Database Problems (308)
Problems tagged with Database. Start with the easy ones, then move to medium and hard.
Learn these first
Introduces core SQL syntax, joins, filtering, and aggregation. These fundamentals form the basis of most database interview problems.
Provides system and schema design thinking, helping you understand how database tables, relationships, and queries work together.
Builds intuition for efficient data lookup and grouping, which parallels how databases use indexing and grouping operations.
Teaches advanced SQL capabilities such as window functions and complex queries that frequently appear in real-world database challenges.
Helps learners understand enterprise SQL features, query optimization, and database-specific functions used in many interview scenarios.
More about Database
Database problems in coding interviews focus on how efficiently you can retrieve, filter, and transform structured data using SQL and database concepts. Instead of traditional algorithmic code, these questions test your ability to write optimized queries, understand relational data models, and work with operations like joins, aggregations, and subqueries. Mastering database problems is essential for backend engineers, data engineers, and full‑stack developers.
Top companies frequently include database questions because real production systems rely heavily on data storage and retrieval. During interviews, you may be asked to write SQL queries that compute rankings, find duplicates, aggregate statistics, or analyze user activity. These problems often combine query logic with algorithmic thinking, similar to topics like Hash Table or Sorting, where efficient data organization and retrieval are key.
Common database problem patterns include:
- Joins: Combining multiple tables using INNER JOIN, LEFT JOIN, or self joins.
- Aggregation: Using COUNT, SUM, AVG, and GROUP BY to compute statistics.
- Subqueries and CTEs: Structuring complex queries for readability and performance.
- Window functions: Ranking rows, running totals, and partition-based analysis.
- Filtering and conditional logic: Using WHERE, CASE, and HAVING to refine results.
Many database interview questions also involve platform-specific features in systems like MySQL, PostgreSQL, and MS SQL Server. Understanding indexing, query optimization, and schema design can make a big difference when solving real-world problems.
On FleetCode, you can practice ranging from beginner SQL queries to advanced analytical challenges. By solving these problems consistently, you'll build the skills needed to write efficient queries, reason about relational data, and confidently tackle database questions in technical interviews.
Database: common questions
Do Database problems require algorithm knowledge?
Yes, to some extent. While SQL handles data retrieval, understanding algorithmic concepts like grouping, sorting, and efficient lookups can help you write better queries and reason about performance.
Is Database knowledge important for FAANG interviews?
Yes. Many FAANG and top tech companies include SQL or database rounds, especially for backend, data engineering, and analytics roles. Candidates are typically expected to write correct queries, understand joins, and analyze data efficiently.
What is the best way to learn Database problem solving?
Start with basic SQL queries like SELECT, WHERE, and GROUP BY. Then practice joins, subqueries, and window functions through structured problem sets. Consistent practice on platforms with real interview-style questions helps reinforce patterns quickly.
What are the most common Database patterns in interviews?
The most common patterns include joins between related tables, grouping and aggregation, filtering with conditional logic, ranking with window functions, and subqueries or CTEs for multi-step analysis.
What are the best Database problems for coding interviews?
The best database interview problems focus on joins, aggregations, window functions, and ranking queries. Common examples include finding duplicate records, calculating running totals, analyzing user activity, and computing top-N results.