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 Apica can upscale your observability game.
What is OpenTelemetry?
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 Apica can upscale your observability game.
Why do enterprises use OpenTelemetry?
Enterprises appreciate OTel 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 OTel 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.
STANDARDIZED INSTRUMENTATION:
- Standardized instrumentation in OpenTelemetry ensures that IT teams can collect and analyze data from multiple sources, making it easier to maintain consistency in their observability practices.
- With consistent instrumentation, it’s easier to collaborate and troubleshoot issues across teams and applications, promoting better communication and faster resolution times.
- Standardized instrumentation also helps enterprises to reduce complexity and improve efficiency in their observability practices.
INTEROPERABILITY:
- Interoperability in OpenTelemetry means that IT teams can leverage their existing investments in monitoring and observability technologies.
- With a vendor-agnostic approach, OTel makes it easier for enterprises to switch between different backend tools and platforms without disrupting their observability practices.
- Interoperability also allows enterprises to integrate with other services and applications, promoting better collaboration and faster innovation.
AUTOMATED INSTRUMENTATION:
- Automated instrumentation in OTel helps enterprises to reduce the effort required to instrument their applications.
- With automated instrumentation, IT teams can focus on more strategic tasks, such as analyzing telemetry data and improving application performance.
- Automated instrumentation also ensures consistency and accuracy in observability practices, reducing the risk of errors and inconsistencies.
FUTURE-READY INSTRUMENTATION:
- OpenTelemetry is a community-driven open-source project, which means that it’s constantly evolving and improving to meet the changing needs of enterprises.
- Future-proof instrumentation in OTel means that enterprises can adapt to new technologies and architectures without having to overhaul their observability practices.
- With future-proof instrumentation, IT teams can ensure that their observability practices remain relevant and effective, even as technology and business need change.
COST-EFFECTIVE OBSERVABILITY:
- OTel is an open-source project, which means that it’s free to use and doesn’t require expensive licensing fees.
- Cost-effective observability in OTel means that enterprises can achieve observability without breaking the bank.
- With cost-effective observability, IT teams can allocate their resources more effectively, promoting better ROI and faster innovation.
These features help enterprises to simplify their observability practices, leverage existing investments, and remain competitive in an ever-changing business landscape.
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.
OpenTelemetry Vs Observability: What’s the difference?
| OpenTelemetry | Observability | |
|---|---|---|
| Focus | Collecting telemetry data from different sources and exporting it to a target system | Understanding the behavior and performance of complex systems by analyzing telemetry data in context and detecting patterns, anomalies, and trends |
| Scope | Standardized means of collecting telemetry data across different platforms and environments | Philosophy that emphasizes understanding the entire system, including its interactions, dependencies, and feedback loops |
| Approach | Uses instrumentation to collect telemetry data and standardizes the data format and API to enable interoperability between different systems | Uses a combination of telemetry data, machine learning, and human analysis to gain insights into the system’s behavior and performance and identify the root causes of issues |
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.
How Apica uses OTel
The Prometheus Remote Write Exporter enables users to send OpenTelemetry metrics to Prometheus-compatible backends. apica.io offers a Prometheus Remote Write backend that allows metric data from OpenTelemetry collectors to be easily sent to Apica. To configure this, simply enable the Prometheus Remote Write Exporter in your OpenTelemetry configuration YAML file and specify the apica.io cluster endpoint where you want to send the remote write data.
To enable TLS, including the certificate and key file names, and to identify the metric, include external labels.
To scrape data from Prometheus endpoints, include a scrape section in your OpenTelemetry config file and push it to the remote Prometheus-compatible write endpoint using the instructions provided above.
For ingesting logs and traces, apica.io supports OpenTelemetry agents and collectors, as well as Jaeger agents and collectors. To configure OTel collector to push logs and traces to apica.io, include the appropriate receivers, exporters, processors, and extensions in your YAML file.
The Synergy of OpenTelemetry and Apica Can Unlock Significantly Higher Benefits
The OpenTelemetry and apica.io combination unlocks a wealth of benefits for organizations looking to optimize their observability landscape.
Teams can gain unparalleled visibility into their complex systems by harnessing the power of OTel’s standardized protocol and the advanced capabilities of Apica’s AI-driven log analytics. This allows teams to identify and resolve issues faster, leading to improved service reliability, reduced downtime, and enhanced customer experience.
With Apica’s high-fidelity distributed tracing, code-level visibility, and advanced diagnostics across cloud-native architectures, the synergy of these two platforms is poised to take your observability to the next level.
In a Glimpse
- OpenTelemetry (OTel) is an open-source observability framework that includes tools, APIs, and SDKs for generating and exporting telemetry data for analysis.
- OTel is the 2nd most active project under the Cloud Native Computing Foundation (CNCF), behind Kubernetes.
- OTel is vendor-agnostic, covers a wide range of signals across traces, metrics, and logs, customizable and extensible, and has an active community of developers and users.
- To use OpenTelemetry effectively, follow best practices such as starting with a clear understanding of your telemetry requirements, following the OpenTelemetry API conventions, using distributed tracing for end-to-end visibility, monitoring performance metrics for optimization, and exporting data to a centralized location.
- The benefits of OpenTelemetry include standardized instrumentation, interoperability, automated instrumentation, future-ready instrumentation, and cost-effective observability.
