Simply put
Container-based distributed systems leverage containerization technology to deploy and manage distributed applications efficiently. Design patterns for such systems can help optimize performance, scalability, and resilience.
Distributed system design patterns
These patterns are used to design complex systems that are distributed across multiple computing nodes. They include patterns like load balancing, fault tolerance, consistency, and replication.
Single-container management patterns
These patterns are used to manage a single containerized application. They include patterns like health check, self-healing,
Single-node, multi-container application patterns
Sidecar pattern
The sidecar pattern involves attaching an additional container to an existing container to provide additional functionality. For example, a logging container can be attached to an application container to handle logging tasks.
Ambassador pattern
The ambassador pattern involves using a proxy container to abstract the network communication between services. This pattern helps to decouple the services from the networking details and provides a centralized point of control.
Adapter pattern
The adapter pattern involves using an intermediate container to provide compatibility between different interfaces or protocols. It is useful when integrating multiple systems with different communication protocols.
Multi-node application patterns
Leader election pattern
The leader election pattern involves selecting a leader among a group of nodes to coordinate the actions of the distributed system. This pattern is commonly used in distributed databases, distributed file systems, or any system that requires a single point of control.
Work queue pattern
Work queue pattern: The work queue pattern involves distributing tasks among multiple nodes using a shared queue. This pattern is commonly used in distributed task processing systems, where multiple workers can process tasks in parallel.
Scatter/gather pattern
The scatter/gather pattern involves splitting a task into smaller sub-tasks, distributing them across multiple nodes, and then aggregating the results. This pattern is commonly used in data-intensive applications where processing a large amount of data can be divided and processed in parallel.
Pros and Cons
Pros of container-based distributed systems:
- Scalability: Containers allow for easy scaling of applications, as they can be quickly replicated and distributed across multiple hosts or clusters.
- Resource isolation: Containers provide resource isolation at the application level, ensuring that applications do not interfere with each other and use only the allocated resources.
- Portability: Containers are portable, meaning that they can be easily moved and deployed across different environments, such as on-premises, cloud, or hybrid setups.
- Ease of deployment: Containers simplify the deployment process, as they package everything needed to run an application, including dependencies and configurations, into a single unit.
- Efficient resource utilization: Containers consume fewer resources compared to traditional virtual machines, as they share the host's operating system kernel and do not require a separate guest OS.
- Faster development cycles: Containers enable rapid development cycles by providing a consistent and reproducible environment that can be shared among developers and easily updated.
Cons of container-based distributed systems:
- Increased complexity: Building and managing container-based distributed systems can be complex, especially when dealing with large-scale deployments and orchestrating container clusters.
- Networking challenges: Containers need to communicate with each other or with external systems, and managing the networking configurations can be challenging, particularly in a distributed environment.
- Learning curve: Adopting container-based distributed systems requires some learning and understanding of containerization technologies and related tools.
- Limited support for legacy applications: Some older or legacy applications may not be well-suited for containerization, as they may heavily rely on specific hardware or operating system dependencies.
- Security concerns: Containers need to be properly secured and isolated from each other to prevent unauthorized access or potential vulnerabilities.
- Performance overhead: While containers are generally lightweight, they add a certain amount of overhead, especially when compared to bare-metal deployments, which may impact application performance to some extent.
https://www.usenix.org/system/files/conference/hotcloud16/hotcloud16_burns.pdf
https://qrs20.techconf.org/QRSC2020_FULL/pdfs/QRS-C2020-4QOuHkY3M10ZUl1MoEzYvg/891500a629/891500a629.pdf