sqlbolt: Examples, How It Works, Best Practices

sqlbolt is a popular interactive tutorial many teams use to learn SQL fundamentals quickly. The pain usually comes after lesson one: real data is messy, schemas differ, and small dialect differences between Postgres, MySQL, and others derail progress. People stall on joins, grouping, date filters, and window functions. AI2sql helps you move from request to correct query faster by translating plain English tasks into production-ready SQL for your database and schema, with explanations so you learn as you go. You can practice the concepts you see on sqlbolt while getting runnable, dialect-aware code.

Takeaway: Use AI2sql to turn your sqlbolt-style question and schema into correct SQL in seconds, then study the explanation to reinforce the concept.

For teams that graduate from tutorials and need to ship queries on real databases, the AI2sql platform handles schema grounding, multiple engines, and variant generation so you can compare approaches and learn what works best.

Understanding sqlbolt

sqlbolt is an interactive SQL tutorial focused on building muscle memory for core topics: SELECT, WHERE filtering, ORDER BY, GROUP BY and aggregates, JOINs, subqueries, and later steps like set operations and window functions. Exercises are deliberately compact, but the habits carry over to production work if you apply them against your own schema and engine.

• Key lessons map to common business questions: listing, filtering, summarizing, joining, ranking, and time series.

• Typical pitfalls: misplaced filters around GROUP BY, accidental Cartesian products in joins, confusing NULL handling, and dialect differences like date functions and case-insensitive matching.

• What you need to practice: a clear schema, precise requirements, and fast feedback on whether your SQL is correct.

Generate SQL for sqlbolt instantly with AI2sql — no technical expertise required.

Step-by-Step Solution

Use this routine whenever you tackle a sqlbolt-style task or a real business question:

1. Identify the data sources: write down the tables and the join keys you need. Example: orders joins to customers on customer_id, and to order_items on order_id.

2. Select only the columns you need: keep SELECT lists tight to catch mistakes early.

3. Filter early: put row filters in WHERE, and remember that post-aggregation filters live in HAVING.

4. Aggregate with intent: group by the grain of the output, then compute aggregates like COUNT, SUM, AVG.

5. Order and limit last: sort for readability and performance testing, and use LIMIT thoughtfully.

6. Validate: spot check counts, totals, and a few records against expectations.

Do it even faster with AI2sql:

1. Paste your schema or connect your database. For Postgres specifics, see our PostgreSQL integration.

2. Choose your engine (MySQL, PostgreSQL, BigQuery, Snowflake, and more).

3. Describe the task in plain English, including filters, time range, and desired columns.

4. Generate SQL and read the explanation; request variants or stricter constraints if needed.

Generate SQL for sqlbolt instantly with AI2sql — no technical expertise required.

Example Queries (multi-DB)

Postgres — Recent orders from California customers in the last 30 days.

SELECT c.customer_id, c.first_name, c.last_name, o.order_id, o.order_date
FROM customers c
JOIN orders o ON o.customer_id = c.customer_id
WHERE c.state = 'CA'
  AND o.order_date >= CURRENT_DATE - INTERVAL '30 days'
ORDER BY o.order_date DESC;

MySQL — 2024 revenue by product category.

SELECT p.category, ROUND(SUM(oi.quantity * oi.unit_price), 2) AS revenue_2024
FROM orders o
JOIN order_items oi ON oi.order_id = o.order_id
JOIN products p ON p.product_id = oi.product_id
WHERE YEAR(o.order_date) = 2024
GROUP BY p.category
ORDER BY revenue_2024 DESC;

Postgres — Practice-style query from sqlbolt: count customers per city.

SELECT city, COUNT(*) AS customers
FROM customers
GROUP BY city
ORDER BY customers DESC, city;

MySQL — Orders with missing shipping date flagged for follow-up.

SELECT o.order_id,
       o.order_date,
       o.shipping_date,
       CASE WHEN o.shipping_date IS NULL THEN 'MISSING' ELSE 'SHIPPED' END AS ship_status
FROM orders o
ORDER BY o.order_date DESC;

Postgres — Top 10 customers by lifetime spend with ranks.

WITH spend AS (
  SELECT o.customer_id, SUM(oi.quantity * oi.unit_price) AS lifetime_spend
  FROM orders o
  JOIN order_items oi ON oi.order_id = o.order_id
  GROUP BY o.customer_id
)
SELECT c.customer_id, c.first_name, c.last_name, s.lifetime_spend,
       DENSE_RANK() OVER (ORDER BY s.lifetime_spend DESC) AS rnk
FROM spend s
JOIN customers c ON c.customer_id = s.customer_id
ORDER BY rnk
LIMIT 10;

MySQL 8 — Contribution of each category to total revenue percent.

WITH revenue AS (
  SELECT p.category, SUM(oi.quantity * oi.unit_price) AS cat_rev
  FROM order_items oi
  JOIN products p ON p.product_id = oi.product_id
  JOIN orders o ON o.order_id = oi.order_id
  GROUP BY p.category
)
SELECT category,
       cat_rev,
       ROUND(100 * cat_rev / SUM(cat_rev) OVER (), 2) AS pct_of_total
FROM revenue
ORDER BY cat_rev DESC;

Postgres — Monthly new customers in 2024.

SELECT DATE_TRUNC('month', created_at)::date AS month_start,
       COUNT(*) AS new_customers
FROM customers
WHERE created_at >= DATE '2024-01-01' AND created_at < DATE '2025-01-01'
GROUP BY month_start
ORDER BY month_start;

MySQL — Products with no orders in the last 90 days.

SELECT p.product_id, p.name
FROM products p
LEFT JOIN (
  SELECT DISTINCT oi.product_id
  FROM orders o
  JOIN order_items oi ON oi.order_id = o.order_id
  WHERE o.order_date >= (CURRENT_DATE - INTERVAL 90 DAY)
) recent ON recent.product_id = p.product_id
WHERE recent.product_id IS NULL
ORDER BY p.product_id;

Generate SQL for sqlbolt instantly with AI2sql — no technical expertise required.

Prevention & Best Practices

• Be explicit in joins: always specify ON keys to avoid accidental cross joins.

• Filter at the right stage: row filters in WHERE, aggregate filters in HAVING.

• Name your aggregates and use clear aliases for readability.

• Mind NULLs: COALESCE when computing derived metrics or sorting.

• Confirm the grain of your GROUP BY matches the columns you return.

• Watch dialect gaps: date functions, case-insensitive search, and string functions vary.

• Prompting AI2sql: include tables, join keys, time range, output columns, sort order, and your engine.

Generate SQL for sqlbolt instantly with AI2sql — no technical expertise required.

Do It Faster with AI2sql

• Natural language to SQL for MySQL, PostgreSQL, BigQuery, Snowflake, SQL Server, and more.

• Understands your schema and constraints, reducing guesswork and errors.

• Explains queries step by step so you learn from every generation.

• Produces variants and optimizations so you can compare approaches quickly.

• Validates dialect details, helping you move from tutorial tasks to production queries confidently.

Generate SQL for sqlbolt instantly with AI2sql — no technical expertise required.

Conclusion: sqlbolt builds the right habits, but getting from concept to correct code on your own data requires speed and feedback. With AI2sql, you describe the task, pick your engine, and receive runnable SQL plus an explanation. Use the examples above to practice core patterns like filtering, aggregation, joins, and ranking, then apply the same prompts on your schema for immediate results. Ready to accelerate your learning and ship correct queries faster? Try AI2sql Free – Generate sqlbolt Solutions.