-operator

By leveraging Kubernetes-native deployments, organizations can ensure a scalable and resilient observability pipeline.

Handling High-Cardinality Data

High-cardinality data, such as user IDs and session IDs, can cause excessive storage and query latency. To mitigate this, consider using histograms instead of raw counts for metric tracking, filtering out low-value attributes in trace spans, and aggregating similar telemetry data before exporting. Managing high-cardinality data effectively helps maintain performance and cost efficiency in large-scale observability systems.

Troubleshooting Common OpenTelemetry Issues

High CPU or Memory Usage

If the OpenTelemetry Collector is consuming excessive resources, reducing sampling rates or applying tail-based sampling can help limit the data load. Optimizing batch processor settings can also improve efficiency by ensuring telemetry is processed in controlled bursts. Scaling the Collector horizontally by deploying multiple instances ensures distributed load balancing across the observability pipeline.

Data Not Appearing in the Observability Platform

Missing telemetry data often results from misconfigurations. Verifying that the correct exporter settings are applied, checking network connectivity between the Collector and backend, and ensuring authentication settings are properly configured can help resolve data visibility issues. Debugging OpenTelemetry’s logs can provide additional insights into potential configuration errors.

Need Help with OpenTelemetry Implementation?

Successfully implementing OpenTelemetry at scale can be complex, especially when integrating with enterprise observability ecosystems like OpenSearch, Elasticsearch, and Grafana. If you need expert advice on designing a high-performance, cost-efficient observability architecture, we’re here to help. O11yAI specializes in optimizing OpenTelemetry deployments for large-scale, mission-critical applications.

Contact us today for a free evaluation of your observability strategy and see how we can help you maximize the value of OpenTelemetry.

Conclusion

OpenTelemetry provides a powerful and flexible approach to observability, but successful adoption requires careful planning and optimization. By instrumenting applications correctly, deploying the OpenTelemetry Collector efficiently, and implementing resource management strategies, organizations can build a robust observability pipeline that scales with their infrastructure.

For further insights, check out this guide on maximizing ROI with observability best practices or explore the OpenTelemetry documentation.