pysqream-sqlalchemy 1.0

SQLAlchemy dialect for SQreamDB

Prerequisites

• Python > 3.9+

• SQLAlchemy = 2.0.27

• SQream DB-API Connector = 3.2.5

• Cython (optional - improves performance)

Installing SQream SQLAlchemy

pip3.9 install pysqream-sqlalchemy -U

Verifying Installation

import sqlalchemy as sa
import pandas as pd
from sqlalchemy import text


conn_str = "sqream://sqream:sqream@localhost:3108/master"
engine = sa.create_engine(conn_str, connect_args={"clustered": True})
session = orm.sessionmaker(bind=engine)()

session.execute(text('create or replace table test (ints int)'))
session.execute(text('insert into test values (5), (6)'))
df = pd.read_sql('select * from test', engine)
print(df)

Connection String

sqream://<user_login_name>:<password>@<host>:<port>/<db_name>

Parameters

Parameter	Description
username	Username of a role to use for connection
password	Specifies the password of the selected role
host	Specifies the hostname
port	Specifies the port number
port_ssl	An optional parameter
database	Specifies the database name
clustered	Establishing a multi-clustered connection. Input values: True, False. Default is False
service	Specifies service queue to use

Example

Pulling a Table into Pandas

The following example shows how to pull a table in Pandas. This example uses the URL method to create the connection string:

import sqlalchemy as sa
import pandas as pd
from sqlalchemy.engine.url import URL


     engine_url = sa.engine.url.URL(
              'sqream',
              username='sqream',
              password='12345',
              host='127.0.0.1',
              port=3108,
              database='master')
     engine = sa.create_engine(engine_url,connect_args={"clustered": True, "service": "admin"})

     table_df = pd.read_sql("select * from nba", con=engine)

Limitations

Arrays

SQream SQLAlchemy doesn’t suppport ARRAY type for columns.

Parameterized Queries

SQream SQLAlchemy supports only the BULK INSERT statement.