OpenTelemetry - definition & overview

OpenTelemetry (OTel) is a set of tools, APIs, and open standards for collecting, processing, and exporting telemetry data from distributed systems. It is a vendor-neutral framework built on open standards and available as open-source software.

OpenTelemetry is used to monitor distributed systems and cloud-native applications. Developers and operators use OTel to collect telemetry data from systems into tools like Sumo Logic. The data is then analyzed to identify performance bottlenecks, troubleshoot errors, and resolve outages. Security teams use telemetry data to understand security posture and investigate breaches.

OpenTelemetry’s API is a standard that developers use to instrument applications and infrastructure across many different technologies. The OTel Collector collects telemetry data and exports it to monitoring systems, logging platforms, and other backends.

OpenTelemetry enables observability across distributed systems, crucial for modern, cloud-native applications. By collecting telemetry data, developers and operators can gain insights into their systems' behavior and identify performance bottlenecks, errors, and outages.

The benefits of OpenTelemetry include:

Standardization: OpenTelemetry provides a vendor-neutral way to instrument applications and infrastructure components, which makes it easier for developers and operators to adopt.

Flexibility: OpenTelemetry supports various languages and platforms, making it a good fit for diverse technology stacks.

Observability: OpenTelemetry enables observability across distributed systems, allowing developers and operators to identify and troubleshoot issues quickly.

Efficiency: OpenTelemetry is designed to be efficient and lightweight, minimizing the impact on application performance.

Community-driven: OpenTelemetry is an open-source project with a vibrant community of contributors, which means it is constantly evolving and improving.

OpenTelemetry provides a set of libraries and SDKs (Software Development Kits) that developers can use to instrument their applications and infrastructure components. These libraries and SDKs provide a standard way to capture telemetry data such as traces, metrics, and logs.

When an application is instrumented with OpenTelemetry, it generates telemetry data as it runs. For example, when a request comes into a web server, the OpenTelemetry library can automatically generate a trace that follows the request's path through the system. The trace can include information about which service handled the request, how long it took, and any errors that occurred.

The telemetry data is then sent to a collector, which aggregates and sends it to a backend system for storage and analysis. OpenTelemetry supports a variety of backends, such as Prometheus, Jaeger, and Zipkin.

OpenTelemetry uses a number of open standards to ensure interoperability between different components. For example, it uses the OpenTracing standard for distributed tracing and the OpenMetrics standard for metrics.

OpenTelemetry also provides a set of APIs that developers can use to customize the telemetry data they collect. For example, they can add custom attributes to traces and metrics to provide additional context.

Overall, OpenTelemetry provides a standardized way for developers to capture and export telemetry data from distributed systems, enabling observability across complex, cloud-native environments.

In OpenTelemetry, a span represents a single unit of work within a trace. A trace is a collection of spans that together represent the path of a request through a system.

For example, imagine handling a user request. The request might start with a front-end server, then go through a load balancer, a caching service and databases. Each of these steps can be represented by a span within a trace. The trace would then be a collection of these spans, showing the request's full path through the system.

Each span contains information such as a unique identifier, the start and end time of the operation, and any metadata that may be useful for understanding the operation.

The choice between automatic and manual instrumentation depends on the application's specific requirements and organization. The main difference between automatic and manual instrumentation in OpenTelemetry is how the telemetry data is captured and exported.

Automatic instrumentation uses pre-built libraries and SDKs to automatically capture telemetry data from an application. This means developers do not need to write additional code to capture telemetry data, as the instrumentation is done for them. Automatic instrumentation can be less error-prone and time-consuming than manual instrumentation, requiring less coding effort.

Manual instrumentation, on the other hand, requires developers to write additional code to capture telemetry data. This can be done using the OpenTelemetry API to create spans and add attributes and events. Manual instrumentation provides greater flexibility and control over the captured telemetry data, as developers can customize the telemetry data to capture business-specific information.

Manual instrumentation in OpenTelemetry can be useful when more than automatic instrumentation is required or where developers want to capture custom data unavailable through automatic instrumentation. For example, manual instrumentation may be the best option if an organization needs to capture application-specific metrics or events.

