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connect/docs/sqlalchemy.mdx
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---
sidebarTitle: 'SQLAlchemy'
keywords: ['clickhouse', 'python', 'sqlalchemy', 'integrate']
description: 'ClickHouse SQLAlchemy support'
slug: /integrations/language-clients/python/sqlalchemy
title: 'SQLAlchemy support'
doc_type: 'reference'
---
ClickHouse Connect includes a SQLAlchemy dialect (`clickhousedb`) built on top of the core driver. It targets SQLAlchemy Core APIs and supports SQLAlchemy 1.4.40+ and 2.0.x.
## Connect with SQLAlchemy {#sqlalchemy-connect}
Create an engine using either `clickhousedb://` or `clickhousedb+connect://` URLs. Query parameters map to ClickHouse settings, client options, and HTTP/TLS transport options.
```python
from sqlalchemy import create_engine, text
engine = create_engine(
"clickhousedb://user:password@host:8123/mydb?compression=zstd"
)
with engine.begin() as conn:
rows = conn.execute(text("SELECT version()"))
print(rows.scalar())
```
Notes on URL/query parameters:
- ClickHouse settings: pass as query parameters (for example, `use_skip_indexes=0`).
- Client options: `compression` (alias for `compress`), `query_limit`, timeouts, and more.
- HTTP/TLS options: options for the HTTP pool and TLS (for example, `ch_http_max_field_name_size=99999`, `ca_cert=certifi`).
See [Connection arguments and Settings](/integrations/language-clients/python/driver-api#connection-arguments) in the sections below for the full list of supported options. These can also be supplied via the SQLAlchemy DSN.
## Core queries {#sqlalchemy-core-queries}
The dialect supports SQLAlchemy Core `SELECT` queries with joins, filters, ordering, limits/offsets, and `DISTINCT`.
```python
from sqlalchemy import MetaData, Table, select
metadata = MetaData(schema="mydb")
users = Table("users", metadata, autoload_with=engine)
orders = Table("orders", metadata, autoload_with=engine)
# Basic SELECT
with engine.begin() as conn:
rows = conn.execute(select(users.c.id, users.c.name).order_by(users.c.id).limit(10)).fetchall()
# JOINs (INNER/LEFT OUTER/FULL OUTER/CROSS)
with engine.begin() as conn:
stmt = (
select(users.c.name, orders.c.product)
.select_from(users.join(orders, users.c.id == orders.c.user_id))
)
rows = conn.execute(stmt).fetchall()
```
Lightweight `DELETE` with a required `WHERE` clause is supported:
```python
from sqlalchemy import delete
with engine.begin() as conn:
conn.execute(delete(users).where(users.c.name.like("%temp%")))
```
## DDL and reflection {#sqlalchemy-ddl-reflection}
You can create databases and tables using the provided DDL helpers and type/engine constructs. Table reflection (including column types and engine) is supported.
```python
import sqlalchemy as db
from sqlalchemy import MetaData
from clickhouse_connect.cc_sqlalchemy.ddl.custom import CreateDatabase, DropDatabase
from clickhouse_connect.cc_sqlalchemy.ddl.tableengine import MergeTree
from clickhouse_connect.cc_sqlalchemy.datatypes.sqltypes import UInt32, String, DateTime64
with engine.begin() as conn:
# Databases
conn.execute(CreateDatabase("example_db", exists_ok=True))
# Tables
metadata = MetaData(schema="example_db")
table = db.Table(
"events",
metadata,
db.Column("id", UInt32, primary_key=True),
db.Column("user", String),
db.Column("created_at", DateTime64(3)),
MergeTree(order_by="id"),
)
table.create(conn)
# Reflection
reflected = db.Table("events", metadata, autoload_with=engine)
assert reflected.engine is not None
```
Reflected columns include dialect-specific attributes such as `clickhousedb_default_type`, `clickhousedb_codec_expression`, and `clickhousedb_ttl_expression` when present on the server.
## Inserts (Core and basic ORM) {#sqlalchemy-inserts}
Inserts work via SQLAlchemy Core as well as with simple ORM models for convenience.
```python
# Core insert
with engine.begin() as conn:
conn.execute(table.insert().values(id=1, user="joe"))
# Basic ORM insert
from sqlalchemy.orm import declarative_base, Session
Base = declarative_base(metadata=MetaData(schema="example_db"))
class User(Base):
__tablename__ = "users"
__table_args__ = (MergeTree(order_by=["id"]),)
id = db.Column(UInt32, primary_key=True)
name = db.Column(String)
Base.metadata.create_all(engine)
with Session(engine) as session:
session.add(User(id=1, name="Alice"))
session.bulk_save_objects([User(id=2, name="Bob")])
session.commit()
```
## Scope and limitations {#scope-and-limitations}
- Core focus: Enable SQLAlchemy Core features like `SELECT` with `JOIN`s (`INNER`, `LEFT OUTER`, `FULL OUTER`, `CROSS`), `WHERE`, `ORDER BY`, `LIMIT`/`OFFSET`, and `DISTINCT`.
- `DELETE` with `WHERE` only: The dialect supports lightweight `DELETE` but requires an explicit `WHERE` clause to avoid accidental full-table deletes. To clear a table, use `TRUNCATE TABLE`.
- No `UPDATE`: ClickHouse is append-optimized. The dialect doesn't implement `UPDATE`. If you need to change data, apply transformations upstream and re-insert, or use explicit text SQL (for example, `ALTER TABLE ... UPDATE`) at your own risk.
- DDL and reflection: Creating databases and tables is supported, and reflection returns column types and table engine metadata. Traditional PK/FK/index metadata isn't present because ClickHouse doesn't enforce those constraints.
- ORM scope: Declarative models and inserts via `Session.add(...)`/`bulk_save_objects(...)` work for convenience. Advanced ORM features (relationship management, unit-of-work updates, cascading, eager/lazy loading semantics) aren't supported.
- Primary key semantics: `Column(..., primary_key=True)` is used by SQLAlchemy for object identity only. It doesn't create a server-side constraint in ClickHouse. Define `ORDER BY` (and optional `PRIMARY KEY`) via table engines (for example, `MergeTree(order_by=...)`).
- Transactions and server features: Two-phase transactions, sequences, `RETURNING`, and advanced isolation levels aren't supported. `engine.begin()` provides a Python context manager for grouping statements but performs no actual transaction control (commit/rollback are no-ops).