1. dialects
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  1. dialects

Databricks dialect

Reference for the databricks SQL dialect
(src/dialects/databricks/index.ts).

Overview#

Databricks SQL is built on Apache Spark SQL and the Delta Lake table
format (the "lakehouse"). Spark SQL quotes identifiers with backticks, exactly
like MySQL, so DatabricksDialect extends MySQLDialect purely to reuse
the backtick quoting/escaping scaffolding and the general query-building
skeleton, then overrides the places where Spark/Delta grammar genuinely
differs from MySQL.
PropertyValue
Dialect namedatabricks
ExtendsMySQLDialect (mysql)
Drivermysql2
Cloud SDKnone
Default port443
Local runtimenone — hosted service only
export class DatabricksDialect extends MySQLDialect {
  readonly name = 'databricks';

  constructor(config: MySQLDialectOptions) {
    // Databricks SQL Warehouses are reached over HTTPS on port 443.
    super({ port: 443, ...config });
  }
  // ...
}
Two things are worth stating honestly up front:
This is a SQL-generation dialect layer. Its job is to emit Spark
SQL / Delta grammar. The synchronous build*Sql/build*Query methods
produce strings (and parameter arrays) without opening any connection —
that is exactly what the test suite exercises. A real deployment would
connect over the Databricks SQL driver (Databricks SQL Warehouse /
@databricks/sql) rather than mysql2; the inherited connection code is
not used against a real cluster.
Default port is 443 (Databricks SQL Warehouses are reached over HTTPS),
set in the constructor rather than MySQL's 3306.
Identifier quoting is inherited verbatim from MySQLDialect:
escapeId(identifier: unknown): string {
  const id = String(identifier ?? '');
  return `\`${id.replace(/`/g, '``')}\``;
}
dialect.escapeId('my_table'); // => `my_table`

Quick start#

Install#

$ npm install ts-prorm-orm mysql2
$ pnpm add ts-prorm-orm mysql2
$ yarn add ts-prorm-orm mysql2
$ bun add ts-prorm-orm mysql2
mysql2 is the same driver the mysql dialect uses.
[!CAUTION] A SQL-generation layer
The driver the catalog lists, mysql2, is the one the parent dialect loads; it does not speak Databricks's own wire protocol, so connecting to a live server is unverified. The builders — everything under Generated SQL — are what this dialect is for. See Caveats.

Connect#

import { Prorm } from 'ts-prorm-orm';

export const db = new Prorm({
  dialect: 'databricks',
  host: 'dbc-a1b2c3d4-e5f6.cloud.databricks.com',
  dialectOptions: {
    catalog: 'main',
    schema: 'default',
  },
});

Define a model#

import { DataTypes } from 'ts-prorm-orm';

const User = db.define('User', {
  id: { type: DataTypes.INTEGER(), primaryKey: true, autoIncrement: true },
  email: { type: DataTypes.STRING(255), allowNull: false, unique: true },
  name: { type: DataTypes.STRING(100) },
});

await db.sync(); // CREATE TABLE — see Generated SQL below

Query#

import { Op } from 'ts-prorm-orm';

await User.create({ email: 'ada@example.com', name: 'Ada' });

const page = await User.findAll({
  where: { name: { [Op.like]: 'A%' } },
  order: [['id', 'ASC']],
  limit: 20,
  offset: 40,
});

Generated SQL#

Each block is the statement the model call above it hands the dialect's query(), captured with the driver stubbed out. The model layer inlines the values.

Create table#

db.sync() for the model in the Quick start:
CREATE TABLE

Insert, update and delete#

await User.create({ email: 'ada@example.com', name: 'Ada' });
await User.update({ name: 'Grace' }, { where: { email: 'ada@example.com' } });
await User.destroy({ where: { id: 1 } });

Upsert#

await User.upsert({ id: 1, email: 'ada@example.com', name: 'Ada Lovelace' });
MERGE statement
upsert() also reads the row by primary key before and after the write, to return the instance.

