1. dialects
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  1. dialects

Amazon Redshift

Amazon Redshift is a columnar, massively-parallel cloud data warehouse. It began life as a fork of PostgreSQL 8.0.2 and still speaks the PostgreSQL wire protocol, so the prorm Redshift dialect reuses the pg driver and mirrors much of the Postgres dialect's query building. That shared heritage is convenient, but Redshift diverges from Postgres in ways that matter a great deal in practice — this guide focuses on those differences and on the analytical features that have no Postgres equivalent.
Implementation: redshift.
Status: Experimental. The dialect implements the full CRUD/DDL surface plus Redshift-specific extras (COPY/UNLOAD, DISTKEY/SORTKEY, datashares, VACUUM/ANALYZE). Some DDL that Redshift accepts but does not enforce (see below) is emitted faithfully rather than silently dropped.

Overview#

Columnar + MPP. Data is stored column-by-column and spread across compute-node slices. This makes wide aggregate scans fast and single-row OLTP-style access comparatively expensive. Design for batch loads and analytical queries, not high-frequency row writes.
Postgres wire, different engine. The pg driver connects and the SQL looks familiar, but the storage engine, planner, and constraint behavior are all different.
No secondary indexes. There is no CREATE INDEX. Physical layout is governed by table-level DISTSTYLE/DISTKEY (distribution) and SORTKEY (on-disk ordering) instead.
Constraints are advisory. PRIMARY KEY, UNIQUE, and FOREIGN KEY DDL is accepted and stored for the planner, but never enforced at write time.
Bulk-first I/O. The idiomatic load path is COPY FROM S3, and the idiomatic export path is UNLOAD TO S3.
PropertyValue
Dialect nameredshift
Extendsnothing — implements Dialect directly
Driverpg
Cloud SDKnone
Default port5439
Local runtimenone — hosted service only

Quick start#

Install#

$ npm install ts-prorm-orm pg
$ pnpm add ts-prorm-orm pg
$ yarn add ts-prorm-orm pg
$ bun add ts-prorm-orm pg

Connect#

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

export const db = new Prorm({
  dialect: 'redshift',
  host: 'my-cluster.abc123.us-east-1.redshift.amazonaws.com',
  database: 'dev',
  username: 'admin',
  password: process.env.DB_PASSWORD,
  dialectOptions: {
    ssl: { rejectUnauthorized: true },
  },
});
import { Prorm } from 'ts-prorm-orm';

export const db = new Prorm({
  dialect: 'redshift',
  url: 'redshift://admin:secret@my-cluster.abc123.us-east-1.redshift.amazonaws.com:5439/dev',
  dialectOptions: {
    ssl: { rejectUnauthorized: 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) },
});

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' });
Upsert statements
upsert() also reads the row by primary key before and after the write, to return the instance.
The staging table's name is generated per call; <timestamp>_<random> stands in for it.

Pagination#

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

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 redshift has none. Run the work in a single transaction, or restructure it so nothing nests.
Type mapping for the portable descriptors: STRING → VARCHAR(255), auto-increment INTEGER → INTEGER, BOOLEAN → BOOLEAN, DATE → TIMESTAMP, UUID → VARCHAR(36), JSON → SUPER. The type comparison lines the major engines up side by side.

Connection#

Select the dialect with dialect: 'redshift'. The dialect connects over the Postgres wire protocol on Redshift's default port 5439 (not 5432).
import { Prorm, DataTypes } from 'ts-prorm-orm';

const prorm = new Prorm({
  dialect: 'redshift',
  host: 'my-cluster.abc123.us-east-1.redshift.amazonaws.com',
  port: 5439,
  database: 'analytics',
  username: 'admin',
  password: process.env.REDSHIFT_PASSWORD,
  dialectOptions: {
    ssl: { rejectUnauthorized: false },
    // Cluster-wide defaults for tables created without an explicit key:
    distKey: 'user_id',
    sortKey: ['created_at'],
  },
});

await prorm.authenticate();
Connection tuning fields (max, idleTimeoutMillis, connectionTimeoutMillis, statementTimeout, queryTimeout, ssl) are passed straight through to the pg pool. The distKey / sortKey entries under dialectOptions are Redshift-specific defaults applied to tables created without their own keys.
The port defaults to 5439 if omitted; the pool defaults to max: 10.

Columnar / analytical nature#

Redshift is built for scan-heavy analytics, and the dialect reflects that:
queryStream() pages through results with repeated LIMIT/OFFSET queries rather than holding a whole result set in memory — appropriate for warehouse-sized reads.
There is no autovacuum/autoanalyze. The planner relies on statistics you refresh explicitly with analyze(), and space from deleted/updated rows is reclaimed with vacuum() (see Maintenance).
Prefer set-based batch operations. Row-by-row INSERT works for compatibility but is slow at scale; use copyFromS3() for real loads.

