diff --git a/Makefile b/Makefile deleted file mode 100644 index d670073..0000000 --- a/Makefile +++ /dev/null @@ -1,31 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= poetry run sphinx-build -SOURCEDIR = ./docsource -BUILDDIR = ./docs/_build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Build the docs -build-docs-local: - poetry run sphinx-apidoc ./src/sql_mock -o "$(SOURCEDIR)" - @$(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -build-docs-github: - @make build-docs-local - @cp -a docs/_build/html/. ./docs - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - - diff --git a/README.md b/README.md index 8496aec..1f878d3 100644 --- a/README.md +++ b/README.md @@ -8,17 +8,25 @@ The primary purpose of this library is to simplify the testing of SQL data models and queries by allowing users to mock input data and create tests for various scenarios. It provides a consistent and convenient way to test the execution of your query without the need to process a massive amount of data. -# Documentation - -A full documentation can be found [on the documentation page](https://deeplcom.github.io/sql-mock/) - -The library currently supports the following databases. -* BigQuery -* Clickhouse -* Redshift -* Snowflake - -## Installation +## Documentation + +* [Installation](#installation) +* [Quickstart](#quickstart) +* Basic usage + * [Create table mocks](/docs/defining_table_mocks.md) + * [Specifying default values](/docs/default_values.md) + * [Specifying the query to test](/docs/your_sql_query_to_test.md) + * [Result assertions](/docs/result_assertion.md) +* System specific usage + * [Use with BigQuery](/docs/bigquery.md) + * [Use with Clickhouse](/docs/clickhouse.md) + * [Use with Redshift](/docs/redshift.md) + * [Use with Snowflake](/docs/snowflake.md) + * [Use with dbt](/docs/dbt.md) + +You can find some examples in the [examples folder](https://github.com/DeepLcom/sql-mock/tree/main/examples). + +### Installation The library can be installed from [PyPI](https://pypi.org/project/sql-mock/) using pip: @@ -42,11 +50,105 @@ If you need to modify this source code, install the dependencies using poetry: poetry install --all-extras ``` +#### Recommended Setup for Pytest + +If you are using pytest, make sure to add a `conftest.py` file to the root of your project. +In the file add the following lines: + +```python +import pytest +pytest.register_assert_rewrite('sql_mock') +``` + +This allows you to get a rich comparison when using the `.assert_equal` method on the table mock instances. + +We also recommend using [pytest-icdiff](https://github.com/hjwp/pytest-icdiff) for better visibility on diffs of failed tests. + +### Quickstart + +Before diving into specific database scenarios, let's start with a simplified example of how SQL Mock works behind the scenes. + +1. You have an original SQL query, for instance: + + ```sql + -- path/to/query_for_result_table.sql + SELECT id FROM data.table1 + ``` + +2. Using SQL Mock, you define table mocks. You can use the built-in column types provided by SQL Mock. Available column types include `Int`, `String`, `Date`, and more. Each database type has their own column types. Define your tables by subclassing a mock table class that fits your database (e.g. `BigQueryTableMock`) and specifying the column types along with default values. In our example we use the `ClickHouseTableMock` class + + ```python + from sql_mock.clickhouse import column_mocks as col + from sql_mock.clickhouse.table_mocks import ClickHouseTableMock + from sql_mock.table_mocks import table_meta + + @table_meta(table_ref='data.table1') + class Table(ClickHouseTableMock): + id = col.Int(default=1) + name = col.String(default='Peter') + + @table_meta(table_ref='data.result_table', query_path='path/to/query_for_result_table.sql') + class ResultTable(ClickHouseTableMock): + id = col.Int(default=1) + ``` + +3. **Creating mock data:** Define mock data for your tables using dictionaries. Each dictionary represents a row in the table, with keys corresponding to column names. Table column keys that don't get a value will use the default. + + ```python + user_data = [ + {}, # This will use the defaults for both id and name + {'id': 2, 'name': 'Martin'}, + {'id': 3}, # This will use defaults for the name + ] + + input_table_mock = Table.from_dicts(user_data) + ``` + +4. **Getting results for a table mock:** Use the `from_mocks` method of the table mock object to generate mock query results based on your mock data. + + ```python + res = ResultTable.from_mocks(input_data=[input_table_mock]) + ``` + +5. Behind the scene SQL Mock replaces table references (e.g. `data.table1`) in your query with Common Table Expressions (CTEs) filled with dummy data. It can roughly be compared to something like this: + + ```sql + WITH data__table1 AS ( + -- Mocked inputs + SELECT + cast('1' AS 'String') AS id, + cast('Peter' AS 'String') AS name + UNION ALL + SELECT + cast('2' AS 'String') AS id, + cast('Martin' AS 'String') AS name + UNION ALL + SELECT + cast('3' AS 'String') AS id, + cast('Peter' AS 'String') AS name + ) + + result AS ( + -- Original query with replaced references + SELECT id FROM data__table1 + ) + + SELECT + cast(id AS 'String') AS id + FROM result + ``` + +6. Finally, you can compare your results to some expected results using the `assert_equal` method. + + ```python + expected = [{'id': '1'},{'id': '2'},{'id': '3'}] + res.assert_equal(expected) + ``` + ## Pydantic V1 vs. V2 SQL Mock's published version supports Pydantic V2. You might run into issues when your code depends on Pydantic V1. -We have an alternative branch you can install from that supports Pydantic V1 in the meanwhile: https://github.com/DeepLcom/sql-mock/tree/pydantic-v1 - +We have an alternative branch you can install from that supports Pydantic V1 in the meanwhile: ## Contributing @@ -56,10 +158,10 @@ We welcome contributions to improve and enhance this open-source project. Whethe If you encounter a bug, have a feature request, or face any issues with the project, we encourage you to report them using the project's issue tracker. When creating an issue, please include the following information: -- A clear and descriptive title. -- A detailed description of the problem or suggestion. -- Steps to reproduce the issue (if applicable). -- Any error messages or screenshots that help clarify the problem. +* A clear and descriptive title. +* A detailed description of the problem or suggestion. +* Steps to reproduce the issue (if applicable). +* Any error messages or screenshots that help clarify the problem. ### Feature Requests @@ -79,3 +181,64 @@ The SQL Mock Buddy can be accessed here: [https://chat.openai.com/g/g-FIXNcqu1l- SQL Mock Buddy should help you to get started quickly with SQL Mock. It is still in beta mode and you should definitely double-check its output! + +## FAQ + +### My database system is not supported yet but I want to use SQL Mock. What should I do? + +We are planning to add more and more supported database systems. However, if your system is not supported yet, you can still use SQL Mock. There