1. dialects
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        • Provision a database
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        • Where a managed database can be put
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      • UpcomingCharges
      • TeamBilling
      • TeamKerberosRealm
      • TeamSsoDomain
      • TeamSsoResponse
      • StoreConnection
      • BatchFailure
      • ProxyStatus
  1. dialects

Azure Synapse Analytics (dedicated SQL pool)

An Azure Synapse Analytics dedicated SQL pool is a provisioned, massively-parallel data warehouse — the engine formerly sold as Azure SQL Data Warehouse. It speaks T-SQL over TDS and is reached with the same mssql driver as SQL Server, so the prorm Synapse dialect extends MSSQLDialect and inherits its connection handling, parameter binding and query building.
The shared grammar is convenient and also the main hazard. A dedicated pool is not SQL Server with extras: it distributes every table across 60 storage distributions and, in exchange, drops a great deal of T-SQL. This guide is mostly about what is missing and what the dialect emits instead.
Implementation: synapse.
Status: Experimental. SQL generation is covered by connection-free unit tests; nothing has been executed against a live pool (see Verification status).

Overview#

MPP, not a bigger SQL Server. A control node distributes work across compute nodes; each table's rows are spread across 60 distributions. Design for batch loads and set-based analytics, not row-at-a-time OLTP.
Physical design is the table definition. DISTRIBUTION (how rows are spread), the storage index (columnstore / rowstore / heap) and range PARTITION boundaries are all declared inline in CREATE TABLE ... WITH (...).
Constraints are advisory or absent. There are no foreign keys and no CHECK constraints at all. PRIMARY KEY and UNIQUE exist only in their NOT ENFORCED spellings, recorded for the optimizer and never checked at write time.
CTAS is the idiomatic transform. CREATE TABLE AS SELECT is fully parallel and sets the new table's distribution and storage in one statement.
Statistics are manual. The pool does not auto-create or auto-update column statistics; the optimizer's distribution decisions depend on them.
PropertyValue
Dialect namesynapse
ExtendsMSSQLDialect (mssql)
Drivermssql
Cloud SDK@azure/identity (Entra only)
Default port1433
Local runtimenone — hosted service only

Quick start#

Install#

$ npm install ts-prorm-orm mssql
$ pnpm add ts-prorm-orm mssql
$ yarn add ts-prorm-orm mssql
$ bun add ts-prorm-orm mssql
mssql is the same driver the mssql dialect uses.

Connect#

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

export const db = new Prorm({
  dialect: 'synapse',
  host: 'my-workspace.sql.azuresynapse.net',
  database: 'my_pool',
  username: 'sqladminuser',
  password: process.env.SYNAPSE_PASSWORD,
  dialectOptions: {
    encrypt: true,
  },
});
import { Prorm } from 'ts-prorm-orm';

export const db = new Prorm({
  dialect: 'synapse',
  url: 'synapse://sqladminuser:secret@my-workspace.sql.azuresynapse.net:1433/my_pool',
  dialectOptions: {
    encrypt: true,
  },
});
A URL is folded into host, port, database, username and password before the dialect is built; any of those passed alongside it wins.

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) },
}, { timestamps: false });

await db.sync(); // CREATE TABLE — see Generated SQL below
[!NOTE] Why timestamps: false
A non-literal DEFAULT on column "createdAt" (CURRENT_TIMESTAMP) is not supported by Azure Synapse Analytics (dedicated SQL pool). A dedicated SQL pool requires the DEFAULT expression to be a literal constant — function calls and CURRENT_TIMESTAMP are rejected. Supply the value from the application on INSERT instead.

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#

[!WARNING] No OFFSET pagination
OFFSET ... FETCH pagination is not supported by Azure Synapse Analytics (dedicated SQL pool). A dedicated SQL pool's ORDER BY clause has no OFFSET/FETCH. Page with a ROW_NUMBER() window function in a subquery or CTE and filter on the resulting row number.

Transaction#

await db.transaction(async (t) => {
  await User.create({ email: 'grace@example.com', name: 'Grace' }, { transaction: t });
  await User.update({ name: 'Grace Hopper' }, { where: { email: 'grace@example.com' }, transaction: t });
});
[!WARNING] No savepoints
Nested transactions need savepoints, and synapse has none. Run the work in a single transaction, or restructure it so nothing nests.
Type mapping for the portable descriptors: STRING → NVARCHAR(255), auto-increment INTEGER → INT, BOOLEAN → BIT, DATE → DATETIME2, UUID → UNIQUEIDENTIFIER, JSON → NVARCHAR(MAX). The type comparison lines the major engines up side by side.

