Docker is a platform that enables developers to package, deploy, and run applications in lightweight, portable containers. This technology isolates applications from their environment, ensuring that they work uniformly despite differences in development and staging environments.
By using Docker, developers can eliminate the common issue of inconsistencies and operational issues due to variations in operating systems and underlying infrastructure. Containers ensure that applications run exactly the same across all environments. In addition, Docker containers are faster, more efficient, and less resource-intensive than traditional virtual machines.
Kubernetes, often referred to as K8s, is an open-source platform designed to automate deploying, scaling, and operating application containers. Developed by Google and now maintained by the Cloud Native Computing Foundation, Kubernetes provides a framework for running distributed systems resiliently, allowing for scaling and failover for your applications.
Kubernetes supports a range of container engines, including Docker, and enables the ability to manage containerized applications in various environments, including physical, virtual, cloud-based, and hybrid infrastructures. It simplifies many aspects of running containerized applications, from managing resource utilization and scalability to providing storage and networking orchestration.
Note: As of the time of this writing, Kubernetes does not officially support Docker containers, although Docker containers can still be used in Kubernetes. It is common to use Docker in development environments but use more lightweight container engines, like containerd, when running them in Kubernetes.
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Docker containers operate by packaging an application and its dependencies into a single container image. This image contains everything the application needs to run, including the code, runtime, libraries, and environment variables.
When a container is run from an image, Docker uses the host operating system’s kernel but isolates the container’s process and file system. This isolation is achieved through kernel features such as namespaces and cgroups, which limit and allocate resources like CPU, memory, and I/O separately for each container.
Unlike virtual machines that require a full operating system to run each application, Docker containers share the host OS kernel and run as isolated processes, ensuring lightweight and fast operation. This efficient use of system resources allows for a high density of containers on a single host, maximizing utilization and minimizing overhead. The ability to quickly start, stop, and replicate containers makes Docker ideal for scaling applications in response to demand.
Here are some of the key benefits of using Docker for application deployment:
Ecosystem and tooling: A vast ecosystem of tools and services has developed around Docker, providing additional functionality such as monitoring, networking, and security.
Kubernetes orchestrates clusters of virtual machines and schedules containers to run on those machines based on the available resources and the requirements of each container. Containers are grouped into pods, the basic operational unit in Kubernetes, which can then be managed as a single entity, simplifying deployment and scaling.
Kubernetes manages the lifecycle of pods, automatically starting, stopping, and replicating them based on the defined policies and the state of the system. It also manages networking between containers, allowing for seamless communication within and outside the cluster. It provides mechanisms for service discovery, load balancing, and securing container communications.
Additionally, Kubernetes offers storage orchestration, allowing containers to automatically mount the storage system of choice, whether from local storage, public cloud providers, or network storage systems.
Here are some of the key benefits of Kubernetes for containerized applications:
This is part of a series of articles about Kubernetes monitoring.
Kubernetes and Docker serve different, albeit complementary, roles in the container ecosystem.
| Feature | Docker | Kubernetes |
| Scope | Containerization platform for packaging, deploying, and running applications | Container orchestration platform for managing clusters of containers |
| Complexity | Simpler setup and usage for individual containers | Higher complexity due to managing multiple containers across clusters |
| Deployment | Focuses on creating and running individual containers | Automates deployment, scaling, and operations of containerized applications |
| Isolation | Container isolation using namespaces and cgroups | Pod-level isolation with additional security policies and network segmentation |
| Storage | Persistent storage through volumes and bind mounts | Storage orchestration with support for various storage systems, dynamic provisioning |
| Usability | Easier for small-scale deployments and development environments | Preferred for large-scale, production-grade deployments with complex requirements |
| Version Control | Versioned container images for rollback and updates | Automated rollouts and rollbacks with application health monitoring |
Docker focuses on containerization, providing the tools needed to create, package, and run containers efficiently. It excels in environments where individual applications need to be encapsulated and isolated, ensuring consistency across different environments. However, Docker alone does not inherently provide orchestration capabilities required for managing multiple containers across multiple hosts. For orchestration, Docker uses Docker Swarm, which offers clustering and scheduling functionalities.
