What is a common reason for increased latency when using Cloud Spanner?

Monotonically increasing primary key patterns can lead to increased latency in Cloud Spanner due to the way data is distributed and stored across the system. Cloud Spanner is designed to handle horizontal scaling and distribution of data across multiple nodes and regions. When primary keys are chosen in a monotonically increasing manner, such as timestamps or sequential IDs, all incoming writes tend to be directed to a single node or a small set of nodes.

This situation can create a bottleneck because the node becomes overloaded with write requests while other nodes remain underutilized. Consequently, this uneven workload distribution slows down the overall system response, leading to increased latency for both read and write operations. To mitigate this, a more random or clustered key distribution pattern should be used, allowing for better load balancing and improved performance.

While other options also have potential impacts on performance, they do not directly link to the way Cloud Spanner handles data distribution and write load as the selected answer does. For instance, data type selection errors and improper indexing might create inefficiencies, but they aren't as fundamentally tied to the system's architecture and its impact on latency as the pattern of primary keys. Network latency, while a factor, is an external condition not intrinsic to the Spanner’s data management strategies.