Distributed Computing Mid
Explain the concept of data locality in distributed computing.
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Data locality means storing all data on a single server.
Data locality refers to encrypting data for security.
Data locality is about backing up data in a local storage device.
Data locality in distributed computing refers to processing data close to where it is stored to reduce latency.
What are the challenges of handling network latency in distributed systems, and what techniques can be used to mitigate latency-related issues?
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Handling network latency is solely about increasing the bandwidth.
Network latency does not affect distributed systems.
Network latency can be ignored in distributed systems.
Handling network latency in distributed systems involves techniques like caching and data replication.
What are the challenges associated with managing distributed system logs, and how can these challenges be addressed?
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Managing distributed system logs only involves backing up data.
Distributed system logs do not require real-time analysis.
Managing distributed system logs is simple due to their uniform structure.
Managing distributed system logs is challenging due to their volume, variety, and the need for real-time analysis.
Explain the concept of distributed tracing and its importance in monitoring and debugging microservices architectures.
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Distributed tracing is a method for data backup in microservices.
Distributed tracing is used to encrypt data across microservices.
Distributed tracing tracks requests as they flow through various services in a microservices architecture.
Distributed tracing speeds up processing in microservices architectures.
Explain the concept of the Two-Phase Commit (2PC) protocol and its use in distributed transaction management.
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The Two-Phase Commit (2PC) protocol speeds up data processing in a single-node system.
The Two-Phase Commit (2PC) protocol is used to encrypt data in a distributed system.
The Two-Phase Commit (2PC) protocol is a method for data backup.
The Two-Phase Commit (2PC) protocol ensures all nodes in a distributed system either commit or abort a transaction.
What is the role of a load balancer in a distributed system, and how does it work?
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A load balancer distributes network or application traffic across multiple servers to ensure no single server becomes overwhelmed.
A load balancer only works in single-node systems.
A load balancer stores backup data for recovery purposes.
A load balancer is used to encrypt data before transmission.
Explain the concept of leader election in distributed systems and its role in fault tolerance.
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Leader election is about backing up data in a distributed system.
Leader election ensures all nodes have the same processing speed.
Leader election in distributed systems determines which node will act as the coordinator for a task.
Leader election is a process of encrypting data in distributed systems.
Explain the concept of distributed consensus and the role of algorithms like Paxos and Raft.
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Distributed consensus is achieved when all nodes in a distributed system agree on a common state.
Distributed consensus is about achieving the highest processing speed in a distributed system.
Distributed consensus ensures data encryption across all nodes.
Distributed consensus is a method for data replication.
What is a distributed lock, and why is it important in distributed systems?
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A distributed lock ensures that only one process can access a resource at a time, preventing conflicts.
A distributed lock speeds up data processing by allowing multiple processes to access the same resource.
A distributed lock is used to encrypt data across multiple nodes.
A distributed lock is a backup mechanism for data recovery.
Explain the concept of eventual consistency in distributed databases.
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Eventual consistency means that updates to a distributed database will eventually be reflected across all nodes.
Eventual consistency means that updates are immediately visible to all nodes.
Eventual consistency ensures data is never lost in a distributed system.
Eventual consistency guarantees real-time data replication.
Explain the concept of distributed caching and its benefits in distributed systems.
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Distributed caching is a method for data encryption.
Distributed caching is a security measure to prevent data breaches.
Distributed caching is used to synchronize data between different databases.
Distributed caching stores frequently accessed data across multiple nodes to improve performance.
What is MapReduce, and how does it work in the context of distributed computing?
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MapReduce is a type of database management system.
MapReduce is a networking protocol for distributed systems.
MapReduce is a programming model for processing large data sets with a distributed algorithm on a cluster.
MapReduce is a file compression algorithm used in distributed systems.