Explanation:
The correct answer is to change the primary key to avoid monotonically increasing values. This is because using a column with values that increase sequentially (like person_id) as the primary key can lead to hotspots in your database. Hotspots occur when all inserts are directed to a single server due to the key range distribution in Cloud Spanner, causing performance bottlenecks.
Reference: Cloud Spanner Schema Design - Preventing Hotspots
The other options do not address the root cause of the performance issue, which is the sequential nature of the primary key values.
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Your customer has implemented a solution using Cloud Spanner and is experiencing read latency-related performance issues on a specific table. This table is accessed solely by their users via a primary key. The table's schema is as follows:
CREATE TABLE Persons (
person_id INT64 NOT NULL, // sequential number based on number of registrations
account_creation_date DATE, // system date
birthdate DATE, // customer birthdate
firstname STRING (255), // first name
lastname STRING (255), // last name
profile_picture BYTES (255) // profile picture
) PRIMARY KEY (person_id)
CREATE TABLE Persons (
person_id INT64 NOT NULL, // sequential number based on number of registrations
account_creation_date DATE, // system date
birthdate DATE, // customer birthdate
firstname STRING (255), // first name
lastname STRING (255), // last name
profile_picture BYTES (255) // profile picture
) PRIMARY KEY (person_id)
What action should you take to resolve the performance issue?
A
Remove the profile_picture field from the table.
B
Add a secondary index on the person_id column.
C
Change the primary key to avoid monotonically increasing values.
D
Create a secondary index using the following Data Definition Language (DDL):
CREATE INDEX person_id_ix
ON Persons (
person_id,
firstname,
lastname
) STORING (
profile_picture
)
CREATE INDEX person_id_ix
ON Persons (
person_id,
firstname,
lastname
) STORING (
profile_picture
)