Connecting to SQream Using Python (pysqream)

The Python connector page describes the following:

Overview 

The SQream Python connector is a set of packages that allows Python programs to connect to SQream DB.

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

The connector supports Python 3.6.5 and newer. The base pysqream package conforms to Python DB-API specifications PEP-249.

Installing the Python Connector 

Prerequisites

Installing the Python connector includes the following prerequisites:

Python 

The connector requires Python 3.6.5 or newer. To verify your version of Python:

$ python --version
Python 3.7.3

PIP 

The Python connector is installed via pip, the Python package manager and installer.

We recommend upgrading to the latest version of pip before installing. To verify that you are on 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 

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

To install OpenSSL on RHEL/CentOS

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

To install OpenSSL on Ubuntu

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

Installing via PIP

The Python connector is available via PyPi.

Install the connector with pip:

$ pip3 install pysqream pysqream-sqlalchemy

pip3 will automatically install all necessary libraries and modules.

Upgrading an Existing Installation

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

$ pip3 install pysqream pysqream-sqlalchemy -U

Validating Your Installation

This section describes how to validate your installation.

To validate your installation:

Create a file called sample.py, containing the following:

Verify that the parameters in the connection have been replaced with your respective SQream installation parameters.
Run the sample file to verify that you can connect to SQream:
```
$ python sample.py
Version: v2020.1
```
If the validation was successful, you can build an application using the SQream Python connector. If you receive a connection error, verify the following:
- You have access to a running SQream database.
- The connection parameters are correct.

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.

This section includes the following examples:

Standard Connection Example 

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

engine_url = URL('sqream'
                          , username='rhendricks'
                          , password='secret_password"
                          , host='localhost'
                          , port=5000
                          , database='raviga'
                          , query={'use_ssl': False})

engine = sa.create_engine(engine_url)

res = engine.execute('create or replace table test (ints int, ints2 int)')
res = engine.execute('insert into test (ints,ints2) values (5,1), (6,2)')
res = engine.execute('select * from test')
for item in res:
print(item)

Multi Cluster Connection Example 

The following example is for using a ServerPicker:

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


engine_url = URL('sqream'
                          , username='rhendricks'
                          , password='secret_password'
                          , host='localhost'
                          , port=5000
                          , database='raviga')

engine = sa.create_engine(engine_url,connect_args={"clustered": True})

res = engine.execute("create or replace table test100 (dor int);")
res = engine.execute('insert into test100 values (5), (6);')
res = engine.execute('select * from test100')
for item in res:
                print(item)

Pulling a Table into Pandas 

The following example shows how to pull a table in Pandas. This examples 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 = URL('sqream'
              , username='rhendricks'
              , password='secret_password"
              , host='localhost'
              , port=5000
              , database='raviga'
              , query={'use_ssl': False})

engine = sa.create_engine(engine_url)

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

API Examples 

This section includes the following examples:

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)
<pysqream.dbapi.Connection object at 0x000002EA952139B0>
>>> 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:

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()

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 varchar(12), ss text, dt date, dtt datetime)'
cur.execute(create)

Load your data into table using the INSERT command.

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

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")

Close this cursor:
```
cur.close()
```

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]}")

Close the cursor:
```
cur.close()
```
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:

Run the following:

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


engine_url = URL('sqream'
              , username='rhendricks'
              , password='secret_password"
              , host='localhost'
              , port=5000
              , database='raviga'
              , query={'use_ssl': False})

engine = sa.create_engine(engine_url)

Build a metadata object and bind it:

metadata = sa.MetaData()
metadata.bind = engine

Create a table in the local metadata:

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

The create_all() function uses the SQream engine object.

Create all the defined table objects:
```
metadata.create_all(engine)
```
Populate your table.

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}
              ]

Build the INSERT command:
```
ins = employees.insert(insert_data)
```
Execute the command:
```
result = engine.execute(ins)
```

For more information, see the python_api_reference_guide.