Computer Science Distributed Systems Akka and Distributed Systems
Akka and Distributed Systems
Akka is an open-source toolkit and runtime for building highly concurrent, distributed, and resilient applications on the Java Virtual Machine (JVM), fundamentally based on the Actor Model. In the context of distributed systems, Akka provides a high-level abstraction that simplifies the inherent complexities of concurrency, parallelism, and fault tolerance. It uses lightweight, isolated processes called "actors" that communicate asynchronously through messages, enabling developers to build scalable systems that can be transparently distributed across a cluster of machines. Key features like location transparency, elastic scalability, and a "let it crash" model of self-healing through supervision hierarchies allow Akka applications to remain responsive and robust in the face of component failures, making it a powerful framework for creating modern, reactive distributed services.
1.1.
Core Problems in Concurrent Programming
1.1.1.
Race Conditions
1.1.1.1. Definition and Characteristics
1.1.1.2. Common Scenarios and Examples
1.1.1.3. Detection Techniques
1.1.1.4. Prevention Strategies
1.1.1.5. Testing for Race Conditions
1.1.2.
Deadlocks
1.1.2.1. Necessary Conditions for Deadlock
1.1.2.2. Deadlock Detection Algorithms
1.1.2.3. Recovery Mechanisms
1.1.2.4. Prevention Strategies
1.1.2.5. Avoidance Techniques
1.1.2.6. Banker's Algorithm
1.1.3.
Livelocks
1.1.3.1. Definition and Characteristics
1.1.3.2. Distinction from Deadlocks
1.1.3.4. Detection Methods
1.1.3.5. Resolution Strategies
1.1.4.
Starvation
1.1.4.1. Causes in Resource Scheduling
1.1.4.2. Priority Inversion
1.1.4.4. Fair Scheduling Algorithms
1.2.
Concurrency vs. Parallelism
1.2.1. Fundamental Definitions
1.2.2. Key Conceptual Differences
1.2.3. Hardware Implications
1.2.4. Use Cases for Concurrency
1.2.5. Use Cases for Parallelism
1.3.
Concurrency Models
1.3.1.
Shared-Memory Concurrency
1.3.1.3. Locks and Mutexes
1.3.1.3.1. Mutual Exclusion Principles
1.3.1.3.2. Lock Granularity
1.3.1.3.3. Lock Contention Issues
1.3.1.3.4. Deadlock Potential
1.3.1.3.5. Lock-Free Programming
1.3.1.4. Semaphores and Monitors
1.3.1.4.1. Counting Semaphores
1.3.1.4.2. Binary Semaphores
1.3.1.4.3. Monitor Constructs
1.3.1.4.4. Condition Variables
1.3.1.5. Atomic Operations
1.3.2.
Message-Passing Concurrency
1.3.2.1. Communicating Sequential Processes
1.3.2.1.1. Channel-Based Communication
1.3.2.1.2. Process Synchronization
1.3.2.1.3. Synchronous Communication
1.3.2.1.4. Asynchronous Communication
1.3.2.2.1. Actors as Computational Units
1.3.2.2.2. Message Passing Semantics
1.3.2.2.3. Location Transparency
1.3.2.2.4. Fault Isolation
1.4.
Challenges in Distributed Systems
1.4.1.
Network Partitions
1.4.1.1. Causes of Network Splits
1.4.1.2. Partition Detection
1.4.1.3. Handling Split Networks
1.4.1.4. Partition Recovery
1.4.2.
Latency and Network Unreliability
1.4.2.1. Sources of Network Latency
1.4.2.2. Jitter and Packet Loss
1.4.2.3. Impact on System Design
1.4.2.4. Latency Mitigation Techniques
1.4.2.5. Timeout Strategies
1.4.3.
Partial Failures
1.4.3.1. Types of System Failures
1.4.3.2. Failure Detection Mechanisms
1.4.3.3. Failure Isolation
1.4.3.4. Recovery Strategies
1.4.4.
Consistency Models
1.4.4.1. Strong Consistency
1.4.4.2. Eventual Consistency
1.4.4.3. Causal Consistency
1.4.4.4. Session Consistency
1.4.4.5. Monotonic Consistency
1.4.4.6. Trade-offs Between Models
1.4.5.
The CAP Theorem
1.4.5.1. Consistency Guarantees
1.4.5.2. Availability Guarantees
1.4.5.3. Partition Tolerance
1.4.5.4. Practical Implications
1.4.5.5. System Design Decisions
1.4.6.
ACID vs. BASE Properties
1.4.6.3. Trade-offs in Distributed Systems
1.4.7.
Consensus Algorithms
1.4.7.3. Byzantine Fault Tolerance