Questions and Answers
What is essential to ensure the smooth implementation of large scale software systems?
What do large enterprises need to carefully evaluate when designing software systems?
Why is understanding business requirements important in system design?
What can enterprises avoid by investing time in understanding bottlenecks?
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In the context of system design, what should be considered along with algorithms and data structures?
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What is a consequence of failing to properly design a large scale software system from the beginning?
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What is emphasized as a key aspect of designing technical architecture in system design?
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Which of the following plays a critical role in the design phase of software development?
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What is the main goal of understanding system design concepts?
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Which of the following statements best describes asynchronous communication?
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Which characteristic applies to synchronous communication?
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In system design, what does consistency refer to?
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What is a primary benefit of asynchronous communication in system design?
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Which aspect is NOT one of the fundamental concepts of system design?
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When is synchronous communication typically preferred?
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What challenges are associated with consistency in distributed systems?
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What is a common requirement for consistency regarding data storage and retrieval?
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Which scenario exemplifies asynchronous communication?
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What is a key characteristic of large-scale software systems?
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In system design, what does fault tolerance refer to?
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What is the purpose of using abstraction in system design?
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What is typically a consideration when deciding between synchronous and asynchronous communication?
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What is the primary purpose of redundancy in system availability?
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What is a key characteristic of fault tolerance in a system?
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How does load balancing contribute to system availability?
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Which type of failover pattern involves multiple systems processing requests in parallel?
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What is a potential drawback of an active-passive failover system?
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In a multi leader replication pattern, what is a challenge that arises?
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What is the main function of a single leader replication pattern?
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What happens if the leader system in a single leader replication pattern fails?
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Which of the following best describes the purpose of failover patterns?
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What is a common risk associated with both failover and replication patterns?
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What is a major advantage of using multi leader replication over single leader replication?
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Which approach effectively limits the risk of data loss in a failover system?
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What defines the active-active failover pattern?
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Which aspect is crucial when choosing a failover pattern?
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What is the primary goal of data replication in distributed systems?
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Which of these techniques helps recover data consistency after a system crash?
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What does monotonic read consistency guarantee?
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Which consistency model guarantees that updates are immediately reflected across all replica nodes?
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In which scenario would conflict resolution be necessary?
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What role do consensus protocols play in distributed systems?
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Which technique involves assigning version numbers to write operations?
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What is a key characteristic of causal consistency?
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What is the purpose of locking mechanisms in data storage systems?
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Which of the following describes eventual consistency?
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What is the impact of failures or delays in distributed systems?
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How does monotonic write consistency affect subsequent reads?
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What does the consistency spectrum model illustrate?
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What is a common challenge in achieving strong consistency?
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What is a primary consequence of using multiple read replicas in a system?
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What typically limits the performance of read replicas compared to the leader system?
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Which metric is used to measure the average time a system can operate without failure?
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What does a low mean time to repair (MTTR) indicate about a system?
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Which fallacy is associated with the assumption that network outages and packet losses are negligible?
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How can reliability and availability be described in the context of system design?
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What is vertical scaling primarily concerned with?
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What must be accounted for when designing systems to manage the inherent limitations of network data transfer speeds?
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Which fallacy refers to underestimating the security risks inherent in a distributed network?
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Which type of scaling is generally more cost-effective for unpredictable traffic patterns?
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What essential component can help achieve high reliability and availability in a system?
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How should systems be designed in relation to changing network conditions?
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Which of the following can indicate a system's reliability?
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Which of the following fallacies involves misjudging the costs associated with network infrastructure?
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What does horizontal scaling achieve in system design?
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What is a primary consequence of the assumption that a network is homogenous?
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When designing systems, why is it critical to account for potential network failure?
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What is the impact of the number of read replicas on a system's processing capabilities?
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In which scenario is vertical scaling typically most advantageous?
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Which AWS Well-Architected Framework pillar is related to managing the fallacy of a single administrator?
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What is a key strategy to mitigate the effects of finite bandwidth in network designs?
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What type of replication pattern can be more efficient for writes than read replicas?
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What is a defining characteristic of a system with high reliability?
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Which of these fallacies highlights the misconception about external threats to data integrity?