Connection#

Select the dialect with dialect: 'synapse'. Connection options are SQL Server's — same driver, same TDS port 1433 — at the workspace's SQL endpoint, plus the Entra ID logins below.
import { Prorm } from 'ts-prorm-orm';

const prorm = new Prorm({
  dialect: 'synapse',
  host: 'my-workspace.sql.azuresynapse.net',
  port: 1433,
  database: 'my_pool',
  username: 'sqladminuser',
  password: process.env.SYNAPSE_PASSWORD,
  dialectOptions: { encrypt: true },
});
A dedicated pool also takes a Microsoft Entra ID login, with the same options as Azure SQL Database: entra (a token from DefaultAzureCredential, or the credential you pick), tokenProvider (any token source), or a tedious-native authentication block. Give at most one. An Entra login replaces username/password and requires verified TLS (encrypt: true, trustServerCertificate: false, the defaults it applies); a certificate that is not verified is refused at construction. Needs @azure/identity installed next to the driver.
const prorm = new Prorm({
  dialect: 'synapse',
  host: 'my-workspace.sql.azuresynapse.net',
  database: 'my_pool',
  dialectOptions: {
    entra: { credential: { type: 'managed-identity', clientId: process.env.AZURE_CLIENT_ID } },
  },
});
dialect.authenticationMode reports 'entra-token', 'tedious-native' or 'sql'.
Identity:
const dialect = new SynapseDialect({ /* ... */ });
dialect.name;    // 'synapse'
dialect.library; // 'mssql' — inherited; there is no separate Synapse driver
The name override is written readonly name = 'synapse' as SynapseDialect['name']. The runtime value is 'synapse'; the cast only satisfies the property-override type check against the base's string-literal type without editing MSSQLDialect.

What differs from MSSQL#

SynapseDialect extends MSSQLDialect (mssql) and
overrides 83 of its methods (Object.getOwnPropertyNames(SynapseDialect.prototype),
computed by the scratch test); everything else is inherited unchanged.
AreaOverridden
Connection and executionqueryStream
DML buildersbuildDeleteQuery, buildInsertQuery, buildLimitOffset, buildSelectQuery, buildUpdateQuery, buildUpsertQuery, bulkInsert
TypesbuildSynapseColumnSql, getDataTypeSql
DDL and schemaaddColumn, addForeignKey, addIndex, addPartition, attachPartition, buildCreateMaterializedViewSQL, buildCreateTableSQL, buildCtasSQL, buildDistributionClause, buildPartitionClause, buildTableIndexClause, buildTableWithClause, changeColumn, createColumnstoreIndex, createComputedColumn, createConstraint, createDatabaseSQL, createExpressionIndex, createFulltextIndex, createIdentityColumn, createMaterializedView, createPartialIndex, createPartition, createPartitionedTable, createSavepointSQL, createSequence, createSpatialIndex, createStatisticsSQL, createStoredProcedure, createTable, createTableAs, createTrigger, createView, detachPartition, disableSystemVersioning, dropDatabaseSQL, dropIfObjectExistsSQL, dropMaterializedView, dropPartition, dropSchema, dropSequence, dropTable, dropTemporalTable, dropTrigger, dropView, refreshMaterializedView, renameTable, splitPartitionSQL
IntrospectionassertLiteralDefault, assertNoReturning, hasSequence, hasTrigger, listSequences, showMaterializedViews
OtherbuildSynapseConstraintSql, commentColumn, commentTable, formatBoundary, insert, mergePartitionSQL, nextSequenceValue, noSpatial, releaseSavepointSQL, rollbackToSavepointSQL, stAsText, stContains, stDWithin, stDistance, stGeomFromText, stIntersects, stWithin, switchPartitionSQL, updateStatisticsSQL

