Coding - In-Memory Database with SQL Operations

The In-Memory Database with SQL Operations interview question at OpenAI typically asks you to design and implement a system that manages data records in memory while supporting a subset of standard SQL-like commands. Hello Interview +1 1 4

Core Problem Statement

The goal is to build a high-performance in-memory database that can store collections of records (often as dictionaries or key-value pairs) and perform operations without using a persistent storage disk. The challenge usually progresses through several parts, starting with basic storage and moving toward complex querying and optimization. Hello Interview +3 0 9 4 5

Required Operations

Most versions of this problem require you to implement the following features:

• Data Insertion/Modification: SET / INSERT: Add a new record or update an existing one based on a primary key (e.g., SET row_id column value). UPSERT: Update a record if it exists, otherwise insert it.
• Data Retrieval (SELECT): Projection: Return only specific columns for each matching record. Filtering (WHERE): Support simple conditions (e.g., =, !=, >, <, >=, <=). Advanced Filtering: Support compound logic like AND or OR.
• Data Organization: Sorting (ORDER BY): Sort results by one or more columns in ascending or descending order.
• Advanced Features (Varies by Round): 0 3 11 9 2 7 8 12 Transactions: Support BEGIN, COMMIT, and ROLLBACK for atomicity. Indexing: Implement inverted indexes to optimize WHERE clause searches from 𝑂(𝑛) to 𝑂(1) or 𝑂(log𝑛). Hello Interview +7

Example Scenario

If you are given a series of records and a query:

1. • INSERT {id: 1, name: "Alice", age: 30}
   • INSERT {id: 2, name: "Bob", age: 25}
   • SELECT * WHERE age > 25 ORDER BY name ASC
2. INSERT {id: 1, name: "Alice", age: 30}
3. INSERT {id: 2, name: "Bob", age: 25}
4. SELECT * WHERE age > 25 ORDER BY name ASC
5. Output: [{id: 1, name: "Alice", age: 30}]. Hello Interview +1

Key Technical Constraints

• Performance: Operations should ideally be 𝑂(1) or 𝑂(log𝑛) using efficient data structures like HashMaps or Treemaps.
• Schema Flexibility: Records in the same table may have different fields (schemaless or flexible schema).
• Code Quality: Interviewers look for modularity, clean interfaces, and proper handling of edge cases (e.g., missing columns or empty results). Hello Interview +3

Would you like to walk through an object-oriented design or a specific Python implementation for this problem?

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