Top OpenTelemetry backends for storage and visualization
Choosing an OpenTelemetry backend depends on several factors, such as your monitoring and observability requirements, current infrastructure, scalability needs, and budget constraints.
What is OpenTelemetry?
OpenTelemetry is an open source observability framework that provides a unified approach to collecting, processing, and exporting telemetry data from applications.
OpenTelemetry simplifies code instrumentation and observability data collection, including metrics, logs, and traces, across all programming languages, frameworks, and infrastructures.
Why does OpenTelemetry need a backend?
OpenTelemetry is a framework that collects telemetry data from applications, including traces, logs, and metrics. It does not, however, include built-in storage or analysis capabilities for this data.
OpenTelemetry requires a backend system to store, process, analyze, and visualize telemetry data collected from applications. The backend is a critical component in the observability pipeline, allowing for insights and actions based on collected telemetry data.
A backend receives, stores, and analyzes the telemetry data collected by OpenTelemetry. It serves as a central hub for data storage and processing, enabling you to collect, analyze, visualize, and gain insights from the telemetry data produced by your applications.
Choosing the right backend
OpenTelemetry does not have a 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:
Feature Set. Evaluate the metrics, traces, and logs offered by each backend. Consider if the backend meets your monitoring and observability needs, including distributed tracing, metric aggregation, anomaly detection, and log analysis capabilities.
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. Evaluate the backend's query and analysis capabilities. Look for features that allow you to perform complex queries, aggregations, and filtering on the telemetry data. Consider whether the backend supports your organization's specific analysis and visualization requirements.
Visualization and alerting. A backend system usually offers visualization and dashboarding features to present telemetry data in a clear and actionable way. This allows you to monitor application performance, track health metrics, and set alerts based on predefined thresholds or conditions.
Integrations and plugins. In many cases, you may 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 select an OpenTelemetry backend that meets your organization's observability goals, scalability requirements, and integration needs, ultimately enabling you to effectively monitor and analyze your telemetry data.
Uptrace
Uptrace is an OpenTelemetry APM built from the ground up to fully embrace OpenTelemetry specification and guidelines.
Uptrace provides end-to-end visibility into requests flowing through multiple services, enabling performance analysis, root cause identification, and performance optimization.
Uptrace is an open source project, which means it is freely available and encourages community contributions and enhancements. It benefits from an active community of developers who contribute to its development, maintenance and support.
Uptrace supports the OpenTelemetry standard, enabling easy integration with OpenTelemetry instrumented applications. It also provides instrumentation libraries and plugins for various frameworks and languages, facilitating the collection of tracing data.
Prometheus
Prometheus is a widely used open-source monitoring system that provides a time-series database for storing metrics data.
By combining Prometheus with OpenTelemetry, you can collect and store metrics from your applications using OpenTelemetry's instrumentation libraries, and send that data to Prometheus for storage and analysis.
Prometheus provides powerful query 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 provides a rich ecosystem of exporters, integrations, and visualization tools, such as Grafana, to visualize and explore the collected metrics.
By integrating Prometheus with OpenTelemetry, you can leverage the flexibility and extensibility of OpenTelemetry to instrument your applications, while benefiting from the robust monitoring and analysis capabilities of Prometheus.
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 use Grafana's powerful visualization capabilities to create interactive dashboards and explore the telemetry data collected by OpenTelemetry.
Grafana provides an easy-to-use interface for creating custom visualizations, charts, and graphs to gain insight and monitor the performance of your applications.
Jaeger
Jaeger is an open source distributed tracing system that integrates with OpenTelemetry to collect and analyze trace data.
By integrating Jaeger with OpenTelemetry, you can 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.
You can configure OpenTelemetry to send log and metric data to Elasticsearch, where it can be indexed and stored in a scalable and distributed manner.
Elasticsearch provides powerful query and search capabilities that allow you to efficiently search and retrieve log data based on various 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 enable you to generate trace data from your applications. These traces represent the execution flow and timing information of requests as they propagate through various services and components.
Zipkin serves 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 solution, enabling you to select the backend that best fits your requirements. This ensures data portability and facilitates migration to alternative systems if necessary in the future.