Table design: distribution, storage, partitioning#

The three Synapse-specific table options are the reason to reach for this dialect at all.
await dialect.createTable(
  'FactSales',
  {
    saleId:    { type: 'BIGINT', allowNull: false },
    region:    { type: 'NVARCHAR(50)', allowNull: false },
    orderDate: { type: 'DATE', allowNull: false },
    amount:    { type: 'DECIMAL(18,2)' },
  },
  {
    distribution: { type: 'HASH', columns: 'region' },
    index: { type: 'CLUSTERED COLUMNSTORE INDEX' },
    partition: { column: 'orderDate', range: 'RIGHT', boundaries: ['2024-01-01', '2025-01-01'] },
  },
);
distribution
ValueEmitsUse for
{ type: 'ROUND_ROBIN' }DISTRIBUTION = ROUND_ROBINStaging tables; the platform default
{ type: 'HASH', columns: 'c' }DISTRIBUTION = HASH([c])Large fact tables joined or grouped on c
{ type: 'HASH', columns: ['a','b'] }DISTRIBUTION = HASH(([a], [b]))Multi-column distribution keys
{ type: 'REPLICATE' }DISTRIBUTION = REPLICATESmall dimension tables
A multi-column hash key is wrapped in its own parentheses — HASH((a, b)) and HASH(a, b) are different productions.
index — { type: 'CLUSTERED COLUMNSTORE INDEX', order?: string[] } (the default; order emits the ordered form), { type: 'CLUSTERED INDEX', columns: [...] }, or { type: 'HEAP' }.
partition — { column, range?: 'LEFT' | 'RIGHT', boundaries }. LEFT is the Synapse default. Unlike SQL Server, there is no separate PARTITION FUNCTION/PARTITION SCHEME; createPartitionedTable() therefore works on this dialect where it throws on mssql.
Reshape partitions afterwards with the statement builders:
dialect.splitPartitionSQL('FactSales', '2026-01-01');            // ALTER TABLE ... SPLIT RANGE (...)
dialect.mergePartitionSQL('FactSales', '2024-01-01');            // ALTER TABLE ... MERGE RANGE (...)
dialect.switchPartitionSQL('Stage', 1, 'FactSales', 3);          // ALTER TABLE ... SWITCH PARTITION 1 TO ...
The generic named-partition API (createPartition, attachPartition, detachPartition, dropPartition, addPartition) throws: Synapse partitions are unnamed boundary ranges, not separate relations.

Constraints#

{ id: { type: 'INT', allowNull: false, primaryKey: true } }
// [id] INT NOT NULL PRIMARY KEY NONCLUSTERED NOT ENFORCED
NONCLUSTERED and NOT ENFORCED are both mandatory — a bare PRIMARY KEY, which the SQL Server base emits, is a parse error. Table-level constraints put NOT ENFORCED after the column list: CONSTRAINT [pk] PRIMARY KEY NONCLUSTERED ([a], [b]) NOT ENFORCED.
Because nothing is enforced, duplicate keys are not rejected — they silently produce wrong results in queries that trust the declaration. Enforce uniqueness in the load pipeline.
FOREIGN KEY and CHECK throw, from createTable, createConstraint and addForeignKey alike.
DEFAULT must be a literal constant. The base dialect passes a string default through as raw SQL, so a model carrying defaultValue: 'GETDATE()' creates fine on SQL Server and fails on Synapse; this dialect rejects it at build time instead. A DEFAULT on the hash-distribution column is also refused.

IDENTITY#

IDENTITY(1,1) is supported and is emitted before the column constraints, which is the order the Synapse grammar uses (SQL Server's base builder emits it last). SET IDENTITY_INSERT works, and insert()/bulkInsert() still accept identityInsert: true.
The semantics differ: values are guaranteed unique within a distribution but are not contiguous — consecutive rows routinely get values like 1 and 61. Never treat an identity value as a row count or an ordering. Adding an IDENTITY column to an existing table throws (there is no way to backfill per-distribution ranges), as does createIdentityColumn(); recreate the table with CTAS instead.

Pagination: TOP, never OFFSET/FETCH#

The Synapse ORDER BY grammar has no <offset_fetch> production at all.
dialect.buildSelectQuery({ tableName: 'FactSales', limit: 10 });
// SELECT TOP(10) * FROM [FactSales]

dialect.buildSelectQuery({ tableName: 'FactSales', limit: 10, offset: 20 });
// throws: OFFSET ... FETCH pagination is not supported by Azure Synapse Analytics (dedicated SQL pool).
There is no trailing clause that can express a limit either, so buildLimitOffset() throws for both forms rather than degrading. queryStream() throws for the same reason: the generic streaming helper pages by re-issuing the query with successive OFFSETs. Page with a ROW_NUMBER() window function in a subquery or CTE.

No OUTPUT clause#

returning throws on buildInsertQuery, insert, buildUpdateQuery, buildDeleteQuery, buildUpsertQuery and bulkInsert. A dedicated pool cannot return affected rows from a DML statement; re-select them afterwards, on a business key rather than on an identity value.

