Python (pysqream)

The current Pysqream connector supports Python version 3.9 and newer. It includes a set of packages that allows Python programs to connect to SQream DB. The base pysqream package conforms to Python DB-API specifications PEP-249.

pysqream is a pure Python connector that can be installed with pip on any operating system, including Linux, Windows, and macOS. pysqream-sqlalchemy is a SQLAlchemy dialect for pysqream.

Installing the Python Connector

Prerequisites

It is essential that you have the following installed:

Python

The connector requires Python version 3.9 or newer.

To see your current Python version, run the following command:

$ python --version

PIP

The Python connector is installed via pip, the standard package manager for Python, which is used to install, upgrade and manage Python packages (libraries) and their dependencies.

We recommend upgrading to the latest version of pip before installing.

To verify that you have the latest version, run the following command:

$ python3 -m pip install --upgrade pip
Collecting pip
   Downloading https://files.pythonhosted.org/packages/00/b6/9cfa56b4081ad13874b0c6f96af8ce16cfbc1cb06bedf8e9164ce5551ec1/pip-19.3.1-py2.py3-none-any.whl (1.4MB)
         |████████████████████████████████| 1.4MB 1.6MB/s
Installing collected packages: pip
  Found existing installation: pip 19.1.1
        Uninstalling pip-19.1.1:
          Successfully uninstalled pip-19.1.1
Successfully installed pip-19.3.1

Note

  • On macOS, you may want to use virtualenv to install Python and the connector, to ensure compatibility with the built-in Python environment

  • If you encounter an error including SSLError or WARNING: pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available. - please be sure to reinstall Python with SSL enabled, or use virtualenv or Anaconda.

OpenSSL for Linux

The Python connector relies on OpenSSL for secure connections to SQream DB. Some distributions of Python do not include OpenSSL.

To install OpenSSL on RHEL/CentOS, run the following command:

$ sudo yum install -y libffi-devel openssl-devel

To install OpenSSL on Ubuntu, run the following command:

$ sudo apt-get install libssl-dev libffi-dev -y

Installing via PIP with an internet connection

The Python connector is available via PyPi.

To install the connector using pip, it is advisable to use the -U or --user flags instead of sudo, as it ensures packages are installed per user. However, it is worth noting that the connector can only be accessed under the same user.

To install pysqream and pysqream-sqlalchemy with the --user flag, run the following command:

$ pip3.9 install pysqream pysqream-sqlalchemy --user

pip3 will automatically install all necessary libraries and modules.

Installing via PIP without an internet connection

  1. To get the .whl package file, contact you SQream support representative.

  2. Run the following command:

tar -xf pysqream_connector_5.0.0.tar.gz
cd pysqream_connector_5.0.0
#Install all packages with --no-index --find-links .
python3 -m pip install *.whl -U --no-index --find-links .
python3.9 -m pip install pysqream-5.0.0.zip -U --no-index --find-links .
python3.9 -m pip install pysqream-sqlalchemy-1.0.zip  -U --no-index --find-links .

Upgrading an Existing Installation

The Python drivers are updated periodically. To upgrade an existing pysqream installation, use pip’s -U flag:

$ pip3.9 install pysqream pysqream-sqlalchemy -U

SQLAlchemy

SQLAlchemy is an Object-Relational Mapper (ORM) for Python. When you install the SQream dialect (pysqream-sqlalchemy) you can use frameworks such as Pandas, TensorFlow, and Alembic to query SQream directly.

