Navigating Kubernetes: Real-world Use Cases
Kubernetes (k8s) is an open-source container orchestration platform that automates many manual processes in deploying, managing, and scaling containerized applications. Google originally designed it, but the Cloud Native Computing Foundation maintains the project.
Kubernetes saves developers and operators a lot of time and effort and lets them focus on building features for their applications instead of figuring out and implementing ways to keep them running well at scale. By keeping applications running despite challenges like failed servers, crashed containers, and traffic spikes, Kubernetes also reduces business impacts, reduces the need for fire drills to bring broken applications back online, and protects against other liabilities, like the costs of failing to comply with Service Level Agreements (SLSs).
Cloud-Native Applications
Cloud-native applications are designed to take advantage of the cloud computing model, where resources are dynamically provisioned and released according to the demand. Cloud-native applications are also resilient, scalable, and portable across different environments.
Use Cases
- Netflix, which uses Kubernetes to run its streaming service on AWS, Azure, and Google Cloud Platform. Netflix leverages Kubernetes to handle millions of requests per second, scale up and down according to the traffic, and deploy new features quickly and safely
- Spotify, which uses Kubernetes to run its music streaming service on Google Cloud Platform. Spotify uses Kubernetes to manage thousands of microservices, orchestrate complex workflows, and enable continuous delivery
- Shopify, which uses Kubernetes to run its e-commerce platform on Google Cloud Platform. Shopify uses Kubernetes to handle peak loads during events like Black Friday, migrate from legacy systems, and empower its developers to experiment and innovate
Machine Learning and Data Processing
Machine learning and data processing require large amounts of computing power and data storage, as well as specialized tools and frameworks. Kubernetes can help simplify and optimize these tasks by providing a unified platform for running different types of workloads.
Kubernetes Features
- Distributed computing: Kubernetes can run parallel and distributed jobs across multiple nodes, leveraging the power of clusters and grids. Kubernetes can also handle failures and retries, ensuring that the jobs are completed successfully
- Resource management: Kubernetes can allocate and limit the resources (such as CPU, memory, disk, and network) for each job, ensuring that they have enough resources to run efficiently and avoiding resource contention or wastage
- Scheduling: Kubernetes can schedule jobs based on various criteria, such as priority, affinity, anti-affinity, and constraints. Kubernetes can also balance the load across the nodes, improving the performance and utilization of the cluster
- Scalability: Kubernetes can scale up or down the number of pods (the basic units of computation in Kubernetes) for each job, depending on the workload and demand. Kubernetes can also scale up or down the size of the cluster itself, adding or removing nodes as needed
- Portability: Kubernetes can run jobs on different environments, such as on-premises, cloud, or hybrid. Kubernetes can also run jobs on different architectures, such as CPU, GPU, or TPU
Use Cases
- TensorFlow, which is a popular framework for building and training deep learning models. TensorFlow can run on Kubernetes using a custom resource called TensorFlow Operator, which allows users to create and manage TensorFlow clusters on Kubernetes
- Apache Spark, which is a popular framework for large-scale data processing. Spark can run on Kubernetes using a native scheduler backend, which allows users to create and manage Spark clusters on Kubernetes
- Kubeflow, which is a platform for machine learning on Kubernetes. Kubeflow provides a set of tools and components that integrate with Kubernetes, such as Jupyter notebooks, pipelines, serving models, hyperparameter tuning
Enable DevOps Practices
Set of principles and practices that aim to improve the collaboration and communication between the development and operations teams. DevOps practices help to achieve faster feedback loops, higher quality, and lower costs. However, they also require a high level of automation and standardization in the application delivery pipeline. Kubernetes facilitates this by providing a consistent and reliable way of building, testing, and deploying applications across different stages and environments.
Kubernetes plays a vital role in enabling organizations to implement robust CI/CD pipelines. By leveraging Kubernetes’ capabilities, organizations can automate the deployment of applications and ensure rapid and reliable delivery.
Kubernetes allows organizations to define deployment strategies, such as blue-green or canary deployments, which enable them to roll out new versions of applications gradually and minimize the impact of potential issues. Kubernetes also integrates well with popular CI/CD tools like Jenkins, GitLab, and Spinnaker, making it easier to orchestrate the entire deployment process.
Edge Computing
Edge computing is a paradigm where computation is performed closer to the source of data or users, rather than in centralized servers or clouds. Edge computing can reduce latency, bandwidth consumption, and privacy risks by processing data locally or regionally.
Kubernetes Features
- Lightweight: Kubernetes can run on devices with limited resources (such as memory or disk), such as IoT devices or edge nodes. Kubernetes can also run on different operating systems (such as Linux or Windows), enabling cross-platform compatibility
- Modular: Kubernetes can be customized and configured according to the needs of each edge scenario. Users can choose which components or features they want to enable or disable on their edge devices or clusters
- Secure: Kubernetes can provide security mechanisms such as encryption, authentication, authorization, and auditing for the edge devices or clusters. Kubernetes can also integrate with external security solutions, such as certificates, firewalls, or VPNs
- Autonomous: Kubernetes can operate independently or in coordination with other edge devices or clusters. Kubernetes can also communicate with the central or cloud servers, synchronizing data or state as needed
Use Cases
- Smart cities, which use Kubernetes to run applications that monitor and control various aspects of urban life, such as traffic, parking, lighting, waste management, etc. These applications can run on edge devices or clusters that are distributed across the city, processing data locally and reducing network congestion and latency
- Autonomous vehicles, which use Kubernetes to run applications that enable self-driving capabilities, such as perception, planning, control, etc. These applications can run on edge devices or clusters that are embedded in the vehicles, processing data from sensors and cameras in real-time and ensuring safety and reliability
- Augmented reality, which use Kubernetes to run applications that enhance the user’s perception of reality, such as gaming, education, entertainment, etc. These applications can run on edge devices or clusters that are attached to the user’s devices, such as smartphones, glasses, or headsets, processing data from cameras and sensors and delivering immersive experiences
Conclusion
Kubernetes is a powerful platform that can support a variety of use cases in different domains. It offers many benefits such as scalability, reliability, security, and portability. However, it also comes with some trade-offs and limitations such as complexity, overhead, and learning curve. Therefore, it is important to understand how Kubernetes works in practice, how it can help to solve real-world problems, and how it can be customized and optimized for specific scenarios.