DISTKEY / SORTKEY (instead of indexes)#

Redshift has no B-tree secondary indexes. Instead you tune tables with distribution and sort keys, passed as extra options to createTable (typed as RedshiftTableOptions).
The Redshift-specific methods below (createTable with key options, copyFromS3, unloadToS3, upsert, vacuum, analyze, ...) live on the dialect instance, which you reach via getDialectInstance(). Some of them (COPY/UNLOAD, datashares) are not part of the generic Dialect interface, so cast the instance to reach them:
// The RedshiftDialect type is not re-exported from the package root; cast to
// reach the Redshift-only methods.
const dialect = prorm.getDialectInstance() as any;

await dialect.createTable(
  'events',
  {
    event_id:   { type: DataTypes.BIGINT, autoIncrement: true },
    user_id:    { type: DataTypes.BIGINT },
    event_type: { type: DataTypes.STRING, encode: 'ZSTD' }, // column compression
    created_at: { type: DataTypes.DATE },
  },
  {
    diststyle: 'KEY',        // EVEN | KEY | ALL | AUTO
    distKey: 'user_id',      // required when diststyle is 'KEY'
    sortKey: ['created_at'], // COMPOUND by default
    sortkeyType: 'COMPOUND', // or 'INTERLEAVED'
  },
);
This emits:
Notes:
diststyle: 'KEY' requires distKey; 'ALL' replicates the table to every node (good for small dimension tables); 'EVEN' round-robins; 'AUTO' lets Redshift decide.
sortKey accepts a single column or an array. sortkeyType is COMPOUND (default) or INTERLEAVED.
Column compression is a first-class perf feature: set encode on a column definition ('AZ64', 'ZSTD', 'LZO', 'BYTEDICT', 'DELTA', 'RAW', ...). The value is passed through verbatim, so future encodings work without a dialect update.
Any request for a traditional index fails loudly rather than being silently ignored. Passing options.indexes to createTable, or calling createPartitionedTable() / createPartition(), throws a DatabaseError pointing you at DISTKEY/SORTKEY. Redshift has no declarative partitioning.

Loading data: COPY and UNLOAD#

The idiomatic bulk-load path is COPY FROM S3, exposed as copyFromS3() (a Redshift-specific method, not part of the generic Dialect interface). Authenticate with either an IAM role ARN (recommended) or static credentials — supplying both, or neither, throws.
await dialect.copyFromS3('events', 's3://my-bucket/events/2026-07-05/', {
  iamRole: 'arn:aws:iam::123456789012:role/RedshiftCopyRole',
  format: 'PARQUET',           // CSV (default) | JSON | PARQUET | AVRO | ORC
  region: 'us-east-1',
  compression: 'GZIP',         // GZIP | BZIP2 | ZSTD
  maxError: 100,               // continue past a threshold of bad rows
});
For CSV you can add delimiter and ignoreHeader; for JSON/AVRO pass jsonOption (a jsonpaths file path or 'auto'). The extra field appends raw COPY options verbatim as an escape hatch.
The write-side counterpart, unloadToS3(), exports the result of a query:
await dialect.unloadToS3(
  'SELECT user_id, count(*) FROM events GROUP BY user_id',
  's3://my-bucket/exports/user_counts/',
  {
    iamRole: 'arn:aws:iam::123456789012:role/RedshiftUnloadRole',
    format: 'PARQUET',   // PARQUET | CSV | JSON (default: delimited text)
    parallel: true,      // set false to force a single output file
    manifest: true,
    allowOverwrite: true,
  },
);
Ordinary bulkInsert() / buildInsertQuery() still work via plain multi-row INSERT for ORM compatibility, but they are not the right tool for large loads.

Upserts#

Redshift has never supported INSERT ... ON CONFLICT, and native MERGE only arrived in 2023. Because of that, buildInsertQuery does not generate ON CONFLICT (calling the internal insert-upsert path throws with guidance), and the default upsert uses AWS's documented staging-table pattern instead:
await dialect.upsert('events', {
  event_id: 42,
  user_id: 7,
  event_type: 'click',
}, {
  conflictFields: ['event_id'],
});
Under the hood this runs, on a single pinned connection inside BEGIN/COMMIT:
Pinning one connection matters: Redshift temp tables are session-scoped, so the staging table must live on the same physical connection for every statement. bulkUpsert() builds one staging table fed by a single multi-row INSERT for a whole array of rows.
If your cluster is new enough, opt into native MERGE with useMerge: true (optionally restrict the updated columns with updateColumns):
await dialect.upsert('events', row, { conflictFields: ['event_id'], useMerge: true });

