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Introduction to Distributed Systems
Definition and Importance
Distributed systems are a collection of independent computers that appear to the users as a single coherent system. These systems are crucial for building scalable and robust applications that can handle large amounts of data and user requests.
Characteristics
- Scalability: Ability to add resources to handle increased load.
- Fault Tolerance: System continues to operate even if some components fail.
- Concurrency: Multiple processes operate simultaneously without interference.
Challenges
- Network Latency: Delays in communication between distributed components.
- Data Consistency: Ensuring all nodes have the same data at any given time.
- Security: Protecting data and operations from unauthorized access.
Scalability in Distributed Systems
Horizontal vs Vertical Scaling
Horizontal scaling involves adding more machines to a system, while vertical scaling involves adding more power (CPU, RAM) to an existing machine.
Load Balancing
Load balancers distribute incoming network traffic across multiple servers to ensure no single server becomes overwhelmed.
Partitioning
Partitioning divides a database into smaller, more manageable pieces, allowing for more efficient data handling and retrieval.
// Example: Load Balancer Implementation
public class LoadBalancer {
private List<Server> servers = new ArrayList<>();
public void addServer(Server server) {
servers.add(server);
}
public Server getServer() {
// Simple round-robin algorithm
return servers.remove(0);
}
}
Example Explanation
The above code demonstrates a simple load balancer using a round-robin algorithm to distribute requests evenly across available servers.
Fault Tolerance in Distributed Systems
Redundancy
Redundancy involves duplicating critical components or functions of a system to increase reliability.
Replication
Replication involves copying data across multiple machines to ensure availability and fault tolerance.
Failover
Failover is the process of switching to a standby database, server, or network if the primary system fails.
// Example: Replication Strategy
public class DataReplicator {
private List<Database> replicas = new ArrayList<>();
public void replicateData(String data) {
for (Database db : replicas) {
db.save(data);
}
}
}
Example Explanation
This code snippet shows a simple data replication strategy where data is saved across multiple database replicas to ensure fault tolerance.
Concurrency in Distributed Systems
Thread Safety
Ensuring that shared data structures are accessed by only one thread at a time to prevent race conditions.
Locking Mechanisms
Locks are used to control access to shared resources in concurrent programming.
Deadlock Prevention
Techniques to prevent deadlock situations where two or more threads are waiting indefinitely for resources held by each other.
// Example: Thread Safety with Locks
public class Counter {
private int count = 0;
private final Lock lock = new ReentrantLock();
public void increment() {
lock.lock();
try {
count++;
} finally {
lock.unlock();
}
}
}
Example Explanation
This example demonstrates how to use locks to ensure thread safety when incrementing a shared counter variable.
Data Consistency in Distributed Systems
Consistency Models
Different models define how changes to data are propagated through a distributed system, ranging from strong consistency to eventual consistency.
CAP Theorem
The CAP theorem states that a distributed system can provide only two out of the following three guarantees: Consistency, Availability, and Partition Tolerance.
Eventual Consistency
A consistency model that guarantees that, given enough time, all replicas will converge to the same state.
// Example: Eventual Consistency
public class DistributedCache {
private Map<String, String> cache = new HashMap<>();
public void put(String key, String value) {
cache.put(key, value);
// Propagate change to other nodes asynchronously
}
public String get(String key) {
return cache.get(key);
}
}
Example Explanation
This example illustrates a simple distributed cache with eventual consistency, where updates are asynchronously propagated to other nodes.
