Databend dialect for SQLAlchemy.

The package is installable through PIP:

pip install databend-sqlalchemy

The DSN format is similar to that of regular Postgres:

from sqlalchemy import create_engine, text
from sqlalchemy.engine.base import Connection, Engine
engine = create_engine(
    f"databend:https://{username}:{password}@{host_port_name}/{database_name}?sslmode=disable"
)
connection = engine.connect()
result = connection.execute(text("SELECT 1"))
assert len(result.fetchall()) == 1

import connector
cursor = connector.connect('databend:https://root:@localhost:8000?sslmode=disable').cursor()
cursor.execute('SELECT * FROM test')
# print(cursor.fetchone())
# print(cursor.fetchall())
for row in cursor:
    print(row)

Databend SQLAlchemy supports upserts via its Merge custom expression. See [Merge](https://docs.databend.com/sql/sql-commands/dml/dml-merge) for full documentation.

The Merge command can be used as below:

from sqlalchemy.orm import sessionmaker
from sqlalchemy import MetaData, create_engine
from databend_sqlalchemy.databend_dialect import Merge

engine = create_engine(db.url, echo=False)
session = sessionmaker(bind=engine)()
connection = engine.connect()

meta = MetaData()
meta.reflect(bind=session.bind)
t1 = meta.tables['t1']
t2 = meta.tables['t2']

merge = Merge(target=t1, source=t2, on=t1.c.t1key == t2.c.t2key)
merge.when_matched_then_delete().where(t2.c.marked == 1)
merge.when_matched_then_update().where(t2.c.isnewstatus == 1).values(val = t2.c.newval, status=t2.c.newstatus)
merge.when_matched_then_update().values(val=t2.c.newval)
merge.when_not_matched_then_insert().values(val=t2.c.newval, status=t2.c.newstatus)
connection.execute(merge)

Databend SQLAlchemy supports databend specific table options for Engine, Cluster Keys and Transient tables

The table options can be used as below:

from sqlalchemy import Table, Column
from sqlalchemy import MetaData, create_engine

engine = create_engine(db.url, echo=False)

meta = MetaData()
# Example of Transient Table
t_transient = Table(
    "t_transient",
    meta,
    Column("c1", Integer),
    databend_transient=True,
)

# Example of Engine
t_engine = Table(
    "t_engine",
    meta,
    Column("c1", Integer),
    databend_engine='Memory',
)

# Examples of Table with Cluster Keys
t_cluster_1 = Table(
    "t_cluster_1",
    meta,
    Column("c1", Integer),
    databend_cluster_by=[c1],
)
#
c = Column("id", Integer)
c2 = Column("Name", String)
t_cluster_2 = Table(
    't_cluster_2',
    meta,
    c,
    c2,
    databend_cluster_by=[cast(c, String), c2],
)

meta.create_all(engine)

• If databend version >= v0.9.0 or later, you need to use databend-sqlalchemy version >= v0.1.0.
• The databend-sqlalchemy use [databend-py](https://github.com/datafuselabs/databend-py) as internal driver when version < v0.4.0, but when version >= v0.4.0 it use [databend driver python binding](https://github.com/datafuselabs/bendsql/blob/main/bindings/python/README.md) as internal driver. The only difference between the two is that the connection parameters provided in the DSN are different. When using the corresponding version, you should refer to the connection parameters provided by the corresponding Driver.