Discover fundamental strategies for optimizing NoSQL database reads, writes, and latency. This quiz focuses on key concepts and best practices to enhance performance in scalable, non-relational data systems.
Which technique most effectively reduces write amplification in NoSQL databases storing frequently updated records?
Explanation: Append-only data structures help minimize write amplification by writing sequentially, reducing the need for frequent in-place updates. Normalizing data extensively can actually complicate write paths and does not address amplification. Random disk writes increase amplification as disk heads move more, causing overhead. High read consistency relates to reading, not to write amplification.
In a NoSQL environment with heavy read loads, which approach best improves read scalability for popular data?
Explanation: Implementing data caching allows frequently accessed data to be retrieved quickly, reducing load on the core database and improving scalability. Data sharding helps with distribution but doesn't directly speed up reads to hot data. Disabling all write operations isn't viable and doesn't enhance reads. Serializing read requests would actually decrease scalability by creating bottlenecks.
How does adding appropriate indexes impact query latency on large datasets in NoSQL databases?
Explanation: Appropriate indexing enables the system to locate data faster for queries, decreasing read latency. Although indexing can slow down write operations due to index updates, it does not guarantee lower write latency. Saying it has no impact on query performance is incorrect as indexes are specifically for lookup speed. Indexes themselves do not inherently increase network congestion.
Which factor most directly increases perceived latency for reads in a distributed NoSQL system across multiple regions?
Explanation: Higher network round-trip times increase the delay between a read request and the corresponding response, directly impacting perceived latency. Data compaction and write consistency primarily affect write paths. Storing larger value objects can increase latency but not as immediately as network delays, which are more dominant in cross-region read scenarios.
What is a simple and effective way to boost write throughput in most NoSQL databases handling heavy write workloads?
Explanation: Batching combines multiple writes into fewer operations, reducing overhead and improving throughput. Reducing buffer memory size often constrains performance. Removing primary keys would harm data organization and not improve writes. Enabling only strong consistency may decrease write throughput due to synchronization overhead.
When designing partition keys to avoid hotspots in a high-traffic NoSQL database, which strategy is generally most effective?
Explanation: Adding a hashed or random element distributes data evenly among partitions, preventing 'hotspots.' Continuously increasing timestamps can cluster recent writes and cause certain partitions to receive disproportionate load. Using a single key creates a major hotspot. Replication factor mainly impacts fault tolerance, not load distribution.
In a situation where low-latency reads are prioritized over absolute accuracy, which consistency setting is most appropriate?
Explanation: Eventual consistency allows the system to serve reads quickly without waiting for absolute accuracy, which is suitable when low latency is preferred. Strong consistency, quorum, and synchronous replication each require more coordination, resulting in higher latency. These other options are better when accuracy is paramount.
How does storing very large documents in a document-oriented NoSQL database typically affect read and write latency?
Explanation: Larger documents take more time to read from, transfer, and write to disk, increasing both read and write latencies. Neither read nor write operations benefit from increased document size. The idea that read latency decreases or that write performance improves doesn’t align with storage realities. Both operations are negatively impacted by large sizes.
Which metric provides the most direct measurement of latency experienced by end-users during NoSQL operations?
Explanation: End-to-end response time captures the actual time users wait for an operation's result, making it the most direct indicator of latency as experienced. Writes-per-second measures throughput, not latency. Index size relates to storage use. Cache hit ratio indicates how often requests are completed from cache, which affects but does not directly represent user-experienced latency.
In NoSQL systems relying heavily on disk storage, how does enabling data compression typically impact performance?
Explanation: Data compression reduces the amount of disk space used and may allow more data to be read into memory at once, possibly improving read speeds. While compression can add CPU overhead, decompression happens mainly during reads, not writes. Compression, by definition, lowers storage costs. When configured properly, it does not cause data corruption.