Introduction to Distributed Systems

Distributed Systems: Foundations and Principles

Core Concepts

Motivations

1. Parallelism: Divide computationally intensive tasks across multiple machines
   
2. Fault Tolerance: Ensure system resilience through redundancy
   
3. Performance Scaling: Horizontal scaling of computational resources
   
4. Geographic Distribution: Leverage physical separation for security and latency optimization

Fundamental Challenges

• Concurrency Management: Coordinating simultaneous operations
  
• Partial Failure Handling: Responding to individual component failures
  
• Performance Optimization: Maintaining efficiency across distributed infrastructure
  
• Consistency Maintenance: Synchronizing state across multiple nodes

System Architecture

Communication Paradigms

• Remote Procedure Calls (RPC): Synchronous method invocation across network
  
• Message Passing: Asynchronous communication between distributed components
  
• Publish-Subscribe Models: Event-driven communication patterns

Concurrency Control Mechanisms

1. Locks and Semaphores
   • Mutual exclusion
     
   • Resource synchronisation
2. Atomic Transactions
   • ACID properties (Atomicity, Consistency, Isolation, Durability)
     
   • Ensuring data integrity

Consistency Models

Strong Consistency

• Guarantees immediate, synchronized view across all nodes
  
• High overhead, lower performance
  
• Suitable for financial, transactional systems

Weak Consistency

• Allows temporary divergence between node states
  
• Lower overhead, higher performance
  
• Appropriate for eventually consistent systems like caches

Replication Strategies

Fault Tolerance Approaches

• Primary-Backup Model
• Peer-to-Peer Replication
• Quorum-Based Replication

Synchronization Challenges

• Network latency
  
• Conflict resolution
  
• Maintaining replica coherence

Performance Considerations

Scalability Principles

• Linear speedup goals
  
• Horizontal scaling
  
• Resource partitioning

Latency and Throughput

• Minimizing inter-node communication
  
• Optimizing data locality
  
• Caching strategies

MapReduce

Map reduce is an example of divide-and-conquer parallel processing: 1. Map - 2. Reduce -

Raft for Fault Tolerance

Replicated K/V store

Sample Implementation: Distributed Key-Value Store

class DistributedKeyValueStore:
    def __init__(self, nodes):
        self.nodes = nodes
        self.replication_factor = 3
    
    def put(self, key, value):
        """Distribute key-value across nodes with replication"""
        target_nodes = self._select_nodes(key)
        for node in target_nodes:
            node.store(key, value)
    
    def get(self, key):
        """Retrieve value with consistency mechanism"""
        nodes_with_key = self._find_nodes_with_key(key)
        return self._resolve_conflicts(nodes_with_key)
    
    def _select_nodes(self, key):
        """Deterministic node selection strategy"""
        hash_value = hash(key)
        return [self.nodes[i % len(self.nodes)] 
                for i in range(self.replication_factor)]

Sharded K/V service

References

[^1] MIT 6.5840 Distributed Systems [^2] UC Berkeley CS267 Applications of Parallel Computers