Kubernetes is designed explicitly for container orchestration at scale. Kubernetes automates the deployment, scaling, and operation of application containers across clusters of machines. It uses a declarative approach, where users define the desired state of their applications, and Kubernetes continuously works to maintain that state. This includes automatically scaling applications up or down based on demand, managing container replication, and handling rolling updates and rollbacks. This makes it suitable for complex, large-scale environments.
Docker provides basic networking capabilities through its built-in network drivers, which facilitate communication between containers on the same host or across different hosts using overlay networks. Docker networks can be configured in various modes, such as bridge, host, and overlay, to suit different networking needs. However, Docker’s networking solutions are relatively simple and may require additional configuration for more complex setups, such as multi-host networking.
Kubernetes offers more advanced networking features tailored for large-scale, distributed systems. In Kubernetes, each pod receives a unique IP address, simplifying the network model and avoiding port conflicts. Kubernetes uses a flat network namespace, allowing all pods to communicate with each other without Network Address Translation (NAT).
For service discovery, Kubernetes employs a built-in DNS service, which automatically creates DNS records for Kubernetes services. This allows applications to easily discover and communicate with each other using service names, regardless of their IP addresses.
Kubernetes also integrates with various network plugins through the Container Network Interface (CNI), providing flexibility and choice in networking solutions, including support for advanced features like network policies and service meshes.
In Docker, resource management and container scheduling are typically handled manually or through Docker Swarm, Docker’s native clustering and orchestration tool. Docker Swarm enables users to define services and scale them across a cluster of Docker hosts. It provides basic scheduling capabilities, but lacks the advanced resource management features found in Kubernetes.
Kubernetes excels in resource management and scheduling through its sophisticated architecture. It uses a declarative model where users specify the desired state of their applications, including resource requirements and constraints.
Kubernetes’ scheduler automatically places containers based on these specifications, ensuring optimal utilization of cluster resources. Kubernetes supports resource quotas and limits, preventing any single application from monopolizing resources and ensuring fair distribution across the cluster. It also provides mechanisms for horizontal and vertical scaling.
In addition, Kubernetes’ scheduling policies can be customized to meet specific needs, such as affinity and anti-affinity rules, which influence where pods are placed based on various criteria.
Docker’s approach to high availability and fault tolerance involves using Docker Swarm or other third-party tools to manage container clusters. Docker Swarm provides basic features for managing high availability, such as container replication and service discovery, but it may require additional setup and configuration to achieve a robust level of fault tolerance.
Kubernetes is built with high availability and fault tolerance as core principles. It continuously monitors the health of nodes and containers, automatically rescheduling and replacing failed components to maintain the desired state of the cluster.
Kubernetes supports multi-master setups, ensuring that the control plane remains operational even if some nodes fail. This distributed architecture enhances resilience and uptime. Kubernetes also includes built-in mechanisms for self-healing, such as automatic restarts for crashed containers, replication controllers to ensure the correct number of pod replicas, and health checks to detect and respond to failures.
Related content: Read our guide to Kubernetes monitoring tools
When choosing between Docker and Kubernetes, use the following considerations:
Many organizations opt to use Docker and Kubernetes together, leveraging the strengths of both to create an effective container management solution. Docker simplifies the process of packaging and containerizing applications, ensuring that they can run consistently across different environments. Kubernetes, on the other hand, excels in orchestrating these containers, managing their deployment, scaling, and operations across clusters of machines.
Using Docker and Kubernetes together is beneficial because it harnesses Docker’s efficient and easy-to-use containerization platform with Kubernetes’ robust and scalable container orchestration system. This integration enables a streamlined workflow where applications are easily packaged, deployed, and managed, allowing for quicker development cycles, more efficient resource use, and higher availability of applications.
Kubernetes Monitoring and Troubleshooting with Lumigo
Lumigo is a troubleshooting platform, purpose-built for microservice-based applications. Developers using Kubernetes to orchestrate their containerized applications can use Lumigo to monitor, trace and troubleshoot issues fast. Deployed with zero-code changes and automated in one-click, Lumigo stitches together every interaction between micro and managed service into end-to-end stack traces. These traces, served alongside request payload data, give developers complete visibility into their container environments. Using Lumigo enables the ability to:
End-to-end virtual stack trace across every micro and managed service that makes up an application, in context
To try out more about Lumigo for Kubernetes, check out our Kubernetes operator on GitHub
