Need advice about which tool to choose?Ask the StackShare community!
Apache Aurora vs Kubernetes: What are the differences?
Introduction: Apache Aurora and Kubernetes are both popular open-source container orchestration platforms that enable the deployment, scaling, and management of containerized applications. While they share similarities, there are key differences between the two that are worth considering when choosing the right platform for a specific use case or environment.
Architecture and Design: Apache Aurora follows a traditional, monolithic architecture where the scheduler and the executor are tightly coupled. In contrast, Kubernetes adopts a more modular and distributed design, with the scheduler (kube-scheduler) responsible for placing containers onto nodes and the kubelet executing them.
Workload Types: Apache Aurora is mainly focused on batch and long-running tasks, such as running production services or workflows that require complex scheduling capabilities. On the other hand, Kubernetes is designed to handle a wide range of workloads, including stateless applications, stateful applications, and batch jobs.
Resource Management: In Apache Aurora, tasks are statically allocated CPU and memory resources, and scaling is managed manually. Kubernetes, however, provides more dynamic resource management through the concept of pods, which allows for fine-grained resource allocation and automatic scaling based on defined resource policies.
Fault Tolerance and High Availability: Apache Aurora offers automatic failover and recovery features for its services, ensuring high availability. Kubernetes also provides fault tolerance and high availability capabilities but requires additional configuration and deployment of external components, such as etcd or distributed storage solutions, to achieve the same level of resilience.
Community and Ecosystem: Kubernetes has gained widespread adoption and has a larger community and ecosystem compared to Apache Aurora. This translates to a wider range of supported integrations, plugins, and tools, making it easier to find solutions and resources when using Kubernetes.
Ease of Use and Learning Curve: While both Apache Aurora and Kubernetes have their learning curves, Kubernetes is often considered more complex and has a steeper learning curve compared to Apache Aurora. Kubernetes offers more flexibility and advanced features but requires more effort and time to master compared to the more straightforward setup and configuration of Apache Aurora.
In summary, Apache Aurora and Kubernetes differ in their architecture, workload types, resource management, fault tolerance, community support, and ease of use. The choice between the two depends on specific requirements, the complexity of the environment, and the level of scalability and flexibility needed for containerized applications.
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
Pros of Apache Aurora
Pros of Kubernetes
- Leading docker container management solution166
- Simple and powerful129
- Open source107
- Backed by google76
- The right abstractions58
- Scale services25
- Replication controller20
- Permission managment11
- Supports autoscaling9
- Simple8
- Cheap8
- Self-healing6
- Open, powerful, stable5
- Reliable5
- No cloud platform lock-in5
- Promotes modern/good infrascture practice5
- Scalable4
- Quick cloud setup4
- Custom and extensibility3
- Captain of Container Ship3
- Cloud Agnostic3
- Backed by Red Hat3
- Runs on azure3
- A self healing environment with rich metadata3
- Everything of CaaS2
- Gke2
- Golang2
- Easy setup2
- Expandable2
- Sfg2
Sign up to add or upvote prosMake informed product decisions
Cons of Apache Aurora
Cons of Kubernetes
- Steep learning curve16
- Poor workflow for development15
- Orchestrates only infrastructure8
- High resource requirements for on-prem clusters4
- Too heavy for simple systems2
- Additional vendor lock-in (Docker)1
- More moving parts to secure1
- Additional Technology Overhead1