1.3 Availability vs consistency

Question 1

What are the three guarantees in the CAP theorem?

Answer 1

Consistency, Availability, Partition Tolerance

These guarantees define the limits of what can be achieved in distributed systems.

Question 2

What does consistency mean in the context of the CAP theorem?

Answer 2

Every read receives the most recent write or an error.

Consistency ensures that all clients see the same data at the same time.

Question 3

Define availability in the CAP theorem.

Answer 3

Every request receives a response, without guarantee that it contains the most recent version of the information.

Availability focuses on ensuring responses are provided even if they are not the latest data.

Question 4

What is partition tolerance?

Answer 4

The system continues to operate despite arbitrary partitioning due to network failures.

This is crucial for systems that cannot afford to go offline during network issues.

Question 5

What is CP in distributed systems?

Answer 5

Consistency and Partition Tolerance.

In CP systems, availability may be compromised to ensure data consistency.

Question 6

What is AP in distributed systems?

Answer 6

Availability and Partition Tolerance.

In AP systems, consistency may be sacrificed to ensure that the system remains operational.

Question 7

What is weak consistency?

Answer 7

After a write, reads may or may not see it. A best effort approach is taken.

Common in systems like memcached and suitable for real-time applications.

Question 8

Define eventual consistency.

Answer 8

After a write, reads will eventually see it, typically within milliseconds.

This approach is often used in systems like DNS and email.

Question 9

What is strong consistency?

Answer 9

After a write, reads will see it. Data is replicated synchronously.

Strong consistency is ideal for systems requiring transactions.

Question 10

What is active-passive failover?

Answer 10

Heartbeats are sent between the active and passive server. If interrupted, the passive server takes over.

The length of downtime depends on the state of the passive server.

Question 11

What is active-active failover?

Answer 11

Both servers manage traffic, spreading the load between them.

Requires DNS or application logic to know about both servers.

Question 12

What are the disadvantages of failover?

Answer 12

Adds more hardware and complexity, potential data loss if the active system fails before replication.

These factors can complicate system design.

Question 13

How is availability quantified?

Answer 13

Availability is measured in uptime as a percentage, often described in terms of ‘nines’.

For example, 99.99% availability is referred to as four nines.

Question 14

What is the acceptable downtime for 99.9% availability?

Answer 14

8h 45min 57s per year.

This level of availability is often referred to as three nines.

Question 15

What happens to overall availability when components are in sequence?

Answer 15

Overall availability decreases.

The formula is Availability (Total) = Availability (Foo) * Availability (Bar).

Question 16

What happens to overall availability when components are in parallel?

Answer 16

Overall availability increases.

The formula is Availability (Total) = 1 - (1 - Availability (Foo)) * (1 - Availability (Bar)).

Question 17

What does the CAP theorem state about distributed systems?

Answer 17

You can only achieve two of the three guarantees: Consistency, Availability, and Partition Tolerance.

One must be sacrificed when designing distributed systems.

Question 18

What is the main flaw in the service model of ‘Remembrance Inc’?

Answer 18

The system is not consistent; updates may not be synchronized between the two operators.

This leads to potential misinformation for customers.

Question 19

What solution was proposed to fix the consistency problem in ‘Remembrance Inc’?

Answer 19

Both operators must inform each other of updates before completing calls.

This ensures that both have the latest information.

Question 20

What issue arises when one operator of ‘Remembrance Inc’ does not report to work?

Answer 20

Availability problem; updates cannot be completed if one person is absent.

This can lead to customer dissatisfaction.

Question 21

What is the proposed solution for maintaining both consistency and availability?

Answer 21

If one operator is unavailable, the other sends an email to update them.

This allows for updates while maintaining service availability.

Question 22

What is the final flaw identified in the system of ‘Remembrance Inc’?

Answer 22

The system is not partition tolerant; if communication fails between operators, they cannot update each other.

This compromises availability during conflicts.

Question 23

What is eventual consistency with a run-around clerk?

Answer 23

A clerk updates notebooks in the background, allowing updates to proceed without blocking.

