r-wheeler/vertica-python

vertica-python is a native Python adapter for the Vertica (http://www.vertica.com) database.

vertica-python

0.5.x changes the connection method to accept kwargs instead of a dict to be more dbapi compliant. copy methods improved and consolidated in 0.5.1

0.4.x breaks some of the older query interfaces (row_handler callback, and connection.query). It replaces the row_handler callback with an iterate() method. Please see examples below If you are on 0.4.x, please upgrade to 0.4.6 as there are various bug fixes

vertica-python is a native Python adapter for the Vertica (http://www.vertica.com) database.

vertica-python is currently in beta stage; it has been tested for functionality and has a very basic test suite. Please use with caution, and feel free to submit issues and/or pull requests (after running the unit tests).

vertica-python has been tested with Vertica 6.1.2/7.0.0+ and Python 2.6/2.7.

Installation

If you're using pip >= 1.4 and you don't already have pytz installed:

pip install --pre pytz

To install vertica-python with pip:

pip install vertica-python

To install vertica-python with pip (with optional namedparams dependencies):

# see 'Using named parameters' section below
pip install 'vertica-python[namedparams]'

Source code for vertica-python can be found at:

http://github.com/uber/vertica-python

Run unit tests

# install nose if you don't have it
pip install -r requirements_test.txt

# you will need to have access to a vertica database.
# connection info is in tests/basic_tests.py

# run tests
nosetests

Usage

Create connection

from vertica_python import connect

conn_info = {'host': '127.0.0.1', 
             'port': 5433, 
             'user': 'some_user', 
             'password': 'some_password', 
             'database': 'a_database'}

# simple connection, with manual close
connection = vertica_python.connect(**conn_info)
# do things
connection.close()

# using with for auto connection closing after usage
with vertica_python.connect(**conn_info) as connection:
    # do things

Stream query results:

cur = connection.cursor()
cur.execute("SELECT * FROM a_table LIMIT 2")
for row in cur.iterate():
    print(row)
# [ 1, 'some text', datetime.datetime(2014, 5, 18, 6, 47, 1, 928014) ]
# [ 2, 'something else', None ]

Streaming is recommended if you want to further process each row, save the results in a non-list/dict format (e.g. Pandas DataFrame), or save the results in a file.

In-memory results as list:

cur = connection.cursor()
cur.execute("SELECT * FROM a_table LIMIT 2")
cur.fetchall()
# [ [1, 'something'], [2, 'something_else'] ]

In-memory results as dictionary:

cur = connection.cursor('dict')
cur.execute("SELECT * FROM a_table LIMIT 2")
cur.fetchall()
# [ {'id': 1, 'value': 'something'}, {'id': 2, 'value': 'something_else'} ]
connection.close()

Query using named parameters:

# Using named parameter bindings requires psycopg2>=2.5.1 which is not includes with the base vertica_python requirements.

cur = connection.cursor()
cur.execute("SELECT * FROM a_table WHERE a = :propA b = :propB", {'propA': 1, 'propB': 'stringValue'})
cur.fetchall()
# [ [1, 'something'], [2, 'something_else'] ]

Insert and commits :

cur = connection.cursor()

# inline commit
cur.execute("INSERT INTO a_table (a, b) VALUES (1, 'aa'); commit;")

# commit in execution
cur.execute("INSERT INTO a_table (a, b) VALUES (1, 'aa')")
cur.execute("INSERT INTO a_table (a, b) VALUES (2, 'bb')")
cur.execute("commit;")

# connection.commit()
cur.execute("INSERT INTO a_table (a, b) VALUES (1, 'aa')")
connection.commit()

Copy :

cur = connection.cursor()
cur.copy("COPY test_copy (id, name) from stdin DELIMITER ',' ",  csv)

Where csv is either a string or a file-like object (specifically, any object with a read() method). If using a file, the data is streamed.

License

MIT License, please see LICENSE for details.

Acknowledgements

Many thanks go to the contributors to the Ruby Vertica gem (https://github.com/sprsquish/vertica), since they did all of the wrestling with Vertica's protocol and have kept the gem updated. They are:

• Matt Bauer
• Jeff Smick
• Willem van Bergen
• Camilo Lopez