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What must systems ensure concerning network traffic due to the assumption of infinite bandwidth?
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What principle helps counter the impact of latency in distributed systems?
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How can developers effectively handle the complexity introduced by multiple administrators in large systems?
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Which fallacy involves the assumption that the network configuration remains stable over time?
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What does eventual consistency guarantee in a distributed system?
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How is availability typically quantified in a system?
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What is the goal for achieving high availability typically measured in?
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What happens when components with 99.9% availability are arranged in sequence?
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In terms of data consistency, what does strong consistency ensure?
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Which of the following could make achieving higher levels of availability more difficult?
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What is an example of a system that might strive for very high availability levels?
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What is the expected result of querying a node under eventual consistency before all replicas are synchronized?
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How does parallel arrangement affect the overall availability of system components?
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What happens when aiming to increase availability by adding more 'nines'?
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Which of the following is true about a system's availability during high load or errors?
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What is the relationship between the components arranged in a sequential system and their overall availability?
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What does a system designer consider when choosing a consistency model?
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What is a primary advantage of horizontal scaling in managing unpredictable traffic?
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Which of the following aspects must be covered to ensure a software system is maintainable?
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Which feature of a distributed system ensures order preservation of updates?
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What is the main goal of fault tolerance in large-scale systems?
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How does replication contribute to fault tolerance?
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What characterizes synchronous checkpointing in a system?
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Why is modifiability important in system design?
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What does lucidity in a system primarily ensure?
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What is a disadvantage of asynchronous checkpointing?
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Which scaling method is recommended for early-stage systems before moving to horizontal scaling?
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What is a key characteristic of operability in system design?
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What is the primary purpose of checkpointing in large-scale systems?
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What is a fundamental challenge of horizontal scaling?
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Which technology can enhance the durability of a database during failures?
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What does fault tolerance help prevent in large-scale systems?
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What is crucial for the successful implementation of large scale software systems?
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Which aspect is NOT considered when designing large scale software systems?
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What should enterprises evaluate to avoid wasted software development effort?
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What is the result of a well-designed technical architecture in large scale software systems?
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Which of the following best describes a key consideration in system design?
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What primary focus should enterprises have while designing large scale software systems?
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What is a potential consequence of neglecting design considerations in large scale system software?
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What role does understanding business requirements play in system design?
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What is the main focus when balancing the trade-offs in system design?
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Which factor is NOT mentioned as a consideration in system design trade-offs?
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How does bandwidth fundamentally differ from throughput?
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What is a common consequence of insufficient bandwidth?
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In the context of latency and throughput, what happens as latency increases?
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Which metric is recommended to capture latency in a system under load?
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What is the trade-off described by the CAP theorem?
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When considering performance vs scalability, what indicates a performance issue?
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What is the relationship between latency and throughput?
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Which trade-off is a key consideration when designing a system with scalability in mind?
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Which of the following is a characteristic of a system that prioritizes maintainability?
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In terms of system design trade-offs, what might sacrificing robustness typically lead to?
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What can using look-up tables in an algorithm help achieve in system design?
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What is a potential downside of focusing too heavily on cost in system design?
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What does the KISS guideline emphasize in system design?
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Which of the following best defines metrics in the context of system performance?
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What is the significance of observability in large-scale systems?
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What does TINSTAAFL advocate regarding system design decisions?
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What does the CAP theorem state regarding distributed systems?
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Which statement most accurately describes a fundamental aspect of system design?
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According to the PACELC theorem, what must be chosen in the absence of network partitions?
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In system design, what role do performance metrics play?
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Which aspect does observing and measuring metrics not help with in system design?
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What trade-off does the CAP theorem highlight during network failures?
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Why is building a system modularly beneficial?
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What does the concept of 'it always depends' suggest in system design?
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What is emphasized in the guideline of simplicity in system design?
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Why is it important to weigh trade-offs in system design?
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Which aspect is not specifically mentioned as a characteristic of modular systems?
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What can be a consequence of failing to think about trade-offs in system design?
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What happens if a system pursues strong consistency through synchronous communication?
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What is the primary focus when measuring system performance?
