Containerization: When Do You Need it and Why?

Richa Chawla
Published On :
June 12, 2023

Containerization has become a popular buzzword in the world of software development and deployment. At its simplest, containerization is a process that enables developers to package their applications and dependencies into lightweight, portable containers. These containers can then be easily deployed and run on any system that supports containerization, regardless of the underlying infrastructure.

One of the primary benefits of containerization is that it allows developers to build, test, and deploy their applications quickly and efficiently. With containers, developers can package all the necessary components of their applications into a single, self-contained unit. This includes the application code and any required libraries, runtime environments,and configuration files.

Because containers are isolated from the underlying host system, developers can be confident that their applications will run consistently across different environments. This makes it easier to deploy applications to different servers or cloud platforms without worrying about compatibility issues or other complications.

But how do you know that you need it? And when do you think you need containerization? Not quite sure? Let us help you with that.

Well, here are some of the common use cases where containerization can provide significant benefits:

1. Developing Microservices: Microservices are a popular architectural pattern that involves breaking down complex applications into smaller, independently deployable components. Containerization is ideal for developing and deploying microservices, as it allows each microservice to be packaged and deployed as a separate container.

2. Scaling Applications: When applications need to be scaled to handle increased traffic or demand, containerization can help simplify the process. By packaging applications into containers, developers can easily spin up new instances of the application as needed, without worrying about compatibility issues or other complexities.

3. Testing Applications: Containers are ideal for testing applications in different environments and configurations. By packaging test environments into containers, developers can easily spin up new test environments as needed and ensure that their applications work correctly in avariety of settings.

4. DevOps Workflows: Containerization is an essential tool for DevOps teams that are responsible for building and deploying applications. By using containers, DevOps teams can ensure that applications run consistently and can be easily deployed to different environments.

To install the Kubernetes cluster with default settings, including a masternode and worker nodes, use a command-line tool (kubectl) and run the following command to deploy Kubernetes:

‘ kubectl create cluster ‘

This will enable you to manage and deploy containerized applications on Kubernetes.

5. Legacy Application Migration: Containerization can also be useful for migrating legacy applications to modern environments. By packaging legacy applications into containers, developers can simplify the migration process and ensure that the application works correctly in its new environment.

When Does One Not Need Containerization?

While containerization can offer many benefits, it is not always the best solution for every application or use case. Here are some situations where containerization may not be necessary or may even be inappropriate:

1. Simple Applications: For very simple applications without complex dependencies or needing to be deployed to multiple environments, containerization may not be necessary. In such cases, a traditional deployment method may suffice.

2. Heavyweight Applications: Applications with a large footprint or requiring significant system resources may not be suitable for containerization. Containerization may add overhead that impacts application performance or scalability.

3. Tight Integration with Host Environment: Some applications may have tight dependencies on the underlying host system or require specific system configurations that cannot be easily replicated in a container. In such cases, containerization may not be feasible.

4. Legacy Applications: Older applications that were not designed with containerization in mind may be difficult to containerize. The application may need to be redesigned or refactored before containerization can be considered.

Ultimately, the decision to use containerization should be based on the specific needs of the application and the development team. While containerization can provide many benefits, it is important to carefully consider the requirements of the application, the available resources, and the development team's expertise before committing to a containerization strategy.

Pros And Cons Of Containerization

Unlike any other technology and tools, containerization has also got its advantages and disadvantages. So, let's take a closer look at the pros and cons of containerization.


1. Portability: Containers are self-contained and portable, meaning they can be easily moved between different environments and infrastructures. This makes deploying and managing applications across different systems and cloud platforms easier.

2. Efficiency: Containers are lightweight and consume fewer resources and can be spun up and destroyed easily. This enables better utilization of the physical/virtual machines to improve efficiency and cost savings.

3. Consistency: Containers ensure that applications run consistently across different environments. By packaging all the dependencies and configurations with the application, developers can be confident that their applications will run the same way no matter where they are deployed.

4. Scalability: Containers can be easily scaled up or down to meet changing demands. Developers can spin up additional containers asneeded to handle increased traffic or scale down containers to save resources during periods of low demand.

5. Isolation: Containers provide an isolated environment for applications, which improves security and reduces the risk of conflicts or errors caused by shared resources.


1. Complexity: Containerization introduces additional complexity to the development and deployment process. Developers need to learn new tools and technologies to work with containers, and deployment pipelines need to be restructured to accommodate containerization.

2. Debugging: Debugging issues that arise in a containerized application can be more challenging than in a traditional deployment. Developers need to have a good understanding of the container environment andhow it interacts with the application.

3. Networking: Containers can introduce networking challenges, particularly when containers need to communicate with each other or with external resources. Network configurations can be complex, and developers need to carefully manage network traffic to ensure that applications function correctly.

4. Container Sprawl: Containers can proliferate quickly, leading to a phenomenon known as "container sprawl." This can make it difficult to manage and monitor containers effectively and can result in wasted resources.

5. Resource Overhead: While containers are more efficient than virtual machines, they still introduce some resource overhead. The additional resources required to run containers can impact performance and may increase infrastructure costs.

In conclusion, containerization is a powerful technology that offers many benefits for software development and deployment. However, it is not without its drawbacks, and developers need to carefully weigh the pros and cons of containerization before deciding to adopt this technology.

SnappyFlow is a growing observability platform that is designed for cloud-native apps with extensive support for Kubernetes and Docker. Feel free to contact us for any support related to Kubernetes and containerization.

What is trace retention

Tracing is an indispensable tool for application performance management (APM) providing insights into how a certain transaction or a request performed – the services involved, the relationships between the services and the duration of each service. This is especially useful in a multi-cloud, distributed microservices environment with complex interdependent services. These data points in conjunction with logs and metrics from the entire stack provide crucial insights into the overall application performance and help debug applications and deliver a consistent end-user experience.
Amongst all observability ingest data, trace data is typically stored for an hour or two. This is because trace data by itself is humongous. For just one transaction, there will be multiple services or APIs involved and imagine an organization running thousands of business transactions an hour which translates to hundreds of millions of API calls an hour. Storing traces for all these transactions would need Tera Bytes of storage and extremely powerful compute engines for index, visualization, and search.

Why is it required

To strike a balance between storage/compute costs and troubleshooting ease, most organizations choose to retain only a couple of hours of trace data. What if we need historical traces? Today, modern APM tools like SnappyFlow have the advantage of intelligently and selectively retaining certain traces beyond this limit of a couple of hours. This is enabled for important API calls and certain calls which are deemed anomalous by the tool. In most troubleshooting scenarios, we do not need all the trace data. For example, a SaaS-based payment solutions provider would want to monitor more important APIs/services related to payments rather than say customer support services.

Intelligent trace retention with SnappyFlow

SnappyFlow by default retains traces for
SnappyFlow by default retains traces for
HTTP requests with durations > 90th percentile (anomalous incidents)
In addition to these rules, users can specify additional rules to filter out services, transaction types, request methods, response codes and transaction duration. These rules are run every 30 minutes and all traces that satisfy these conditions are retained for future use.
With the built-in trace history retention and custom filters enabled, SREs and DevOps practitioners can look further to understand historical API performance, troubleshoot effectively and provide end-users with a consistent and delightful user experience.
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