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View management for Amazon's Redshift

Project description

Room with a View

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View management for Amazon’s Redshift

Features

Room with a view is a python script that automatically parses a collection of SQL files to find View and Function definitions and their dependencies. It then makes it easy to sync those views with Redshift, automatically dropping and recreating dependent views as necessary so that there are no errors. The key benefits are:

  • No writing code to send view SQL to Redshift: the script does it for you.

  • Edit any view and sync it without worrying about needing to drop or recreate views that depend on it.

  • Error handling tells you exactly where there are errors in your views.

  • The script runs in an atomic transaction, so you can’t accidentally enter a broken state by syncing some views and not others.

Setup

  • Copy settings.yaml.default to settings.yaml, and edit the file to configure your Redshift connection and the location of your .sql files. Example settings.yaml file:

connections:
  default:
    host: localhost
    port: 5432
    user: awsuser
    password: **CHANGEME**
    dbname: postgres

directories:
  default: .
  • Install requirements with pip install -r requirements.txt

Usage

usage: room_with_a_view.py [-h] [--view-names [VIEW-NAME [VIEW-NAME ...]]]
                           [--file-names [FILE-PATH [FILE-PATH ...]]]
                           [--connection CONNECTION]
                           [--directories [DIRECTORY [DIRECTORY ...]]]
                           [--settings SETTINGS]
                           {sync-all,sync,drop,drop-all}

Manages Redshift SQL views. Possible actions:
        sync-all: Syncs all views in all .sql files in a set of directories (identified by the --directories parameter). The directory will be searched recursively
        sync: Syncs specific views (identified by the --view-names or --file-names parameters)
        drop: Drops specific views (identified by the --view-names or --file-names parameters)
        drop-all: Drops all views in all .sql files in a set of directories (identified by the --directories parameter). The directory will be searched recursively

positional arguments:
  {sync-all,sync,drop,drop-all}
                        The action to perform.

optional arguments:
  -h, --help            show this help message and exit
  --view-names [VIEW-NAME [VIEW-NAME ...]]
                        View names to manage.
  --file-names [FILE-PATH [FILE-PATH ...]]
                        Paths to .sql files to manage.
  --connection CONNECTION
                        Name of the Redshift connection to use (or "default",
                        if not specified). The name must match a connection in
                        settings.yaml
  --directories [DIRECTORY [DIRECTORY ...]]
                        Directory names to search for SQL files (or "default"
                        if not specified). Names must match directories in
                        settings.yaml
  --settings SETTINGS   Location of the settings file (settings.yaml by
                        default)

Examples

  • room_with_a_view.py sync-all: Syncs all views in all SQL files in the default directory specified in settings.yaml. Drops and recreates existing views, and makes sure views are created in dependency order.

  • room_with_a_view.py sync --view-names my_view1 my_view2 --file-names ../sql/my_file.sql: Syncs the specific views my_view1 and my_view2, as well as all views in the file ../sql/my_file.sql.

  • room_with_a_view.py drop-all --connection other_connection: Drops all views in the default directory, using the connection info specified in settings.yaml under the name other_connection to connect to Redshift.

  • room_with_a_view.py drop --view-names my_view1 --directories other_dir1 other_dir2 --settings /path/to/fancy_settings.yaml: Drops the view my_view1, looking for SQL files that contain the view and its dependents in the directories specified by other_dir1 and other_dir2 in the settings file located in /path/to/fancy_settings.yaml.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2018-05-28)

  • First release on PyPI.

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