Pagination#

await User.findAll({
  where: { name: { [Op.like]: 'A%' } },
  order: [['id', 'ASC']],
  limit: 20,
  offset: 40,
});

Transaction with a savepoint#

await db.transaction(async (t) => {
  await User.create({ email: 'grace@example.com', name: 'Grace' }, { transaction: t });
  // A nested transaction is a SAVEPOINT; its failure undoes only its own work.
  await db
    .transaction(async (sp) => {
      await User.destroy({ where: { id: 1 }, transaction: sp });
      throw new Error('keep the row');
    }, { transaction: t })
    .catch(() => {});
});
Type mapping for the portable descriptors: STRING → STRING, auto-increment INTEGER → INT, BOOLEAN → BOOLEAN, DATE → TIMESTAMP, UUID → STRING, JSON → STRING. The type comparison lines the major engines up side by side.

Connection#

import { Prorm } from 'ts-prorm-orm';

const orm = new Prorm({
  dialect: 'databricks',
  host: 'dbc-xxxxxxxx-xxxx.cloud.databricks.com',
  // port defaults to 443 (HTTPS SQL Warehouse); no need to set it
});
You can also construct the dialect directly, e.g. for SQL generation in tests
or tooling:
import {
  DatabricksDialect,
  createDatabricksDialect,
} from 'prorm/dialects/databricks';

const dialect = new DatabricksDialect({});
// or the factory that mirrors the other dialects:
const dialect2 = createDatabricksDialect();

What differs from MySQL#

DatabricksDialect extends MySQLDialect (mysql) and
overrides 53 of its methods (Object.getOwnPropertyNames(DatabricksDialect.prototype),
computed by the scratch test); everything else is inherited unchanged.
AreaOverridden
DML buildersbuildDeleteQuery, buildInsertQuery, buildLimitOffset, buildMergeSql, buildSetColumnCommentSql, buildSetColumnNullabilitySql, buildSetTablePropertiesSql, buildUpdateQuery, buildUpsertQuery, quoteTable
TypesbuildAddColumnSql, buildColumnDefinitionSql, buildDropColumnSql, buildRenameColumnSql, getDataTypeSql, legacySparkTypeSql, mapNativeType, renderSparkType
DDL and schemaaddColumn, addIndex, buildAddColumnSpecSql, buildCreateTableAsSelect, buildCreateTableSql, buildDropTableSql, changeColumn, createFulltextIndex, createIndex, createSpatialIndex, createTable, dropIndex, dropTable, optimize, removeColumn, removeIndex, renameColumn, vacuum
OtherbuildCopyIntoSql, buildOptimizeSql, buildRestoreSql, buildTimeTravelSelectSql, buildUnsetTablePropertiesSql, buildVacuumSql, copyInto, mergeInto, qualifiedTable, restore, runtimeAtLeast, selectAsOf, setColumnComment, setColumnNullability, setTableProperties, structFieldName, unsetTableProperties

Delta / Spark features#

CREATE TABLE (Delta/Spark grammar)#

buildCreateTableSql(tableName, columns, options?) emits Delta/Spark
CREATE TABLE grammar. The Databricks-specific options are:
OptionEmits
using (DatabricksTableFormat)USING <format> — defaults to DELTA. Values: DELTA, PARQUET, CSV, JSON, ORC, AVRO
partitionedBy: string[]PARTITIONED BY (cols)
clusteredBy: { columns, buckets }CLUSTERED BY (cols) INTO n BUCKETS
location: stringLOCATION '...' (external/unmanaged table)
comment: stringtrailing COMMENT '...'
tblproperties: Record<string,string>TBLPROPERTIES ('k'=v, ...)
ifNotExists: booleanCREATE TABLE IF NOT EXISTS
There is no MySQL ENGINE= / ROW_FORMAT= / charset concept.
dialect.buildCreateTableSql(
  'events',
  {
    id: { type: 'BIGINT' },
    country: { type: 'STRING' },
    created_at: { type: 'DATE' },
  },
  { using: 'DELTA', partitionedBy: ['country'] },
);
using defaults to DELTA when omitted:
dialect.buildCreateTableSql('t', { id: { type: 'BIGINT' } });
// => CREATE TABLE `t` (`id` BIGINT) USING DELTA
Bucketing, external location, and table properties:
dialect.buildCreateTableSql(
  'sales',
  { id: { type: 'BIGINT' }, region: { type: 'STRING' } },
  {
    clusteredBy: { columns: ['id'], buckets: 8 },
    location: 's3://bucket/sales',
    tblproperties: { 'delta.appendOnly': 'true' },
  },
);
The generated SQL contains:
The overall clause order emitted by buildCreateTableSql is:
USING → PARTITIONED BY → CLUSTERED BY ... INTO n BUCKETS → LOCATION
→ COMMENT → TBLPROPERTIES.

