schemaql 0.0.4

A testing and auditing tool inspired by dbt, for those not using dbt.

SchemaQL

A testing and auditing tool inspired by dbt, for those not using dbt.

Installation

1. Fork and clone to repo to a local folder.
2. Create a new Python virtual environment in this folder and activate it.
3. In the local repo folder, run pip install -r requirements to install the dependent packages in this virtualenv.
4. In the local repo folder, run pip install -e . to install a dev version locally.

You should now be able to run schemaql -h from the command line.

schemaql -h
usage: schemaql [-h] [-p None] [-c config.yml] [-x connections.yml] [action]

positional arguments:
  action                Action ('test', or 'generate')

optional arguments:
  -h, --help            show this help message and exit
  -p None, --project None
                        Project
  -c config.yml, --config-file config.yml
                        Config file
  -x connections.yml, --connections-file connections.yml
                        Connections file

Configuration

SchemaQL needs to *.yml for configuration:

In connections.yml you define how to connect to one or more of your data warehouse connections like so:

project-1-snowflake:
type: snowflake
  account: <my_account>
  user: <my_user>
  password: <my_password>
  database: <my_database>
  # optional:
  warehouse: <my_warehouse>
  role: <my_role>
  schema: <my_initial_schema>

project-2-bigquery:
type: bigquery
  database: <my_bigquery_project>
  credentials_path: <path_to_my_service_account_credentials.json>
  supports_multi_insert: False

In config.yml, you define the following:

• Logging, where output is a directory relative to the project path.

logs:
  output: logs

• a collector which is a definition for how you want test results to be collected. Currently supported are:
  • json
  • csv
  • database

For json and csv, you only need to provide an output path.

collector:
  type: csv
  output: output 

For database collection, you need to provide the name of a connection (from connections.yml) and a destination table via output:

collector:
  type: database
  connection: project-1-snowflake
  output: test_results

The collector connection does not need to the same connection, or even the same connection type as the project connection. So, tests could be run against BigQuery but tests results could be collected using Snowflake or Postgres.

• projects which is a combination of a connection and a list of which databases and schemas you want to work with. If you don't define any schemas within the database key, all schemas will be processed.

projects:
  project-1:
    connection: project-1-snowflake
    schema:
      database_1:
      - schema_1
      - schema_2
  project-2:
    connection: project-2-bigquery
    schema:
      my_bg_project_1
      my_bg_project_2
      - data_set_1
      - data_set_2

Usage

Generate

SchemaQL runs tests against schema information contained in yml files. You can either write these from scratch, use your existing dbt schema files, or use schemaql to generate them.

Generates schema files for all projects:

schemaql generate

Generates schema files for "my_project" only:

schemaql generate -p my_project

Test

schemaql test

Test "my_project" only:

schemaql test -p my_project

Built-in Tests

Schema Tests

not_null

Checks if column values are NULL

models:
  - name: my_table
    columns:
      - name: col_1
        tests:
          - not_null

relationships

Checks if column values match values from column in other entity

models:
  - name: my_table
    columns:
      - name: col_1
        tests:
          - relationships:
              to: my_other_table
              field: col_1

unique

Checks if column values are unique

models:
  - name: my_table
    columns:
      - name: col_1
        tests:
          - unique

Data Tests

accepted_values

Checks if column values match a predefined list of accepted values

models:
  - name: my_table
    columns:
      - name: day_of_week
        description: 
        tests:
          - accepted_values:
            values: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

at_least_one

Checks if column has at least one value

models:
  - name: my_table
    columns:
      - name: col_1
        tests:
          - at_least_one

equal_expression (TBD)

frequency (TBD)

not_constant

Checks if column has at more than one value

models:
  - name: my_table
    columns:
      - name: col_1
        tests:
          - not_constant

recency (TBD)

unique_rows

Checks if table rows are unique. If columns are not specified, uses all columns.

models:
  - name: my_table
    tests:
      - unique_rows:
          columns: [col_1, col_2, col_3]