Bulk insert#

bulkInsert() issues one parameterized INSERT per row, wrapped in a single transaction. The base dialect batches rows into a multi-row table value constructor, which Synapse rejects outright ("Table value constructor is not supported in Azure Synapse Analytics").
This is correct but deliberately not fast. A real load on this platform belongs in COPY/PolyBase or a CTAS from an external table; row-at-a-time INSERT against an MPP engine is slow by construction.

CTAS and materialized views#

await dialect.createTableAs(
  'AggSales',
  'SELECT region, SUM(amount) AS total FROM [FactSales] GROUP BY region',
  { distribution: { type: 'HASH', columns: 'region' }, index: { type: 'CLUSTERED COLUMNSTORE INDEX' } },
);
// CREATE TABLE [AggSales] WITH (CLUSTERED COLUMNSTORE INDEX, DISTRIBUTION = HASH([region])) AS SELECT ...
The WITH (...) clause is required by the grammar, so a call that names no distribution still gets DISTRIBUTION = ROUND_ROBIN spelled out. ORDER BY is not allowed in a CTAS select.
Materialized views are the real thing here, not SQL Server's indexed-view emulation:
await dialect.createMaterializedView({
  name: 'mvSales',
  schema: 'dbo',
  query: 'SELECT region, SUM(amount) AS total FROM [FactSales] GROUP BY region',
  distribution: { type: 'HASH', columns: 'region' },
});
// CREATE MATERIALIZED VIEW [dbo].[mvSales] WITH (DISTRIBUTION = HASH([region])) AS SELECT ...
The pool maintains them incrementally, so refreshMaterializedView() emits ALTER MATERIALIZED VIEW ... REBUILD. REPLICATE distribution is refused. showMaterializedViews() looks for a clustered columnstore index (sys.indexes.type = 5) rather than the unique clustered rowstore index the base looks for, which a Synapse materialized view never has.
Ordinary views have no SCHEMABINDING and no CREATE OR ALTER, so createView(..., { replace: true }) emits a guarded DROP VIEW followed by CREATE VIEW.

Indexes#

addIndex() builds a plain CREATE [CLUSTERED|NONCLUSTERED] INDEX name ON table (cols). unique, where (filtered), include (covering) and expression each throw by name — none exist in the dedicated pool's CREATE INDEX surface, and there are no computed columns to emulate an expression index with.
createColumnstoreIndex() supports the clustered form plus ORDER (cols); the nonclustered columnstore form and COMPRESSION_DELAY throw. Full-text and spatial indexes throw.

Data types#

Type mapping is inherited from SQL Server, including NVARCHAR(MAX), VARBINARY(MAX), UNIQUEIDENTIFIER and DATETIMEOFFSET, which a dedicated pool does support.
getDataTypeSql() throws for the types it does not: GEOMETRY, GEOGRAPHY, HIERARCHYID, XML, SQL_VARIANT, ROWVERSION/TIMESTAMP and the legacy TEXT/NTEXT/IMAGE LOB types. The st* spatial helpers throw for the same reason. Table types and table variables (DECLARE @t TABLE) do not exist either; use a #temp table.

Other divergences#

SynapseEmitted instead of the base's
DROP TABLE/VIEW/MATERIALIZED VIEW with ifExistsNo IF EXISTSIF OBJECT_ID('T', 'U') IS NOT NULL DROP TABLE [T]
dropSchema with ifExistsNo IF EXISTSIF SCHEMA_ID('s') IS NOT NULL DROP SCHEMA [s]
renameTableNo sp_rename for tablesRENAME OBJECT [old] TO [new]
createStoredProcedureNo CREATE OR ALTER; CREATE PROCEDURE must start its batchGuard issued as its own statement, then a plain CREATE PROCEDURE
changeColumnALTER COLUMN takes only type/collation/nullabilityNullability always spelled out; DEFAULT/PRIMARY KEY dropped
Row locksNo table-hint syntaxthrows
Recursive CTEsNot supportedthrows (plain CTEs work)
Savepointsno — a nested transaction() throws
Table/column commentsNo sp_addextendedpropertythrows
CREATE/DROP DATABASEAzure control-plane operationthrows
Triggers, sequences and system-versioned (temporal) tables have no equivalent and every related method throws. Row-level security (CREATE SECURITY POLICY), CROSS/OUTER APPLY, OPENJSON, the JSON scalar functions, window functions, MERGE, SELECT ... INTO and DELETE/UPDATE ... FROM ... JOIN are all supported and inherited unchanged.
Statistics are manual on this platform:
dialect.createStatisticsSQL('FactSales', 'st_region', ['region']);
dialect.updateStatisticsSQL('FactSales');