GENERATED ALWAYS AS IDENTITY#

Auto-increment columns use Delta identity columns instead of MySQL's
AUTO_INCREMENT. A primaryKey column is emitted as an inline (informational)
PRIMARY KEY — see Caveats.
dialect.buildCreateTableSql('users', {
  id: { type: 'BIGINT', primaryKey: true, autoIncrement: true },
  name: { type: 'STRING' },
});
Per-column definition rules (buildColumnDefinitionSql): NOT NULL is emitted
when allowNull === false; GENERATED ALWAYS AS IDENTITY when autoIncrement;
DEFAULT <value> when defaultValue is set and the column is not an
identity column; PRIMARY KEY when primaryKey; and a trailing COMMENT.

CTAS helper#

buildCreateTableAsSelect(tableName, selectQuery, options?) emits
CREATE TABLE ... USING <format> AS SELECT .... Only format,
partitioning, location, comment, ifNotExists, and tblproperties apply —
the column list and types are inferred from the SELECT. A trailing semicolon
on the select is stripped.
dialect.buildCreateTableAsSelect(
  'summary',
  'SELECT country, COUNT(*) AS n FROM events GROUP BY country',
  { using: 'DELTA' },
);

MERGE INTO upsert (buildUpsertQuery)#

Delta has no MySQL-style ON DUPLICATE KEY UPDATE. Upserts are expressed as a
Delta Lake MERGE INTO statement where the row to upsert is supplied as an
inline source relation (SELECT ? AS col, ...) and matched on the conflict
key columns.
const { sql, values } = dialect.buildUpsertQuery(
  'users',
  { id: 1, name: 'Ada', email: 'ada@example.com' },
  { conflictFields: ['id'] },
);
values is [1, 'Ada', 'ada@example.com'] (parameterized in column order).
conflictFields picks the ON/match key columns; it defaults to all
columns
when omitted or empty.
updateOnDuplicate restricts which columns the WHEN MATCHED branch
updates; it defaults to all non-key columns (columns not in
conflictFields). If there are no non-key columns to update, the
WHEN MATCHED branch is omitted entirely.
A Literal value is inlined into the source SELECT (<val> AS col)
rather than parameterized.
At least one column is required, otherwise buildUpsertQuery throws.
buildInsertQuery routes to this MERGE builder whenever options.upsert is
set (a plain insert is inherited from MySQL unchanged):
dialect.buildInsertQuery('users', { id: 1, name: 'Ada' });
// => INSERT INTO `users` (`id`, `name`) VALUES (?, ?)

dialect.buildInsertQuery(
  'users',
  { id: 1, name: 'Ada' },
  { upsert: true, conflictFields: ['id'] },
);
// => MERGE INTO `users` ...  (never ON DUPLICATE KEY)