are only 2 things you need to do: + +#### Create your `TableMock` class + +First, you need to create a `TableMock` class for your database system that inherits from `sql_mock.table_mocks.BaseTableMock`. + +That class needs to implement the `_get_results` method which should make sure to fetch the results of a query (e.g. produced by `self._generate_query()`) and return it as list of dictionaries. + +Look at one of the existing client libraries to see how this could work (e.g. [BigQueryTableMock](https://github.com/DeepLcom/sql-mock/blob/main/src/sql_mock/bigquery/table_mocks.py)). + +You might want to create a settings class as well in case you need some specific connection settings to be available within the `_get_results` method. + +#### Create your `ColumnMocks` + +Your database system might support specific database types. In order to make them available as column types, you can use the `sql_mock.column_mocks.BaseColumnMock` class as a base and inherit your specific column types from it. +For most of your column mocks you might only need to specify the `dtype` that should be used to parse the inputs. + +A good practise is to create a `BaseColumnMock` class that is specific to your database and inherit all your column types from it, e.g.: + +```python +from sql_mock.column_mocks import BaseColumnMock + +class MyFanceDatabaseColumnMock(BaseColumnMock): + # In case you need some specific logic that overwrites the default behavior, you can do so here + pass + +class Int(MyFanceDatabaseColumnMock): + dtype = "Integer" + +class String(MyFanceDatabaseColumnMock): + dtype = "String" +``` + +#### Contribute your database setup + +There will definitely be folks in the community that are in the need of support for the database you just created all the setup for. +Feel free to create a PR on this repository that we can start supporting your database system! + +### I am missing a specific BaseColumnMock type for my model fields + +We implemented some basic column types but it could happen that you don't find the one you need. +Luckily, you can easily create those with the tools provided. +The only thing you need to do is to inherit from the `BaseColumnMock` that is specific to your database system (e.g. `BigQueryColumnMock`) and write classes for the column mocks you are missing. Usually you only need to set the correct `dtype`. This would later be used in the `cast(col to )` expression. + +```python +# Replace the import with the database system you are using +from sql_mock.bigquery.column_mock import BigQueryColumnMock + +class MyFancyMissingColType(BigQueryColumnMock): + dtype = "FancyMissingColType" + + # In case you need to implement additional logic for casting, you can do so here + ... +``` + +**Don't forget to create a PR in case you feel that your column mock type could be useful for the community**! diff --git a/docs/.buildinfo b/docs/.buildinfo deleted file mode 100644 index 94c9eec..0000000 --- a/docs/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: c82d665cc338348dd2b6777f2bd67a49 -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/.nojekyll b/docs/.nojekyll deleted file mode 100644 index e69de29..0000000 diff --git a/docs/_sources/faq.md.txt b/docs/_sources/faq.md.txt deleted file mode 100644 index 095a1a0..0000000 --- a/docs/_sources/faq.md.txt +++ /dev/null @@ -1,62 +0,0 @@ - -# FAQ - -## My database system is not supported yet but I want to use SQL Mock. What should I do? - -We are planning to add more and more supported database systems. However, if your system is not supported yet, you can still use SQL Mock. There are only 2 things you need to do: - -### Create your `MockTable` class - -First, you need to create a `MockTable` class for your database system that inherits from `sql_mock.table_mocks.BaseMockTable`. - -That class needs to implement the `_get_results` method which should make sure to fetch the results of a query (e.g. produced by `self._generate_query()`) and return it as list of dictionaries. - -Look at one of the existing client libraries to see how this could work (e.g. [BigQueryMockTable](https://github.com/DeepLcom/sql-mock/blob/main/src/sql_mock/bigquery/table_mocks.py)). - -You might want to create a settings class as well in case you need some specific connection settings to be available within the `_get_results` method. - -### Create your `ColumnMocks` - -Your database system might support specific database types. In order to make them available as column types, you can use the `sql_mock.column_mocks.ColumnMock` class as a base and inherit your specific column types from it. -For most of your column mocks you might only need to specify the `dtype` that should be used to parse the inputs. - -A good practise is to create a `ColumnMock` class that is specific to your database and inherit all your column types from it, e.g.: - -```python -from sql_mock.column_mocks import ColumnMock - -class MyFanceDatabaseColumnMock(ColumnMock): - # In case you need some specific logic that overwrites the default behavior, you can do so here - pass - -class Int(MyFanceDatabaseColumnMock): - dtype = "Integer" - -class String(MyFanceDatabaseColumnMock): - dtype = "String" -``` - -### Contribute your database setup - -There will definitely be folks in the community that are in the need of support for the database you just created all the setup for. -Feel free to create a PR on this repository that we can start supporting your database system! - - -## I am missing a specific ColumnMock type for my model fields - -We implemented some basic column types but it could happen that you don't find the one you need. -Luckily, you can easily create those with the tools provided. -The only thing you need to do is to inherit from the `ColumnMock` that is specific to your database system (e.g. `BigQueryColumnMock`) and write classes for the column mocks you are missing. Usually you only need to set the correct `dtype`. This would later be used in the `cast(col to )` expression. - -```python -# Replace the import with the database system you are using -from sql_mock.bigquery.column_mock import BigQueryColumnMock - -class MyFancyMissingColType(BigQueryColumnMock): - dtype = "FancyMissingColType" - - # In case you need to implement additional logic for casting, you can do so here - ... -``` - -**Don't forget to create a PR in case you feel that your column mock type could be useful for the community**! diff --git a/docs/_sources/getting_started/installation.md.txt b/docs/_sources/getting_started/installation.md.txt deleted file mode 100644 index 0a36650..0000000 --- a/docs/_sources/getting_started/installation.md.txt +++ /dev/null @@ -1,39 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Installation - -The library can be installed from [PyPI](https://pypi.org/project/sql-mock/) using pip: - -```shell -# BigQuery -pip install --upgrade "sql-mock[bigquery]" - -# Clickhouse -pip install --upgrade "sql-mock[clickhouse]" - -# Redshift -pip install --upgrade "sql-mock[redshift]" - -# Snowflake -pip install --upgrade "sql-mock[snowflake]" -``` - -If you need to modify this