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Leetcode 2408. Design SQL

Asked at:

OpenAI

DESCRIPTION

Given one or more relational tables, construct a SQL query that produces a specified result set using JOIN operations, GROUP BY aggregations, conditional logic, and potentially window functions for ranking or partitioning. Handle edge cases including NULL values, duplicate records, and optimize with appropriate subqueries or indexing strategies. For example, given employees and departments tables, find the highest-paid employee per department.

Input:


Tables:
employees (id, name, dept_id, salary)
departments (id, dept_name)

Task: Find highest-paid employee per department

Output:


SELECT d.dept_name, e.name, e.salary
FROM employees e
JOIN departments d ON e.dept_id = d.id
WHERE (e.dept_id, e.salary) IN (
  SELECT dept_id, MAX(salary)
  FROM employees
  GROUP BY dept_id
)

Explanation: Uses JOIN to combine tables, subquery with MAX() aggregation to find top salary per department, and WHERE IN clause for filtering

Constraints:

Must handle NULL values appropriately (e.g., in join keys or aggregation columns)
Consider duplicate records when selecting top-N results
Optimize query performance with proper indexing on join and filter columns
Use window functions (ROW_NUMBER(), RANK()) for per-group ordering when needed

Understanding the Problem

The core challenge is translating business requirements into correct SQL syntax while managing relational data across multiple tables. You must identify the appropriate JOIN type (INNER, LEFT, RIGHT), determine grouping levels for aggregations, and decide between subqueries versus window functions for ranking. Edge cases like NULL handling in joins (which exclude rows) and tie-breaking in top-N queries require careful consideration. Performance optimization through indexed columns and avoiding Cartesian products is essential for large datasets.

Building Intuition

A naive approach uses nested subqueries for each filtering step, leading to redundant table scans and poor performance. A better approach leverages window functions like ROW_NUMBER() OVER (PARTITION BY dept_id ORDER BY salary DESC) to rank rows within groups in a single pass, then filters with WHERE rank = 1. For example, finding the top employee per department becomes a single query with one table scan instead of multiple subqueries.

Efficient SQL queries are critical for real-time analytics and application performance where databases handle millions of records. Poor query design causes exponential slowdowns through Cartesian products or missing indexes. Mastering joins, aggregations, and window functions enables complex reporting (sales rankings, moving averages) and data transformations without loading entire datasets into application memory.

Common Pitfalls

Using INNER JOIN when LEFT JOIN is needed - causes data loss when right table has no match; always verify join semantics match requirements

Forgetting DISTINCT or ROW_NUMBER() for duplicates - multiple employees with same max salary return duplicate rows; use window functions with tie-breaking logic

Ignoring NULL in aggregations - COUNT(column) excludes NULLs while COUNT(*) includes them; use COALESCE() or IS NOT NULL filters explicitly

Inefficient subquery placement - correlated subqueries in SELECT clause execute per row; move to FROM clause or use window functions for set-based operations

Implementation

Join Strategy and Table Relationships

Identify the relationship type between tables (one-to-many, many-to-many) and select appropriate JOIN operations. Use INNER JOIN when both tables must have matching records, LEFT JOIN to preserve all rows from the primary table even without matches, and junction tables for many-to-many relationships. For example, joining orders to customers with LEFT JOIN ensures all orders appear even if customer data is missing, while INNER JOIN would silently drop orphaned orders.

# Join Strategy Examples

# INNER JOIN: Only matching records from both tables
inner_query = """
    SELECT o.order_id, c.customer_name
    FROM orders o
    INNER JOIN customers c ON o.customer_id = c.customer_id
"""

# LEFT JOIN: All orders, even without customer match
left_query = """
    SELECT o.order_id, c.customer_name
    FROM orders o
    LEFT JOIN customers c ON o.customer_id = c.customer_id
"""

# Many-to-many with junction table
many_to_many_query = """
    SELECT s.student_name, c.course_name
    FROM students s
    INNER JOIN enrollments e ON s.student_id = e.student_id
    INNER JOIN courses c ON e.course_id = c.course_id
"""

Aggregation and Grouping Logic

Apply GROUP BY to collapse rows into summary statistics using COUNT(), SUM(), AVG(), MAX(), or MIN(). The grouping columns define the granularity of results (e.g., per department, per month). Use HAVING to filter aggregated results after grouping, unlike WHERE which filters before aggregation. For instance, GROUP BY dept_id HAVING AVG(salary) > 50000 finds departments with average salary above threshold.

