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        "description": "# db_utils library\n\n## Introduction\ndb-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:\n\n * Redshift\n * Snowflake\n * Postgres\n * Mysql\n * Sqlserver\n * sqlite\n * s3\n * dynamoDB\n\n\n## Docker\nOne 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.\n\n```bash\ndocker run -p 8888:8888 hannaj06/db-utils:latest\n```\n\n## docker-compose\n\nBelow 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.\n\n`docker-compose.yaml`\n```bash\nversion: \"2.1\"\nservices:\n  dbutils_juypter:\n    image: hannaj06/db-utils\n    ports:\n      - 8888:8888\n    volumes:\n      - ${HOME}/.databases.conf:/root/.databases.conf\n      - <local_notebooks>:/notebooks\n\n```\n\n`.databases.conf`\n```ini\n[redshift]\nhost=<redshift_host>\nuser=<user>\npassword=<redshift_password>\nport=<port>\ndatabase=<db>\n\n[s3]\naws_access_key_id=<access_id>\naws_secret_access_key=<secret_access>\nregion=<aws_region>\ndefault_bucket=<default_bucket>\n```\n\n## Installation on local envoirnment\n * sudo apt-get update\n\nRequired system packages:\n * sudo apt-get install python3-dev (Ubuntu)\n * sudo apt-get apt-get install g++ (Ubuntu)\n * sudo apt-get install libpq-dev (Ubuntu)\n * sudo apt-get install unixodbc-dev (Ubuntu)\n * brew install postgresql (MacOS)\n\n```bash\npip install db_utils\n```\n\n### pg_connect class (previously DBUtil)\nA database connection class to interact with  Postgres or Redshift\n\nBasic Usage:\n * create database configuration file\n * example below is called .databases.conf\n\n```\n    [redshift_example]\n    host=redshift.example.com\n    user=test_user\n    password=password\n    port=5439\n    database=test_db\n\n    >>> from db_utils.pg_connect import pg_connect\n    >>>\n    >>> db = pg_connect('redshift_example', '.databases.conf')\n    >>> db.get_arr_from_query('select * from test', pprint=True)\n```\n\n### snowflake_connect class\nA database connection class to interact with snowflake\n\nBasic Usage:\n * create database configuration file\n * example below is called .databases.conf\n\n```\n    [snowflake]\n    account=abc123.us-east-1\n    host=abc123.us-east-1.snowflakecomputing.com\n    user=test_user\n    password=password\n    port=443\n    database=test_db\n    aws_access_key_id=<key_id>\n    aws_secret_access_key=<secret_key>\n```\n\n### snowflake_s3 class\nA child class of snowflake_connect class used to retrieve large datasets in small chunks\n\nBasic Usage:\n * create database configuration file\n * example below is called .databases.conf\n * note the additional fields required\n\n```\n    [snowflake]\n    account=abc123.us-east-1\n    host=abc123.us-east-1.snowflakecomputing.com\n    user=test_user\n    password=password\n    port=443\n    database=test_db\n    aws_access_key_id=<key_id>\n    aws_secret_access_key=<secret_key>\n    default_bucket=\n```\n\nexample) Loading large data set into memory in chunks\n```\n    >>> from db_utils.snowflake_connect import snowflake_s3\n    >>> import os\n    >>>\n    >>> file_format = '''\n    TYPE = CSV\n    COMPRESSION = NONE\n    '''\n    >>>\n    >>>\n    >>> with snowflake_s3('snowflake', '.databases.conf') as db:\n    >>>     db.cursor('SELECT * FROM example_large_table', file_format=file_format, pprint=True)\n    >>>\n    >>>     while True:\n    >>>         file = db.fetch(contents=True)\n    >>>\n    >>>         if file:\n    >>>             for row in file:\n    >>>                 print(row)\n    >>>\n    >>>         else:\n    >>>             break\n\n\n```\n\n### sqlite_connect class\nA database connection class to interact with SQLite\n\n```\n\n    >>> from db_utils.sqlite_connect import sqlite_connect\n    >>>\n    >>> db = sqlite_connect('test.db')\n    >>> db.get_df_from_query('select * from test_table', pprint=True)\n```\n\n### s3_connect class\nConnection library for interacting with S3\n\nBasic Usage:\n * add s3 section to .databases.conf file (created in previous example)\n\n```\n    [s3]\n        aws_access_key_id=<key_id>\n        aws_secret_access_key=<secret_key>\n        default_bucket=<bucket>\n\n\n    >>> from db_utils.s3_connect import s3_connect\n    >>>\n    >>> s3 = s3_connect('.databases.conf', 's3')\n    >>> s3.list_keys(prefix='examples')\n\n```\n\nexample) grab file from s3 into memory as stringIO object\n```\n    >>> from db_utils.s3_connect import s3_connect\n    >>>\n    >>> s3 = s3_connect('.databases.conf', 's3')\n    >>> s3.get_contents('example_file', stringIO=True)\n    >>> s3.read()\n\n```\n\n\n### sql_server connect class\n\nRequirements: \n * 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\n\nBasic Usage:\n * add sql server section to .databases.conf file (created in previous example)\n\n\n```\n    [sql_server]\n    driver=ODBC Driver 17 for SQL Server\n    server=127.0.0.1\n    user=bill\n    password=gates\n    database=master\n\n    >>> from db_utils.sql_server_connect import sql_server_connect\n    >>> db = sql_server_connect('sql_server', 'databases.conf')\n    >>>\n    >>> db.get_arr_from_query('''SELECT * FROM SYSOBJECTS''', pprint=True))\n```\n\n\n### dynamodb_connect class\nConnection library for interacting with Dynamodb\n\n\n### timer class\nHelper class to time long running processes\n\nBasic Usage:\n\n```\n>>> from db_utils.timer import timer\n>>>\n>>> t = timer()\n>>> t.lap('s')\n5.469961\n```\n\n\n",
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