A public library consortium stores loan records in one Amazon DynamoDB table. Two access patterns matter most: the members' app lists a member's current loans, and the catalog team looks up which members hold a given book. The first design used branch_id as the partition key, so one busy central branch receives most of the writes.
The team wants both access patterns served by queries rather than scans, and it wants the writes spread evenly across partitions.
Which table design meets these requirements?
Choose one answer.
AA. Keep branch_id as the partition key, and add a global secondary index on member_id so that the member's loans can be queried.
BB. Use book_id as the partition key with no sort key, and rely on scans for the member's loans, which the app runs once a day.
CC. Use a random number from 1 to 10 appended to branch_id as the partition key, and scan all ten suffixes for each member lookup.
DD. Use member_id as the partition key and loan_date as the sort key, and add a GSI with book_id as its partition key.
Show the answer and explanation
Answer: D
A. Wrong: The base table still concentrates writes on the busy branch, so the hot partition remains. The global secondary index helps the member lookup but does not spread the writes the team asked to balance.
B. Wrong: Scans read the whole table and are expensive at scale, and the team wants queries. A partition key on book_id can also concentrate popular titles on a few partitions.
C. Wrong: Write sharding spreads the writes, but reading a member's loans then needs ten queries for each lookup. The option also scans for the catalog pattern, which the team wanted served by queries.
DynamoDB spreads items by partition key, so a key with many distinct values, such as member ID, avoids a single busy partition. A sort key on loan date keeps a member's loans in one query, and a global secondary index on book ID serves the catalog lookup by query. Keeping branch as the key or scanning either access pattern leaves one of the requirements unmet.
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