ClickHouse dialect for SQLAlchemy to ClickHouse database.

The package can be installed using pip:

pip install clickhouse-sqlalchemy

• native [recommended] (TCP) via clickhouse-driver
• http via requests

ClickHouse SQLAlchemy uses the following syntax for the connection string:

'clickhouse+<driver>:https://<user>:<password>@<host>:<port>/<database>[?key=value..]'

Where:

• driver is driver to use. Possible choices: http, native. http is default.
• database is database connect to. Default is default.

There are several options can be specified in query string.

• port is port ClickHouse server is bound to. Default is 8123.
• timeout in seconds. There is no timeout by default.
• protocol to use. Possible choices: http, https. http is default.

Connection string to database test in default ClickHouse installation:

'clickhouse:https://default:@localhost/test'

When you are using nginx as proxy server for ClickHouse server connection string might look like:

'clickhouse:https://user:[email protected]:8124/test?protocol=https'

Where 8124 is proxy port.

Please note that native connection is not encrypted. All data including user/password is transferred in plain text. You should use this connection over SSH or VPN (for example) while communicating over untrusted network.

Connection string to database test in default ClickHouse installation:

'clickhouse+native:https://default:@localhost/test'

All connection string parameters are proxied to clickhouse-driver. See it's parameters.

Both declarative and constructor-style tables support:

from sqlalchemy import create_engine, Column, MetaData, literal

from clickhouse_sqlalchemy import Table, make_session, get_declarative_base, types, engines

uri = 'clickhouse:https://default:@localhost/test'

engine = create_engine(uri)
session = make_session(engine)
metadata = MetaData(bind=engine)

Base = get_declarative_base(metadata=metadata)

class Rate(Base):
    day = Column(types.Date, primary_key=True)
    value = Column(types.Int32)
    other_value = Column(
        types.DateTime,
        clickhouse_codec=('DoubleDelta', 'ZSTD'),
    )

    __table_args__ = (
        engines.Memory(),
    )

another_table = Table('another_rate', metadata,
    Column('day', types.Date, primary_key=True),
    Column('value', types.Int32, server_default=literal(1)),
    engines.Memory()
)

Tables created in declarative way have lowercase with words separated by underscores naming convention. But you can easy set you own via SQLAlchemy __tablename__ attribute.

You can emit simple DDL. Example CREATE/DROP table:

table = Rate.__table__
table.create()
another_table.create()


another_table.drop()
table.drop()

Simple batch INSERT:

from datetime import date, timedelta
from sqlalchemy import func

today = date.today()
rates = [{'day': today - timedelta(i), 'value': 200 - i} for i in range(100)]

# Emits single INSERT statement.
session.execute(table.insert(), rates)

order_by, filter, limit, offset, etc. are supported:

session.query(func.count(Rate.day)) \
    .filter(Rate.day > today - timedelta(20)) \
    .scalar()

session.query(Rate.value) \
    .order_by(Rate.day.desc()) \
    .first()

session.query(Rate.value) \
    .order_by(Rate.day) \
    .limit(10) \
    .all()

session.query(func.sum(Rate.value)) \
    .scalar()

INSERT FROM SELECT statement:

from sqlalchemy import cast

# Labels must be present.
select_query = session.query(
    Rate.day.label('day'),
    cast(Rate.value * 1.5, types.Int32).label('value')
).subquery()

# Emits single INSERT FROM SELECT statement
session.execute(
    another_table.insert()
    .from_select(['day', 'value'], select_query)
)

Many but not all of SQLAlchemy features are supported out of the box.

UNION ALL example:

from sqlalchemy import union_all

select_rate = session.query(
    Rate.day.label('date'),
    Rate.value.label('x')
)
select_another_rate = session.query(
    another_table.c.day.label('date'),
    another_table.c.value.label('x')
)

union_all(select_rate, select_another_rate).execute().fetchone()

Currently can be used with native interface.

ext = Table(
    'ext', metadata, Column('x', types.Int32),
    clickhouse_data=[(101, ), (103, ), (105, )], extend_existing=True
)

rv = session.query(Rate) \
    .filter(Rate.value.in_(session.query(ext.c.x))) \
    .execution_options(external_tables=[ext]) \
    .all()

print(rv)

• SELECT query:

  • WITH TOTALS
  • SAMPLE
  • lambda functions: x -> expr
  • JOIN

See tests for examples.

Set lower priority to query and limit max number threads to execute the request.

rv = session.query(func.sum(Rate.value)) \
    .execution_options(settings={'max_threads': 2, 'priority': 10}) \
    .scalar()

print(rv)

mkvirtualenv testenv && python setup.py test

pip will automatically install all required modules for testing.

ClickHouse SQLAlchemy is distributed under the MIT license.

1. Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug.
2. Fork the repository on GitHub to start making your changes to the master branch (or branch off of it).
3. Write a test which shows that the bug was fixed or that the feature works as expected.
4. Send a pull request and bug the maintainer until it gets merged and published.