Verification status#

Be honest about the boundary of what has been checked.
Verified against Microsoft Learn documentation. Each refusal above was checked against the azure-sqldw-latest version of the relevant T-SQL reference page rather than against general SQL Server knowledge. The load-bearing ones are quote-backed in the source comments: the absent <offset_fetch> production in the Synapse ORDER BY syntax block; "Table value constructor is not supported in Azure Synapse Analytics"; "a common table expression that includes references to itself … isn't supported"; the OUTPUT clause's "applies to" list omitting Synapse; the FROM grammar's missing table-hint production; IF EXISTS documented as SQL Server only; OR ALTER present only in the SQL Server and Fabric procedure/view blocks; "PRIMARY KEY is only supported when NONCLUSTERED and NOT ENFORCED are both used".
SQL-generation verified (no database required). Every builder and refusal is exercised by connection-free unit tests in tests/dialects/synapse.test.ts that assert exact SQL strings. Several tests additionally assert the base MSSQLDialect's divergent output next to the Synapse one, so the specific regression each override prevents is pinned rather than merely described.
Not verified against a live pool. A dedicated SQL pool has no Docker image and no emulator — it exists only as a provisioned Azure service — so nothing here has been executed. In particular the following are unconfirmed by execution:
That every generated statement is accepted by a real pool, including the WITH (...) clause ordering, the ordered clustered columnstore ORDER (...) form, and the RENAME OBJECT and ALTER MATERIALIZED VIEW ... REBUILD statements.
Runtime behaviour of MERGE. The docs are genuinely ambiguous — the feature overview marks it "Yes (preview)" while the MERGE reference lists Synapse without a preview flag and documents distribution-corruption bugs fixed in specific server builds. WHEN NOT MATCHED THEN INSERT is also documented as unsupported against a table with an IDENTITY column, which this dialect does not detect. Check your pool's build before relying on upserts.
Savepoints, which are refused here on the strength of absence rather than an explicit statement: the dedicated-pool transaction documentation covers only BEGIN/COMMIT/ROLLBACK and isolation level, and the SAVE TRANSACTION reference page could not be retrieved. If a pool does accept SAVE TRANSACTION, this dialect is over-restrictive there.
sp_addextendedproperty, likewise refused on the strength of Synapse's absence from its "applies to" list as reported by search rather than a directly fetched page.
Column renames. renameColumn() is inherited unchanged, emitting sp_rename ... 'COLUMN'. That matches the shape of the documented Synapse column-rename support, which is in preview and cannot be used on the distribution column; it is not confirmed available on every pool.
Whether a PRIMARY KEY/UNIQUE NOT ENFORCED column must be NOT NULL, and whether it may be the distribution column. Neither is settled by the documentation, so neither is checked.
In short: the dialect is confirmed to generate SQL consistent with Microsoft's published dedicated-pool grammar; it has not been confirmed to execute against a live pool.

See also#

Source: synapse and its README
SQL Server dialect — the base class, for everything inherited unchanged
Amazon Redshift — the other MPP warehouse dialect, with the same unenforced-constraint caveats

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.
Capabilitysynapse
Transactions atomicyes
Savepointsno — a nested transaction() throws
Isolationup to SERIALIZABLE; native levels SNAPSHOT
Transaction modesread-only no; deferrable no; constraint deferral no
RETURNINGrefused — The OUTPUT clause (returning on INSERT) is not supported by Azure Synapse Analytics (dedicated SQL pool). A dedicated SQL pool cannot return affected rows fro
UpsertMERGE INTO … WHEN MATCHED / WHEN NOT MATCHED
JSON columnNVARCHAR(MAX) (JSONB → NVARCHAR(MAX))
Vector columnrefused — The VECTOR data type is not supported by Azure Synapse Analytics (dedicated SQL pool). A dedicated SQL pool has no vector type. Store the embedding as NVARCHAR(
Array columnNVARCHAR(255)
WHERE builder18 operator keys; $and/$or/$not; $exists: yes; $in subquery: no; expression operands: no
SQL objectsfamily mssql: triggers no; procedures yes; functions multiStatement; materialized views emulated (indexedView); sequences yes; enums check; RLS no
StreamingqueryStream() present; LISTEN absent; createExtension() present

Testing#

tests/dialects/synapse.test.ts exercises the dialect with a mocked driver, so nothing touches a server.

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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