Guest post by Jay Swamidass, Logiq.ai
If you’re a DevOps engineer, IT personnel, or developer, you’re likely very familiar with telemetry data. After all, it’s what provides you with valuable insights into an application’s health and performance.
Although technology providers offer agents to gather telemetry data, relying on these agents may lead to vendor lock-in. Enter OpenTelemetry, which provides a vendor-neutral standard for telemetry data, as well as the necessary tools to collect and export data from cloud-native applications.
OpenTelemetry is a unified open-source distributed tracing initiative that integrates two similar projects, namely OpenTracing and OpenCensus, into a single collaborative endeavor.
Simply put, OpenTelemetry helps you equip your application with instrumentation in a manner that is vendor-neutral. Following this, you may examine the resulting telemetry data through whichever backend tool you prefer, including but not limited to Prometheus, Jaeger, Zipkin, and other similar options.
Despite the prevailing nature of such open-source projects, there’s a lot of doubt and unclarity surrounding them as well, perhaps due to their sheer scope. In this article, we aim to paint a clear picture of OpenTelemetry. We’ll demystify its uses, dive deep into its functionalities, talk about its undisputed benefits, and see how integrating it with LOGIQ can upscale your observability game.
What is OpenTelemetry?
OpenTelemetry, also known as OTel, is a comprehensive open-source observability framework that includes a variety of tools, APIs, and SDKs. With OpenTelemetry, IT teams can equip their applications with instrumentation, generate telemetry data, collect it, and export it for analysis, ultimately gaining a better understanding of software performance and behavior. This observability framework aims to cover a wide range of signals across traces, metrics, and logs.
As of August 2021, OpenTelemetry is an incubating project under the Cloud Native Computing Foundation (CNCF). The CNCF dev stats shows that OpenTelemetry is currently the 2nd most active CNCF project, surpassed only by Kubernetes.
OpenTelemetry comprises various components, namely APIs and SDKs for generating and emitting telemetry in different programming languages. It also includes a collector component that receives, processes, and exports telemetry data, as well as the OTLP protocol used for transmitting telemetry data.
Why do enterprises use OpenTelemetry?
Enterprises appreciate OpenTelemetry and its capabilities for the several following reasons:
- Vendor-agnostic approach: OpenTelemetry allows enterprises to use it with a variety of different backend tools and monitoring platforms, providing the flexibility to choose the tools that work best for their needs.
- Comprehensive observability signals: OpenTelemetry covers traces, metrics, and logs, giving IT teams a complete understanding of their systems and applications to identify and troubleshoot issues more quickly.
- Customizable and extensible: With APIs and SDKs available for a variety of programming languages, OpenTelemetry can be tailored to specific use cases and requirements, ensuring that the data collected provides the necessary insights for optimal performance and reliability.
- Open-source community support: OpenTelemetry is backed by a vibrant and active community of developers and users, helping to keep the project up-to-date and relevant, and quickly identify and address any issues or bugs.
These factors make OpenTelemetry a popular choice for enterprises looking to optimize their observability approach and gain a competitive advantage through improved performance and reliability of their applications and systems.
What is Telemetry Data?
If you ask a DevOps person what is Telemetry data about? You’ll likely get a response along the lines of “Telemetry data is the lifeblood of modern software development and operations, and OpenTelemetry is a powerful tool for harnessing its full potential.”
But what does telemetry data tell you about? And What’s its significance in relation to OpenTelemetry? Let’s find out.
In simple terms, telemetry data refers to information that’s collected from different sources about the performance and behavior of a system or application. This data can include various metrics, such as response times, throughput, error rates, and resource utilization.
Telemetry data matters for the following reasons:
- Telemetry data is essential for monitoring and troubleshooting modern distributed systems, as it helps to detect issues before they cause significant problems.
- With the rise of cloud computing, microservices, and containerization, telemetry data has become increasingly critical for ensuring the reliability and scalability of complex applications.
