PCD Question 87

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose C1

  The most likely cause of the performance issue is C. The use of Version 1 UUIDs as primary keys that increase monotonically. Here's why: Version 1 UUIDs and Monotonicity: Version 1 UUIDs are generated based on timestamps and MAC addresses. When used as primary keys, they can lead to performance issues in Cloud Spanner due to their non-monotonic nature. As new UUIDs are generated, they are not guaranteed to be in a sequential order, which can cause fragmentation in the underlying storage and lead to slower queries.

• 1

  Let's analyze why the other options are less likely: A. The use of 64-bit numeric types for 32-bit numbers: While using larger data types than necessary can impact storage space, it's unlikely to significantly affect performance in a way that would cause non-linear scaling. B. The use of the STRING data type for arbitrary-precision values: Using the STRING data type for numeric values can impact performance, but it's not the primary cause of non-linear scaling. Cloud Spanner is optimized for numeric data types. D. The use of LIKE instead of STARTS_WITH keyword for parameterized SQL queries: While using LIKE can be less efficient than STARTS_WITH for certain queries, it's unlikely to cause a significant performance bottleneck that would prevent linear scaling.

• Chose C1

  C. The use of Version 1 UUIDs as primary keys that increase monotonically. When designing schemas for Cloud Spanner, it is important to consider how the choice of primary keys can impact performance, especially under heavy load. Cloud Spanner splits data among servers based on the primary key values, so if the keys are monotonically increasing, as is the case with Version 1 UUIDs, new inserts are constantly added to the end of the table. This can create hotspots, where a single node receives a disproportionate amount of read and write requests, leading to performance bottlenecks and preventing linear scaling.