Next-Level SQL Functions

🚀📊 Next-Level SQL Functions to Optimize Large Dataset Queries Like a Pro!

Tired of the same old SQL tips? Let’s explore lesser-known, database-specific functions that can drastically speed up your large dataset workflows! From magic window functions to niche optimizers, these gems will make your queries fly. ✨

1. LATERAL Joins – Unleash Correlated Power 🔄

Supported Databases: PostgreSQL, Oracle 12c+, SQL Server (via CROSS APPLY)

What it does: Allows subqueries in the FROM clause to reference columns from preceding tables. Perfect for row-by-row calculations or JSON/array unpacking.

Example: Get the top 3 sales per region without a subquery mess:

SELECT regions.name, top_sales.*  
FROM regions  
LEFT JOIN LATERAL (  
    SELECT *  
    FROM sales  
    WHERE sales.region_id = regions.id  
    ORDER BY revenue DESC  
    LIMIT 3  
) AS top_sales ON true;  

Optimization Benefit: Avoids redundant scans by pushing down conditions into correlated subqueries.

2. FILTER Clause – Cleaner Aggregations 🧹

Supported Databases: PostgreSQL, SQLite

What it does: Applies conditional filters directly to aggregate functions, replacing verbose CASE statements.

Example: Calculate average salary for managers vs. non-managers in one query:

SELECT  
    AVG(salary) FILTER (WHERE is_manager = true) AS avg_manager_salary,  
    AVG(salary) FILTER (WHERE is_manager = false) AS avg_employee_salary  
FROM employees;  

Optimization Benefit: Simplifies code and improves readability while maintaining performance.

3. DISTINCT ON – Smarter Deduplication 🎯

Supported Databases: PostgreSQL

What it does: Returns the first row for each group defined by DISTINCT ON columns. Like GROUP BY but without aggregating!

Example: Fetch the latest order per customer:

SELECT DISTINCT ON (customer_id)  
    customer_id, order_date, amount  
FROM orders  
ORDER BY customer_id, order_date DESC;  

Optimization Benefit: Faster than window functions for simple “first per group” use cases.

4. TABLESAMPLE – Lightning-Fast Sampling 🌩️

Supported Databases: PostgreSQL, SQL Server, IBM Db2

What it does: Retrieves a random sample of data without scanning the entire table.

Example: Analyze 10% of a massive log table:

SELECT *  
FROM server_logs  
TABLESAMPLE SYSTEM (10); -- 10% of the table  

Optimization Benefit: Saves time on statistical analysis by avoiding full-table scans.

5. MATERIALIZED VIEW – Cache Complex Queries 💾

Supported Databases: PostgreSQL, Oracle, SQL Server

What it does: Stores the result of a query physically, like a table, and refreshes it on demand.

Example: Precompute daily sales aggregates:

CREATE MATERIALIZED VIEW daily_sales AS  
SELECT  
    sale_date,  
    SUM(revenue) AS total_revenue  
FROM sales  
GROUP BY sale_date;  

-- Refresh to update data  
REFRESH MATERIALIZED VIEW daily_sales;  

Optimization Benefit: Eliminates repetitive heavy computations for frequently used reports.

6. UNNEST() – Explode Arrays Like JSON/Arrays 💥

Supported Databases: PostgreSQL, Google BigQuery

What it does: Converts arrays or JSON elements into rows. Ideal for denormalized datasets.

Example: List all product tags stored as an array:

SELECT product_id, UNNEST(tags) AS tag  
FROM products;  

Bonus (BigQuery):

SELECT product_id, tag  
FROM products, UNNEST(tags) AS tag;  

Optimization Benefit: Faster than app-level processing for nested data structures.

7. GENERATE_SERIES() – Fill Time Gaps Effortlessly ⏳

Supported Databases: PostgreSQL, SQL Server 2022+

What it does: Generates a sequence of numbers or dates. Perfect for time-series reporting with missing data.

Example: Track daily sales, including days with zero sales:

WITH date_series AS (  
    SELECT GENERATE_SERIES(  
        '2023-01-01'::date,  
        '2023-01-31'::date,  
        '1 day'  
    ) AS date  
)  
SELECT  
    date_series.date,  
    COALESCE(SUM(sales.amount), 0) AS daily_sales  
FROM date_series  
LEFT JOIN sales ON date_series.date = sales.sale_date  
GROUP BY date_series.date;  

Optimization Benefit: Avoids gaps in reports without tedious app-side date generation.

8. JSONB_ARRAY_ELEMENTS() – Crush JSON Datasets 🗜️

Supported Databases: PostgreSQL

What it does: Expands JSONB array elements into rows. A must-have for modern JSON-heavy tables.

Example: Extract user IDs from a JSONB log field:

SELECT  
    log_id,  
    JSONB_ARRAY_ELEMENTS(log_data->'user_ids') AS user_id  
FROM activity_logs;  

Optimization Benefit: Faster and cleaner than parsing JSON in application code.

🎯 Pro Tips for Maximum Speed:

• Combine LATERAL + UNNEST to flatten nested data while joining.
• Use MATERIALIZED VIEW + REFRESH CONCURRENTLY (PostgreSQL) for zero-downtime refreshes.
• Pair TABLESAMPLE with WHERE clauses for targeted sampling.

💡 Final Thoughts

Large datasets demand creativity! These niche functions help you avoid brute-force scans, leverage database-specific optimizations, and keep data processing inside the database where it’s fastest. 🏎️

Which function will you try first? Let me know below! 👇

#SQL #DataEngineering #DatabaseHacks #BigData
(Cover image: A rocket ship (🚀) blasting through a slow turtle (🐢) labeled “Inefficient Queries”)