DML

INSERT

INSERT INTO orders (customer_name, amount)
VALUES ('Alice Smith', 149.99);

RETURNING

Return values from the inserted row:

INSERT INTO orders (customer_name, amount)
VALUES ('Bob Johnson', 75.50)
RETURNING id;

INSERT INTO orders (customer_name, amount)
VALUES ('Carol', 200.00)
RETURNING *;

Insert from a Query

INSERT INTO archive (customer_name, amount)
SELECT customer_name, amount FROM orders WHERE status = 'delivered';

INSERT … ON CONFLICT (Upsert)

Skip conflicting rows:

INSERT INTO users (email, name)
VALUES ('alice@example.com', 'Alice')
ON CONFLICT DO NOTHING;

Update on conflict with specific columns:

INSERT INTO users (email, name)
VALUES ('alice@example.com', 'Alice Updated')
ON CONFLICT (email) DO UPDATE SET name = 'Alice Updated';

Can be combined with RETURNING:

INSERT INTO users (email, name)
VALUES ('alice@example.com', 'Alice')
ON CONFLICT (email) DO UPDATE SET name = 'Alice'
RETURNING *;

UPDATE

UPDATE orders SET status = 'shipped' WHERE amount > 100;

Bulk Update from VALUES

PostgreSQL-style UPDATE ... FROM:

UPDATE orders
  SET status = d.status
  FROM (VALUES ('ord-1', 'shipped'), ('ord-2', 'delivered'))
       AS d (id, status)
  WHERE orders.id = d.id;

UPDATE with RETURNING

UPDATE orders SET status = 'shipped'
WHERE id = 42
RETURNING id, status;

DELETE

DELETE FROM orders WHERE status = 'delivered';

DELETE … USING

Join other tables to determine which rows to delete:

DELETE FROM orders
USING customers
WHERE orders.customer_id = customers.id
  AND customers.status = 'inactive';

Multiple USING tables:

DELETE FROM order_items
USING products, categories
WHERE order_items.product_id = products.id
  AND products.category_id = categories.id
  AND categories.name = 'discontinued';

DELETE with RETURNING

DELETE FROM orders WHERE status = 'delivered'
RETURNING *;

COPY

Bulk import and export data in CSV format.

COPY FROM

Import rows from a CSV file:

COPY orders FROM 'data/orders.csv';
COPY orders FROM 'data/orders.csv' WITH (HEADER);
COPY orders FROM 'data/orders.csv' WITH (HEADER, DELIMITER '|');
COPY orders (customer_name, amount) FROM 'data/partial.csv' WITH (HEADER);

COPY TO

Export a table or query to a CSV file:

COPY orders TO 'export/orders.csv';
COPY orders TO 'export/orders.csv' WITH (HEADER);
COPY (SELECT * FROM orders WHERE status = 'pending') TO 'export/pending.csv' WITH (HEADER);

Options

Option	Description
HEADER	First row is a header (skipped on import, written on export)
DELIMITER 'c'	Field separator character (default: ,)

csv_file() — Query CSV Files Directly

Query a CSV file as a virtual table without importing:

SELECT * FROM csv_file('data/sales.csv');
SELECT name, age::int FROM csv_file('data/people.csv') WHERE age::int > 25;

All values are returned as TEXT — use ::type to cast. Supports WHERE, ORDER BY, LIMIT, JOIN, and aggregates.

Options:

csv_file('path')                        -- with header (default)
csv_file('path', false)                 -- no header (columns named column1, column2, ...)
csv_file('path', true, '|')            -- custom delimiter

Join CSV files with each other or with database tables:

SELECT e.name, d.department
FROM csv_file('employees.csv') e
JOIN csv_file('departments.csv') d ON e.dept_id = d.id;

Virtual Tables

Register external data sources as queryable tables using CREATE VIRTUAL TABLE:

CREATE VIRTUAL TABLE sales USING csv('data/sales.csv');
CREATE VIRTUAL TABLE sales USING csv('data/sales.csv', 'no_header', '|');

SELECT * FROM sales WHERE amount::int > 100;
DROP TABLE sales;

Virtual tables appear in SHOW TABLES and support SELECT, WHERE, JOIN, ORDER BY, and aggregates. They are read-only — INSERT, UPDATE, and DELETE are not supported.

The built-in csv module is registered by default. Custom modules can be registered via the Scala API:

db.registerVirtualTableModule("mymodule", new VirtualTableModule { ... })