Type mapping#

getDataTypeSql(dataType) maps ORM types to Spark SQL / Delta types. The
signature difference from MySQL is that character types collapse to STRING
(Spark SQL has no VARCHAR(n)/TEXT), and the complex types
ARRAY<>/MAP<>/STRUCT<> are supported.
ORM type (key)Spark SQL type
STRING, CHAR, TEXT, ENUM, SET, UUID, JSON, JSONBSTRING
INTEGER (default)INT
INTEGER length: 1TINYINT
INTEGER length: 2SMALLINT
INTEGER length: 8BIGINT
BIGINTBIGINT
FLOATFLOAT
DOUBLEDOUBLE
DECIMALDECIMAL(precision,scale) (defaults 10,0)
BOOLEANBOOLEAN
DATETIMESTAMP (date + time)
DATEONLYDATE
TIMESTRING (Spark has no standalone TIME)
BLOBBINARY
ARRAYARRAY<elem>
MAPMAP<keyType, valueType>
STRUCTSTRUCT<field: type, ...>
anything elseSTRING
For ARRAY/MAP, an unspecified element/key/value type defaults to STRING.
Raw string types are also normalized: a string matching
VARCHAR, CHARACTER VARYING, NVARCHAR, CHAR, TEXT/NTEXT,
TINYTEXT, MEDIUMTEXT, LONGTEXT, or CLOB returns STRING; any other
raw string is returned unchanged.
dialect.getDataTypeSql('VARCHAR(255)');                 // 'STRING'
dialect.getDataTypeSql({ key: 'DECIMAL', precision: 12, scale: 4 }); // 'DECIMAL(12,4)'
dialect.getDataTypeSql({ key: 'BLOB' });                // 'BINARY'
dialect.getDataTypeSql({ key: 'DATE' });                // 'TIMESTAMP'
dialect.getDataTypeSql({ key: 'ARRAY', type: { key: 'STRING' } });   // 'ARRAY<STRING>'
dialect.getDataTypeSql({
  key: 'MAP',
  keyType: { key: 'STRING' },
  valueType: { key: 'BIGINT' },
});                                                       // 'MAP<STRING, BIGINT>'
dialect.getDataTypeSql({
  key: 'STRUCT',
  fields: { a: { key: 'STRING' }, b: { key: 'BIGINT' } },
});                                                       // 'STRUCT<a: STRING, b: BIGINT>'

Caveats#

PRIMARY KEY / FOREIGN KEY are informational, not enforced#

In Databricks, PRIMARY KEY / FOREIGN KEY constraints are informational
only
(declared NOT ENFORCED, used by the optimizer for query planning);
they do not enforce uniqueness or referential integrity. This dialect emits an
inline PRIMARY KEY when a column requests it (see the identity example above)
but never relies on it for correctness.

RETURNING is unsupported#

Delta does not support a RETURNING clause. The dialect throws rather than
emit SQL a cluster would reject:
buildUpsertQuery(..., { returning: true }) throws
Databricks (Delta MERGE) does not support the RETURNING clause. ...
buildUpdateQuery(..., { returning: true }) throws
buildDeleteQuery(..., { returning: true }) throws
Re-fetch the row(s) after the write instead.

UPDATE / DELETE have no trailing LIMIT#

Delta UPDATE and DELETE support WHERE but not a trailing LIMIT. Any
limit option is stripped before delegating to the inherited builder:
dialect.buildUpdateQuery('users', { name: 'Ada' }, { id: 1 }, { limit: 5 });
// => UPDATE `users` SET `name` = ? WHERE ...   (no LIMIT), values ['Ada', 1]

dialect.buildDeleteQuery('users', { id: 1 }, { limit: 5 });
// => DELETE FROM `users` WHERE ...             (no LIMIT)

LIMIT / OFFSET#

Unlike MySQL, Spark SQL accepts a standalone OFFSET, so buildLimitOffset
drops MySQL's LIMIT 18446744073709551615 OFFSET n sentinel hack:
dialect.buildLimitOffset(undefined, 20); // ' OFFSET 20'
dialect.buildLimitOffset(5);             // ' LIMIT 5'
dialect.buildLimitOffset(5, 10);         // ' LIMIT 5 OFFSET 10'
OFFSET is only valid on the outermost query and requires a recent
Spark/DBR runtime (Spark 3.4 / Databricks Runtime 11+); on older runtimes it
raises a parser error. Prefer a keyset/WHERE predicate for deep pagination.

ALTER TABLE uses Spark grammar#

buildAddColumnSql emits Spark's plural
ALTER TABLE t ADD COLUMNS (col type [COMMENT '...']) — not MySQL's
singular ADD COLUMN. NOT NULL, DEFAULT, PRIMARY KEY, and identity
clauses are intentionally omitted on an added column (Spark rejects them
there):
dialect.buildAddColumnSql('users', 'age', { type: 'INT' });
// => ALTER TABLE `users` ADD COLUMNS (`age` INT)