# Aggregation and Grouping Logic
from typing import List, Dict, Any

def build_aggregation_query(
    table: str,
    group_by_cols: List[str],
    agg_functions: Dict[str, str],
    having_clause: str = None
) -> str:
    """Build SQL query with GROUP BY and aggregation functions."""
    agg_exprs = [f"{func}({col}) AS {alias}" 
                 for alias, (func, col) in agg_functions.items()]
    
    query = f"SELECT {', '.join(group_by_cols + agg_exprs)} FROM {table}"
    query += f" GROUP BY {', '.join(group_by_cols)}"
    
    if having_clause:
        query += f" HAVING {having_clause}"
    
    return query

Window Functions for Per-Group Ranking

Use window functions like ROW_NUMBER(), RANK(), or DENSE_RANK() with PARTITION BY to assign rankings within groups without collapsing rows. The OVER clause defines partitioning and ordering: ROW_NUMBER() OVER (PARTITION BY dept_id ORDER BY salary DESC) numbers employees 1, 2, 3... within each department by descending salary. Wrap in a subquery and filter WHERE rank = 1 to select top records per group, handling ties based on chosen ranking function.

# Using pandas for window function simulation
import pandas as pd

def get_top_per_group(df, partition_col, order_col, ascending=False):
    """Assign rank within each group and filter top records."""
    df['rank'] = df.groupby(partition_col)[order_col].rank(
        method='first', ascending=ascending
    )
    return df[df['rank'] == 1].drop(columns=['rank'])

# Alternative with row_number equivalent
def get_top_with_row_number(df, partition_col, order_col, ascending=False):
    """Use sort + groupby for ROW_NUMBER() behavior."""
    sorted_df = df.sort_values([partition_col, order_col], ascending=[True, ascending])
    sorted_df['rn'] = sorted_df.groupby(partition_col).cumcount() + 1
    return sorted_df[sorted_df['rn'] == 1].drop(columns=['rn'])

NULL Handling and Edge Cases

Explicitly manage NULL values which behave unexpectedly in joins (excluded from matches) and aggregations (COUNT(col) ignores them). Use COALESCE(column, default_value) to replace NULLs or IS NOT NULL filters to exclude them. For duplicate handling, add tie-breaking columns to ORDER BY clauses or use DISTINCT when appropriate. For example, ORDER BY salary DESC, hire_date ASC ensures deterministic ranking when salaries match.

# NULL handling in SQL query construction
def handle_nulls_and_duplicates(query_parts):
    # Replace NULLs with defaults using COALESCE
    select_with_coalesce = f"COALESCE({query_parts['column']}, {query_parts['default']}) AS {query_parts['alias']}"
    
    # Filter out NULLs explicitly
    where_not_null = f"{query_parts['column']} IS NOT NULL"
    
    # Handle duplicates with tie-breaking ORDER BY
    order_with_tiebreak = f"ORDER BY {query_parts['primary_sort']} DESC, {query_parts['tiebreaker']} ASC"
    
    # COUNT with NULL awareness (COUNT(col) vs COUNT(*))
    count_non_null = f"COUNT({query_parts['column']})"  # Ignores NULLs
    count_all = "COUNT(*)"  # Includes NULLs
    
    return {
        'select': select_with_coalesce,
        'where': where_not_null,
        'order': order_with_tiebreak,
        'count_non_null': count_non_null,
        'count_all': count_all
    }

What We've Learned

Pattern: Use window functions with `PARTITION BY` for per-group rankings instead of correlated subqueries for better performance
Use Case: Essential for analytics dashboards (top products per category), leaderboards, and time-series analysis (moving averages)
Optimization: Index join columns and `WHERE` clause predicates; avoid `SELECT *` and retrieve only needed columns
Edge Cases: Always test with `NULL` values, empty result sets, and duplicate records to verify query correctness

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Company

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Late August, 2025

OpenAI

Senior

Early April, 2025

OpenAI

Senior

Early April, 2025

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Leetcode 2408. Design SQL

DESCRIPTION

Understanding the Problem

Building Intuition

Common Pitfalls

Implementation

Join Strategy and Table Relationships

Aggregation and Grouping Logic

Window Functions for Per-Group Ranking

NULL Handling and Edge Cases

What We've Learned

Problems to Practice

Question Timeline

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