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How can observability affect system reliability?
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What principle is highlighted in the guideline of isolation?
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What often results from choosing a simpler design solution?
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What does eventual consistency imply in distributed systems?
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What is a potential trade-off in achieving high levels of system performance?
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Which statement about the PACELC theorem is accurate?
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What guideline advises to ensure easy usability of the system?
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Which factor is crucial when designing modular systems according to the content?
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What characterizes systems that are designed using the PACELC theorem?
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Which principle ensures that modules can be reused in different projects?
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What is the primary characteristic of synchronous communication in system design?
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Which of the following best describes the difference between synchronous and asynchronous communication?
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In the context of data storage, what does consistency ensure?
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What is an example of asynchronous communication?
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Which concept refers to the ability of a system to continue operating in the event of a failure?
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When is synchronous communication most appropriately used in a software system?
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What is meant by 'abstraction' in system design?
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What does scalability refer to in system design?
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In distributed systems, what does a consistency issue often refer to?
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Why is asynchronous communication considered flexible?
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What is a common challenge associated with consistency in large-scale systems?
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What role does 'fault tolerance' play in system design?
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What aspect of system design is concerned with how effectively a system can remain available?
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Why might a system designer choose asynchronous communication over synchronous?
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What technique is primarily used to log writes before applying them to data?
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Which of the following models ensures that once a client reads a value, all subsequent reads return the same or a more recent value?
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What is used to resolve conflicts when multiple replica nodes attempt to update the same data simultaneously?
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Which consistency level guarantees that all replica nodes reflect the same data at all times?
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What does locking in data storage systems primarily ensure?
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What is the primary purpose of consensus protocols in distributed systems?
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What technique allows concurrent writes while ensuring reads return the most recent write?
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What does causal consistency guarantee in the context of operations?
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Which statement best describes 'eventual consistency'?
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What is the primary challenge when implementing strong consistency in distributed systems?
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What does monotonic write consistency ensure about write operations?
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Which technique is fundamental for restoring data consistency after a system crash?
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What does the consistency spectrum model help reason about in distributed systems?
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Which of the following describes the active-passive failover pattern?
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What is a potential issue with multi leader replication?
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Which technique is primarily used to improve system availability?
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In the context of failover systems, what is the primary advantage of the active-active pattern?
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What can be a consequence of using single leader replication?
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Which of the following is NOT a technique to enhance system availability?
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What is a key trade-off of an active-active failover system?
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How does replication contribute to system availability?
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What is a characteristic of the active-active failover strategy?
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What role does load balancing play in system design?
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Which of the following can lead to data loss in failover systems?
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What is the primary objective of using redundancy in a system?
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Which type of replication pattern allows writing to multiple systems at the same time?
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What effect does the use of multiple read replicas have on replication lag?
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Which of the following best defines reliability in system design?
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What does the term Mean Time Between Failures (MTBF) measure?
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How is Mean Time to Repair (MTTR) characterized?
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Which is true about the relationship between reliability and availability?
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What is vertical scaling in the context of system design?
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In which scenario is vertical scaling particularly useful?
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What challenge arises with the use of multiple read replicas?
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What does scalability in system design ensure?
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Which of the following statements is true regarding the implementation of redundancy?
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What is the overall goal of using MTBF and MTTR measurements in a system?
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Which challenge is associated with vertical scaling?
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What does eventual consistency guarantee in a distributed system?
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Which metric is used to quantify the availability of a system?
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What is the primary trade-off involved in the consistency spectrum model?
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Which of the following scenarios describes a system with high availability?
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How does the arrangement of components in a system affect overall availability?
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What is a key challenge in achieving 'five nines' availability?
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If two components both have an availability of 99.9% and are arranged in sequence, what will be the overall availability?
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Which factor does NOT affect the realism of achieving high levels of availability?
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What does the term 'availability percentages represented in 9s' indicate?
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What is commonly involved in maintaining high availability in a system?
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What does a higher level of availability often require regarding system architecture?
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What is the implication of assuming that the network is reliable in distributed system design?
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Why is the assumption that latency is zero problematic in distributed systems?
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What happens to the overall availability in a sequential system if one component fails?
