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A supermarket chain wants you to implement an in-memory database that can be used to access facts about the products they sell. Although a snapshot of this database will be periodically copied to disk, its contents fit in memory, and your component of the application will operate only on data stored in memory.

Here are the requirements specified by the managers of the supermarket chain:

A) They want to be able to retrieve product records by specifying the name of the product.

B) They want to be able to specify the first n characters of a product name and to retrieve all records that begin with those characters.

C) They want the time required to retrieve a record to be as efficient as possible – on the order of 20 operations per retrieval, given a database of approximately one million records.

D) They want to be able to increase the size of the database – adding large sets of new records – without taking the system offline.

Answer :

Final answer:

Implementing an in-memory database for a supermarket chain that meets specific requirements such as retrieving records by product name and prefix, and allowing for scalable growth requires using a trie or prefix tree combined with a hash table and tactics like sharding for efficient operation.

Explanation:

Implementing an In-Memory Database for a Supermarket Chain

To fulfill the requirements of a supermarket chain that wishes to have an efficient and scalable in-memory database, one would need to consider data structures that allow for quick retrieval and efficient storage. Given the need to retrieve product records by name and also by the first n characters of a product name, a trie or a prefix tree would be an appropriate choice. These data structures offer fast lookup times and are efficient in handling a large set of strings while allowing for prefix-based search queries, which meets the requirement of retrieving all records that begin with the specified characters.

For achieving the desired retrieval efficiency, with operations on the order of 20 per retrieval in a database of approximately one million records, using a hash table in conjunction with the trie could provide constant time complexity for lookups by full product names. To handle the scalability requirement and adding records without taking the system offline, techniques such as sharding and consistent hashing can be implemented to distribute the data and workload evenly across the system.

The chosen data structure and techniques must align with the in-memory nature of the database and its periodic snapshot backups to disk, ensuring that the system remains fast and responsive while allowing for growth and data persistence.

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