Container Observability: Challenges and Key Considerations

What Is Container Observability?

Containers are a method of packaging and distributing software applications in a way that makes them easy to deploy and run consistently, regardless of the underlying hardware or operating system. They are a core component of many cloud native applications, built to take advantage of cloud computing environments and designed to be scalable, resilient, and highly available.

Observability is the ability to monitor and understand the behavior and performance of a system or application. It enables organizations to identify and diagnose issues in their systems, understand how their systems are behaving under different workloads and conditions, and make informed decisions about how to optimize and improve the performance and reliability of their systems.

Container observability is the ability to monitor and understand the behavior and performance of containerized applications. Understanding how these applications are behaving and performing is critical for ensuring their reliability and performance.

To achieve container observability, organizations typically use a combination of monitoring and analysis tools that are specifically designed to work with containerized applications. These tools can collect data about the performance and behavior of the containers, as well as the underlying infrastructure and resources that the containers are using. This data can then be analyzed to identify issues and trends, and to trigger alerts when certain thresholds or conditions are met.

This is part of a series of articles about container monitoring.

In this article

Why Is Container Observability Important?

Container observability is a crucial aspect of running and managing containerized applications in production. It is important for several reasons:

Identifying and troubleshooting issues: Helps to identify and troubleshoot issues that may arise in production, such as slow performance, errors, or downtime. By collecting and analyzing data from various sources within the container ecosystem, such as logs, metrics, and traces, it is possible to gain insights into the health and performance of the containers and the applications they host, and identify the root cause of any issues that may arise.
Optimizing performance and resource utilization: Helps optimize the performance and resource utilization of the containers and the underlying infrastructure. This can include identifying bottlenecks or inefficiencies in the application and identifying ways to improve resource utilization and reduce waste.
Ensuring compliance and security: Helps ensure compliance with regulations and industry standards, as well as identify and address security vulnerabilities. By collecting and analyzing data from the containers and the surrounding environment, it is possible to identify potential compliance or security issues and take the necessary steps to address them.
Improving the user experience: Ultimately, container observability helps to improve the user experience by ensuring that the application is running smoothly and meeting the needs of users. By identifying and addressing issues promptly, it is possible to deliver a more stable and reliable service to users.

Visibility Challenges in Containerized Environments

There are several visibility challenges that organizations may face when working with containerized environments:

Lack of built-in observability: Containers are designed to be lightweight and portable, and as a result, they often lack built-in observability features. This can make it difficult for organizations to monitor and understand the performance and behavior of their containerized applications.
Microservices environments: Containerized applications are often built using microservices architectures, which involve breaking down a monolithic application into smaller, independent services that communicate with each other over a network. This can make it difficult to understand how the different services are interacting and performing, and can make it harder to identify issues and trends across the system as a whole.
Inability to use traditional monitoring tools: Traditional monitoring tools are often not designed to work with containerized environments, and may not be able to collect the types of data that are needed to understand the performance and behavior of containerized applications. This can make it difficult for organizations to effectively monitor and understand their containerized environments.
Diverse workloads and technologies: Containerized environments often involve a diverse set of workloads and technologies, including different types of containers, container runtimes, and operating systems. This can make it difficult to collect and analyze data from these environments in a consistent and reliable way.

To address these challenges, organizations often use specialized tools and practices that are specifically designed to work with containerized environments. This can involve using tools that are able to collect and analyze data from containerized environments, as well as adopting best practices for monitoring and debugging containerized applications.

Implementing Observability in a Containerized Environment

There are several main steps for implementing observability in a containerized environment:

Identify the key metrics and data points to collect: The first step in implementing observability is to identify the key metrics and data points that are relevant to the application and its performance. This will typically involve collecting data about the containers and their resource usage, as well as data about the interactions between the containers and other components in the system.
Select and configure observability tools: Once the key metrics and data points have been identified, the next step is to select and configure the observability tools that will be used to collect and analyze the data. This will typically involve selecting tools that are specifically designed to work with containerized environments, and configuring them to collect the desired data points.
Set up alerts and notifications: To ensure that issues are identified and addressed in a timely manner, it is important to set up alerts and notifications that will be triggered when certain thresholds or conditions are met. This can involve using tools that are able to automatically trigger alerts and notifications based on the collected data, as well as configuring manual alerts and notifications as needed.
Analyze and interpret the data: Once the observability tools are in place and collecting data, the next step is to analyze and interpret the data to understand the performance and behavior of the application. This can involve using tools that provide insights and recommendations based on the data, as well as manually analyzing the data to identify trends and issues.
Debug and fix issues: If issues are identified in the system, the final step is to use the collected data to debug and fix the issues. This can involve identifying the root cause of the issue, replicating the issue in a test environment, and implementing a fix or workaround.

By following these steps, organizations can effectively implement observability in their containerized environments, enabling them to monitor and understand the performance and behavior of their applications.

Key Considerations for Container Observability

There are several key considerations when implementing observability for containerized applications:

Automation

To ensure that observability is effective and efficient, it is important to automate as much of the process as possible. This can involve using tools that are able to collect and analyze data from containerized environments automatically, as well as using automation to trigger alerts and notifications when certain thresholds or conditions are met.

Context

To effectively understand and debug issues in containerized applications, it is important to have access to a rich set of context about the performance and behavior of the application. This can include data about the containers and their resource usage, as well as data about the interactions between the containers and other components in the system.

Actionability

The data collected for observability should be actionable, meaning that it should be easy to understand and use to identify and fix issues in the system. This can involve using tools that provide insights and recommendations based on the data, as well as providing clear and concise alerts and notifications that can be acted upon quickly.

Container Observability with Lumigo

Lumigo is an observability and debugging platform that delivers automated distributed tracing across the full spectrum of cloud services used in modern microservice applications, from AWS Lambda to serverless managed services to containers in Amazon ECS.

Lumigo uses distributed tracing at its core, automatically correlating logs and metrics to extract events from the application and stitch together the different components of an application in one complete view. Its advanced distributed tracing helps developers understand how services in their application interact with and impact each other, enabling them to catch issues early and resolve them quickly.

Trace end-to-end applications running on Amazon ECS, AWS Lambda and consuming AWS services and 3rd party APIs
Easily monitor and debug ECS clusters and underlying services and tasks in real-time
Setup automatic alerts to notify you in Slack, Pagerduty and other workflow tools

Get started with Lumigo today!