Mammoth: A type-safe Postgres query builder pur sang for TypeScript

npm i @ff00ff/mammoth@1.0.0-rc.7

Mammoth is a type-safe query builder. It only supports Postgres which we consider a feature. It's syntax is as close to SQL as possible so you already know how to use it. It's autocomplete features are great. It helps you avoid mistakes so you can develop applications faster.

const rows = await db
  .select(db.foo.id, db.bar.name)
  .from(db.foo)
  .leftJoin(db.bar)
  .on(db.foo.barId.eq(db.bar.id))
  .where(db.foo.id.eq(`1`));

The above query produces the following SQL:

SELECT
  foo.id,
  bar.name
FROM foo
LEFT JOIN bar ON (foo.bar_id = bar.id)
WHERE
  foo.id = $1

More importantly, the resulting type of rows is { id: string; name: string | undefined }[]. Notice how the name is automatically nullable because of the left join.

Query examples

Basic update

const updateCount = await db.update(db.foo).set({ name: `Test` }).where(db.foo.value.gt(0));

UPDATE foo
SET
  name = $1
WHERE
  value > $2

Basic insert

const rows = await db
  .insertInto(db.foo)
  .values({
    name: `Test`,
    value: 123,
  })
  .returning(`id`);

INSERT INTO foo (
  name,
  value
) VALUES (
  $1,
  $2
)
RETURNING
  id

Insert into select

const affectedCount = await db
  .insertInto(db.foo, ['name'])
  .select(db.bar.name)
  .from(db.bar)
  .where(db.bar.name.isNotNull());

INSERT INTO foo (name)
SELECT
  bar.name
FROM bar
WHERE
  bar.name IS NOT NULL

Select with count(*)

db.select(count()).from(db.foo);

SELECT COUNT(*) FROM foo

Select with aggregate expression

db.select(arrayAgg(db.foo.name.orderBy(db.foo.name.desc()))).from(db.foo);

SELECT array_agg(foo.name ORDER BY foo.name DESC) "arrayAgg" FROM foo

With (CTE) query

db.with(
  `regionalSales`,
  () =>
    db
      .select(db.orderLog.region, sum(db.orderLog.amount).as(`totalSales`))
      .from(db.orderLog)
      .groupBy(db.orderLog.region),
  `topRegions`,
  ({ regionalSales }) =>
    db
      .select(regionalSales.region)
      .from(regionalSales)
      .where(
        regionalSales.totalSales.gt(
          db.select(sum(regionalSales.totalSales).divide(10)).from(regionalSales),
        ),
      ),
  ({ topRegions }) =>
    db
      .select(
        db.orderLog.region,
        db.orderLog.product,
        sum(db.orderLog.quantity).as(`productUnits`),
        sum(db.orderLog.amount).as(`productSales`),
      )
      .from(db.orderLog)
      .where(db.orderLog.region.in(db.select(topRegions.region).from(topRegions)))
      .groupBy(db.orderLog.region, db.orderLog.product),
);

WITH "regionalSales" AS (SELECT order_log.region, SUM (order_log.amount) "totalSales" FROM order_log GROUP BY order_log.region), "topRegions" AS (SELECT "regionalSales".region FROM "regionalSales" WHERE "regionalSales"."totalSales" > (SELECT SUM ("regionalSales"."totalSales") / $1 FROM "regionalSales")) SELECT order_log.region, order_log.product, SUM (order_log.quantity) "productUnits", SUM (order_log.amount) "productSales" FROM order_log WHERE order_log.region IN (SELECT "topRegions".region FROM "topRegions") GROUP BY order_log.region, order_log.product

Quick start

Mammoth is a query builder pur sang so it doesn't include a database driver. You need to create a db and pass a callback to execute the query.

import { defineDb } from '@ff00ff/mammoth';
import { foo, bar } from './tables';

const db = defineDb({ foo, bar }, async (query, parameters) => {
  const result = await pool.query(query, parameters);

  return {
    affectedCount: result.rowCount,
    rows: result.rows,
  };
});

In the defineDb call you pass all your tables so they can be access through the db instance. You have to define all the tables to make sure Mammoth understands the type information. This should be close to the CREATE TABLE syntax.

const foo = defineTable({
  id: uuid().primaryKey().default(`gen_random_uuid()`),
  createDate: timestampWithTimeZone().notNull().default(`now()`),
  name: text().notNull(),
  value: integer(),
});

You should keep your column names camelCase in the defineTable call as they are automatically transformed to train_case throughout Mammoth.

