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1. What is a Distributed System?

A distributed system is a collection of independent computers that appear to its users as a single coherent system. These computers (or nodes) communicate and coordinate their actions by passing messages to achieve a common goal.

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Key Characteristics:

  • Multiple Nodes: Composed of multiple independent machines.
  • Concurrency: Nodes operate concurrently.
  • No Global Clock: Nodes have their own clocks, making synchronization challenging.
  • Independent Failures: Nodes can fail independently without affecting the entire system.

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2. Goals of Distributed Systems

  1. Transparency:
    • Access Transparency: Hide differences in data representation and resource access.
    • Location Transparency: Hide where resources are located.
    • Failure Transparency: Hide failures and recovery.
    • Scalability Transparency: Hide the system’s ability to scale.
  2. Scalability: The system should handle growth in users, data, and resources.
  3. Fault Tolerance: The system should continue functioning even if some components fail.
  4. Performance: The system should provide efficient and timely responses.

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3. Types of Distributed Systems

  1. Cluster Computing:
    • A group of connected computers working together as a single system.
    • Example: Hadoop clusters for big data processing.
  2. Cloud Computing:
    • A system that provides on-demand access to shared computing resources over the internet.
    • Example: AWS, Azure, Google Cloud.
  3. Peer-to-Peer (P2P) Systems:
    • A decentralized system where each node acts as both a client and a server.
    • Example: BitTorrent, blockchain networks.

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4. Key Components of Distributed Systems

  1. Nodes: Individual machines or servers in the system.
  2. Communication Protocols:
    • Rules and conventions for communication between nodes.
    • Examples: HTTP, TCP/IP, gRPC.
  3. Middleware:
    • Software that connects different components of a distributed system.
    • Examples: Apache Kafka, RabbitMQ.
  4. Distributed File Systems:
    • File systems that store data across multiple nodes.
    • Examples: HDFS (Hadoop Distributed File System), Google File System (GFS).
  5. Distributed Databases:
    • Databases that store data across multiple nodes.
    • Examples: Cassandra, MongoDB, Amazon DynamoDB.

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5. Challenges in Distributed Systems

  1. Consistency:
    • Ensuring all nodes see the same data at the same time.
    • Example: CAP Theorem trade-offs.
  2. Fault Tolerance:
    • Handling node failures without disrupting the system.
    • Techniques: Replication, redundancy.
  3. Scalability:
    • Adding more nodes to handle increased load.
    • Types: Horizontal scaling (adding more machines) vs. Vertical scaling (adding more resources to a single machine).
  4. Synchronization:
    • Coordinating actions and data across nodes.
    • Techniques: Distributed locks, consensus algorithms (e.g., Paxos, Raft).
  5. Security:
    • Protecting data and ensuring secure communication.
    • Techniques: Encryption, authentication, authorization.

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6. Key Concepts in Distributed Systems

  1. CAP Theorem: In a distributed system, you can only guarantee two out of three properties: Consistency, Availability, and Partition Tolerance.
  2. Consensus Algorithms:
    • Algorithms that ensure all nodes agree on a single value.
    • Examples: Paxos, Raft.
  3. Replication:
    • Storing multiple copies of data across nodes to ensure fault tolerance and availability.
    • Types: Master-slave replication, peer-to-peer replication.
  4. Load Balancing:
    • Distributing workloads across multiple nodes to ensure efficient resource utilization.
    • Techniques: Round-robin, least connections, weighted distribution.
  5. Distributed Transactions:
    • Ensuring atomicity, consistency, isolation, and durability (ACID) across multiple nodes.
    • Techniques: Two-phase commit (2PC), three-phase commit (3PC).

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7. Real-World Examples of Distributed Systems

  1. Google Search: A distributed system that indexes and retrieves information from the web.
  2. AWS, Azure, GCP: A cloud computing platform that provides distributed computing resources.
  3. Bitcoin: A decentralized cryptocurrency that uses a distributed ledger (blockchain).
  4. Netflix: A streaming service that uses distributed systems for content delivery and recommendation.

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8. Tools and Technologies for Distributed Systems

  1. Apache Hadoop: A framework for distributed storage and processing of large datasets.
  2. Apache Kafka: A distributed streaming platform for real-time data processing.
  3. Kubernetes: A container orchestration platform for managing distributed applications.
  4. Docker: A platform for developing, shipping, and running distributed applications in containers.
  5. Zookeeper: A centralized service for maintaining configuration information and providing distributed synchronization.

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9. Best Practices for Designing Distributed Systems

  1. Design for Failure: Assume that components will fail and build mechanisms to handle failures.
  2. Use Redundancy: Replicate data and services to ensure fault tolerance.
  3. Monitor and Log: Implement robust monitoring and logging to detect and diagnose issues.
  4. Optimize for Performance: Use efficient algorithms and data structures to minimize latency and maximize throughput.
  5. Ensure Security: Implement strong security measures to protect data and communication.

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10. Key Takeaways

  • Distributed systems consist of multiple independent nodes that work together as a single system.
  • Key goals include transparency, scalability, fault tolerance, and performance.
  • Challenges include consistency, fault tolerance, scalability, synchronization, and security.
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