Data types#

getDataTypeSql maps prorm DataTypes to Redshift's type system. The mappings that differ from Postgres:
prorm DataTypeRedshift typeNote
STRINGVARCHAR(255)length honored
TEXTVARCHAR(65535)Redshift has no unbounded TEXT
FLOATREAL
DOUBLEDOUBLE PRECISION
DATETIMESTAMPdate+time
DATEONLYDATE
BLOBVARBYTEno BYTEA
JSON / JSONBSUPERsemi-structured type
ARRAYSUPERno native array types
UUIDVARCHAR(36)no native UUID
ENUMVARCHAR(n)no native ENUM; widened to fit values
The semi-structured SUPER type is Redshift's answer to JSON and nested/array data. Array JS values are escaped with JSON_PARSE('...') so they round-trip into SUPER columns.
Auto-increment columns use Redshift's IDENTITY(seed, step) clause rather than Postgres SERIAL/BIGSERIAL (Redshift has no SERIAL type and no sequence objects). The column keeps its declared width — a SMALLINT identity stays SMALLINT IDENTITY, it is not silently widened. Seed/step come from an optional identity field on the column definition and default to (1, 1).

Maintenance: VACUUM / ANALYZE#

Because there is no background autovacuum, reclaim space and refresh planner stats explicitly — especially after the DELETE-heavy staging-table upsert path:
await dialect.vacuum('events', { mode: 'FULL', boost: true }); // FULL | SORT ONLY | DELETE ONLY | REINDEX
await dialect.analyze('events', { predicateColumns: true });   // or { columns: ['user_id'] }
await dialect.analyzeCompression('events');                    // recommends column encodings
vacuum() also accepts toPercent (1–100) to stop once a table is sufficiently sorted. analyze() rejects passing both columns and predicateColumns.

Transactions#

Redshift supports only two isolation levels: READ COMMITTED (default) and SERIALIZABLE. It has no MVCC snapshot mode equivalent to REPEATABLE READ and no dirty-read READ UNCOMMITTED. Requesting either of the unsupported levels is rejected outright (rather than silently downgraded to a different, unrequested guarantee). Transactions may additionally be marked readOnly via RedshiftTransactionOptions.

How Redshift differs from Postgres#

Even though the driver and much of the SQL are shared, do not assume Postgres semantics. Key divergences the dialect encodes:
Default port 5439, not 5432.
No enforced constraints. PRIMARY KEY / UNIQUE / FOREIGN KEY DDL is emitted and stored for the planner and for ERD/documentation tooling, but inserts/updates that violate them succeed silently. Enforce integrity in application code. CHECK constraints are not supported at all and throw.
No B-tree indexes / no partitioning. Use DISTKEY/SORTKEY; index and partition APIs throw.
No SERIAL/sequences. Auto-increment is IDENTITY(seed, step); createSequence() throws.
No CREATE EXTENSION, no triggers, no row-level-security policies. All throw a clear DatabaseError. (Redshift's answer to external/federated data is Spectrum's CREATE EXTERNAL SCHEMA and federated queries, exposed separately.)
SUPER instead of JSON/JSONB, VARBYTE instead of BYTEA, no native TEXT/UUID/ENUM/array types.
No ON CONFLICT. Upserts use the staging-table pattern (or opt-in native MERGE).
Manual maintenance. No autovacuum/autoanalyze; call vacuum()/analyze() yourself.
Redshift does support views, materialized views (with optional AUTO REFRESH, BACKUP NO, and their own DISTKEY/SORTKEY), and a limited form of plpgsql stored procedures (CREATE OR REPLACE PROCEDURE ... LANGUAGE plpgsql), all implemented much like Postgres.
The RedshiftTableOptions, RedshiftColumnDefinition, CopyFromS3Options, UnloadOptions, RedshiftUpsertOptions, and related option interfaces are declared alongside RedshiftDialect in the dialect source.

See also#

Source: redshift
GAPS and redshift in the dialect directory

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.
Capabilityredshift
Transactions atomicyes
Savepointsno — a nested transaction() throws
Isolationup to SERIALIZABLE
Transaction modesread-only no; deferrable no; constraint deferral no
RETURNINGRETURNING on INSERT
Upserta plain INSERT: the engine has no conflict clause (see the page)
JSON columnSUPER (JSONB → SUPER)
Vector columnrefused — redshift has no column type for DataTypes.VECTOR: Redshift has no vector type.
Array columnSUPER
WHERE builder35 operator keys; $and/$or/$not; $exists: yes; $in subquery: yes; expression operands: yes
SQL objectsfamily postgres: triggers yes; procedures yes; functions multiStatement; materialized views native; sequences yes; enums named; RLS policy
StreamingqueryStream() present; LISTEN absent; createExtension() present

Testing#

tests/dialects/redshift.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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