Kubernetes pods vs containers: 4 key differences and how they work together

Kubernetes is an open-source platform for automating the deployment, scaling, and management of containerized applications. Its core components are pods and containers. Containers are lightweight, stand-alone packages that include everything needed to run a piece of software, while pods are the smallest deployable units that encapsulate one or more containers in Kubernetes, and enable operations like resource management and Kubernetes autoscaling.

By learning how pods and containers interact within Kubernetes, developers and operations teams can better manage application lifecycles. We’ll discuss how each of these components works, how pods manage containers, and best practices for making effective use of containers within your Kubernetes environment.

This is part of a series of articles about Kubernetes architecture

In this article:

Understanding Containers in Kubernetes

Containers are the building blocks of modern applications in Kubernetes. They contain the application and its dependencies, ensuring consistent behavior across environments. This portability makes containers useful for microservices architectures, allowing applications to be broken down into smaller, manageable pieces.

Definition and Purpose of Containers

Containers encapsulate software code and required dependencies to run uniformly across different computing environments. This capability is useful for DevOps practices, enabling continuous integration and deployment. Containers share the host OS kernel, making them lightweight compared to virtual machines, which include a separate operating system instance.

Containers enable isolation, ensuring that processes running within them do not interfere with one another. This isolation simplifies dependency management because each container operates independently, reducing compatibility issues that typically arise when different applications run on the same system.

Container Runtime Environments

Container runtime environments, such as Docker and containerd, execute and manage containers on a host system. These environments are responsible for creating, running, and managing the full lifecycle of containers, including network configuration and storage provisioning. With Kubernetes, container runtimes operate on each node to support workload orchestration.

The choice of container runtime can influence system efficiency and compatibility with Kubernetes. While Docker is a popular option, Kubernetes has gradually shifted towards more specialized runtimes like containerd and CRI-O that better align with the Kubernetes environment.

Understanding Pods in Kubernetes

Pods are the smallest deployable units in the system. They encapsulate one or more containers, which share networking and storage resources, allowing them to work collectively as a single service. Pods manage networking and storage, abstracting complexities in dealing with separate containers.

Pods as the Smallest Deployable Unit

In Kubernetes, a pod is the smallest unit that a developer can manage and deploy. It can house one or more containers that share the same network space and can communicate with each other using localhost. This grouping allows containers within a pod to share resources and dependencies.

Pods provide a unified management layer, which is beneficial for scenarios requiring multiple web servers or application components to work in tandem. By packaging containers that need to work together in a pod, Kubernetes simplifies deployment strategies and resource management.

Single-Container vs Multi-Container Pods

Single-container pods are common for simple applications, while multi-container pods are used for closely coupled services. For example, sidecar containers might offer additional functionalities like logging and monitoring to the main application.

The choice between single and multiple containers in pods often depends on application architecture and performance requirements. Multi-container pods enable resource and process sharing, but they must be carefully managed to balance resource consumption and performance.

Key Differences Between Pods and Containers

Pods and containers serve distinct roles in Kubernetes, although their differences are often subtle.

Containers within a pod share the same network namespace, meaning they can communicate via localhost. This shared environment allows efficient resource utilization, as no separate networking setup is needed for inter-container communication. However, each pod operates in its own isolated context from others, maintaining security and minimizing resource conflicts.

Resource sharing within a pod also includes storage, where containers can access the same volumes. This shared access simplifies data management for applications needing common storage, but it requires careful handling to avoid contention.

2. Networking Differences

Pods encapsulate containers with a unique IP address in the Kubernetes network. This encapsulation allows seamless communication for containers within the same pod. Networking between pods, enabled through services and other abstractions, is more complex but scalable, supporting all network policies Kubernetes provides.

Networking models dictate how traffic flows externally and internally, impacting application latency and throughput. Kubernetes abstracts the complexities of pod-networking, allowing developers to focus on application logic instead of handling intricate network configurations.

3. Storage Differences

Pods manage storage through persistent volumes, which offer reliable data across container restarts. Container storage is ephemeral and lost when the container is terminated. Kubernetes abstracts storage, offering various classes that fit different scenarios, from cloud-based solutions to on-premises configurations.

Pod-level storage simplifies managing data longevity and availability, differing from containers that only use transient storage. This allows applications to maintain state, critical for data-heavy operations.

4. Lifecycle Management

The container lifecycle is managed by the container runtime, focusing on creation, start, restart, and termination phases. Kubernetes extends this by managing pod lifecycles with states like Pending, Running, Succeeded, or Failed, overseeing their entire deployment process.

Lifecycle management in Kubernetes includes policies that handle pod behavior during failures and updates. Such policies ensure applications remain consistent and available, automatically restarting or rescheduling pods as needed.

