db_utils library

Introduction

db-utils is a Python package that standardizes interactions with various types of databases. db-utils is a collection of modules that lowers the bar to viewing, extracting, and analyzing data from various sources including:

• Redshift
• Snowflake
• Postgres
• Mysql
• Sqlserver
• sqlite
• s3
• dynamoDB

Docker

One line command to start a Docker container with db-utils installed. This will run on any system that is running Docker. A jupyter notebook will open up on port 8888, just copy and paste the url from the terminal into your preferred browser.

docker run -p 8888:8888 hannaj06/db-utils:latest

docker-compose

Below is an example docker-compose.yaml file. With this configuration jupyter notebook changes persist in the location defined by <local_notebooks>. Below is a sample of the .databases.conf file which will be bindmounted to the docker image.

docker-compose.yaml

version: "2.1"
services:
  dbutils_juypter:
    image: hannaj06/db-utils
    ports:
      - 8888:8888
    volumes:
      - ${HOME}/.databases.conf:/root/.databases.conf
      - <local_notebooks>:/notebooks

.databases.conf

[redshift]
host=<redshift_host>
user=<user>
password=<redshift_password>
port=<port>
database=<db>

[s3]
aws_access_key_id=<access_id>
aws_secret_access_key=<secret_access>
region=<aws_region>
default_bucket=<default_bucket>

Installation on local envoirnment

• sudo apt-get update

Required system packages:

• sudo apt-get install python3-dev (Ubuntu)
• sudo apt-get apt-get install g++ (Ubuntu)
• sudo apt-get install libpq-dev (Ubuntu)
• sudo apt-get install unixodbc-dev (Ubuntu)
• brew install postgresql (MacOS)

pip install db_utils

pg_connect class (previously DBUtil)

A database connection class to interact with Postgres or Redshift

Basic Usage:

• create database configuration file
• example below is called .databases.conf

    [redshift_example]
    host=redshift.example.com
    user=test_user
    password=password
    port=5439
    database=test_db

    >>> from db_utils.pg_connect import pg_connect
    >>>
    >>> db = pg_connect('redshift_example', '.databases.conf')
    >>> db.get_arr_from_query('select * from test', pprint=True)

snowflake_connect class

A database connection class to interact with snowflake

Basic Usage:

• create database configuration file
• example below is called .databases.conf

    [snowflake]
    account=abc123.us-east-1
    host=abc123.us-east-1.snowflakecomputing.com
    user=test_user
    password=password
    port=443
    database=test_db
    aws_access_key_id=<key_id>
    aws_secret_access_key=<secret_key>

snowflake_s3 class

A child class of snowflake_connect class used to retrieve large datasets in small chunks

Basic Usage:

• create database configuration file
• example below is called .databases.conf
• note the additional fields required

    [snowflake]
    account=abc123.us-east-1
    host=abc123.us-east-1.snowflakecomputing.com
    user=test_user
    password=password
    port=443
    database=test_db
    aws_access_key_id=<key_id>
    aws_secret_access_key=<secret_key>
    default_bucket=

example) Loading large data set into memory in chunks

    >>> from db_utils.snowflake_connect import snowflake_s3
    >>> import os
    >>>
    >>> file_format = '''
    TYPE = CSV
    COMPRESSION = NONE
    '''
    >>>
    >>>
    >>> with snowflake_s3('snowflake', '.databases.conf') as db:
    >>>     db.cursor('SELECT * FROM example_large_table', file_format=file_format, pprint=True)
    >>>
    >>>     while True:
    >>>         file = db.fetch(contents=True)
    >>>
    >>>         if file:
    >>>             for row in file:
    >>>                 print(row)
    >>>
    >>>         else:
    >>>             break

sqlite_connect class

A database connection class to interact with SQLite

    >>> from db_utils.sqlite_connect import sqlite_connect
    >>>
    >>> db = sqlite_connect('test.db')
    >>> db.get_df_from_query('select * from test_table', pprint=True)

s3_connect class

Connection library for interacting with S3

Basic Usage:

• add s3 section to .databases.conf file (created in previous example)

    [s3]
        aws_access_key_id=<key_id>
        aws_secret_access_key=<secret_key>
        default_bucket=<bucket>


    >>> from db_utils.s3_connect import s3_connect
    >>>
    >>> s3 = s3_connect('.databases.conf', 's3')
    >>> s3.list_keys(prefix='examples')

example) grab file from s3 into memory as stringIO object

    >>> from db_utils.s3_connect import s3_connect
    >>>
    >>> s3 = s3_connect('.databases.conf', 's3')
    >>> s3.get_contents('example_file', stringIO=True)
    >>> s3.read()

sql_server connect class

Requirements:

• sql server drivers - https://docs.microsoft.com/en-us/sql/connect/odbc/linux-mac/installing-the-microsoft-odbc-driver-for-sql-server?view=sql-server-2017

Basic Usage:

• add sql server section to .databases.conf file (created in previous example)

    [sql_server]
    driver=ODBC Driver 17 for SQL Server
    server=127.0.0.1
    user=bill
    password=gates
    database=master

    >>> from db_utils.sql_server_connect import sql_server_connect
    >>> db = sql_server_connect('sql_server', 'databases.conf')
    >>>
    >>> db.get_arr_from_query('''SELECT * FROM SYSOBJECTS''', pprint=True))

dynamodb_connect class

Connection library for interacting with Dynamodb

timer class

Helper class to time long running processes

Basic Usage:

>>> from db_utils.timer import timer
>>>
>>> t = timer()
>>> t.lap('s')
5.469961