Distributed Systems
tags:
- data-engineering
- software-engineering
- cloud
- distributed-processing
- distributed-systems
Introduction to Distributed Systems
Understanding the basics of distributed systems Exploring the concepts of distribution, concurrency, and fault tolerance Types of distributed systems: Client-server architecture, Peer-to-peer (P2P) networks, Distributed databases, etc. Challenges and advantages of distributed systems
Set up a basic distributed system environment using virtual machines or containers Experiment with communication between nodes using simple messaging protocols (e.g., HTTP, TCP/IP) Explore the use of distributed file systems for sharing files between nodes (e.g., Hadoop Distributed File System, GlusterFS)
Consistency and Consensus in Distributed Systems
Understanding consistency models in distributed systems (e.g., eventual consistency, strong consistency) Consensus algorithms: Paxos, Raft, Byzantine Fault Tolerance (BFT) Trade-offs between consistency, availability, and partition tolerance (CAP theorem) Real-world applications of consensus algorithms
Implement a simple distributed key-value store using Paxos or Raft algorithm Explore different consistency levels and observe their impact on system behavior Experiment with network partitions and observe how the system behaves under different failure scenarios
Scalability and Load Balancing
Understanding scalability in distributed systems: Horizontal vs. Vertical scaling Load balancing strategies: Round-robin, Least Connections, Weighted Round-robin Techniques for handling increasing loads: Sharding, Replication, Caching Monitoring and scaling distributed systems dynamically
Set up a cluster of nodes and deploy a simple web application Implement a load balancer to distribute incoming requests among multiple nodes Experiment with different load balancing algorithms and observe their impact on request distribution and system performance Monitor system metrics (e.g., CPU usage, memory usage, network traffic) and scale the system dynamically based on load
Fault Tolerance and Resilience
Understanding fault tolerance mechanisms in distributed systems Techniques for detecting and handling failures: Heartbeating, Failure detectors Recovery strategies: Checkpointing, Logging, Replication Designing resilient distributed systems
Introduce faults into the distributed system environment (e.g., node failures, network partitions) Implement fault detection mechanisms to detect and respond to failures automatically Experiment with fault recovery strategies such as checkpointing and logging to restore the system to a consistent state after failures Evaluate the system’s resilience to various failure scenarios
Distributed Data Processing and Stream Processing
Introduction to distributed data processing frameworks: Apache Hadoop, Apache Spark Real-time data processing: Apache Kafka, Apache Flink Use cases for distributed data processing and stream processing Best practices for designing and implementing distributed data processing pipelines
Set up a distributed data processing environment using Apache Spark or Apache Flink Implement a simple data processing pipeline to analyze large datasets Experiment with stream processing using Apache Kafka or Apache Flink Visualize the results of data processing tasks and gain insights from the analyzed data
Key Focus for Distributed Systems
- Understanding distributed computing principles and concepts
- Familiarity with distributed system architectures (e.g., client-server, peer-to-peer)
- Proficiency in distributed data management and synchronization techniques
- Experience with distributed system scalability and fault tolerance strategies
- Understanding of distributed system security and data privacy considerations
- Proficient in designing and implementing distributed system workflows and pipelines
- Familiarity with distributed system monitoring and performance optimization techniques