As a database consultant who has spent years optimizing PostgreSQL implementations for businesses of all sizes, I’m frequently asked about query techniques that can transform complex database operations into maintainable, performant code. Common Table Expressions (CTEs) stand out as one of PostgreSQL’s most powerful features—and they’re often underutilized by developers who could benefit tremendously from understanding them.

What Are CTEs?

A Common Table Expression is a temporary named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. Think of it as a way to create a virtual table that exists only for the duration of your query. In PostgreSQL, you define a CTE using the WITH clause:WITH sales_summary AS (
SELECT
product_id,
SUM(quantity) as total_quantity,
SUM(amount) as total_revenue
FROM orders
WHERE order_date >= ‘2026-01-01’
GROUP BY product_id
)
SELECT
p.product_name,
s.total_quantity,
s.total_revenue
FROM products p
JOIN sales_summary s ON p.product_id = s.product_id
WHERE s.total_revenue > 10000
ORDER BY s.total_revenue DESC;

Why CTEs Excel in PostgreSQL

1. Dramatic Improvement in Code Readability

One of the most compelling reasons to use CTEs is their impact on query readability. Instead of nesting subqueries within subqueries—creating what I call “query spaghetti”—CTEs allow you to break complex logic into named, sequential steps that read almost like natural language.

Consider a scenario where you need to calculate year-over-year sales growth for products, filter by performance thresholds, and join with inventory data. Without CTEs, this becomes a nested mess. With CTEs, each logical step gets its own named block, making the query self-documenting and significantly easier to debug.

2. Enhanced Query Maintainability

In production environments, queries evolve. Requirements change, business logic shifts, and performance optimizations become necessary. CTEs shine here because modifying one section of your query doesn’t require untangling nested parentheses or tracking which subquery feeds into which JOIN.

3. Reusability Within the Same Query

A CTE can be referenced multiple times within the same query. This is particularly valuable when you need the same intermediate result set for different purposes. PostgreSQL materializes the CTE results, meaning the subquery executes once and the results are reused, rather than re-executing the same logic multiple times.

For example, if you need to calculate monthly aggregates and then use those results in both a summary table and a detailed breakdown, defining the aggregation as a CTE means you write the logic once and reference it twice.

4. Recursive Queries Made Possible

One of PostgreSQL’s standout capabilities is recursive CTEs, enabled by the WITH RECURSIVE clause. This feature allows you to traverse hierarchical data—organizational charts, bill-of-materials structures, graph relationships—with elegant, set-based operations rather than procedural loops.Here’s a classic example of finding all employees in a management hierarchy:

WITH RECURSIVE employee_hierarchy AS (
— Anchor: Start with top-level managers
SELECT
employee_id,
employee_name,
manager_id,
1 as level
FROM employees
WHERE manager_id IS NULL

UNION ALL

— Recursive: Find employees reporting to current level
SELECT
e.employee_id,
e.employee_name,
e.manager_id,
eh.level + 1
FROM employees e
INNER JOIN employee_hierarchy eh ON e.manager_id = eh.employee_id
)
SELECT * FROM employee_hierarchy
ORDER BY level, employee_name;

This type of query would be significantly more complex—or even impossible—without CTEs.

5. Cleaner Alternative to Temporary Tables

Before CTEs became widely adopted, developers often created temporary tables to store intermediate results. While temporary tables have their place, they come with overhead: you need to explicitly create them, manage their lifecycle, and clean them up. CTEs provide a cleaner, more declarative approach for many use cases where you simply need a named intermediate result.

Temporary tables still make sense for very large intermediate result sets that you’ll query multiple times with different filters, but for most analytical queries, CTEs offer a more elegant solution.

Performance Considerations: The Truth About CTEs

It’s important to address a common misconception: CTEs don’t automatically improve performance. In fact, prior to PostgreSQL 12, CTEs were always materialized (essentially creating an internal temporary result set), which could sometimes hurt performance if the query optimizer couldn’t push down predicates.

PostgreSQL 12 introduced an optimization fence behavior change where simple CTEs can be inlined, allowing the optimizer to treat them more like subqueries when beneficial. This gives you the best of both worlds: readable code with intelligent optimization.

If you need to force materialization (for example, to compute an expensive aggregation only once), you can use the MATERIALIZED keyword:

WITH sales_data AS MATERIALIZED (
    SELECT 
        region,
        SUM(revenue) as total_revenue
    FROM large_sales_table
    GROUP BY region
)
SELECT * FROM sales_data WHERE total_revenue > 1000000
UNION ALL
SELECT * FROM sales_data WHERE total_revenue < 100000;

Conversely, if you want to ensure the CTE is inlined, use NOT MATERIALIZED.

Real-World Use Cases

Over my years working with PostgreSQL in production environments, I’ve found CTEs particularly valuable in several scenarios:

Complex Reporting Queries: When building dashboards or analytical reports, CTEs allow you to break down multi-step calculations into logical units. Each CTE represents one business metric or data transformation, making the entire query comprehensible to both developers and business analysts.

Data Quality Checks: CTEs excel at multi-stage data validation where you need to apply successive filters or transformations and track results at each stage.

Hierarchical Data Processing: Any time you’re working with self-referential data—organization structures, category trees, thread conversations—recursive CTEs provide the cleanest solution.

ETL Operations: When transforming data for analytics or migration, CTEs let you define each transformation step explicitly, making the pipeline transparent and debuggable.

Best Practices for Using CTEs

Based on my experience optimizing PostgreSQL queries for performance-critical applications:

Monitor performance: Use EXPLAIN ANALYZE to understand how PostgreSQL is executing your CTEs. In modern PostgreSQL versions, pay attention to whether CTEs are being materialized or inlined.

Don’t overuse them: CTEs are powerful, but not every query needs them. Simple queries with a single subquery are often clearer without the CTE wrapper.

Conclusion

Common Table Expressions represent one of PostgreSQL’s most elegant features for writing maintainable, readable SQL. They transform complex queries from intimidating walls of nested logic into clear, sequential operations that any developer can understand and modify.

Whether you’re building analytical reports, processing hierarchical data, or simply trying to make your queries more maintainable, CTEs should be in your PostgreSQL toolkit. They won’t always be the fastest solution, but they provide a level of code clarity that pays dividends in development velocity, debugging efficiency, and long-term maintainability.

As database systems grow more complex and queries become more sophisticated, the ability to express logic clearly becomes just as important as raw performance. CTEs give you that clarity without sacrificing PostgreSQL’s powerful optimization capabilities.

If you’re not already using CTEs in your PostgreSQL applications, I encourage you to start experimenting with them. Your future self—and your teammates—will thank you for writing queries that are not just functional, but genuinely understandable.

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David Sterling is a PostgreSQL consultant specializing in database optimization and architecture for high-performance applications.I’ve worked with clients whose legacy codebases contained queries spanning hundreds of lines with deeply nested subqueries. Converting these to CTEs reduced debugging time by as much as 60% because developers could immediately identify which logical section needed attention.

Use descriptive names: Your CTE names should clearly indicate what data they contain. Names like “monthly_sales_aggregates” or “active_customers_last_90_days” make queries self-documenting.

Keep CTEs focused: Each CTE should represent one logical transformation or data subset. If a CTE is doing too much, consider breaking it into multiple CTEs.

Be mindful of scope: CTEs are scoped to the query they’re defined in. If you need to reuse logic across multiple queries, consider creating a view or function instead.