dialect.buildAddColumnSql('users', 'bio', {
  type: 'STRING', allowNull: false, defaultValue: 'x', comment: 'biography',
});
// => ALTER TABLE `users` ADD COLUMNS (`bio` STRING COMMENT 'biography')
buildRenameColumnSql uses RENAME COLUMN old TO new (not MySQL CHANGE).
buildDropColumnSql → ALTER TABLE t DROP COLUMN col (requires Delta
column-mapping mode enabled on the table).
changeColumn() throws — MySQL-style CHANGE col <full-def> is not
valid Spark SQL. Use renameColumn(), setColumnComment()
(ALTER COLUMN col COMMENT '...'), or setColumnNullability()
(ALTER COLUMN col SET|DROP NOT NULL) for the supported in-place edits.
buildDropTableSql never emits CASCADE (Spark has no DROP TABLE ... CASCADE); a passed cascade option is ignored.
Table properties: buildSetTablePropertiesSql →
ALTER TABLE t SET TBLPROPERTIES (...), buildUnsetTablePropertiesSql →
ALTER TABLE t UNSET TBLPROPERTIES [IF EXISTS] (...).

Secondary indexes are unsupported#

Spark SQL has no generic CREATE INDEX. addIndex, createIndex,
removeIndex, dropIndex, createFulltextIndex, and createSpatialIndex
all throw, directing you to liquid clustering / Z-ORDER
(OPTIMIZE ... ZORDER BY), partitioning, or a Bloom-filter index instead.

Verification status#

SQL-generation verified (asserted by tests/dialects/databricks.test.ts,
which exercises only the synchronous builders — no connection is opened):
Dialect name and inherited backtick escapeId.
CREATE TABLE with USING DELTA, PARTITIONED BY,
CLUSTERED BY ... INTO n BUCKETS, LOCATION, TBLPROPERTIES, default
format DELTA, and GENERATED ALWAYS AS IDENTITY + informational
PRIMARY KEY.
CTAS (buildCreateTableAsSelect).
MERGE upsert (buildUpsertQuery) SQL shape and parameter order, plus the
RETURNING rejection.
Plain vs. upsert INSERT routing, and bulkInsert multi-row VALUES.
SELECT clause assembly and standalone OFFSET (no BIGINT sentinel).
UPDATE/DELETE without LIMIT, increment, and RETURNING rejection.
ALTER TABLE grammar (ADD COLUMNS, DROP COLUMN, RENAME COLUMN,
ALTER COLUMN comment/nullability, SET/UNSET TBLPROPERTIES,
DROP TABLE without CASCADE, changeColumn rejection).
Secondary-index rejections.
The full type-mapping table.
Not verified against a live warehouse. No end-to-end execution against a
real Databricks SQL Warehouse is covered — there is no databricks-crud
integration test, and the dialect does not use the real @databricks/sql
driver. Whether each generated statement runs successfully depends on the
target Databricks Runtime version (e.g. standalone OFFSET on DBR 11+, Delta
column-mapping mode for DROP COLUMN, identity-column support). Treat the SQL
here as verified-by-shape, and validate against your actual warehouse before
relying on it in production.

Capabilities#

Computed from the code — capabilitiesOf() (src/transaction-support.ts),
whereSupportOf() (src/query-builders/expression-render.ts),
sqlObjectCapabilitiesOf() (src/sql-objects/capabilities.ts) and the
builders — by the scratch test described in the
capability matrix.
Capabilitydatabricks
Transactions atomicyes
Savepointsyes
Isolationup to SERIALIZABLE
Transaction modesread-only yes; deferrable no; constraint deferral no
RETURNINGnot emitted; returning is accepted and ignored
UpsertMERGE INTO … WHEN MATCHED / WHEN NOT MATCHED
JSON columnSTRING (JSONB → STRING)
Vector columnARRAY<FLOAT>
Array columnARRAY<INT>
WHERE builder28 operator keys plus the $json: prefix; $and/$or/$not; $exists: no; $in subquery: no; expression operands: no
SQL objectsfamily unknown: triggers no; procedures no; functions no; materialized views no; sequences no; enums check; RLS no
StreamingqueryStream() present; LISTEN absent; createExtension() present

Related reading#

All dialects — what each engine supports
Data types — how each type maps to this engine
Querying — how a FindOptions becomes SQL
Modified at 2026-10-04 00:40:16
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