- By using OpenTelemetry, developers and DevOps teams can gain valuable insights via telemetry data into the performance and behavior of their systems and applications, improve their observability, and streamline their troubleshooting processes.
OpenTelemetry: Best Practices
OpenTelemetry enables the observability and monitoring of complex distributed systems, making troubleshooting issues and optimizing performance easier. To make sure that OpenTelemetry is set up and used with best practices, follow these guidelines:
- Start with a clear understanding of your telemetry requirements: Before you start instrumenting your application with OpenTelemetry, it’s important to identify the telemetry data that you need to collect and the sources from which you need to collect it. This will help you avoid collecting unnecessary data and ensure that you collect the data that you need to effectively monitor and optimize your application.
- Follow the OpenTelemetry API conventions: OpenTelemetry has a well-defined API for instrumentation that you should follow to ensure consistency and compatibility across your application. This includes using the correct semantic conventions for your telemetry data, such as span names, metric names, and attribute keys.
- Use distributed tracing for end-to-end visibility: OpenTelemetry’s distributed tracing capabilities enable you to trace requests and operations across multiple services and components in your application. This gives you end-to-end visibility into the performance and behavior of your application, allowing you to quickly identify and resolve issues.
- Monitor performance metrics for optimization: OpenTelemetry’s metric collection capabilities enable you to monitor key performance indicators (KPIs) and other metrics that are important for optimizing your application’s performance. By collecting and analyzing metrics such as request latency, error rates, and throughput, you can identify bottlenecks and other issues that may be impacting the performance of your application.
- Export data to a centralized location: OpenTelemetry supports a wide range of export options, including popular monitoring platforms such as Prometheus, Jaeger, and Zipkin. By exporting your telemetry data to a centralized location, you can more easily analyze and visualize your data, and share it with other stakeholders.
To effectively monitor and optimize the performance of your applications and gain valuable insights into their behavior and usage, you can follow these best practices when using OpenTelemetry.
The Benefits of OpenTelemetry
OpenTelemetry offers consistent and streamlined observability, simplifies the choice between observability frameworks, and provides the telemetry data needed to ensure stable and reliable business processes.
Besides that, the following are other key benefits as well:
- Consistency: With OpenTelemetry, you can capture telemetry data and transmit it to a backend in a consistent way, regardless of the application or service. This makes it easier to get a holistic understanding of an application’s performance.
- Simplicity: OpenTelemetry merges the code of OpenTracing and OpenCensus, offering the best of both in a single solution. You don’t have to choose between them anymore, and there’s no risk in switching to OpenTelemetry if you were using one or the other.
- Streamlined observability: OpenTelemetry provides a convenient interface for developers to view and analyze observability data in real-time from any device or web browser.
- Achieving business goals: The ultimate benefit of OpenTelemetry is gaining the observability necessary to achieve business goals. With the ability to track and analyze performance data in real-time, organizations can identify issues and fix root causes before they result in service interruption, leading to more excellent stability and reliability for supporting business processes.
What are the use cases of OpenTelemetry?
OpenTelemetry provides a flexible and extensible framework for collecting telemetry data, which can be used for a wide range of use cases, such as:
- Distributed Tracing: OpenTelemetry can be used to trace a request across a distributed system, enabling developers to understand the end-to-end flow of a request and identify bottlenecks or errors. For example, if a user complains about slow response times, you can use OpenTelemetry to trace the request through all the services and identify the service that is causing the delay.
- Performance Monitoring: You can collect metrics from applications and infrastructure, such as CPU usage, memory usage, network traffic, and response times. This data can be used to monitor the performance of an application or infrastructure, identify performance bottlenecks, and optimize resource usage.
- Logging: OpenTelemetry can be used to collect logs from applications and infrastructure, enabling developers to debug issues and troubleshoot errors. For example, if an application is crashing, you can use OpenTelemetry to collect logs from the application and identify the root cause of the issue.
- Cloud Monitoring: It can be used to monitor cloud infrastructure, such as Kubernetes clusters, AWS services, or Google Cloud Platform services. This data can be used to optimize resource usage, identify security issues, and troubleshoot issues.