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Which of the following describes the primary difference between strong consistency and eventual consistency?
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What consequence might arise from assuming infinite bandwidth in network design?
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Which fallacy relates to the misconception that network security is guaranteed?
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What is a consequence of arranging components in a sequential system?
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How does the assumption of a fixed topology complicate distributed system design?
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What is a primary concern when inferring a single administrator for distributed systems?
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What does the assumption of zero transport cost overlook in network design?
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Why is it important to account for a heterogeneous network when designing distributed systems?
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What might be a direct result of neglecting the fallacies in distributed systems during implementation?
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Which AWS Well-Architected Framework pillar addresses the fallacy of assuming a secure network?
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How can the assumption of network reliability impact system administration complexity?
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What approach can help mitigate the risks associated with assuming zero latency in distributed systems?
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What is a potential effect of neglecting the fallacy of infinite bandwidth in distributed network designs?
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What is a primary benefit of horizontal scaling for managing unpredictable traffic?
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What aspect of maintainability involves a system being easy to modify or extend?
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Which mechanism ensures that a system can recover from a failure and continue to serve requests?
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What does synchronous checkpointing require from the system during the checkpointing process?
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Which of the following is NOT a component of maintainability in system design?
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What is a primary risk associated with asynchronous checkpointing?
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Which aspect of a system does operability emphasize on?
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To adapt to changing business needs, software systems must prioritize which of these aspects?
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How does replication contribute to fault tolerance?
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What is the main function of checkpointing in a system?
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In large-scale systems, what does fault tolerance primarily aim to eliminate?
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What is the role of lucidity in a software system?
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Which of the following is a vital characteristic of highly maintainable systems?
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What is the purpose of having multiple copies of data in replication?
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What is the primary goal when balancing trade-offs in system design?
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What does the CAP theorem address in system design?
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Which trade-off involves managing the speed of requests versus the ability to handle increased demand?
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Which metric is more empirical and measures actual data transmission in a network?
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What does it mean if a system is experiencing high latency?
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If a system prioritizes cost, which of the following factors may be sacrificed?
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Which of the following best defines latency in a network context?
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What happens to throughput as latency increases?
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A system that is designed for both high reliability and scalability may result in which trade-off?
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Why is average latency not used as a metric in system design?
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In what situation might you prioritize scalability over performance?
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Which of these concepts relates to the actual capacity of a network under specific conditions?
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What would likely be a consequence of insufficient bandwidth in a network?
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Which guarantees can a distributed system provide simultaneously according to the CAP theorem?
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When a network partition occurs, what trade-off must a distributed system make according to the CAP theorem?
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What does the PACELC theorem specify when there are no network partitions?
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Which guideline focuses on restructuring a system into smaller independent components?
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Which approach supports reusability in system design?
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What is the main consequence of prioritizing complexity over simplicity in system design?
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What is a characteristic of synchronous communication within a distributed system?
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Which of the following would NOT align with the Keep it Simple, Silly (KISS) principle in system design?
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Which is a key advantage of maintaining modularity in a system design?
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Why might a team opt for eventual consistency in a distributed system?
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What aspect should be prioritized when designing to accommodate growth in large scale systems?
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What does TINSTAAFL imply in system design?
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Which of the following is NOT a factor considered in system design?
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How do metrics contribute to system performance management?
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Why is it necessary to measure before building systems?
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In system design, what is the significance of balancing competing factors?
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What happens when simplicity is prioritized excessively in system design?
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Study Notes
System Design Overview
- Large-scale software systems are fundamental to modern technological advancements, evidenced by companies like Google, Amazon, Oracle, and SAP.
- First principles thinking is critical in designing technical architecture to prevent issues later in the implementation process.
Importance of System Design
- Successful system design focuses on business requirements, customer needs, and various trade-offs to ensure long-term functionality.
- Careful consideration of system bottlenecks and user access patterns is essential for effective system design.
Foundational Concepts in System Design
- Key concepts include:
- Communication
- Consistency
- Availability
- Reliability
- Scalability
- Fault tolerance
- System maintainability
Communication Mechanisms
-
Synchronous Communication:
- Example: Real-time phone conversations where both parties communicate simultaneously.