To manually instrument an application with OpenTelemetry, developers can use the OpenTelemetry API to create spans and add attributes and events to those spans. For example, they might create a span to represent a particular operation within the application, add metadata to that span to provide additional context, and then export the telemetry data to a backend system for analysis.

Automatic instrumentation can capture common telemetry data such as HTTP request/response information, database queries, and system metrics. It is often used for monitoring systems and platforms where instrumentation needs to be standardized across many applications and services.

Define clear objectives: Clearly define what you want to achieve with OpenTelemetry. Identify the telemetry data you need to collect, the performance metrics you want to track, and the key business metrics you want to measure.

Instrumentation: Use automatic instrumentation wherever possible to capture telemetry data. Use manual instrumentation to capture custom telemetry data where automatic instrumentation is impossible.

Consistency: Use consistent naming conventions and attribute keys to ensure that your telemetry data is standardized and can be easily compared across different systems.

Sampling: Use sampling to reduce the amount of telemetry data collected. Sampling can reduce overhead and storage costs while providing a representative sample of the telemetry data.

Error reporting: Use OpenTelemetry to capture information and report errors to your monitoring system. This can help to identify and resolve issues quickly.

Performance monitoring: Use OpenTelemetry to monitor application performance and to track key performance metrics such as latency, throughput, and error rates. This can help to identify performance bottlenecks and to optimize application performance.

Security monitoring: Use OpenTelemetry to monitor security-related events such as authentication failures, access violations, and data breaches. This can help to detect and respond to security threats quickly.

Collaboration: Collaborate with other teams in your organization to ensure that OpenTelemetry is used consistently across different applications and services. Share best practices and develop common standards to ensure that your telemetry data is standardized and can be easily compared and analyzed.

Integration: Integrate your existing monitoring systems and tools with OpenTelemetry to ensure your telemetry data can be easily analyzed and acted upon.

Following these best practices can help ensure that OpenTelemetry is used effectively and that the data you collect is standardized, consistent, and meaningful.

OpenTelemetry and Prometheus are both open-source tools used for collecting, storing, and analyzing telemetry data, but they have some key differences in their capabilities and focus.

OpenTelemetry is a vendor-neutral observability framework that provides a unified set of APIs, libraries, and agents for collecting telemetry data from various sources, including applications, infrastructure, and security tools. It can be used to instrument applications and services in a language-agnostic way, and it supports a wide range of telemetry data types, including traces, metrics, and logs. OpenTelemetry is designed to be extensible and customizable, allowing users to integrate it with various backends, including logging systems, tracing platforms, and monitoring tools.

On the other hand, Prometheus is a monitoring and alerting system primarily focused on time-series metrics data. It is designed to collect metrics data from various sources, including applications, services, and infrastructure components, and to store them in a time-series database. Prometheus provides a powerful query language for analyzing metrics data, and it supports a wide range of alerting options.

While both OpenTelemetry and Prometheus can be used for monitoring and analytics, they have different strengths and use cases. OpenTelemetry is more focused on observability and telemetry data collection, while Prometheus is more focused on monitoring and alerting based on metrics data. OpenTelemetry is generally better suited for distributed systems that generate various telemetry data types, while Prometheus is better suited for monitoring infrastructure components and services that generate metrics data.

Site reliability engineers (SREs) need help to simplify and streamline the process of monitoring and understanding the performance and behavior of complex distributed systems. Sumo Logic’s OpenTelemetry Collector provides a single unified agent to send logs, metrics, traces, and metadata for observability to Sumo Logic. With Sumo Logic’s OTel Collector, you can identify and diagnose issues and improve overall system reliability and efficiency faster and more easily.

What makes the Sumo Logic OTel Collector unique is its flexibility and scalability. It can be easily deployed as a containerized application on any cloud platform, and it supports a wide range of data sources, including AWS CloudWatch, Prometheus, and Jaeger. This means that organizations can use the collector to gain deeper observability data across their systems, no matter where they are hosted.

Once the data is collected, the Sumo Logic platform provides powerful analytics capabilities, enabling users to gain insights into their applications and systems, troubleshoot issues, and optimize their operations. With its user-friendly interface and powerful features, the Sumo Logic OTel Collector is an ideal choice for organizations looking to understand their systems with better visibility and improve their overall performance and reliability.

Use the common OpenTelemetry demo application with Sumo Logic.