Limitations

Creating a Standard Connection

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

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


engine_url = sa.engine.url.URL('sqream',
                               username='<user_name>',
                               password='<password>',
                               host='<host_name>',
                               port=<port_number>,
                               port_ssl=<port_ssl>,
                               database='<database_name>')

engine = sa.create_engine(engine_url,connect_args={"clustered": False, "service": "<service_name>"})
session = orm.sessionmaker(bind=engine)()

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)

API

Using the Cursor

The DB-API specification includes several methods for fetching results from the cursor. This sections shows an example using the nba table, which looks as follows:

nba

Name

Team

Number

Position

Age

Height

Weight

College

Salary

Avery Bradley

Boston Celtics

0.0

PG

25.0

6-2

180.0

Texas

7730337.0

Jae Crowder

Boston Celtics

99.0

SF

25.0

6-6

235.0

Marquette

6796117.0

John Holland

Boston Celtics

30.0

SG

27.0

6-5

205.0

Boston University

R.J. Hunter

Boston Celtics

28.0

SG

22.0

6-5

185.0

Georgia State

1148640.0

Jonas Jerebko

Boston Celtics

8.0

PF

29.0

6-10

231.0

5000000.0

Amir Johnson

Boston Celtics

90.0

PF

29.0

6-9

240.0

12000000.0

Jordan Mickey

Boston Celtics

55.0

PF

21.0

6-8

235.0

LSU

1170960.0

Kelly Olynyk

Boston Celtics

41.0

C

25.0

7-0

238.0

Gonzaga

2165160.0

Terry Rozier

Boston Celtics

12.0

PG

22.0

6-2

190.0

Louisville

1824360.0

As before, you must import the library and create a Connection(), followed by execute() on a simple SELECT * query:

import pysqream


con = pysqream.connect(host='127.0.0.1',
                       port=3108,
                       database='master',
                       username='rhendricks',
                       password='Tr0ub4dor&3',
                       clustered=True)

cur = con.cursor() # Create a new cursor
# The select statement:
statement = 'SELECT * FROM nba'
cur.execute(statement)

When the statement has finished executing, you have a Connection cursor object waiting. A cursor is iterable, meaning that it advances the cursor to the next row when fetched.

You can use fetchone() to fetch one record at a time:

first_row = cur.fetchone() # Fetch one row at a time (first row)

second_row = cur.fetchone() # Fetch one row at a time (second row)

To fetch several rows at a time, use fetchmany():

# executing `fetchone` twice is equivalent to this form:
third_and_fourth_rows = cur.fetchmany(2)

To fetch all rows at once, use fetchall():

# To get all rows at once, use `fetchall`
remaining_rows = cur.fetchall()

cur.close()


# Close the connection when done
con.close()

The following is an example of the contents of the row variables used in our examples:

>>> print(first_row)
('Avery Bradley', 'Boston Celtics', 0, 'PG', 25, '6-2', 180, 'Texas', 7730337)
>>> print(second_row)
('Jae Crowder', 'Boston Celtics', 99, 'SF', 25, '6-6', 235, 'Marquette', 6796117)
>>> print(third_and_fourth_rows)
[('John Holland', 'Boston Celtics', 30, 'SG', 27, '6-5', 205, 'Boston University', None), ('R.J. Hunter', 'Boston Celtics', 28, 'SG', 22, '6-5', 185, 'Georgia State', 1148640)]
>>> print(remaining_rows)
[('Jonas Jerebko', 'Boston Celtics', 8, 'PF', 29, '6-10', 231, None, 5000000), ('Amir Johnson', 'Boston Celtics', 90, 'PF', 29, '6-9', 240, None, 12000000), ('Jordan Mickey', 'Boston Celtics', 55, 'PF', 21, '6-8', 235, 'LSU', 1170960), ('Kelly Olynyk', 'Boston Celtics', 41, 'C', 25, '7-0', 238, 'Gonzaga', 2165160),
[...]

Note

Calling a fetch command after all rows have been fetched will return an empty array ([]).

Reading Result Metadata

When you execute a statement, the connection object also contains metadata about the result set, such as column names, types, etc).