This may lead to temporary inconsistencies but improves system performance.

Question 24

What is the significance of the CAP theorem in distributed system design?

Answer 24

It helps in understanding trade-offs between consistency, availability, and partition tolerance.

This is crucial for making informed decisions in system architecture.

Question 25

What is Partition Tolerance in distributed systems?

Answer 25

The system will continue to function when network partitions occur.

Question 26

True or False: Object Oriented Programming is the same as Network Programming.

Answer 26

False

Question 27

What is a common fallacy of distributed computing regarding networks?

Answer 27

Networks are reliable.

Question 28

What must be tolerated in a distributed system due to network unreliability?

Answer 28

Partitions

Question 29

According to the CAP theorem, what two options are available when a partition occurs?

Answer 29

Consistency and Availability

Question 30

What does CP stand for in the context of the CAP theorem?

Answer 30

Consistency/Partition Tolerance

Question 31

What happens in a CP system when a partition occurs?

Answer 31

Wait for a response from the partitioned node, which could result in a timeout error.

Question 32

What is the main choice in a CP system regarding business requirements?

Answer 32

Choose Consistency over Availability when atomic reads and writes are needed.

Question 33

What does AP stand for in the context of the CAP theorem?

Answer 33

Availability/Partition Tolerance

Question 34

What does an AP system return when a partition occurs?

Answer 34

The most recent version of the data, which could be stale.

Question 35

When should you choose Availability over Consistency?

Answer 35

When business requirements allow flexibility around data synchronization.

Question 36

What is a compelling reason to choose Availability?

Answer 36

When the system needs to continue to function despite external errors.

Question 37

What type of decision is the choice between Consistency and Availability?

Answer 37

A software trade-off.

Question 38

What must be acknowledged about network outages?

Answer 38

They are a fact of life and occur unexpectedly.

Question 39

What are the advantages of building distributed systems?

Answer 39

Many advantages, but also adds complexity.

Question 40

What is vital for the success of your application in distributed systems?

Answer 40

Understanding the trade-offs available in the face of network errors.

Question 41

What could happen if the trade-offs between Consistency and Availability are not handled correctly?

Answer 41

Your application could fail before deployment.

Question 42

What is consistency in distributed systems and what does the CAP theorem state about it?

Answer 42

Consistency in distributed systems refers to how data synchronization is handled when there are multiple copies of the same data: CAP Theorem Definition: Consistency means every read operation receives either the most recent write or an error It's one of the three key components of the CAP theorem (Consistency, Availability, Partition Tolerance) Systems must balance these properties as you can only guarantee two out of three Implementation Considerations: Multiple data copies require synchronization strategies Different consistency models offer different trade-offs Choice of consistency model impacts system behavior and user experience Must consider network latency and partition scenarios Business requirements often dictate consistency needs

Question 43

What is weak consistency and when should it be used?

Answer 43

Weak consistency is the most relaxed consistency model where: Core Characteristics: Reads may or may not reflect the most recent write No guarantees about when data will be consistent Best-effort approach to data synchronization Fastest performance among consistency models Lowest consistency guarantees Use Cases: Real-time communication systems (VoIP) Video chat applications Multiplayer games Systems where temporary data inconsistency is acceptable Applications where speed is more important than accuracy Example Scenario: Phone call with lost reception When connection resumes, missed audio is not replayed System prioritizes real-time communication over data consistency Implementation Example: Memcached uses this model Provides high performance Sacrifices data consistency for speed No guarantee of data synchronization timing

Question 44

What is eventual consistency and how does it work?

Answer 44

Eventual consistency provides a middle-ground approach where: Core Characteristics: Data will become consistent over time Reads will eventually reflect all completed writes Typically achieves consistency within milliseconds Data replication happens asynchronously Better performance than strong consistency Implementation Details: Updates propagate gradually through the system No immediate synchronization requirement Systems can continue operating during network partitions May serve stale data temporarily Conflicts resolved through various mechanisms (vector clocks, etc.) Common Applications: DNS systems Email systems Distributed databases Social media platforms Content delivery networks Advantages: Higher availability Better scalability Lower latency Continues functioning during network partitions Good for systems that don't require immediate consistency

Question 45

What is strong consistency and what are its implications?