How Pods Manage Containers

In Kubernetes, pods act as a management layer for containers, handling their lifecycle, networking, and storage configurations within a unified structure. Each pod is responsible for coordinating the operations of the containers it holds, simplifying the deployment and management of multi-container applications:

Container lifecycle coordination: Pods in Kubernetes oversee container lifecycle events like creation, scheduling, and termination, enabling containers within the same pod to start and stop in a synchronized manner. Kubernetes controllers monitor the state of pods and apply policies, such as restarting containers if they fail or rescheduling pods to other nodes if resources become constrained.
Networking and communication: Pods assign a shared IP address to all containers within them, allowing for direct communication via localhost. This network model simplifies the interaction between containers, enabling them to collaborate without complex network configurations. Kubernetes abstracts the complexities of networking between pods, making it easy to connect different application components.
Storage and data persistence: Each pod can be configured with shared storage volumes, which are accessible to all containers within the pod. This allows containers to share files or persistent data, supporting applications that require coordinated data access. Kubernetes also manages the mounting and unmounting of these volumes based on pod lifecycle events, preserving data across container restarts.
Health monitoring and readiness: Kubernetes enables health and readiness checks at the pod level, providing continuous monitoring of container health within each pod. Liveness probes help Kubernetes detect and recover unresponsive containers by automatically restarting them, while readiness probes determine if containers are ready to handle traffic. These checks help ensure that applications remain highly available and resilient.

Best Practices for Using Pods and Containers in Kubernetes

Adopting the following practices helps optimize performance, scalability, and manageability when designing pods and containers within Kubernetes environments.

Designing Effective Pod Architectures

Pods should be designed to maximize resource utilization and minimize overhead. Consider using single-container pods for simple tasks and multi-container pods for complex service interdependencies.

Effective pod architecture also involves defining resource requests and limits appropriately to avoid resource starvation or over-utilization. This allocation improves application stability by ensuring consistent access to computational resources and adapting to varying load patterns.

Optimizing Resource Requests and Limits

Resource requests specify the minimum CPU and memory required for a container, while limits define the maximum resources a container can consume. Proper configuration of these parameters helps avoid resource contention and ensures that applications have the resources to function reliably.

When setting resource requests, it’s critical to understand the application’s baseline requirements. For applications with predictable load, set requests close to the average usage to prevent unnecessary scaling and resource waste. For limits, configure a buffer above expected peak usage to allow flexibility without overwhelming the node’s resources.

Learn more in our detailed guide to Kubernetes resource limits (coming soon)

Implementing Security Contexts and Policies

Securing pods and containers in Kubernetes involves setting security contexts and implementing policies that restrict access and limit potential vulnerabilities. Security contexts define the privileges and access control settings for containers within a pod, such as setting specific user IDs, limiting root access, or enabling read-only root file systems.

Implementing Kubernetes security policies, such as Pod Security Standards (PSS) or Open Policy Agent (OPA), helps enforce security controls across the cluster. These policies can prevent privileged operations, enforce namespace restrictions, and control network access between pods.

Monitoring and Logging Strategies

Kubernetes provides built-in tools, such as kube-state-metrics and the Metrics Server, which offer insights into cluster health, resource utilization, and pod-level metrics. Integrating external monitoring solutions like Prometheus and Grafana helps visualize these metrics, supporting real-time alerts and trend analysis.

For logging, tools like Fluentd, Elasticsearch, and Kibana (the EFK stack) enable centralized log aggregation, making it easier to troubleshoot and audit application activities. Kubernetes also supports logging at the container level, ensuring each container’s log output is captured and stored for analysis. Log rotation and retention policies help manage storage consumption.

Managing Pod Updates and Rollbacks

Kubernetes provides deployment strategies, such as rolling updates and blue-green deployments, to gradually replace old pods with new ones, minimizing downtime and user impact. Rolling updates incrementally replace pods, allowing teams to monitor each stage and halt updates if issues arise.

In case of failed deployments, Kubernetes supports rollbacks, enabling teams to revert to a previously stable version. Defining deployment configurations and monitoring update progress helps ensure reliable updates. By adopting automated tools like Argo CD or Flux for GitOps-based deployments, teams can further simplify and control these processes.

Automating Kubernetes Infrastructure with Spot by NetApp

Spot Ocean from Spot by NetApp frees DevOps teams from the tedious management of their cluster’s worker nodes while helping reduce cost by up to 90%. Spot Ocean’s automated optimization delivers the following benefits:

Container-driven autoscaling for the fastest matching of pods with appropriate nodes
Easy management of workloads with different resource requirements in a single cluster
Intelligent bin-packing for highly utilized nodes and greater cost-efficiency
Cost allocation by namespaces, resources, annotation and labels
Reliable usage of the optimal blend of spot, reserved and on-demand compute pricing models
Automated infrastructure headroom ensuring high availability
Right-sizing based on actual pod resource consumption

Learn more about Spot Ocean today!