Distributed Systems Quiz

Introduction to Distributed Systems

• Recent distributed systems include Amazon's Dynamo, Google's BigTable, and Apache's Hadoop.
• Main ideas are accessibility, key concepts for further reading, and understanding core constraints of distributed programming.
• Two crucial aspects: information travels at light speed and independent failures occur independently.
• Focus on interaction between distance, time, and consistency models in commercial data centers.
• Key protocols and new methods like CRDTs and the CALM theorem are introduced.

Basics of Distributed Systems

• Distributed programming solves problems using multiple computers instead of a single machine.
• Central tasks: storage and computation, often due to size and cost constraints.
• Commodity hardware offers the best value at scale, emphasizing fault-tolerant software.
• Performance improvements are limited by communication bottlenecks between nodes.
• Scalability principles define how performance and capacity must improve with added nodes.

Key Concepts of Scalability

• Scalability is the ability to manage growing workloads without performance degradation.
• Size scalability: More nodes should enhance performance linearly.
• Geographic scalability: Multiple data centers reduce response times and manage latency.
• Administrative scalability: Adding nodes shouldn't increase administrative overhead.
• Performance metrics include response time, throughput, and resource utilization, each with its own tradeoffs.

Performance and Latency

• Performance measures the useful work relative to time and resources used.
• Latency refers to the delay between an action and its observable effect, linked to physical travel times and hardware limits.
• Minimum latency cannot be avoided, constrained by speed of light and hardware operations.
• High latency in distributed systems can arise from operational distance and requires careful management.

Availability and Fault Tolerance

• System availability reflects the proportion of time a system functions correctly.
• Fault tolerance is the system's capacity to handle failures gracefully without complete breakdown.
• Redundancy is key for high availability across various components (nodes, servers, data centers).
• Availability metrics are quantified (e.g., 99.999% equates to about 5 minutes of downtime annually).

Constraints in Distributed Systems

• Two major physical limitations: node count and inter-node distance, impacting performance and administrative costs.
• More independent nodes raise failure probabilities, reducing overall system reliability.
• Distance contributes to communication latency, necessitating careful system design to mitigate downsides.

Abstractions and Models

• Abstractions simplify complex systems by identifying relevant facets pertinent to solving specific problems.
• Different models, such as system models (synchronous/asynchronous) and consistency models (strong/eventual), provide clarity.
• Effective abstractions enhance operability but must balance performance with comprehensibility.

Data Distribution Techniques

• Key methods for organizing data are partitioning and replication.
• Partitioning divides datasets for parallel processing, enhancing performance and resilience to independent failures.
• Replication copies data to multiple nodes, improving availability and reducing latency for client interactions.
• Choosing between methods is dependent on application needs and performance criteria.

Summary of Design Techniques

• Smart system design leverages partitioning to manage data growth while ensuring operational efficiency and independent node reliability.
• Understanding and applying distributed algorithms critically shapes successful implementation, aligning with specific system goals.### Partitioning and Replication
• Partitioning involves dividing data into segments optimized for expected access patterns.
• Independent partitions can lead to inefficiencies, such as cross-partition access and uneven growth rates.
• Replication creates copies of data across multiple servers, enhancing computation and reducing latency.

Advantages of Replication

• Increases performance by providing additional computing power and bandwidth through data copies.
• Enhances availability by having multiple copies of data, requiring more failures before downtime occurs.
• Supports scaling and fault tolerance in systems.
• Addresses slow computation and I/O by replicating data to reduce latency and improve throughput.

Consistency Models

• The consistency model determines how replicated data remains synchronized across nodes.
• Strong consistency allows programming as if data isn't replicated, ensuring reliability.
• Weaker consistency models can reduce latency and enhance availability but may complicate programming semantics.

Levels of Abstraction

• Distributed programming involves managing multiple nodes while striving for a unified system experience.
• Abstractions simplify complex systems but inevitably exclude unique aspects of each scenario.
• Proper abstractions allow for manageable problem statements while retaining essential characteristics.

System Models in Distributed Systems

• Distributed systems operate with no shared memory or synchronized clocks; nodes execute concurrently and independently.
• Nodes possess local knowledge only, leading to potential delays or inaccuracies in global state representation.
• Failures can occur independently, complicating system behavior.

Assumptions and Properties

• System models specify assumptions regarding nodes, communication links, and timing.
• Robust models are based on minimal assumptions, increasing algorithmic tolerance to diverse environments.
• Nodes are designed to execute programs, with capabilities for volatile and stable data storage.

Communication and Network Links

• Links connect nodes and facilitate message passing, often with assumptions of FIFO ordering and possible message loss.
• A network partition can disrupt communication without node failures, causing messages to be lost or delayed.
• Understanding failure models is crucial; most systems use a crash-recovery model, while Byzantine fault tolerance addresses arbitrary faults but is rarely practical.

Impossibility Results

• Impossibility results clarify limitations within distributed systems based on specific assumptions or constraints.
• They highlight essential characteristics that must be preserved in system design for optimal performance and reliability.

Test your knowledge on distributed systems concepts, including principles like independent failures and consistency models such as the CALM theorem. This quiz covers various key aspects of distributed programming and matching relevant terms to their definitions.