Cluster Computing Quiz

A computing system combining multiple standalone PCs (servers or nodes) to act as a single, powerful resource. Each node runs its own operating system, connected via a local area network (LAN).

The ability of a cluster to add or remove nodes seamlessly without disrupting operations, allowing flexible resource allocation.

The ability of a cluster to continue operating even if some nodes fail, ensuring uninterrupted service.

The system's ability to minimize service disruptions by tolerating failures in individual nodes.

The ability to use standard, inexpensive hardware (commodity hardware) to build a cost-effective cluster system.

Utilizing cluster computing for apps that process massive amounts of data, providing high-speed computational power and efficiency.

A cluster's ability to dynamically expand or shrink based on operational needs. This allows for managing server shutdowns and handling increased workload efficiently.

Sharing computing resources within a cluster allows multiple apps to run concurrently and adapt to changing demands. It provides a cost-effective and scalable solution for big data processing.

The process of copying data to multiple nodes in a system, ensuring that each node has an identical copy of the data.

A technique used to distribute incoming traffic across multiple servers in a cluster, improving performance and fault tolerance.

A load balancing algorithm where requests are sequentially distributed to servers in a circular pattern.

A load balancing algorithm that considers pre-defined weights assigned to servers, distributing requests proportionally based on their capacity.

A load balancing algorithm that randomly routes requests to servers in a cluster. It's suitable for homogeneous clusters where servers have similar processing power.

A load balancing algorithm that remembers the server a client initially connected to and directs subsequent requests from that client to the same server.

A cluster structure where each node functions independently and can run applications. The setup is simple and straightforward.

A cluster structure where one node acts as the primary or head node, serving as a gateway between users and other nodes.

Distributing large datasets across multiple servers to manage data efficiently and reduce the load on individual nodes.

A technique that divides a large dataset into smaller chunks called shards, each stored on a separate node. Each shard has the same schema, and together they represent the complete dataset.

Shards are stored on separate nodes to improve performance and fault tolerance. If a node fails, only the data stored in that shard is affected.

The process of creating copies of data across multiple servers to enhance fault tolerance and data availability. If one server fails, other copies ensure data remains accessible.

Sharding promotes data accessibility even in case of server failure. If one node goes down, the remaining nodes continue to hold the data.

Replication increases data availability. Even if a server crashes, data replicas on other servers maintain service continuity.

Sharding reduces the amount of data and transactions each node handles, improving performance and throughput.

Replication makes data retrieval faster. With a copy on each server, users access the data from the nearest server.

A copy of a block of data in a distributed system.

A replication architecture where one master node manages data modification and multiple slave nodes provide read access.

A model in a distributed system where a single node (master) controls the actions of other nodes (slaves).

A replication architecture where all nodes have equal privilege and can both read and write data.

The ability of a system to continue operating even if some nodes fail.

The ability of a system to provide access to data even when some nodes are unavailable.

Combines the benefits of sharding and replication, where multiple shards act as slaves to a single master shard. This creates multiple masters and allows for scalability and fault tolerance in read operations while maintaining write consistency through the master shard.

Each shard is replicated to multiple peers, and each peer is responsible for a subset of the overall dataset. This ensures that no single point of failure exists and allows for scalability and fault tolerance for both read and write operations.

Each node acts both as a master and a slave for different shards, ensuring that data is replicated and accessible from multiple nodes.

Peer-to-peer replication enables scalability by distributing data across multiple nodes, allowing the system to handle larger workloads. It also makes the system more fault-tolerant, as data is available even if some nodes fail.

A database that doesn't follow the rigid structure of traditional relational databases (RDBMS). Data is stored in a more flexible way, often using key-value pairs or document-like structures.

A database management system (DBMS) that uses a fixed schema to define the structure of data. This means data must adhere to predefined rules and formats.

NoSQL databases are often described as horizontally scalable. This means you can increase their capacity by simply adding more servers to the system. This is in contrast to RDBMS, which are typically vertically scalable, where performance improvements come from upgrading individual servers.

NoSQL databases emphasize availability and partitioning data across multiple servers to ensure high availability. RDBMS prioritize consistency, ensuring data integrity but potentially sacrificing availability in case of failures.

NoSQL databases often employ the BASE (Basically Available, Soft state, Eventually consistent) model. While data might not be completely up-to-date, it's still available. Eventually, the data will become consistent across the system.

NoSQL databases are particularly well-suited for handling big data, where speed and flexibility are crucial. They can scale horizontally to manage massive amounts of data and cope with high velocity data streams.

NoSQL databases are typically used for storing data with less structure. This is especially useful for unstructured data like images, videos, and social media posts.

While commonly used, NoSQL databases are not always the best choice. If your application requires strict data integrity and ACID properties, a traditional RDBMS might still be the preferred option.