SQL

July 10, 2026 ยท View on GitHub

MongrelDB ships a DataFusion-backed SQL engine at POST /sql. From Crystal, run SQL with Client#sql:

rows = db.sql("SELECT 1")

This guide covers the SQL surface - DDL, DML, CREATE TABLE AS SELECT, recursive CTEs, and window functions - and when to reach for SQL versus the native query builder.

How sql behaves

Client#sql(sql) sends {"sql": "...", "format": "json"} to /sql. It requests JSON output and returns the decoded rows when the daemon replies with a JSON result set, and an empty array with a nil error otherwise.

In practice:

  • DDL and DML (CREATE TABLE, INSERT, UPDATE, DELETE) reply with a non-JSON status body. sql returns [] - success is the signal.
  • SELECT in most daemon builds streams Arrow IPC bytes rather than JSON. sql therefore returns [] for SELECTs too. Use the native QueryBuilder for typed row retrieval in application code, and use sql for statements whose execution is the goal (DDL/DML/admin).

Errors are mapped to the same typed exceptions as everything else: an HTTP 400 or 5xx raises QueryError; 409 raises ConflictError; and so on. See errors.md.

begin
  db.sql("INSERT INTO orders (id, customer, amount) VALUES (99, 'Zoe', 999.0)")
rescue ex : MongrelDB::ConflictError
  STDERR.puts "duplicate row: #{ex.message}" if ex.error_code == "UNIQUE_VIOLATION"
end

CREATE TABLE

Define a table in SQL instead of via Client#create_table. Column ids are assigned by the server when not stated.

db.sql(<<-SQL)
  CREATE TABLE products (
    id          INT64 PRIMARY KEY,
    name        VARCHAR,
    price       FLOAT64,
    category    VARCHAR,
    in_stock    BOOLEAN
  )
SQL

INSERT

db.sql("INSERT INTO products (id, name, price, category, in_stock) VALUES (1, 'Widget', 9.99, 'tools', true)")
db.sql("INSERT INTO products VALUES (2, 'Gadget', 19.99, 'tools', true)")

For bulk inserts, the native batch transaction (Client#begin_transaction) is usually faster because it stages ops in one round trip without re-parsing SQL.

UPDATE

db.sql("UPDATE products SET price = 14.99 WHERE id = 1")
db.sql("UPDATE orders SET amount = 200.0 WHERE customer = 'Bob'")

DELETE

db.sql("DELETE FROM products WHERE in_stock = false")
db.sql("DELETE FROM products WHERE id = 2")

SELECT

db.sql("SELECT id, name FROM products WHERE category = 'tools' ORDER BY price")
db.sql("SELECT category, COUNT(*) AS n FROM products GROUP BY category")

Remember SELECT bodies usually arrive as Arrow IPC, so sql returns an empty array. To read rows back into Crystal, mirror the same lookup with the QueryBuilder.

CREATE TABLE AS SELECT

Materialize a query result into a new table. Great for snapshots, rollups, and denormalized aggregates.

# Snapshot all high-value orders into a new table.
db.sql("CREATE TABLE archive AS SELECT * FROM orders WHERE amount > 500")

# Roll up sales by customer.
db.sql(<<-SQL)
  CREATE TABLE sales_by_customer AS
  SELECT customer, SUM(amount) AS total
  FROM orders
  GROUP BY customer
SQL

The new table inherits column types from the query. Query it afterward with the native builder or SQL.

Recursive CTEs

WITH RECURSIVE is fully supported. Classic use cases: series generation, hierarchy/graph traversal.

# Generate the numbers 1..10.
db.sql(<<-SQL)
  WITH RECURSIVE r(n) AS (
    SELECT 1
    UNION ALL
    SELECT n + 1 FROM r WHERE n < 10
  )
  SELECT n FROM r
SQL

A common practical example is walking an adjacency list:

db.sql(<<-SQL)
  WITH RECURSIVE descendants(id) AS (
    SELECT id FROM categories WHERE id = 1
    UNION ALL
    SELECT c.id FROM categories c
    JOIN descendants d ON c.parent_id = d.id
  )
  SELECT id FROM descendants
SQL

Window functions

Window functions compute aggregates/rankings across a moving window without collapsing rows. Useful for top-N-per-group, running totals, and row numbers.

# Row number within each customer, ordered by amount descending.
db.sql(<<-SQL)
  SELECT id, customer, amount,
         ROW_NUMBER() OVER (PARTITION BY customer ORDER BY amount DESC) AS rn
  FROM orders
SQL

# Running total per customer.
db.sql(<<-SQL)
  SELECT id, customer, amount,
         SUM(amount) OVER (PARTITION BY customer ORDER BY id) AS running_total
  FROM orders
SQL

RANK(), DENSE_RANK(), LAG(), LEAD(), NTILE(), and the usual window-frame clauses are available through DataFusion.

When to use SQL vs. the query builder

Both read from the same tables, but they are optimized for different jobs.

Reach forWhen
QueryBuilderPoint lookups, range scans, bitmap filters, full-text, and vector similarity that map to a native index. Sub-millisecond, no parser overhead, and rows decode into Crystal directly.
SQLDDL (CREATE TABLE, schemas, materialized views), multi-statement setup, joins, recursive CTEs, window functions, and arbitrary aggregates. Also the natural choice for admin scripts and one-off analysis.

Rules of thumb:

  • Need a typed Array(JSON::Any) of matching rows? Use the query builder.
  • Building/dropping tables, or running a CREATE TABLE AS SELECT? Use SQL.
  • Joining multiple tables, computing rankings, or walking a graph? Use SQL.
  • Filtering by one or more indexed columns? Use the query builder - it is faster and avoids Arrow-to-Crystal decoding.

Mix freely: create tables with SQL, write rows with Client#put, read them back with QueryBuilder, and run analytics with SQL.

Next steps