- The application waits for responses before proceeding, potentially causing perceived latency.
-
Asynchronous Communication:
- Example: Email exchanges allowing delayed responses.
- The sender does not wait for replies, facilitating flexibility and resilience in applications.
Consistency in Systems
- Consistency ensures all parts of a distributed system view data uniformly, pertinent in contexts like data storage and retrieval.
-
Consistency Techniques in distributed systems:
- Data Replication: Multiple replicas are updated simultaneously for uniformity.
- Consensus Protocols: Ensure agreement on data updates among nodes.
- Conflict Resolution: Mechanisms to handle simultaneous conflicting updates from different replicas.
Consistency in Data Storage
- Techniques to maintain consistency in data storage include:
- Write-ahead Logging: Logs write operations before application to data.
- Locking Mechanisms: Control concurrent write access.
- Data Versioning: Allows multiple concurrent writes while preserving read consistency.
Consistency Spectrum Model
- Consistency ranges from Eventual Consistency (leading to flexibility with potential data stale states) to Strong Consistency (ensuring all replicas are updated immediately after a write).
Availability in Systems
- Availability measures a system's capacity to serve requests effectively, even under failures.
- Calculated as the proportion of uptime to total operational time, expressed as a percentage of the “nines” (e.g., 99.9999% represents six nines).
Achieving High Availability
- Each increment in availability comes with increased cost and complexity.
- Techniques include:
- Redundancy: Having backup components to maintain function amid failures.
- Fault Tolerance: System resilience against unpredictable errors.
System Arrangement Impacting Availability
- Sequential Systems: The overall availability is multiplied across components; e.g., two 99.9% components yield 99.8% availability.
- Parallel Systems: Availability is significantly improved as components can serve requests simultaneously, leading to a maintained uptime (e.g., two 99.9% components yield 99.9999% availability).
Ensuring System Availability
- Critical for maintaining performance and reliability through methods like redundancy and fault tolerance to navigate failure scenarios effectively.### Availability Mechanisms
- Systems can achieve high availability through error-handling mechanisms, redundant hardware, or self-healing systems.
- Load balancing distributes incoming requests across multiple servers to efficiently manage heavy loads and enhance availability.
- Active-active and active-passive are the two primary failover patterns utilized to maintain system availability.
Failover Patterns
- Active-active failover: Multiple systems process requests in parallel; if one fails, others continue operations, providing flexibility but increasing complexity.
- Active-passive failover: One primary system handles requests while passive backups wait to take over if the primary fails. This method is simpler but can cause delays during failover, reducing availability.
Replication Patterns
- Replication maintains multiple data copies to enhance availability and fault tolerance, with multi-leader and single-leader formats being the two main types.
- Multi-leader replication: Multiple systems can read and write data, offering flexibility but increasing complexity and potential latency due to conflict resolution.
- Single-leader replication: A single leader manages commands while followers replicate data for read operations only. This approach risks data loss if the leader fails and can lead to replication lag.
Reliability Measurement
- Reliability reflects a system's consistency in performing intended functions. Key metrics include:
- Mean Time Between Failures (MTBF): Time a system operates without failure; higher is more reliable.
- Mean Time to Repair (MTTR): Time to restore a system after failure; lower is better.
Reliability vs. Availability
- Reliability and availability are interrelated; a reliable but unavailable system fails at critical times, while an available but unreliable system may perform erratically.
- Meeting service level objectives (SLOs) requires incorporating redundancy and failover mechanisms alongside regular maintenance.
Scalability
- Scalability ensures system performance improves with additional resources in response to increased workloads, whether from user requests or data storage needs.
- Vertical scaling enhances a single server's capabilities but has limits and high costs associated with resource upgrades.
- Horizontal scaling involves adding multiple servers, providing cost-effective scalability for variable traffic levels but adds management complexity.
Maintainability
- Maintainability allows a system to adapt to changing user needs without disrupting operations. Three key aspects include:
- Operability: The system should function smoothly and resume operations quickly after faults.
- Lucidity: A clear and understandable system promotes efficient collaboration and easier maintenance.
