SQL

July 10, 2026 ยท View on GitHub

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

val 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

MongrelDB.sql(sql) sends {"sql": "...", "format": "json"} to /sql, asking the daemon for JSON output. It returns the decoded rows when the daemon replies with a JSON result set, and an empty list otherwise.

In practice:

  • DDL and DML (CREATE TABLE, INSERT, UPDATE, DELETE) reply with a non-JSON status body. sql returns an empty list - success is the signal.
  • SELECT returns its rows as a JSON array of row objects keyed by column name, so sql decodes them into a List<Map<String, Any?>>. Use the native QueryBuilder when you need conditions that map to a specialized index (bitmap, range, full-text, vector); use sql for joins, CTEs, window functions, and arbitrary aggregates.

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

try {
    db.sql("INSERT INTO orders (id, customer, amount) VALUES (99, 'Zoe', 999.0)")
} catch (e: ConflictException) {
    if (e.code == "UNIQUE_VIOLATION") {
        println("duplicate row: ${e.message}")
    }
}

CREATE TABLE

Define a table in SQL instead of via MongrelDB.createTable. Column ids are assigned by the server when not stated.

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

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 (MongrelDB.beginTransaction) 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")

The client requests JSON output (format: "json"), so each SELECT returns its rows decoded into a List<Map<String, Any?>> keyed by column name.

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(
    """
    CREATE TABLE sales_by_customer AS
    SELECT customer, SUM(amount) AS total
    FROM orders
    GROUP BY customer
    """.trimIndent(),
)

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(
    """
    WITH RECURSIVE r(n) AS (
      SELECT 1
      UNION ALL
      SELECT n + 1 FROM r WHERE n < 10
    )
    SELECT n FROM r
    """.trimIndent(),
)

A common practical example is walking an adjacency list:

db.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
    """.trimIndent(),
)

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(
    """
    SELECT id, customer, amount,
           ROW_NUMBER() OVER (PARTITION BY customer ORDER BY amount DESC) AS rn
    FROM orders
    """.trimIndent(),
)

// Running total per customer.
db.sql(
    """
    SELECT id, customer, amount,
           SUM(amount) OVER (PARTITION BY customer ORDER BY id) AS running_total
    FROM orders
    """.trimIndent(),
)

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 Kotlin maps 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 List<Map<String, Any?>> of matching rows? Either works: the query builder for indexed lookups, or sql for ad-hoc queries.
  • 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 maps directly to a specialized index and skips the SQL parser.

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

Next steps