Compatibility

Below is a list of clauses per query and a short description on what we Mammoth supports.

SELECT

[ WITH [ RECURSIVE ] with_query [, ...] ] — Partial support. Recursive not supported yet.
SELECT [ ALL | DISTINCT [ ON ( expression [, ...] ) ] ] — Mostly supported. Distinct not yet.
[ * | expression [ [ AS ] output_name ] [, ...] ] — mostly supported. Selecting certain expressions like update queries, insert and delete queries are not supported yet. Select queries are though.
[ FROM from_item [, ...] ] — partially supported. Only 1 table is currently supported in the from.
[ WHERE condition ] — mostly supported. The condition concept is pretty broad but it should contain a lot of cases.
[ GROUP BY grouping_element [, ...] ] — supported.
[ HAVING condition [, ...] ] — supported.
[ WINDOW window_name AS ( window_definition ) [, ...] ] — not supported.
[ { UNION | INTERSECT | EXCEPT } [ ALL | DISTINCT ] select ] — not supported yet
[ ORDER BY expression [ ASC | DESC | USING operator ] [ NULLS { FIRST | LAST } ] [, ...] ] — supported, but expressions are pretty broad and there might be cases not covered yet.
[ LIMIT { count | ALL } ] — supported.
[ OFFSET start [ ROW | ROWS ] ] — supported.
[ FETCH { FIRST | NEXT } [ count ] { ROW | ROWS } ONLY ] — supported
[ FOR { UPDATE | NO KEY UPDATE | SHARE | KEY SHARE } [ OF table_name [, ...] ] [ NOWAIT | SKIP LOCKED ] [...] ] — supported

UPDATE

[ WITH [ RECURSIVE ] with_query [, ...] ] — Partial support. Recursive not supported yet.
UPDATE [ ONLY ] table_name [ * ] [ [ AS ] alias ] — supported
SET { column_name = { expression | DEFAULT } | — supported, but expression concept is very broad and might be incomplete
( column_name [, ...] ) = [ ROW ] ( { expression | DEFAULT } [, ...] ) | — supported, but expression concept is very broad and might be incomplete in some cases
( column_name [, ...] ) = ( sub-SELECT ) — not supported
} [, ...]
[ FROM from_item [, ...] ] — partially supported. Only 1 table as from item is supported
[ WHERE condition | WHERE CURRENT OF cursor_name ] — supported, but the condition concept is very broad and is incomplete in some cases.
[ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] — supported, but up to 10 expressions

DELETE

[ WITH [ RECURSIVE ] with_query [, ...] ] — Partial support. Recursive not supported yet.
DELETE FROM [ ONLY ] table_name [ * ] [ [ AS ] alias ] — supported
[ USING from_item [, ...] ] — supported
[ WHERE condition | WHERE CURRENT OF cursor_name ] — supported, but the condition concept is very broad and might be incomplete
[ RETURNING * | output_expression [ [ AS ] output_name ] [, ... ] ] — supported, but up to 10 expressions

INSERT

[ WITH [ RECURSIVE ] with_query [, ...] ] — Partial support. Recursive not supported yet.
INSERT INTO table_name [ AS alias ] [ ( column_name [, ...] ) ] — supported
[ OVERRIDING { SYSTEM | USER } VALUE ] — not supported
{ DEFAULT VALUES | VALUES ( { expression | DEFAULT } [, ...] ) [, ...] | query } - supported, but expression is a broad concept and may not be complete
[ ON CONFLICT [ conflict_target ] conflict_action ] — supported
[ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] — supported, but limited to 10 expressions

Column data type

This is a list of all the data types and it's companioning data type function.

Function	SQL data type
binary()	BYTEA
blob()	BYTEA
bytea()	BYTEA
caseInsensitiveText()	CITEXT
citext()	CITEXT
date()	DATE
decimal()	DECIMAL
integer()	INTEGER
interval()	INTERVAL
jsonb<T>()	JSONB
json<T>()	JSON
money()	MONEY
number()	IntegerColumn
serial()	SERIAL
string()	TextColumn
text()	TEXT
text<T>()	TEXT
time()	TIME
timestamp()	TIMESTAMP
timestampWithoutTimeZone()	TIMESTAMP WITHOUT TIME ZONE
timestampWithTimeZone()	TIMESTAMP WITH TIME ZONE
timeWithoutTimeZone()	TIME WITHOUT TIME ZONE
timeWithTimeZone()	TIME WITH TIME ZONE
uuid()	UUID

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