Answer 45

Strong consistency is the most rigid consistency model that: Core Characteristics: All reads reflect the most recent write Data is replicated synchronously Provides immediate consistency across all nodes Highest consistency guarantees Most impactful on performance Implementation Requirements: Synchronous replication Coordination between all nodes Consensus protocols Transaction management Conflict prevention mechanisms Common Applications: File systems Relational databases (RDBMS) Banking systems Financial transactions Systems requiring ACID properties Trade-offs: Highest consistency guarantees Lower availability during partitions Higher latency for operations More complex implementation Resource intensive

Question 46

What are the key availability patterns in distributed systems?

Answer 46

Availability patterns focus on ensuring system uptime through: Primary Approaches: Fail-over: Systems switch to backup when primary fails Requires redundant hardware Can be active-passive or active-active Requires heartbeat monitoring Needs failover automation Replication: Data copied across multiple nodes Can be synchronous or asynchronous Supports different consistency models Provides redundancy Enables load distribution Implementation Considerations: Hardware requirements Network configuration Data synchronization Monitoring systems Recovery procedures

Question 47

What is active-passive failover and how does it work?

Answer 47

Active-passive failover (also known as master-slave failover) is a high availability pattern that: Core Components: Active server handling all traffic Passive server on standby Heartbeat mechanism between servers IP address takeover capability Monitoring system Operational Details: Normal Operation: Active server handles all requests Passive server maintains standby state Regular heartbeat checks between servers Continuous data synchronization System monitoring active Failover Process: Heartbeat interruption detected Passive server activates IP address migration occurs Services resume on passive server System alerts generated Standby Modes: Hot Standby: Passive server running and ready Minimal startup time Higher resource usage Faster failover More expensive Cold Standby: Passive server inactive Longer startup time Lower resource usage Slower failover More cost-effective Disadvantages: Additional hardware costs Complex configuration Potential data loss during failover Resource underutilization Higher maintenance overhead

Question 48

How does active-active failover work and what are its characteristics?

Answer 48

Active-active failover (also known as master-master failover) is a more complex availability pattern where: Core Characteristics: Multiple active servers Load distribution across servers Simultaneous traffic handling Synchronized data states No standby resources Implementation Requirements: Public-Facing Systems: DNS configuration for multiple IPs Load balancer configuration Health checking mechanisms Traffic distribution rules Failover procedures Internal Systems: Application awareness of multiple servers Connection management Load distribution logic State synchronization Conflict resolution Advantages: Better resource utilization Higher throughput capacity Natural load balancing Improved fault tolerance Easier maintenance procedures Challenges: Complex data synchronization Potential consistency issues More sophisticated monitoring needed Higher implementation complexity Increased operational overhead

Question 49

What are the key considerations for availability percentages and their implications?

Answer 49

Availability measurements and calculations involve: Availability Metrics: Three Nines (99.9%): 8h 45min 57s downtime per year 43m 49.7s downtime per month 10m 4.8s downtime per week 1m 26.4s downtime per day Suitable for non-critical systems Four Nines (99.99%): 52min 35.7s downtime per year 4m 23s downtime per month 1m 5s downtime per week 8.6s downtime per day Required for critical systems Calculation Patterns: Sequential Systems: Availability decreases with each component Formula: A(total) = A(component1) * A(component2) Example: Two 99.9% components = 99.8% total More components reduce overall availability Requires higher component reliability Parallel Systems: Availability increases with redundancy Formula: A(total) = 1 - (1-A(comp1)) * (1-A(comp2)) Example: Two 99.9% components = 99.9999% total Better fault tolerance Higher overall availability Implementation Considerations: Cost vs availability requirements System architecture decisions Component reliability needs Monitoring and alerting thresholds Maintenance windows impact