source code, install the dependencies using poetry: - -```shell -poetry install --all-extras -``` - - -## Recommended Setup for Pytest -If you are using pytest, make sure to add a `conftest.py` file to the root of your project. -In the file add the following lines: -```python -import pytest -pytest.register_assert_rewrite('sql_mock') -``` -This allows you to get a rich comparison when using the `.assert_equal` method on the table mock instances. - -We also recommend using [pytest-icdiff](https://github.com/hjwp/pytest-icdiff) for better visibility on diffs of failed tests. diff --git a/docs/_sources/getting_started/quickstart.md.txt b/docs/_sources/getting_started/quickstart.md.txt deleted file mode 100644 index e8b2cd4..0000000 --- a/docs/_sources/getting_started/quickstart.md.txt +++ /dev/null @@ -1,81 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Quickstart - -Before diving into specific database scenarios, let's start with a simplified example of how SQL Mock works behind the scenes. - - -1. You have an original SQL query, for instance: - ```sql - -- path/to/query_for_result_table.sql - SELECT id FROM data.table1 - ``` - - -2. Using SQL Mock, you define mock tables. You can use the built-in column types provided by SQL Mock. Available column types include `Int`, `String`, `Date`, and more. Each database type has their own column types. Define your tables by subclassing a mock table class that fits your database (e.g. `BigQueryMockTable`) and specifying the column types along with default values. In our example we use the `ClickHouseTableMock` class - ```python - from sql_mock.clickhouse import column_mocks as col - from sql_mock.clickhouse.table_mocks import ClickHouseTableMock - from sql_mock.table_mocks import table_meta - - @table_meta(table_ref='data.table1') - class Table(ClickHouseTableMock): - id = col.Int(default=1) - name = col.String(default='Peter') - - @table_meta(table_ref='data.result_table', query_path='path/to/query_for_result_table.sql') - class ResultTable(ClickHouseTableMock): - id = col.Int(default=1) - ``` - -3. **Creating mock data:** Define mock data for your tables using dictionaries. Each dictionary represents a row in the table, with keys corresponding to column names. Table column keys that don't get a value will use the default. - ```python - user_data = [ - {}, # This will use the defaults for both id and name - {'id': 2, 'name': 'Martin'}, - {'id': 3}, # This will use defaults for the name - ] - - input_table_mock = Table.from_dicts(user_data) - ``` - - -4. **Getting results for a table mock:** Use the `from_mocks` method of the table mock object to generate mock query results based on your mock data. - ```python - res = ResultTable.from_mocks(input_data=[input_table_mock]) - ``` - -5. Behind the scene SQL Mock replaces table references (e.g. `data.table1`) in your query with Common Table Expressions (CTEs) filled with dummy data. It can roughly be compared to something like this: - ```sql - WITH data__table1 AS ( - -- Mocked inputs - SELECT - cast('1' AS 'String') AS id, - cast('Peter' AS 'String') AS name - UNION ALL - SELECT - cast('2' AS 'String') AS id, - cast('Martin' AS 'String') AS name - UNION ALL - SELECT - cast('3' AS 'String') AS id, - cast('Peter' AS 'String') AS name - ) - - result AS ( - -- Original query with replaced references - SELECT id FROM data__table1 - ) - - SELECT - cast(id AS 'String') AS id - FROM result - ``` - -6. Finally, you can compare your results to some expected results using the `assert_equal` method. - ```python - expected = [{'id': '1'},{'id': '2'},{'id': '3'}] - res.assert_equal(expected) - ``` diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt deleted file mode 100644 index b78bacb..0000000 --- a/docs/_sources/index.rst.txt +++ /dev/null @@ -1,60 +0,0 @@ -.. SQL Mock documentation master file, created by - sphinx-quickstart on Wed Nov 1 07:36:03 2023. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to SQL Mock's documentation! -==================================== - -The primary purpose of this library is to simplify the testing of SQL data models and queries by allowing users to mock input data and create tests for various scenarios. -It provides a consistent and convenient way to test the execution of your query without the need to process a massive amount of data. - -.. meta:: - :google-site-verification: ohDOHPn1YMLYMaWQpFmqQfPW_KRZXNK9Gq47pP57VQM - -.. automodule:: sql_mock - :members: - :undoc-members: - :show-inheritance: - -.. toctree:: - :maxdepth: 3 - :caption: Getting Started - - getting_started/installation - getting_started/quickstart - faq - -.. toctree:: - :maxdepth: 3 - :caption: Basic Usage - - usage/defining_table_mocks - usage/dbt - usage/your_sql_query_to_test - usage/result_assertion - usage/default_values - usage/examples - -.. toctree:: - :maxdepth: 3 - :caption: Database Specifics - - usage/bigquery/index - usage/clickhouse/index - usage/redshift/index - usage/snowflake/index - -.. toctree:: - :maxdepth: 3 - :caption: API Reference - - API Reference - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/_sources/modules.rst.txt b/docs/_sources/modules.rst.txt deleted file mode 100644 index f4baeab..0000000 --- a/docs/_sources/modules.rst.txt +++ /dev/null @@ -1,8 +0,0 @@ -sql_mock -======== - -.. toctree:: - :maxdepth: 4 - :hidden: - - sql_mock diff --git a/docs/_sources/robots.txt b/docs/_sources/robots.txt deleted file mode 100644 index 7557cc5..0000000 --- a/docs/_sources/robots.txt +++ /dev/null @@ -1,3 +0,0 @@ -User-agent: * - -Sitemap: https://deeplcom.github.io/sql-mock/sitemap.xml diff --git a/docs/_sources/sql_mock.bigquery.rst.txt b/docs/_sources/sql_mock.bigquery.rst.txt deleted file mode 100644 index 0ec2aea..0000000 --- a/docs/_sources/sql_mock.bigquery.rst.txt +++ /dev/null @@ -1,37 +0,0 @@ -sql\_mock.bigquery package -========================== - -Submodules ----------- - -sql\_mock.bigquery.column\_mocks module ---------------------------------------- - -.. automodule:: sql_mock.bigquery.column_mocks - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.bigquery.settings module ----------------------------------- - -.. automodule:: sql_mock.bigquery.settings - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.bigquery.table\_mocks module --------------------------------------- - -.. automodule:: sql_mock.bigquery.table_mocks - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: sql_mock.bigquery - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_sources/sql_mock.clickhouse.rst.txt b/docs/_sources/sql_mock.clickhouse.rst.txt deleted file mode 100644 index d8577e0..0000000 --- a/docs/_sources/sql_mock.clickhouse.rst.txt +++ /dev/null @@ -1,37 +0,0 @@ -sql\_mock.clickhouse package -============================ - -Submodules ----------- - -sql\_mock.clickhouse.column\_mocks module ------------------------------------------ - -.. automodule:: sql_mock.clickhouse.column_mocks - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.clickhouse.settings module ------------------------------------- - -.. automodule:: sql_mock.clickhouse.settings - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.clickhouse.table\_mocks module ----------------------------------------- - -.. automodule:: sql_mock.clickhouse.table_mocks - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: sql_mock.clickhouse - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_sources/sql_mock.redshift.rst.txt b/docs/_sources/sql_mock.redshift.rst.txt deleted file mode 100644 index 1a3a286..0000000 --- a/docs/_sources/sql_mock.redshift.rst.txt +++ /dev/null @@ -1,37 +0,0 @@ -sql\_mock.redshift package -========================== - -Submodules ----------- - -sql\_mock.redshift.column\_mocks module ---------------------------------------- - -.. automodule:: sql_mock.redshift.column_mocks - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.redshift.settings module ----------------------------------- - -.. automodule:: sql_mock.redshift.settings - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.redshift.table\_mocks module --------------------------------------- - -.. automodule:: sql_mock.redshift.table_mocks - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: sql_mock.redshift - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_sources/sql_mock.rst.txt b/docs/_sources/sql_mock.rst.txt deleted file mode 100644 index 407d078..0000000 --- a/docs/_sources/sql_mock.rst.txt +++ /dev/null @@ -1,54 +0,0 @@ -sql\_mock package -================= - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - sql_mock.bigquery - sql_mock.clickhouse - -Submodules ----------- - -sql\_mock.column\_mocks module ------------------------------- - -.. automodule:: sql_mock.column_mocks - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.constants module --------------------------- - -.. automodule:: sql_mock.constants - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.exceptions module ---------------------------- - -.. automodule:: sql_mock.exceptions - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.table\_mocks module ------------------------------ - -.. automodule:: sql_mock.table_mocks - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: sql_mock - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_sources/sql_mock.snowflake.rst.txt b/docs/_sources/sql_mock.snowflake.rst.txt deleted file mode 100644 index ba3ca51..0000000 --- a/docs/_sources/sql_mock.snowflake.rst.txt +++ /dev/null @@ -1,37 +0,0 @@ -sql\_mock.snowflake package -=========================== - -Submodules ----------- - -sql\_mock.snowflake.column\_mocks module ----------------------------------------- - -.. automodule:: sql_mock.snowflake.column_mocks - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.snowflake.settings module ------------------------------------ - -.. automodule:: sql_mock.snowflake.settings - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.snowflake.table\_mocks module ---------------------------------------- - -.. automodule:: sql_mock.snowflake.table_mocks - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: sql_mock.snowflake - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_sources/usage/bigquery/examples.md.txt b/docs/_sources/usage/bigquery/examples.md.txt deleted file mode 100644 index 99523fa..0000000 --- a/docs/_sources/usage/bigquery/examples.md.txt +++ /dev/null @@ -1,65 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Example: Testing Subscription Counts in BigQuery - -```python -import datetime -from sql_mock.bigquery import column_mocks as col -from sql_mock.bigquery.table_mocks import BigQueryMockTable -from sql_mock.table_mocks import table_meta - -# Define mock tables for your data model that inherit from BigQueryMockTable -@table_meta(table_ref='data.users') -class UserTable(BigQueryMockTable): - user_id = col.Int(default=1) - user_name = col.String(default='Mr. T') - - -@table_meta(table_ref='data.subscriptions') -class SubscriptionTable(BigQueryMockTable): - subscription_id = col.Int(default=1) - period_start_date = col.Date(default=datetime.date(2023, 9, 5)) - period_end_date = col.Date(default=datetime.date(2023, 9, 5)) - user_id = col.Int(default=1) - - -# Define a mock table for your expected results -class SubscriptionCountTable(BigQueryMockTable): - subscription_count = col.Int(default=1) - user_id = col.Int(default=1) - -# Your original SQL query -query = """ -SELECT - count(*) AS subscription_count, - user_id -FROM data.users -LEFT JOIN data.subscriptions USING(user_id) -GROUP BY user_id -""" - -# Create mock data for the 'data.users' and 'data.subscriptions' tables -users = UserTable.from_dicts([{'user_id': 1}, {'user_id': 2}]) -subscriptions = SubscriptionTable.from_dicts([ - {'subscription_id': 1, 'user_id': 1}, - {'subscription_id': 2, 'user_id': 1}, - {'subscription_id': 2, 'user_id': 2}, -]) - -# Define your expected results -expected = [ - {'user_id': 1, 'subscription_count': 2}, - {'user_id': 2, 'subscription_count': 1} -] - -# Simulate the SQL query using SQL Mock -res = SubscriptionCountTable.from_mocks( - query=query, - input_data=[users, subscriptions] -) - -# Assert the results -res.assert_equal(expected) -``` diff --git a/docs/_sources/usage/bigquery/index.rst.txt b/docs/_sources/usage/bigquery/index.rst.txt deleted file mode 100644 index 5af047d..0000000 --- a/docs/_sources/usage/bigquery/index.rst.txt +++ /dev/null @@ -1,10 +0,0 @@ -BigQuery -===================== - -This section documents the specifics on how to use SQL Mock with BigQuery - -.. toctree:: - :maxdepth: 4 - - ./settings.md - ./examples.md diff --git a/docs/_sources/usage/bigquery/settings.md.txt b/docs/_sources/usage/bigquery/settings.md.txt deleted file mode 100644 index 9e261ad..0000000 --- a/docs/_sources/usage/bigquery/settings.md.txt +++ /dev/null @@ -1,9 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Settings - -In order to use sql-mock with BigQuery, ensure you have the `GOOGLE_APPLICATION_CREDENTIALS` environment variable correctly set to point to a service account file. - -You need to service account file in order to let SQL Mock connect to BigQuery while running the tests. diff --git a/docs/_sources/usage/clickhouse/index.rst.txt b/docs/_sources/usage/clickhouse/index.rst.txt deleted file mode 100644 index 4bca7b1..0000000 --- a/docs/_sources/usage/clickhouse/index.rst.txt +++ /dev/null @@ -1,10 +0,0 @@ -Clickhouse -===================== - -This section documents the specifics on how to use SQL Mock with Clickhouse - -.. toctree:: - :maxdepth: 4 - - ./settings.md - ./examples.md diff --git a/docs/_sources/usage/clickhouse/settings.md.txt b/docs/_sources/usage/clickhouse/settings.md.txt deleted file mode 100644 index 8e9d6fb..0000000 --- a/docs/_sources/usage/clickhouse/settings.md.txt +++ /dev/null @@ -1,13 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Settings - -In order to use SQL Mock with Clickhouse, you need to provide the following environment variables when you run tests: -* `SQL_MOCK_CLICKHOUSE_HOST`: Host of your Clickhouse instance -* `SQL_MOCK_CLICKHOUSE_USER`: User you want to use for the connection -* `SQL_MOCK_CLICKHOUSE_PASSWORD`: Password of your user -* `SQL_MOCK_CLICKHOUSE_PORT`: Port of your Clickhouse instance - -Having those environment variables enables SQL Mock to connect to your Clickhouse instance. diff --git a/docs/_sources/usage/dbt.md.txt b/docs/_sources/usage/dbt.md.txt deleted file mode 100644 index 5d30985..0000000 --- a/docs/_sources/usage/dbt.md.txt +++ /dev/null @@ -1,130 +0,0 @@ -# Use with dbt - -## Introduction - -This guide will provide a quick start on how to use SQLMock with dbt (data build tool). You can use it to mock dbt models, sources, and seed models. We'll cover how to use these features effectively in your unit tests. - -## Prerequisites - -- A working dbt project with a `manifest.json` file **that has the latest compiled run.