Simple ClickHouse SQLAlchemy Dialect
Project description
ClickHouse SQLAlchemy
ClickHouse dialect for SQLAlchemy to ClickHouse database.
Installation
The package can be installed using pip:
pip install clickhouse-sqlalchemy
Interfaces support
http via requests
native (TCP) via clickhouse-driver
Connection Parameters
ClickHouse SQLAlchemy uses the following syntax for the connection string:
'clickhouse+<driver>://<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.
Drivers options
There are several options can be specified in query string.
HTTP
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://default:@localhost/test'
When you are using nginx as proxy server for ClickHouse server connection string might look like:
'clickhouse://user:password@example.com:8124/test?protocol=https'
Where 8124 is proxy port.
Native
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://default:@localhost/test'
All connection string parameters are proxied to clickhouse-driver. See it’s parameters.
Features
SQLAlchemy declarative support
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://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) __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.
Basic DDL support
You can emit simple DDL. Example CREATE/DROP table:
table = Rate.__table__ table.create() another_table.create() another_table.drop() table.drop()
Basic INSERT clause support
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)
Common SQLAlchemy query method chaining
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()
Advanced INSERT clause support
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()
External data for query processing
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)
Supported ClickHouse-specific SQL
- SELECT query:
WITH TOTALS
SAMPLE
lambda functions: x -> expr
JOIN
See tests for examples.
Overriding default query settings
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)
Running tests
mkvirtualenv testenv && python setup.py test
pip will automatically install all required modules for testing.
License
ClickHouse SQLAlchemy is distributed under the MIT license.
How to Contribute
Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug.
Fork the repository on GitHub to start making your changes to the master branch (or branch off of it).
Write a test which shows that the bug was fixed or that the feature works as expected.
Send a pull request and bug the maintainer until it gets merged and published.
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