- Modifiability: Modular systems enable smooth changes without impacting other components.
Fault Tolerance
- Fault tolerance enables continuous operation despite failures through effective request rerouting and redundancy.
- Replication: Clones services and data across multiple servers for safety and inherent data accessibility.
- Checkpointing: Backups the system's state to restore it following data loss or corruption, employing synchronous or asynchronous methods for checkpoint creation.
Fallacies of Distributed Computing
- Reliable Network: Networks are often unstable; design for potential faults.
- Zero Latency: Latency is unavoidable; optimize proximity to data through edge-computing and strategic server placement.
- Infinite Bandwidth: Network resource contention leads to limits; use lightweight data formats and multiplexing to optimize bandwidth.
- Secure Network: A network is not inherently secure; adopt a security-first approach and conduct thorough assessments.
- Fixed Topology: Network topologies fluctuate continuously due to system changes; design must account for dynamism.### System Design Fallacies
- Fixed topology assumptions can lead to issues such as latency and bandwidth problems; systems should be designed to be topology-agnostic.
- The assumption of a "Single Administrator" fails in large-scale distributed systems due to multiple teams and OS; systems need decoupled designs for easier troubleshooting.
- "Zero Transport Cost" is a fallacy; network infrastructure requires investment in hardwares, software, and teams, thus costs must be accounted in budgets.
- Networks are not homogeneous; variations in device configurations and protocols necessitate an emphasis on interoperability among subsystems.
AWS Well-Architected Framework
- The framework consists of six core pillars designed to guide system design and mitigate common fallacies.
- Pillars include:
- Operational Excellence: Avoids issues related to Single Administrator and Homogeneous Network.
- Security: Addresses the Secure Network fallacy.
- Reliability: Counters Reliable Network and Fixed Topology fallacies.
- Performance Efficiency: Tackles Zero Latency and Infinite Bandwidth assumptions.
- Cost Optimization & Sustainability: Overcome the Zero Transport Cost assumption.
System Design Trade-offs
- Balancing cost, scalability, reliability, maintainability, and robustness is crucial when designing large-scale systems.
- Performance trade-offs may require decisions between higher reliability with greater costs versus budget constraints impacting robustness and scalability.
Time vs Space Trade-off
- Time-memory trade-offs are essential; choosing between quick calculations using more memory or time-consuming recalculations must be respected in algorithm design.
Latency vs Throughput
- Latency is the time a request waits, while throughput measures actual data processed; these metrics have an inverse relationship, as increased latency reduces throughput.
- Percentile metrics (e.g., p90 latency) gauge performance more effectively than average latency.
Performance vs Scalability
- Performance focuses on single request efficiency; scalability deals with system behavior under increased load; both aspects require careful management to meet user demands.
Consistency vs Availability (CAP Theorem)
- CAP Theorem states it's impossible to guarantee consistency, availability, and partition tolerance simultaneously in a distributed system.
- Systems must prioritize either consistency or availability during network failures, emphasizing partition tolerance in designs.
PACELC Theorem
- PACELC expands on CAP, indicating the need to balance between availability and consistency during partitions and between latency and consistency otherwise.
System Design Guidelines
- Isolation: Develop modular systems for ease of maintenance, reusability, scalability, and reliability.
- Simplicity: Employ KISS principles to build straightforward systems that focus on core requirements without unnecessary complexity.
- Performance: Utilize metrics and observability as critical components to assess system performance and preempt issues.
- Trade-offs: Recognize that optimizing one factor often affects others; value careful consideration in system design choices.
- Use Cases: Understand that each design decision depends on specific user needs, constraints, and contextual factors, emphasizing custom solutions over one-size-fits-all approaches.
Conclusion
- Effective system design requires balancing competing factors and understanding the broader implications of decisions.
- Future chapters will delve into foundational concepts related to data storage, caching, load balancing, and networking within system architecture.
System Design Overview
- Large-scale software systems are fundamental to modern technological advancements, evidenced by companies like Google, Amazon, Oracle, and SAP.
- First principles thinking is critical in designing technical architecture to prevent issues later in the implementation process.
