pysqream 3.0.1

DB-API connector for SQream DB

• Version: 3.0.0

• Supported SQream DB versions: >= 2.13, 2019.2 or newer recommended

The Python connector for SQream DB is a Python DB API 2.0-compliant interface for developing Python applications with SQream DB.

The SQream Python connector provides an interface for creating and running Python applications that can connect to a SQream DB database. It provides a lighter-weight alternative to working through native C++ or Java bindings, including JDBC and ODBC drivers.

pysqream conforms to Python DB-API specifications PEP-249

pysqream is native and pure Python, with minimal requirements. It can be installed with pip on any operating system, including Linux, Windows, and macOS.

For more information and a full API reference, see SQream documentation’s pysqream guide .

Requirements

• Python 3.6.5+, with 3.7+ highly recommended

• Cython (Optional, faster performance) - pip3 install cython

Installing the Python connector

Prerequisites

1. Python

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

$ python --version
Python 3.7.3

Note: If both Python 2.x and 3.x are installed, you can run python3 and pip3 instead of python and pip respectively for the rest of this guide

2. 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:

$ python -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.

3. 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

4. Cython (optional)

Optional but recommended is Cython, which improves performance of Python applications.

$ pip install cython

Install via pip

The Python connector is available via PyPi.

Install the connector with pip:

$ pip install pysqream

pip will automatically installs all necessary libraries and modules.

Validate the installation

Create a file called test.py (make sure to replace the parameters in the connection with the respective parameters for your SQream DB installation):

#!/usr/bin/env python

import pysqream

"""
Connection parameters include:
* IP/Hostname
* Port
* database name
* username
* password
* Connect through load balancer, or direct to worker (Default: false - direct to worker)
* use SSL connection (default: false)
* Optional service queue (default: 'sqream')
"""

# Create a connection object

con = pysqream.connect(host='127.0.0.1', port=5000, database='master'
                   , username='sqream', password='sqream'
                   , clustered=False)

# Create a new cursor
cur = con.cursor()

# Prepare and execute a query
cur.execute('select show_version()')

result = cur.fetchall() # `fetchall` gets the entire data set

print (f"Version: {result[0][0]}")

# This should print the SQream DB version. For example ``Version: v2020.1``.

# Finally, close the connection

con.close()

Run the test file to verify that you can connect to SQream DB:

$ python test.py
Version: v2020.1

If all went well, you are now ready to build an application using the SQream DB Python connector!

If any connection error appears, verify that you have access to a running SQream DB and that the connection parameters are correct.

Further examples

Data load example

This example loads 10,000 rows of dummy data to a SQream DB instance

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)

# 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 nvarchar(20), dt date, dtt datetime)'
con.execute(create)

# After creating the table, we can load data into it with the INSERT command

# Create dummy data which matches the table we 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
cur = con.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
# Get a new cursor
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]}")

# When done, close the cursor
cur.close()

# Close the connection
con.close()

Example of data retrieval methods

# Assume a table structure:
# "CREATE TABLE table_name (int_column int, varchar_column varchar(10))"

# The select statement:
statement = 'SELECT int_column, varchar_column FROM table_name'
con.execute(statement)

first_row = con.fetchone() # Fetch one row at a time (first row)
second_row = con.fetchone() # Fetch one row at a time (second row)

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

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

con.close()

Example of a SET data loop for data loading

# Assume a table structure:
# "CREATE TABLE table_name (int_column int, varchar_column varchar(10))"

# Each `?` placeholder represents a column value that will be inserted
statement = 'INSERT INTO table_name(int_column, varchar_column) VALUES(?, ?)'

# To insert data, we execute the statement with `executemany`, and pass an array of values alongside it
data_rows = [(1, 's1'), (2, 's2'), (3, 's3')] # Sample data
con.executemany(statement, data_rows)

con.close()

Example inserting data from a CSV

def insert_from_csv(con, table_name, csv_filename, field_delimiter = ',', null_markers = []):

    # We will first ask SQream DB for some table information.
    # This is important for understanding the number of columns, and will help
    # to create an INSERT statement

    column_info = con.execute(f"select * from {table_name} limit 0").description


    def parse_datetime(v):
        try:
            return datetime.datetime.strptime(row[i], '%Y-%m-%d %H:%M:%S.%f')
        except ValueError:
            try:
                return datetime.datetime.strptime(row[i], '%Y-%m-%d %H:%M:%S')
            except ValueError:
                return datetime.datetime.strptime(row[i], '%Y-%m-%d')

    # Create enough placeholders (`?`) for the INSERT query string
    qstring = ','.join(['?'] * len(column_info))
    insert_statement = f"insert into {table_name} values ({qstring})"

    # Open the CSV file
    with open(csv_filename, mode='r') as csv_file:
        csv_reader = csv.reader(csv_file, delimiter=field_delimiter)

    # Execute the INSERT statement with the CSV data
    con.executemany(insert_statement, [row for row in csv_reader]):

Example saving the results of a query to a csv file

def save_query(con, query, csv_filename, field_delimiter, null_marker):
    # The query string has been passed from the outside, so we will now execute it:
    column_info = con.execute(query).description

    # With the query information, we will write a new CSV file
    with open(csv_filename, 'x', newline='') as csvfile:
        wr = csv.writer(csvfile, delimiter=field_delimiter,quoting=csv.QUOTE_MINIMAL)
        # For each result row in a query, write the data out
        for result_row in con:
                csv_row = []
                wr.writerow(result_row)