sql postgresql: Examples, How It Works, Best Practices

Searching for sql postgresql usually means you want accurate PostgreSQL queries without the trial-and-error. Manually writing SQL can be slow: remembering dialect nuances, handling joins, window functions, JSONB operators, and time math often leads to mistakes and rework. AI2sql turns plain-language prompts into production-ready SQL tailored for Postgres, so analysts, PMs, and engineers can move from question to correct query faster. Whether you are exploring a new schema or standardizing reporting across teams, AI2sql reduces the back-and-forth and explains what it generates. This guide shows what sql postgresql entails, how to connect your database, and how to turn prompts into runnable SQL with copy-paste examples for Postgres and sibling engines like MySQL and BigQuery.

sql postgresql Overview (dialect specifics)

PostgreSQL is a robust, standards-first SQL database with powerful features: ILIKE for case-insensitive search, rich window functions, JSONB with operators and indexes, arrays and array functions, CTEs and recursive queries, and flexible upserts via ON CONFLICT. Compared with other engines, Postgres differs in timestamp math (interval syntax), case sensitivity rules for identifiers, regular expressions, and JSONB operators. If you are evaluating broader connectivity, see our integrations starting with PostgreSQL integration.

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Connect Your Database to AI2sql (steps)

Follow these steps to go from prompt to query in minutes:

1. Create an account on the AI2sql platform.

2. Open the builder and choose PostgreSQL as your target dialect.

3. Provide connection details (host, port, database, user) or upload a schema snapshot. For read-only analysis, a limited-access user is recommended.

4. Optionally import a sample schema or let AI2sql infer tables and relationships.

5. Type a natural-language prompt (for example: show top 5 products by revenue in the last 30 days) and review the generated SQL plus the explanation.

6. Run it in your SQL client or connect AI2sql to execute in a safe, read-only context.

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Prompts to SQL for sql postgresql (multiple examples)

Below are practical, copy-ready examples that reflect common analytics tasks. Each sql postgresql example includes PostgreSQL plus sibling-engine variants to highlight dialect differences.

Business context: top 5 products by revenue in the last 30 days.

PostgreSQL:

SELECT p.id, p.name, SUM(oi.quantity * oi.price) AS revenue FROM orders o JOIN order_items oi ON oi.order_id = o.id JOIN products p ON p.id = oi.product_id WHERE o.status = 'paid' AND o.created_at >= now() - interval '30 days' GROUP BY p.id, p.name ORDER BY revenue DESC LIMIT 5;

MySQL:

SELECT p.id, p.name, SUM(oi.quantity * oi.price) AS revenue FROM orders o JOIN order_items oi ON oi.order_id = o.id JOIN products p ON p.id = oi.product_id WHERE o.status = 'paid' AND o.created_at >= NOW() - INTERVAL 30 DAY GROUP BY p.id, p.name ORDER BY revenue DESC LIMIT 5;

BigQuery:

SELECT p.id, p.name, SUM(oi.quantity * oi.price) AS revenue FROM orders o JOIN order_items oi ON oi.order_id = o.id JOIN products p ON p.id = oi.product_id WHERE o.status = 'paid' AND o.created_at >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY) GROUP BY p.id, p.name ORDER BY revenue DESC LIMIT 5;

Business context: case-insensitive user search by name containing smith.

PostgreSQL:

SELECT id, full_name FROM users WHERE full_name ILIKE '%smith%' LIMIT 50;

MySQL:

SELECT id, full_name FROM users WHERE LOWER(full_name) LIKE '%smith%' LIMIT 50;

BigQuery:

SELECT id, full_name FROM users WHERE LOWER(full_name) LIKE '%smith%' LIMIT 50;

Business context: orders containing a line item with sku ABC stored in JSON arrays.

PostgreSQL (JSONB array of objects):

SELECT o.id, o.order_number FROM orders o WHERE EXISTS (SELECT 1 FROM jsonb_array_elements(o.line_items) AS li WHERE li ->> 'sku' = 'ABC');

MySQL 8 (JSON_TABLE):

SELECT o.id, o.order_number FROM orders o JOIN JSON_TABLE(o.line_items, '$[*]' COLUMNS(sku VARCHAR(64) PATH '$.sku')) jt ON jt.sku = 'ABC';

BigQuery (UNNEST JSON):

SELECT o.id, o.order_number FROM orders o, UNNEST(JSON_EXTRACT_ARRAY(o.line_items, '$')) it WHERE JSON_VALUE(it, '$.sku') = 'ABC';

Business context: monthly active users for the last 6 months.