** (make sure to run `dbt compile`). -- The SQLMock library installed in your Python environment. - -## Configuration - -### Setting the dbt Manifest Path - -Initialize your testing environment by setting the global path to your dbt manifest file: - -```python -from sql_mock.config import SQLMockConfig - -SQLMockConfig.set_dbt_manifest_path('/path/to/your/dbt/manifest.json') -``` - -## Creating Mock Tables - -SQLMock offers specialized decorators for different dbt entities: models, sources, and seeds. - -### dbt Model Mock Table - -For dbt models, use the `dbt_model_meta` decorator from `sql_mock.dbt`. This decorator is suited for mocking the transformed data produced by dbt models. - -```python -from sql_mock.dbt import dbt_model_meta -from sql_mock.bigquery.table_mocks import BigQueryMockTable - -@dbt_model_meta(model_name="your_dbt_model_name") -class YourDBTModelTable(BigQueryMockTable): - # Define your table columns and other necessary attributes here - ... -``` - -### dbt Source Mock Table - -For dbt sources, use the `dbt_source_meta` decorator from `sql_mock.dbt`. This is ideal for mocking the raw data sources that dbt models consume. - -```python -from sql_mock.dbt import dbt_source_meta -from sql_mock.bigquery.table_mocks import BigQueryMockTable - -@dbt_source_meta(source_name="your_source_name", table_name="your_source_table") -class YourDBTSourceTable(BigQueryMockTable): - # Define your table columns and other necessary attributes here - ... -``` - -### dbt Seed Mock Table - -For dbt seeds, which are static data sets loaded into the database, use the `dbt_seed_meta` decorator from `sql_mock.dbt`. - -```python -from sql_mock.dbt import dbt_seed_meta -from sql_mock.bigquery.table_mocks import BigQueryMockTable - -@dbt_seed_meta(seed_name="your_dbt_seed_name") -class YourDBTSeedTable(BigQueryMockTable): - # Define your table columns and other necessary attributes here - ... -``` - -## Example: Testing a dbt Model with Upstream Source and Seed Data - -Let’s consider a dbt model named `monthly_user_spend` that aggregates data from a source `user_transactions` and a seed `user_categories`. - -### Step 1: Define Your Source and Seed Mock Tables - -```python -@dbt_source_meta(source_name="transactions", table_name="user_transactions") -class UserTransactionsTable(BigQueryMockTable): - transaction_id = col.Int(default=1) - user_id = col.Int(default=1) - amount = col.Float(default=1.0) - transaction_date = col.Date(default='2023-12-24') - -@dbt_seed_meta(seed_name="user_categories") -class UserCategoriesTable(BigQueryMockTable): - user_id = col.Int(default=1) - category = col.String(default='foo') -``` - -### Step 2: Define Your Model Mock Table - -```python -@dbt_model_meta(model_name="monthly_user_spend") -class MonthlyUserSpendTable(BigQueryMockTable): - user_id = col.Int(default=1) - month = col.String(default='foo') - total_spend = col.Float(default=1.0) - category = col.String(default='foo') -``` - -### Step 3: Write Your Test Case - -```python -import datetime - -def test_monthly_user_spend_model(): - # Mock input data for UserTransactionsTable and UserCategoriesTable - transactions_data = [ - {"transaction_id": 1, "user_id": 1, "amount": 120.0, "transaction_date": datetime.date(2023, 1, 10)}, - {"transaction_id": 2, "user_id": 2, "amount": 150.0, "transaction_date": datetime.date(2023, 1, 20)}, - ] - - categories_data = [ - {"user_id": 1, "category": "Premium"}, - {"user_id": 2, "category": "Standard"} - ] - - transactions_table = UserTransactionsTable.from_dicts(transactions_data) - categories_table = UserCategoriesTable.from_dicts(categories_data) - - # Expected result - expected_output = [ - {"user_id": 1, "month": "2023-01", "total_spend": 120.0, "category": "Premium"}, - {"user_id": 2, "month": "2023-01", "total_spend": 150.0, "category": "Standard"}, - ] - - monthly_spend_table = MonthlyUserSpendTable.from_mocks(input_data=[transactions_table, categories_table]) - - monthly_spend_table.assert_equal(expected_output) -``` diff --git a/docs/_sources/usage/default_values.md.txt b/docs/_sources/usage/default_values.md.txt deleted file mode 100644 index 471c94d..0000000 --- a/docs/_sources/usage/default_values.md.txt +++ /dev/null @@ -1,71 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Default values - -Testing SQL queries can often involve repetitive setup for mock tables. In SQLMock, one effective way to streamline this process is by using default values. By setting reasonable defaults, you can significantly reduce the boilerplate code in your tests, especially when dealing with multiple input tables or complex queries. Let’s explore how you can efficiently implement this. - -## Utilizing Default Values in MockTable Fields - -Defining default values at the field level in your mock tables is straightforward. -The default argument in the field definition allows you to set default values consistency across all test scenarios in one step. -They are particularly useful for ensuring that joins and other query functionalities operate correctly. - -Here's an example: - -```python -@table_meta(table_ref="data.users") -class UserTable(BigQueryMockTable): - user_id = col.Int(default=1) - user_name = col.String(default="Mr. T") - -# Create instances of the UserTable with various combinations of defaults and specified values -users = UserTable.from_dicts([ - {}, # Left empty {} uses default values --> {"user_id": 1, "user_name": "Mr. T"} - {"user_id": 2}, # Overrides user_id but uses default for user_name - {"user_id": 3, "user_name": "Nala"} # No defaults used here -]) -``` - -## Setting Mock Defaults with table_meta - - -When defining your MockTable classes, the `table_meta` decorator accepts a `default_inputs` argument. -The Mock instances passed here, will be used as defaults in the `from_mocks` method. - -Consider this example: - -```python -@table_meta( - query_path="./examples/test_query.sql", - default_inputs=[UserTable([]), SubscriptionTable([])] # We can provide defaults for the class if needed. -) -class MultipleSubscriptionUsersTable(BigQueryMockTable): - user_id = col.Int(default=1) - -# Setting up different scenarios to demonstrate the use of defaults -users = UserTable.from_dicts([ - {"user_id": 1}, - {"user_id": 2} -]) -subscriptions = SubscriptionTable.from_dicts( - [ - {"subscription_id": 1, "user_id": 1}, - {"subscription_id": 2, "user_id": 1}, - {"subscription_id": 