Distributed Systems Architecture

Study Notes

Distributed Systems Architecture

System Components

Nodes:
- Individual computing units (servers, machines) that make up the distributed system.
- Can be homogeneous or heterogeneous in terms of hardware and software.
Communication:
- Protocols: Enabling data exchange between nodes (e.g., TCP/IP, HTTP).
- Message Passing: Asynchronous or synchronous methods for nodes to communicate.
- Remote Procedure Calls (RPC): Allow execution of code on a different machine as if it were local.
Data Storage:
- Distributed Databases: Databases spread across multiple nodes to handle larger datasets.
- Data Replication: Copying data across nodes for redundancy and reliability.
- Consistency Models: Ensuring data integrity (e.g., strong consistency, eventual consistency).
Middleware:
- Software that facilitates communication and management of distributed systems.
- Provides services like message queuing, object request brokers, and transaction management.
Resource Management:
- Tools and algorithms for managing resources across nodes, including load balancing and fault tolerance.

Scalability Challenges

Horizontal vs. Vertical Scaling:
- Horizontal scaling involves adding more nodes (scale out).
- Vertical scaling involves adding more resources (CPU, RAM) to existing nodes (scale up).
Load Balancing:
- Distributing workloads evenly across nodes to optimize resource use.
- Challenge: Achieving efficient load distribution without bottlenecks.
Network Latency:
- Delay in communication between nodes can slow down the system.
- Challenge: Minimizing latency impacts through efficient protocols and data locality.
Data Consistency:
- As systems scale, maintaining consistency across nodes becomes complex.
- Challenge: Choosing the right consistency model based on application requirements.
Fault Tolerance:
- Ensuring system reliability in the event of node failures.
- Challenge: Designing systems that can detect failures and recover without significant downtime.
Partitioning:
- Dividing data across multiple nodes to enhance performance.
- Challenge: Balancing load and managing data distribution can be complex, especially in dynamic environments.
Deployment and Management:
- Complexity increases with the number of nodes, affecting deployment and ongoing operations.
- Challenge: Automating deployment and monitoring of distributed systems for performance and health checks.

Distributed Systems Architecture

Nodes are the fundamental building blocks of distributed systems, forming a network of interconnected computers. These nodes can have different hardware and software configurations.
Communication within distributed systems relies on protocols like TCP/IP and HTTP, enabling nodes to exchange data.
Message passing is a method where nodes communicate asynchronously or synchronously, while Remote Procedure Calls (RPC) allow code execution on another machine as if it were local.
Data storage strategies in distributed systems include distributed databases - spreading the database across multiple nodes for scalability, and data replication - copying data for redundancy and reliability. Consistency models define data integrity, ensuring accurate data across nodes.
Middleware acts as the middleman, simplifying communication and management of distributed systems. It provides services such as message queuing, object request brokers, and transaction management.
Resource management in distributed systems involves using tools and algorithms to manage resources effectively across nodes. This includes vital practices like load balancing and fault tolerance.

Scalability Challenges

Horizontal scaling involves adding more nodes to handle increased workloads, while vertical scaling enhances existing nodes with added resources.
Load balancing aims to distribute workloads evenly across nodes to optimize resource utilization.
Network latency is the delay in communication between nodes, affecting system performance.
Data consistency becomes complex as systems scale, requiring careful consideration of the appropriate consistency model to maintain data integrity.
Fault tolerance is critical to ensure system reliability. Designing systems that can detect and recover from node failures is crucial.
Partitioning divides data across multiple nodes for improved performance.
Deployment and management become increasingly complex with larger numbers of nodes. Automating deployment and monitoring is essential for efficient operations and performance.

Description

Explore the fundamental components of distributed systems, including nodes, communication protocols, data storage strategies, and middleware. Understand how these elements work together to create robust and scalable architectures. This quiz will test your knowledge of key concepts in distributed systems.