The metadata is stored in the Connection.description object of the cursor:

import pysqream


con = pysqream.connect(host='127.0.0.1',
                       port=3108,
                       database='master',
                       username='rhendricks',
                       password='Tr0ub4dor&3',
                       clustered=True)
cur = con.cursor()
statement = 'SELECT * FROM nba'
cur.execute(statement)
print(cur.description)
# [('Name', 'STRING', 24, 24, None, None, True), ('Team', 'STRING', 22, 22, None, None, True), ('Number', 'NUMBER', 1, 1, None, None, True), ('Position', 'STRING', 2, 2, None, None, True), ('Age (as of 2018)', 'NUMBER', 1, 1, None, None, True), ('Height', 'STRING', 4, 4, None, None, True), ('Weight', 'NUMBER', 2, 2, None, None, True), ('College', 'STRING', 21, 21, None, None, True), ('Salary', 'NUMBER', 4, 4, None, None, True)]

You can fetch a list of column names by iterating over the description list:

>>> [ i[0] for i in cur.description ]
['Name', 'Team', 'Number', 'Position', 'Age (as of 2018)', 'Height', 'Weight', 'College', 'Salary']

Loading Data into a Table

This example shows how to load 10,000 rows of dummy data to an instance of SQream.

To load data 10,000 rows of dummy data to an instance of SQream:

  1. Run the following:

    import pysqream
    from datetime import date, datetime
    from time import time
    
    
    con = pysqream.connect(host='127.0.0.1',
                           port=3108,
                           database='master',
                           username='rhendricks',
                           password='Tr0ub4dor&3',
                           clustered=True)
    
    cur = con.cursor()
    
  2. Create a table for loading:

    create = 'create or replace table perf (b bool, t tinyint, sm smallint, i int, bi bigint, f real, d double, s text(12), ss text, dt date, dtt datetime)'
    cur.execute(create)
    
  3. Create a session:

    session = orm.sessionmaker(bind=engine)()
    
  4. Load your data into table using the INSERT command.

  5. Create dummy data matching the table you created:

    data = (False, 2, 12, 145, 84124234, 3.141, -4.3, "Marty McFly" , u"キウイは楽しい鳥です" , date(2019, 12, 17), datetime(1955, 11, 4, 1, 23, 0, 0))
    
    row_count = 10**4
    
  6. Get a new cursor:

    insert = 'insert into perf values (?,?,?,?,?,?,?,?,?,?,?)'
    start = time()
    cur.executemany(insert, [data] * row_count)
    print (f"Total insert time for {row_count} rows: {time() - start} seconds")
    
  7. Close this cursor:

    cur.close()
    
  8. Verify that the data was inserted correctly:

    cur = con.cursor()
    cur.execute('select count(*) from perf')
    result = cur.fetchall() # `fetchall` collects the entire data set
    print (f"Count of inserted rows: {result[0][0]}")
    
  9. Close the cursor:

    cur.close()
    
  10. Close the connection:

con.close()

Using SQLAlchemy ORM to Create and Populate Tables

This section shows how to use the ORM to create and populate tables from Python objects.

To use SQLAlchemy ORM to create and populate tables:

  1. Run the following:

    import sqlalchemy as sa
    import pandas as pd
    
    engine_url = "sqream://rhendricks:secret_password@localhost:5000/raviga"
    
    engine = sa.create_engine(engine_url)
    
  2. Build a metadata object and bind it:

    metadata = sa.MetaData()
    metadata.bind = engine
    
  3. Create a table in the local metadata:

    employees = sa.Table(
        'employees',
        metadata,
        sa.Column('id', sa.Integer),
        sa.Column('name', sa.TEXT(32)),
        sa.Column('lastname', sa.TEXT(32)),
        sa.Column('salary', sa.Float)
    )
    

    The create_all() function uses the SQream engine object.

  4. Create all the defined table objects:

    metadata.create_all(engine)
    
  5. Populate your table.

  6. Build the data rows:

    insert_data = [ {'id': 1, 'name': 'Richard','lastname': 'Hendricks',   'salary': 12000.75},
                    {'id': 3,  'name': 'Bertram', 'lastname': 'Gilfoyle', 'salary': 8400.0},
                    {'id': 8,  'name': 'Donald', 'lastname': 'Dunn', 'salary': 6500.40}]
    
  7. Build the INSERT command:

    ins = employees.insert(insert_data)
    
  8. Execute the command:

    result = session.execute(ins)