- Security Monitoring: Another way OpenTelemetry can be used is to monitor security events, such as failed login attempts, suspicious user activity, or malware attacks. This data can be used to identify security threats and respond to them in real-time.
How does OpenTelemetry work?
OpenTelemetry enables developers and operators to get a comprehensive view of the monitored system’s behavior and performance, which is crucial for detecting and resolving issues, optimizing resource usage, and improving user experience.
Moreover, OpenTelemetry provides a vendor-neutral and standardized approach to telemetry data collection, which simplifies the integration of different telemetry data sources and systems.
The data life cycle in OpenTelemetry involves several steps, including:
- Instrumentation: Developers instrument their code with APIs to specify what metrics to gather and how to gather them.
- Data pooling: The data is pooled using SDKs and transported for processing and exporting.
- Data breakdown: The data is broken down, sampled, filtered, and enriched using multi-source contextualization.
- Data conversion and export: The data is converted and exported.
- Filtering in time-based batches: The data is further filtered in time-based batches.
- Ingestion: There are two principal ways to ingest data: local ingestion, where data is safely stored within a local cache, and span ingestion, where trace data is ingested in span format.
- Moving data to a backend: The data is moved to a predetermined backend for storage, analysis, and visualization.
These methods are pivotal to the entire pipeline, as the process cannot work without tapping into this information.
Why DevOps need OpenTelemetry?
DevOps is all about finding ways to optimize and streamline the development and delivery of software applications. And that’s where OpenTelemetry comes in. It’s a powerful tool that simplifies the process of alerting, troubleshooting, and debugging applications.
You see, collecting and analyzing telemetry data has always been important in understanding system behavior. But with the increasing complexity of modern networks, it’s become more challenging than ever. Trying to track down the cause of an incident in these complex systems can take hours or even days using conventional methods.
That’s where OTel comes to the rescue. It brings together traces, logs, and metrics from across applications and services in a correlated manner. This makes it easier to identify and resolve incidents quickly and efficiently. And because it’s an open-source project, it removes roadblocks to instrumentation, so organizations can focus on vital functions like application performance monitoring (APM) and other key tasks.
In short, OpenTelemetry is essential for DevOps because it streamlines the process of troubleshooting and debugging applications. With its help, DevOps teams can work more efficiently, identify issues faster, and improve the overall reliability of their services.
What is OTLP?
OpenTelemetry Protocol (OTLP) is a protocol specification that’s a key part of the OpenTelemetry project. It’s a vendor and tool-agnostic protocol designed to transmit traces, metrics, and logs telemetry data.
The beauty of OTLP is that it’s so flexible. You can use it to transmit data from the SDK to the Collector, as well as from the Collector to the backend tool of your choice. And because it defines the encoding, transport, and delivery mechanism for the data, it’s the future-proof choice.
If you’re already using third-party tools and frameworks with built-in instrumentation that don’t use OTLP, like Zipkin or Jaeger formats, no worries. OpenTelemetry Collector can ingest data from those sources as well, using the appropriate Receivers.
But perhaps the best thing about OTLP is how easy it makes it to switch out backend analysis tools. All you need to do is change a few configuration settings on the collector, and you’re good to go. This level of flexibility is what makes OpenTelemetry such a powerful and valuable tool for anyone who’s serious about collecting and analyzing telemetry data.
OpenTelemetry is currently in its early stages and project teams are working on stabilizing its core components, integrating automated instrumentation, and improving metrics capabilities.
With considerable support from the technology community and being one of CNCF’s biggest open-source projects, OTel is expected to become the dominant observability framework in the cloud-native telemetry landscape.
As customers demand better integration and less lock-in, supporting OpenTelemetry will be table stakes for vendors involved in applications and software development. OTel’s extensive portability, greater developer control, and strong vendor and cloud provider support help improve achievable results across the application infrastructure, from software development to security.