For an application that prioritizes latency over consistency, how should you perform reads from Cloud Spanner?

When prioritizing latency over consistency in an application that uses Cloud Spanner, performing stale reads using single-read methods is the most effective approach. Stale reads allow you to access data that may not be the most recent, providing faster response times because they do not require locking and the overhead associated with ensuring that the data is consistent.

In Cloud Spanner, stale reads can be executed by specifying a timestamp that is behind the most current timestamp in the system. This facilitates quick access to data without the need for the system to wait for locks to be released or synchronized state across replicas, thereby minimizing latency.

Using single-read methods enables you to efficiently retrieve data for analytical queries or user requests where near-real-time data is acceptable, and the latest data is not critical for decision-making or user experience. This makes it optimal for scenarios where the application can tolerate some degree of staleness in the underlying data while still achieving high speed in data retrieval.

Other options involve either strong consistency or transaction overhead, which can increase latency. Strong reads require more extensive checks to ensure the most current data is available, while transactions may involve more steps to ensure all involved data is consistent. As such, these methods are typically not suited for applications focused on minimizing latency.