Assess your understanding of handling hotspots in sharded databases, including common causes, prevention techniques, detection strategies, and best practices. Improve your knowledge of sharding patterns and hotspot mitigation methods crucial for database scalability and performance.
In a sharded database, what is the primary cause of a hotspot when most user activity targets the same shard, such as all users logging in at midnight UTC?
Explanation: Skewed data distribution leads to hotspots when most operations are directed to a single shard, causing uneven load and reduced performance. Network congestion affects the system differently and is not directly related to shard targeting. Insufficient storage would impact all data, not just a single shard receiving all activity. Synchronized backups can stress the system, but do not specifically lead to hotspots from user activity.
Which sharding strategy is most effective in reducing write hotspots for timestamp-based identifiers, where all new entries use the current time?
Explanation: Hash-based sharding distributes incoming writes more evenly by hashing the key, which prevents focusing activity on the latest time-based range. Range-based sharding would localize writes to the most recent range, intensifying hotspots. Vertical partitioning separates tables by columns, not rows, and does not solve write concentration. Manual replication copies data but does not address uneven writing patterns.
What is a common symptom indicating a hotspot has developed on a specific shard, such as slow response times for certain queries?
Explanation: Consistently high latency on one shard commonly signals uneven load and possible hotspots. A reduction in total database size does not usually indicate hotspots. Schema changes and server time fluctuations are unrelated and do not directly reflect shard-specific load issues.
Why should a sharding key, such as ‘user ID’, be chosen to ensure an even distribution of data and access patterns across shards?
Explanation: Choosing a sharding key that spreads data and requests evenly helps prevent any one shard from becoming a hotspot. Simplifying backups is a different concern and not a reason to pick a sharding key. Complying with naming conventions is important for clarity but not for load balancing. Increasing index size is usually undesirable for performance.
Which access pattern is most likely to generate a hotspot in a sharded database, as seen when all inserts target recent keys?
Explanation: Monotonically increasing keys, such as auto-incremented IDs or timestamps, can cause all new writes to hit the same shard, leading to a hotspot. Randomized key distribution and uniformly distributed reads help spread the load. Sparse updates do not typically focus traffic on a single shard.
How does adding a random 'salt' prefix to shard keys help prevent hotspots in a database where orders are based on sequential numbers (for example: order_1, order_2)?
Explanation: Salting introduces randomness to the shard key, breaking the sequential pattern and distributing writes across multiple shards. While it slightly increases key size, it does not necessarily impact overall storage requirements. It does not reduce the number of queries per second or compress the data; its main benefit is even distribution.
What is a likely outcome if a hotspot persists on a single shard, as in the case of high-concurrency updates to a popular item?
Explanation: A persistent hotspot can degrade the performance of the entire application due to slow responses and increased contention. Immediate data loss is rare in this context. Error rates are more likely to rise, not fall, and automatic rebalancing does not always happen unless built-in mechanisms exist.
Why can queries that always filter by a fixed value, like 'country = USA', cause a hotspot in a sharded database?
Explanation: Repeatedly filtering on a fixed value, such as 'country = USA', can result in all such queries being served by one shard, concentrating load and creating a hotspot. This does not adjust shard counts, modify read/write types, or affect key uniqueness.
What does resharding involve when mitigating an existing hotspot in a sharded database?
Explanation: Resharding spreads data more evenly by redistributing it across additional or reconfigured shards. Deleting a busy shard would cause data loss and is not a viable solution. Partitions handle data differently and may not solve hotspot issues. Lowering index fragmentation can improve performance but does not redistribute hotspot traffic.
Which metric should be closely monitored to detect the development of hotspots in a sharded database cluster?
Explanation: Monitoring CPU usage and request rates on a per-shard basis allows fast detection of overload or hotspots. Overall database size and version number provide limited insight into real-time hotspot formation. Shard naming conventions are administrative and do not reveal operational issues.