Importance of System Design
- Successful system design focuses on business requirements, customer needs, and various trade-offs to ensure long-term functionality.
- Careful consideration of system bottlenecks and user access patterns is essential for effective system design.
Foundational Concepts in System Design
- Key concepts include:
- Communication
- Consistency
- Availability
- Reliability
- Scalability
- Fault tolerance
- System maintainability
Communication Mechanisms
-
Synchronous Communication:
- Example: Real-time phone conversations where both parties communicate simultaneously.
- The application waits for responses before proceeding, potentially causing perceived latency.
-
Asynchronous Communication:
- Example: Email exchanges allowing delayed responses.
- The sender does not wait for replies, facilitating flexibility and resilience in applications.
Consistency in Systems
- Consistency ensures all parts of a distributed system view data uniformly, pertinent in contexts like data storage and retrieval.
-
Consistency Techniques in distributed systems:
- Data Replication: Multiple replicas are updated simultaneously for uniformity.
- Consensus Protocols: Ensure agreement on data updates among nodes.
- Conflict Resolution: Mechanisms to handle simultaneous conflicting updates from different replicas.
Consistency in Data Storage
- Techniques to maintain consistency in data storage include:
- Write-ahead Logging: Logs write operations before application to data.
- Locking Mechanisms: Control concurrent write access.
- Data Versioning: Allows multiple concurrent writes while preserving read consistency.
Consistency Spectrum Model
- Consistency ranges from Eventual Consistency (leading to flexibility with potential data stale states) to Strong Consistency (ensuring all replicas are updated immediately after a write).
Availability in Systems
- Availability measures a system's capacity to serve requests effectively, even under failures.
- Calculated as the proportion of uptime to total operational time, expressed as a percentage of the “nines” (e.g., 99.9999% represents six nines).
Achieving High Availability
- Each increment in availability comes with increased cost and complexity.
- Techniques include:
- Redundancy: Having backup components to maintain function amid failures.
- Fault Tolerance: System resilience against unpredictable errors.
System Arrangement Impacting Availability
- Sequential Systems: The overall availability is multiplied across components; e.g., two 99.9% components yield 99.8% availability.
- Parallel Systems: Availability is significantly improved as components can serve requests simultaneously, leading to a maintained uptime (e.g., two 99.9% components yield 99.9999% availability).
Ensuring System Availability
- Critical for maintaining performance and reliability through methods like redundancy and fault tolerance to navigate failure scenarios effectively.### Availability Mechanisms
- Systems can achieve high availability through error-handling mechanisms, redundant hardware, or self-healing systems.
- Load balancing distributes incoming requests across multiple servers to efficiently manage heavy loads and enhance availability.
- Active-active and active-passive are the two primary failover patterns utilized to maintain system availability.
Failover Patterns
- Active-active failover: Multiple systems process requests in parallel; if one fails, others continue operations, providing flexibility but increasing complexity.
- Active-passive failover: One primary system handles requests while passive backups wait to take over if the primary fails. This method is simpler but can cause delays during failover, reducing availability.
Replication Patterns
- Replication maintains multiple data copies to enhance availability and fault tolerance, with multi-leader and single-leader formats being the two main types.
- Multi-leader replication: Multiple systems can read and write data, offering flexibility but increasing complexity and potential latency due to conflict resolution.
- Single-leader replication: A single leader manages commands while followers replicate data for read operations only. This approach risks data loss if the leader fails and can lead to replication lag.
Reliability Measurement
- Reliability reflects a system's consistency in performing intended functions. Key metrics include:
- Mean Time Between Failures (MTBF): Time a system operates without failure; higher is more reliable.
- Mean Time to Repair (MTTR): Time to restore a system after failure; lower is better.
Reliability vs. Availability
- Reliability and availability are interrelated; a reliable but unavailable system fails at critical times, while an available but unreliable system may perform erratically.
- Meeting service level objectives (SLOs) requires incorporating redundancy and failover mechanisms alongside regular maintenance.
Scalability
- Scalability ensures system performance improves with additional resources in response to increased workloads, whether from user requests or data storage needs.
- Vertical scaling enhances a single server's capabilities but has limits and high costs associated with resource upgrades.