PostgreSQL:

SELECT date_trunc('month', occurred_at)::date AS month, COUNT(DISTINCT user_id) AS mau FROM events WHERE occurred_at >= date_trunc('month', now()) - interval '6 months' GROUP BY month ORDER BY month;

MySQL:

SELECT DATE_FORMAT(occurred_at, '%Y-%m-01') AS month, COUNT(DISTINCT user_id) AS mau FROM events WHERE occurred_at >= DATE_SUB(DATE_FORMAT(NOW(), '%Y-%m-01'), INTERVAL 6 MONTH) GROUP BY month ORDER BY month;

BigQuery:

SELECT DATE_TRUNC(DATE(occurred_at), MONTH) AS month, COUNT(DISTINCT user_id) AS mau FROM events WHERE occurred_at >= TIMESTAMP_SUB(TIMESTAMP_TRUNC(CURRENT_TIMESTAMP(), MONTH), INTERVAL 6 MONTH) GROUP BY month ORDER BY month;

Business context: upsert inventory increments by sku.

PostgreSQL:

INSERT INTO inventory (sku, qty) VALUES ('A1', 10) ON CONFLICT (sku) DO UPDATE SET qty = inventory.qty + EXCLUDED.qty;

MySQL:

INSERT INTO inventory (sku, qty) VALUES ('A1', 10) ON DUPLICATE KEY UPDATE qty = qty + VALUES(qty);

BigQuery:

MERGE inventory t USING (SELECT 'A1' AS sku, 10 AS qty) s ON t.sku = s.sku WHEN MATCHED THEN UPDATE SET qty = t.qty + s.qty WHEN NOT MATCHED THEN INSERT (sku, qty) VALUES (s.sku, s.qty);

Business context: flag likely-valid emails via regex.

PostgreSQL:

SELECT id, email FROM users WHERE email ~* '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+[.][A-Za-z]{2,}$';

MySQL:

SELECT id, email FROM users WHERE REGEXP_LIKE(email, '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+[.][A-Za-z]{2,}$');

BigQuery:

SELECT id, email FROM users WHERE REGEXP_CONTAINS(email, r'^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+[.][A-Za-z]{2,}$');

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Common Errors and Fixes

• String quoting: In Postgres, use single quotes for string literals; double-quoted identifiers ("Name") change case sensitivity. Prefer lowercase identifiers and single-quoted strings.

• Time math: Postgres uses interval syntax like interval '30 days'; MySQL and BigQuery have different interval functions. Ask AI2sql to target your engine explicitly.

• Case-insensitive search: Use ILIKE in Postgres; in MySQL and BigQuery use LOWER(column) LIKE pattern.

• JSON vs JSONB: For Postgres, store JSONB and add GIN indexes for operators like @> or jsonb_path_ops to speed up filters.

• GROUP BY differences: Postgres requires grouped columns listed; avoid relying on positional groupings for portability.

• Upserts: Postgres uses ON CONFLICT; MySQL uses ON DUPLICATE KEY; BigQuery uses MERGE. Let AI2sql pick the right pattern.

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FAQs: sql postgresql with AI2sql

Does AI2sql support native Postgres features? Yes. Prompts can generate queries using ILIKE, window functions, CTEs, JSONB, arrays, and ON CONFLICT patterns.

How do I switch dialects? Choose PostgreSQL, MySQL, BigQuery, Snowflake, or others in the builder. AI2sql adapts functions, date math, and DML syntax automatically.

Can AI2sql explain queries? Yes. You get an explanation of joins, filters, and calculations alongside the SQL, useful for code review and onboarding.

What if I only have a schema snippet? Paste a sample schema or table DDL and use plain-language prompts; AI2sql infers relationships and proposes joins you can adjust.

Where can I learn more? Start with our PostgreSQL integration page and explore the builder.

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

Conclusion

PostgreSQL gives you power and flexibility, but writing perfect SQL on the first try can slow teams down. With AI2sql, you describe your goal in plain language and receive correct, dialect-aware SQL plus explanations you can trust. Connect your database, prompt, review, and ship faster across analytics, reporting, and product use cases. Try the builder to convert everyday questions into performant queries and accelerate your Postgres workflow. Try AI2sql Free – Generate sql postgresql Solutions.