2, "user_id": 2}, - ] -) - -# Utilizing the default inputs set in the table_meta -res = MultipleSubscriptionUsersTable.from_mocks(input_data=[]) -res = MultipleSubscriptionUsersTable.from_mocks(input_data=[users]) # Using only users, defaults for others -res = MultipleSubscriptionUsersTable.from_mocks(input_data=[users, subscriptions]) # Overriding defaults -``` - -## When is this useful? - -* **Safe time and code by changing only the data you need for your test case:** You can only change single columns for the data you provide for a test case. The rest will be filled by defaults. -* **Simplifying Happy Path Testing:** Validate basic functionality and syntax correctness of your SQL queries with minimal setup. -* **Testing Subset Logic:** When certain tables in your query don't require data, default values can help focus on specific test scenarios. -* **Provide reasonable defaults for joins:** In tests with numerous input tables you can specify inputs that joins between tables work. For frequent addition of new tables, defaults can prevent the need for extensive refactoring. diff --git a/docs/_sources/usage/defining_table_mocks.md.txt b/docs/_sources/usage/defining_table_mocks.md.txt deleted file mode 100644 index 826430b..0000000 --- a/docs/_sources/usage/defining_table_mocks.md.txt +++ /dev/null @@ -1,51 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Defining table mocks - -When you want to provide mocked data to test your SQL model, you need to create MockTable classes for all upstream data that your model uses, as well as for the model you want to test. Those mock tables can be created by inheriting from a `BaseMockTable` class for the database provider you are using (e.g. `BigQueryMockTable`). - -**We recommend to have a central `model.py` file where you create those models that you can easily reuse them across your tests** - -```python -# models.py - -from sql_mock.bigquery import column_mocks as col -from sql_mock.bigquery.table_mocks import BigQueryMockTable, table_meta - -# The models you are goign to use as inputs need to have a `table_ref` specified -@table_meta(table_ref='data.table1') -class Table(BigQueryMockTable): - id = col.Int(default=1) - name = col.String(default='Peter') - -@table_meta( - table_ref='data.result_table', - query_path='path/to/query_for_result_table.sql', - default_inputs=[Table()] # You can provide default inputs on a class level -) -class ResultTable(BigQueryMockTable): - id = col.Int(default=1) -``` - -Some important things to mention: - -**The models you are goign to use as inputs need to have a `table_ref` specified.** -The `table_ref` is how the table will be referenced in your production database (usually some pattern like `.`) - -**The result model needs to have a query.** -There are currently 2 ways to provide a query to the model: - -1. Pass a path to your query file in the class definition using the `table_meta` decorator. This allows us to only specify it once. - ```python - @table_meta(table_ref='data.result_table', query_path='path/to/query_for_result_table.sql') - class ResultTable(BigQueryMockTable): - ... - ``` -2. Pass it as `query` argument to the `from_mocks` method when you are using the model in your test. This will also overwrite whatever query was read from the `query_path` in the `table_meta` decorator. - ```python - res = ResultTable.from_mocks(query='SELECT 1', input_data=[]) - ``` - -More details on how to handle queries can be found [in the "Your SQL query to test" section](./your_sql_query_to_test.md) diff --git a/docs/_sources/usage/examples.md.txt b/docs/_sources/usage/examples.md.txt deleted file mode 100644 index 39763e6..0000000 --- a/docs/_sources/usage/examples.md.txt +++ /dev/null @@ -1,7 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Examples - -You can find some examples in the [examples folder](https://github.com/DeepLcom/sql-mock/tree/main/examples). diff --git a/docs/_sources/usage/redshift/examples.md.txt b/docs/_sources/usage/redshift/examples.md.txt deleted file mode 100644 index 24691c3..0000000 --- a/docs/_sources/usage/redshift/examples.md.txt +++ /dev/null @@ -1,61 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Example: Testing Subscription Counts in Redshift - -```python -import datetime -from sql_mock.redshift import column_mocks as col -from sql_mock.redshift.table_mocks import RedshiftMockTable -from sql_mock.table_mocks import table_meta - -# Define mock tables for your data model that inherit from RedshiftMockTable -@table_meta(table_ref="data.users") -class UserTable(RedshiftMockTable): - user_id = col.INTEGER(default=1) - user_name = col.VARCHAR(default="Mr. T") - - -@table_meta(table_ref="data.subscriptions") -class SubscriptionTable(RedshiftMockTable): - subscription_id = col.INTEGER(default=1) - period_start_date = col.DATE(default=datetime.date(2023, 9, 5)) - period_end_date = col.DATE(default=datetime.date(2023, 9, 5)) - user_id = col.INTEGER(default=1) - -# Define a mock table for your expected results -class SubscriptionCountTable(RedshiftMockTable): - subscription_count = col.INTEGER(default=1) - user_id = col.INTEGER(default=1) - -# Your original SQL query -query = """ -SELECT - count(*) AS subscription_count, - user_id -FROM data.users -LEFT JOIN data.subscriptions USING(user_id) -GROUP BY user_id -""" - -# Create mock data for the 'data.users' and 'data.subscriptions' tables -users = UserTable.from_dicts([{'user_id': 1}, {'user_id': 2}]) -subscriptions = SubscriptionTable.from_dicts([ - {'subscription_id': 1, 'user_id': 1}, - {'subscription_id': 2, 'user_id': 1}, - {'subscription_id': 2, 'user_id': 2}, -]) - -# Define your expected results -expected = [ - {'user_id': 1, 'subscription_count': 2}, - {'user_id': 2, 'subscription_count': 1} -] - -# Simulate the SQL query using SQL Mock -res = SubscriptionCountTable.from_mocks(query=query, input_data=[users, subscriptions]) - -# Assert the results -res.assert_equal(expected) -``` diff --git a/docs/_sources/usage/redshift/index.rst.txt b/docs/_sources/usage/redshift/index.rst.txt deleted file mode 100644 index b2f7caa..0000000 --- a/docs/_sources/usage/redshift/index.rst.txt +++ /dev/null @@ -1,10 +0,0 @@ -Redshift -===================== - -This section documents the specifics on how to use SQL Mock with Redshift - -.. toctree:: - :maxdepth: 4 - - ./settings.md - ./examples.md diff --git a/docs/_sources/usage/redshift/settings.md.txt b/docs/_sources/usage/redshift/settings.md.txt deleted file mode 100644 index fdf1ba0..0000000 --- a/docs/_sources/usage/redshift/settings.md.txt +++ /dev/null @@ -1,14 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Settings - -In order to use SQL Mock with Redshift, you need to provide the following environment variables when you run tests: - -* `SQL_MOCK_REDSHIFT_HOST`: The host of your Redshift instance -* `SQL_MOCK_REDSHIFT_USER`: The user of your Redshift instance -* `SQL_MOCK_REDSHIFT_PASSWORD`: The password of your Redshift instance -* `SQL_MOCK_REDSHIFT_PORT`: The port of your Redshift instance - -Having those environment variables enables SQL Mock to connect