- Horizontal scaling involves adding multiple servers, providing cost-effective scalability for variable traffic levels but adds management complexity.
Maintainability
- Maintainability allows a system to adapt to changing user needs without disrupting operations. Three key aspects include:
- Operability: The system should function smoothly and resume operations quickly after faults.
- Lucidity: A clear and understandable system promotes efficient collaboration and easier maintenance.
- Modifiability: Modular systems enable smooth changes without impacting other components.
Fault Tolerance
- Fault tolerance enables continuous operation despite failures through effective request rerouting and redundancy.
- Replication: Clones services and data across multiple servers for safety and inherent data accessibility.
- Checkpointing: Backups the system's state to restore it following data loss or corruption, employing synchronous or asynchronous methods for checkpoint creation.
Fallacies of Distributed Computing
- Reliable Network: Networks are often unstable; design for potential faults.
- Zero Latency: Latency is unavoidable; optimize proximity to data through edge-computing and strategic server placement.
- Infinite Bandwidth: Network resource contention leads to limits; use lightweight data formats and multiplexing to optimize bandwidth.
- Secure Network: A network is not inherently secure; adopt a security-first approach and conduct thorough assessments.
- Fixed Topology: Network topologies fluctuate continuously due to system changes; design must account for dynamism.### Fallacies in System Design
- Fixed topology assumptions lead to system issues due to latency and bandwidth constraints; systems must be agnostic to underlying topology.
- The “Single Administrator” fallacy fails in large distributed systems; design should be decoupled for easier repair and troubleshooting given multiple teams and OSs.
- The notion of “Zero Transport Cost” overlooks network infrastructure expenses, necessitating budget considerations for servers, switches, and maintenance teams.
- Networks are heterogeneous, contrary to the “Homogeneous Network” fallacy; interoperability is essential for systems to function across diverse devices and protocols.
AWS Well-Architected Framework
- Comprises six core pillars for designing robust AWS systems:
- Operational Excellence: Addresses the fallacies of Single Administrator and Homogeneous Network.
- Security: Tackles the assumption of a Secure Network.
- Reliability: Mitigates Fixed Topology and Reliable Network assumptions.
- Performance Efficiency: Resolves issues related to Zero Latency and Infinite Bandwidth.
- Cost Optimization and Sustainability: Counteracts Zero Transport Cost misconceptions.
System Design Trade-offs
- System design necessitates balancing cost, scalability, reliability, maintainability, and robustness to meet user needs.
- Performance and scalability must be weighed; reliable systems may require expensive components for future scalability.
- The Time vs Space trade-off arises when algorithmic performance is optimized using additional memory or storage.
- Latency vs Throughput: As system load increases, latency metrics decline when aiming for higher throughput. Throughput measures actual data transmission, whereas bandwidth indicates potential limits.
- Performance vs Scalability: A scalable system improves performance proportionally with additional resources, but may encounter latency under heavy user demand.
- Consistency vs Availability: The CAP theorem states a distributed system cannot ensure consistency, availability, and partition tolerance simultaneously; typically, two of these are prioritized when faced with network partitions.
CAP and PACELC Theorems
- CAP Theorem: In distributed systems, one must choose between consistency and availability during network partitions.
- PACELC Theorem: Extends CAP by indicating that in absence of network partition, trade-offs exist between latency and consistency.
System Design Guidelines
- Isolation: Modular systems enhance maintainability, reusability, scalability, and reliability by breaking down complexity into independent components.
- Simplicity: KISS principle focuses on minimizing complexities and unnecessary features. Prioritize core requirements and avoid over-engineering.
- Performance Metrics: Metrics and observability are critical; they provide baseline measurements for assessing system performance and identifying issues.
- Trade-offs: Recognize that all design decisions involve trade-offs; optimizing one aspect often compromises another.
- Use Cases: Emphasize that design depends on specific factors, and there is no universal approach in system design solutions.
Conclusion
- Effective system design requires balancing various trade-offs, understanding fallacies, and following established guidelines.
- Next chapters will delve into fundamental aspects of systems, such as data storage, caching, load balancing, and communication networks.
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