OpenTelemetry is a framework for collecting telemetry data from applications, including traces, logs, and metrics. However, OpenTelemetry itself does not include built-in storage or analysis capabilities for this data. That's where a backend system comes into play.
What is OpenTelemetry?
OpenTelemetry is an open-source observability framework that provides a unified approach for collecting, processing, and exporting telemetry data from applications.
OpenTelemetry aims to simplify the instrumentation of code and the collection of observability data, such as metrics, logs, and traces, regardless of the programming language, framework, or infrastructure being used.
OpenTelemetry helps organizations to achieve a standardized and flexible approach to observability, enabling them to gain deep insights into their applications' performance, troubleshoot issues, and optimize system behavior.
Why does OpenTelemetry need a backend?
OpenTelemetry needs a backend system to store, process, analyze, and visualize the telemetry data collected from applications. The backend acts as a crucial component in the observability pipeline, enabling you to derive insights and take actions based on the collected telemetry data.
The backend is responsible for receiving, storing, and analyzing the telemetry data collected by OpenTelemetry. It acts as a central repository or processing pipeline for the data, allowing you to aggregate, query, visualize, and gain insights from the telemetry data generated by your applications.
Choosing the right backend
OpenTelemetry does not have its own built-in backend. Instead, it allows you to export data to various backend systems and services to store and analyze telemetry data.
There are several factors to consider when choosing an OpenTelemetry backend:
Data storage. Telemetry data generated by applications can be voluminous, especially in large-scale distributed systems. Storing this data efficiently and reliably requires a dedicated backend system that can handle the data volume and provide the necessary durability.
Querying and analysis. Assess the querying and analysis capabilities of the backend. Look for features that enable you to perform complex queries, aggregations, and filtering on the telemetry data. Consider if the backend supports the specific analytics and visualization requirements of your organization.
Visualization and alerting. A backend system typically provides visualization and dashboarding capabilities to present telemetry data in a clear and actionable manner. This empowers you to effectively monitor application performance, track health metrics, and establish alerts based on predefined thresholds or conditions.
Integrations and plugins. In many cases, you might already have existing monitoring, logging, or analytics systems in your infrastructure. The backend system can integrate with these tools, allowing you to consolidate telemetry data with other observability data sources and leverage existing workflows and processes.
By carefully considering these factors, you can choose an OpenTelemetry backend that aligns with your organization's observability goals, scalability requirements, and integration needs, ultimately enabling effective monitoring and analysis of your telemetry data.
Uptrace
Uptrace is an OpenTelemetry APM built from the ground up to fully embrace OpenTelemetry specification and guidelines.
Prometheus
Prometheus is a widely used open-source monitoring system that provides a time-series database for storing metrics data.
By combining Prometheus and OpenTelemetry, you can collect and store metrics data from your applications using OpenTelemetry's instrumentation libraries and send that data to Prometheus for storage and analysis.
Prometheus provides powerful querying and alerting capabilities, allowing you to perform complex queries on the collected metrics data and set up alerts based on predefined conditions or thresholds. It also offers a rich ecosystem of exporters, integrations, and visualization tools, such as Grafana, for visualizing and exploring the collected metrics.
Integrating Prometheus with OpenTelemetry enables you to leverage the flexibility and extensibility of OpenTelemetry for instrumenting your applications, while benefiting from Prometheus' robust monitoring and analysis capabilities.
Grafana
Grafana is a popular open-source visualization and analytics platform that can be used to visualize and explore telemetry data collected by OpenTelemetry.
By integrating Grafana with OpenTelemetry, you can leverage Grafana's powerful visualization capabilities to create interactive dashboards and explore the telemetry data collected by OpenTelemetry.
Grafana provides a user-friendly interface for building custom visualizations, charts, and graphs, allowing you to gain insights and monitor the performance of your applications.
Jaeger
Jaeger is an open-source distributed tracing system that can be integrated with OpenTelemetry to capture and analyze trace data.
Integrating Jaeger with OpenTelemetry enables you to leverage the power of distributed tracing for troubleshooting, performance optimization, and root cause analysis of your applications.
Jaeger provides end-to-end visibility into the execution path of requests, making it easier to identify performance bottlenecks, latency issues, or errors across your distributed systems.
Elasticsearch
Elasticsearch is a scalable search and analytics engine that can be used to store and analyze logs and other telemetry data collected by OpenTelemetry.
OpenTelemetry can be configured to send log and metric data to Elasticsearch, where it can be indexed and stored in a scalable and distributed manner.
Elasticsearch offers powerful querying and search capabilities, allowing you to efficiently search and retrieve log data based on different criteria, such as time range, keywords, or specific fields.
Zipkin
Zipkin is another distributed tracing system that can be integrated with OpenTelemetry to collect and analyze trace data.
OpenTelemetry provides instrumentation libraries that allow you to generate trace data from your applications. These traces represent the execution flow and timing information of requests as they propagate across various services and components.
Zipkin acts as the backend system for collecting, storing, and visualizing these traces. OpenTelemetry exporters can send the generated trace data to Zipkin, where it can be stored and indexed for further analysis.
Conclusion
OpenTelemetry provides a vendor-agnostic approach, allowing you to choose the backend that best suits your requirements. It allows for data portability and easy migration to alternative systems if needed in the future.