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://{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://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 copy into operations through it's CopyIntoTable and CopyIntoLocation methods See [CopyIntoLocation](https://docs.databend.com/sql/sql-commands/dml/dml-copy-into-location) or [CopyIntoTable](https://docs.databend.com/sql/sql-commands/dml/dml-copy-into-table) for full documentation.
The CopyIntoTable command can be used as below:
from sqlalchemy.orm import sessionmaker
from sqlalchemy import MetaData, create_engine
from databend_sqlalchemy import (
CopyIntoTable, GoogleCloudStorage, ParquetFormat, CopyIntoTableOptions,
FileColumnClause, CSVFormat,
)
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']
gcs_private_key = 'full_gcs_json_private_key'
case_sensitive_columns = True
copy_into = CopyIntoTable(
target=t1,
from_=GoogleCloudStorage(
uri='gcs://bucket-name/path/to/file',
credentials=base64.b64encode(gcs_private_key.encode()).decode(),
),
file_format=ParquetFormat(),
options=CopyIntoTableOptions(
force=True,
column_match_mode='CASE_SENSITIVE' if case_sensitive_columns else None,
)
)
result = connection.execute(copy_into)
result.fetchall() # always call fetchall() to ensure the cursor executes to completion
# More involved example with column selection clause that can be altered to perform operations on the columns during import.
copy_into = CopyIntoTable(
target=t2,
from_=FileColumnClause(
columns=', '.join([
f'${index + 1}'
for index, column in enumerate(t2.columns)
]),
from_=GoogleCloudStorage(
uri='gcs://bucket-name/path/to/file',
credentials=base64.b64encode(gcs_private_key.encode()).decode(),
)
),
pattern='*.*',
file_format=CSVFormat(
record_delimiter='\n',
field_delimiter=',',
quote='"',
escape='',
skip_header=1,
empty_field_as='NULL',
compression=Compression.AUTO,
),
options=CopyIntoTableOptions(
force=True,
)
)
result = connection.execute(copy_into)
result.fetchall() # always call fetchall() to ensure the cursor executes to completion
The CopyIntoLocation command can be used as below:
from sqlalchemy.orm import sessionmaker
from sqlalchemy import MetaData, create_engine
from databend_sqlalchemy import (
CopyIntoLocation, GoogleCloudStorage, ParquetFormat, CopyIntoLocationOptions,
)
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']
gcs_private_key = 'full_gcs_json_private_key'
copy_into = CopyIntoLocation(
target=GoogleCloudStorage(
uri='gcs://bucket-name/path/to/target_file',
credentials=base64.b64encode(gcs_private_key.encode()).decode(),
),
from_=select(t1).where(t1.c['col1'] == 1),
file_format=ParquetFormat(),
options=CopyIntoLocationOptions(
single=True,
overwrite=True,
include_query_id=False,
use_raw_path=True,
)
)
result = connection.execute(copy_into)
result.fetchall() # always call fetchall() to ensure the cursor executes to completion
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/databendlabs/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/databendlabs/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.
