Explore essential topics in CouchDB cluster configuration, node management, and sharding mechanisms. This quiz is designed to help users understand distributed database clustering, sharding strategies, replication, partitioning, and key concepts for maintaining a scalable, reliable database cluster.
Which role does a node play in a CouchDB cluster when it is responsible for storing actual data shards and servicing read and write requests?
Explanation: A data node is the part of a cluster that stores shards and directly handles database requests. Arbiters do not store data and are not typical in such clusters. Client nodes usually refer to external applications, not actual cluster components. Monitor nodes suggest a monitoring-only role, not direct data storage or request handling.
Why is sharding used in CouchDB clusters, such as splitting a large database into smaller, manageable pieces?
Explanation: Sharding enables the database to spread data and queries across multiple nodes, improving scalability and fault tolerance. While sharding can influence resource usage, its primary purpose is not simply reducing memory. It does not remove the use of indexes, nor does it provide built-in data encryption.
What is the main function of a shard key in a CouchDB cluster when inserting new documents?
Explanation: The shard key is used to determine how documents are distributed across shards for balanced storage. It does not guarantee data encryption nor is it responsible for automatically creating document IDs. Document compression is also unrelated to the shard key function.
How does a replica set enhance reliability in a CouchDB cluster, considering node failures?
Explanation: Replica sets increase fault tolerance by storing each shard’s data on more than one node, so if one fails, another can take over. Grouping nodes in one location does not improve reliability and might reduce it. Disabling sharding and requiring manual synchronization limit the benefits of clustering.
Which requirement must be met before nodes can successfully join a CouchDB cluster?
Explanation: Node communication is essential for cluster operations like replication and sharding. Requiring different software versions can cause compatibility issues. Nodes do not need identical data prior to joining, as synchronization happens afterwards. The cluster does not need to restart after every addition.
How does sharding help balance load in a CouchDB cluster experiencing high traffic?
Explanation: Sharding balances user requests and data storage over several nodes to prevent bottlenecks. Disabling backups is unrelated and not recommended. Compressing network traffic is a different optimization, and stopping replication would harm data safety.
What typically limits the maximum number of nodes in a CouchDB cluster?
Explanation: Network conditions and managing many nodes are practical limits for large clusters. RAM matters for node performance, but not total cluster size. There are no imposed hardware vendor limits within this context, and user logins do not affect cluster capacity.
When dividing data among nodes, which technique helps ensure even distribution and easy scaling by mapping keys onto a ring?
Explanation: Consistent hashing maps data to a circular space, balancing data and minimizing reassignment when nodes are added or removed. Round-robin is for distributing tasks, not data. Binary search is a search algorithm, and mirror replication only provides copies, not distribution.
If one node in a sharded cluster fails, what ensures that users can still access all their data?
Explanation: Replicas provide redundancy, so data remains accessible from healthy nodes. Temporarily pausing nodes does not help data access. Forcing indefinite retries is impractical and not a recovery method. Reducing shards does not directly increase availability or solve data loss.
When scaling a CouchDB cluster, what is the benefit of adding more nodes, for example during peak usage periods?
Explanation: Adding nodes distributes data and queries, boosting the cluster’s ability to handle more operations. Shutting down nodes would reduce availability. Removing replication compromises reliability. Merging data to one node defeats the purpose of clustering.