to your Redshift instance. diff --git a/docs/_sources/usage/result_assertion.md.txt b/docs/_sources/usage/result_assertion.md.txt deleted file mode 100644 index fecaa11..0000000 --- a/docs/_sources/usage/result_assertion.md.txt +++ /dev/null @@ -1,88 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Result assertion - -There are 2 ways how you can check the output of your query given the mocked input data on your Table Mock instance: - -1. **`assert_equal` method:** Assert the normal result of the query (running the full query with your input mocks) -2. **`assert_cte_equal` method:** Assert the result of a **CTE** in your query. A lot of times when we built more complicated data models, they include a bunch of CTEs that map to separate logical steps. In those cases, when we unit test our models, we want to be able to not only check the final result but also the single steps. To do this, you can use the `assert_cte_equal` method. - - -Let's assume we have the following query: - -```sql -WITH subscriptions_per_user AS ( - SELECT - count(sub.user_id) AS subscription_count, - users.user_id - FROM data.users AS users - LEFT JOIN data.subscriptions AS sub ON sub.user_id = users.user_id - GROUP BY user_id -), - -users_with_multiple_subs AS ( - SELECT - * - FROM subscriptions_per_user - WHERE subscription_count >= 2 -) - -SELECT user_id FROM users_with_multiple_subs -``` - -... and we define the following mock tables: - -```python -import datetime - -from sql_mock.bigquery import column_mocks as col -from sql_mock.bigquery.table_mocks import BigQueryMockTable -from sql_mock.table_mocks import table_meta - -@table_meta(table_ref="data.users") -class UserTable(BigQueryMockTable): - user_id = col.Int(default=1) - user_name = col.String(default="Mr. T") - - -@table_meta(table_ref="data.subscriptions") -class SubscriptionTable(BigQueryMockTable): - subscription_id = col.Int(default=1) - period_start_date = col.Date(default=datetime.date(2023, 9, 5)) - period_end_date = col.Date(default=datetime.date(2023, 9, 5)) - user_id = col.Int(default=1) - - -@table_meta(query_path="./examples/test_query.sql") -class MultipleSubscriptionUsersTable(BigQueryMockTable): - user_id = col.Int(default=1) -``` - -Now we can use the different testing methods the following way: - -```python -def test_model(): - users = UserTable.from_dicts([{"user_id": 1}, {"user_id": 2}]) - subscriptions = SubscriptionTable.from_dicts( - [ - {"subscription_id": 1, "user_id": 1}, - {"subscription_id": 2, "user_id": 1}, - {"subscription_id": 2, "user_id": 2}, - ] - ) - - subscriptions_per_user__expected = [{"user_id": 1, "subscription_count": 2}, {"user_id": 2, "subscription_count": 1}] - users_with_multiple_subs__expected = [{"user_id": 1, "subscription_count": 2}] - end_result__expected = [{"user_id": 1}] - - res = MultipleSubscriptionUsersTable.from_mocks(input_data=[users, subscriptions]) - - # Check the results of the subscriptions_per_user CTE - res.assert_cte_equal('subscriptions_per_user', subscriptions_per_user__expected) - # Check the results of the users_with_multiple_subs CTE - res.assert_cte_equal('users_with_multiple_subs', users_with_multiple_subs__expected) - # Check the end result - res.assert_equal(end_result__expected) -``` diff --git a/docs/_sources/usage/snowflake/examples.md.txt b/docs/_sources/usage/snowflake/examples.md.txt deleted file mode 100644 index 645621b..0000000 --- a/docs/_sources/usage/snowflake/examples.md.txt +++ /dev/null @@ -1,63 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Example: Testing Subscription Counts in Snowflake - -```python -import datetime -from sql_mock.snowflake import column_mocks as col -from sql_mock.snowflake.table_mocks import SnowflakeMockTable -from sql_mock.table_mocks import table_meta - -# Define mock tables for your data model that inherit from SnowflakeMockTable -@table_meta(table_ref="data.users") -class UserTable(SnowflakeMockTable): - user_id = col.INTEGER(default=1) - user_name = col.STRING(default="Mr. T") - - -@table_meta(table_ref="data.subscriptions") -class SubscriptionTable(SnowflakeMockTable): - subscription_id = col.INTEGER(default=1) - period_start_date = col.DATE(default=datetime.date(2023, 9, 5)) - period_end_date = col.DATE(default=datetime.date(2023, 9, 5)) - user_id = col.INTEGER(default=1) - - -# Define a mock table for your expected results -class SubscriptionCountTable(SnowflakeMockTable): - subscription_count = col.INTEGER(default=1) - user_id = col.INTEGER(default=1) - -# Your original SQL query -query = """ -SELECT - count(*) AS subscription_count, - user_id -FROM data.users -LEFT JOIN data.subscriptions USING(user_id) -GROUP BY user_id -""" - -def test_something(): - # Create mock data for the 'data.users' and 'data.subscriptions' tables - users = UserTable.from_dicts([{'user_id': 1}, {'user_id': 2}]) - subscriptions = SubscriptionTable.from_dicts([ - {'subscription_id': 1, 'user_id': 1}, - {'subscription_id': 2, 'user_id': 1}, - {'subscription_id': 2, 'user_id': 2}, - ]) - - # Define your expected results - expected = [ - {"USER_ID": 1, "SUBSCRIPTION_COUNT": 2}, - {"USER_ID": 2, "SUBSCRIPTION_COUNT": 1}, - ] - - # Simulate the SQL query using SQL Mock - res = SubscriptionCountTable.from_mocks(query=query, input_data=[users, subscriptions]) - - # Assert the results - res.assert_equal(expected) -``` diff --git a/docs/_sources/usage/snowflake/index.rst.txt b/docs/_sources/usage/snowflake/index.rst.txt deleted file mode 100644 index 6c143df..0000000 --- a/docs/_sources/usage/snowflake/index.rst.txt +++ /dev/null @@ -1,10 +0,0 @@ -Snowflake -===================== - -This section documents the specifics on how to use SQL Mock with Snowflake - -.. toctree:: - :maxdepth: 4 - - ./settings.md - ./examples.md diff --git a/docs/_sources/usage/snowflake/settings.md.txt b/docs/_sources/usage/snowflake/settings.md.txt deleted file mode 100644 index 2c9143d..0000000 --- a/docs/_sources/usage/snowflake/settings.md.txt +++ /dev/null @@ -1,13 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Settings - -In order to use SQL Mock with Snowflake, you need to provide the following environment variables when you run tests: - -* `SQL_MOCK_SNOWFLAKE_ACCOUNT`: The name of your Snowflake account -* `SQL_MOCK_SNOWFLAKE_USER`: The name of your Snowflake user -* `SQL_MOCK_SNOWFLAKE_PASSWORD`: The password for your Snowflake user - -Having those environment variables enables SQL Mock to connect to your Snowflake instance. diff --git a/docs/_sources/usage/your_sql_query_to_test.md.txt b/docs/_sources/usage/your_sql_query_to_test.md.txt deleted file mode 100644 index e662f95..0000000 --- a/docs/_sources/usage/your_sql_query_to_test.md.txt +++ /dev/null @@ -1,56 +0,0 @@ -# Your SQL query to test - -There are multiple ways on how you can provide the SQL query that you want to test. Let's walk through them and also cover some specifics. - -## Ways to provide your SQL query to be tested - -### Option 1 (recommended): Use the `table_meta` decorator - -When defining your [Table Mock classes](./defining_table_mocks.md), you can pass a path to your query (`query_path` argument) or the query as string (`query` argument) to the `table_meta` decorator of the table mock you want to test. - -```python -# Pass the query path -@table_meta(table_ref='data.result_table', query_path='path/to/query_for_result_table.sql') -class ResultTable(BigQueryTableMock): - id = col.Int(default=1) - -# Pass the query itself -query = 'SELECT user_id AS id FROM data.users' -@table_meta(table_ref='data.result_table', query=query) -class ResultTable(BigQueryTableMock): - id = col.Int(default=1) -``` - -The advantage of that option is that you only need to define your Table Mock class once (e.g. in a `models.py` file). After that you can reuse it for many tests. - -### Option 2: Pass the query in the `.from_mocks` call - -You can also pass your query in your test case when you call the `from_mocks` method. - -```python -res = ResultTable.from_mocks(query='SELECT 1', input_data=[]) -``` - -Note that this will overwride whatever was specified by using the `table_meta` decorator. - -## Queries with Jinja templates - -Sometimes we need to test queries that use jinja templates (e.g. for dbt). -In those cases, you can provide the necessary context for rendering your query using the `from_mocks` call. - -**Example**: - -Let's assume the following jinja template query: - -```jinja -SELECT * FROM data.users -WHERE created_at > {{ creation_date }} -``` - -We can provide the `creation_date` variable in a dictionary using the `query_template_kwargs` argument: - -```python -res = ResultTable.from_mocks(input_data=[your_input_mock_instances], query_template_kwargs={'creation_date': '2023-09-05'}) -``` - -This will automatically render your query using the given input. diff --git a/docs/_static/_sphinx_javascript_frameworks_compat.js b/docs/_static/_sphinx_javascript_frameworks_compat.js deleted file mode 100644 index 8141580..0000000 --- a/docs/_static/_sphinx_javascript_frameworks_compat.js +++ /dev/null @@ -1,123 +0,0 @@ -/* Compatability shim for jQuery and underscores.js. - * - * Copyright Sphinx contributors - * Released under the two clause BSD licence - */ - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x - } - return decodeURIComponent(x.replace(/\+/g, ' ')); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; - -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - var bbox = node.parentElement.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } - } - } - else if (!jQuery(node).is("button, select, textarea")) { - 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- -/** - * Simple result scoring code. - */ -if (typeof Scorer === "undefined") { - var Scorer = { - // Implement the following function to further tweak the score for each result - // The function takes a result array [docname, title, anchor, descr, score, filename] - // and returns the new score. - /* - score: result => { - const [docname, title, anchor, descr, score, filename] = result - return score - }, - */ - - // query matches the full name of an object - objNameMatch: 11, - // or matches in the last dotted part of the object name - objPartialMatch: 6, - // Additive scores depending on the priority of the object - objPrio: { - 0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5, // used to be unimportantResults - }, - // Used when the priority is not in the mapping. - objPrioDefault: 0, - - // query found in title - title: 15, - partialTitle: 7, - // query found in terms - term: 5, - partialTerm: 2, - }; -} - -const _removeChildren = (element) => { - while (element && element.lastChild) element.removeChild(element.lastChild); 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- }); - Search.output.appendChild(listItem); -}; -const _finishSearch = (resultCount) => { - Search.stopPulse(); - Search.title.innerText = _("Search Results"); - if (!resultCount) - Search.status.innerText = Documentation.gettext( - "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." - ); - else - Search.status.innerText = _( - `Search finished, found ${resultCount} page(s) matching the search query.` - ); -}; -const _displayNextItem = ( - results, - resultCount, - searchTerms, - highlightTerms, -) => { - // results left, load the summary and display it - // this is intended to be dynamic (don't sub resultsCount) - if (results.length) { - _displayItem(results.pop(), searchTerms, highlightTerms); - setTimeout( - () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), - 5 - ); - } - // search finished, update title and status message - else _finishSearch(resultCount); -}; - -/** - * Default splitQuery function. Can be overridden in ``sphinx.search`` with a - * custom function per language. - * - * The regular expression works by splitting the string on consecutive characters - * that are not Unicode letters, numbers, underscores, or emoji characters. - * This is the same as ``\W+`` in Python, preserving the surrogate pair area. - */ -if (typeof splitQuery === "undefined") { - var splitQuery = (query) => query - .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) - .filter(term => term) // remove remaining empty strings -} - -/** - * Search Module - */ -const Search = { - _index: null, - _queued_query: null, - _pulse_status: -1, - - htmlToText: (htmlString) => { - const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); - const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent !== undefined) return docContent.textContent; - console.warn( - "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." - ); - return ""; - }, - - init: () => { - const query = new URLSearchParams(window.location.search).get("q"); - document - .querySelectorAll('input[name="q"]') - .forEach((el) => (el.value = query)); - if (query) Search.performSearch(query); - }, - - loadIndex: (url) => - (document.body.appendChild(document.createElement("script")).src = url), - - setIndex: (index) => { - Search._index = index; - if (Search._queued_query !== null) { - const query = Search._queued_query; - Search._queued_query = null; - Search.query(query); - } - }, - - hasIndex: () => Search._index !== null, - - deferQuery: (query) => (Search._queued_query = query), - - stopPulse: () => (Search._pulse_status = -1), - - startPulse: () => { - if (Search._pulse_status >= 0) return; - - const pulse = () => { - Search._pulse_status = (Search._pulse_status + 1) % 4; - Search.dots.innerText = ".".repeat(Search._pulse_status); - if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); - }; - pulse(); - }, - - /** - * perform a search for something (or wait until index is loaded) - */ - performSearch: (query) => { - // create the required interface elements - const searchText = document.createElement("h2"); - searchText.textContent = _("Searching"); - const searchSummary = document.createElement("p"); - searchSummary.classList.add("search-summary"); - searchSummary.innerText = ""; - const searchList = document.createElement("ul"); - searchList.classList.add("search"); - - const out = document.getElementById("search-results"); - Search.title = out.appendChild(searchText); - Search.dots = Search.title.appendChild(document.createElement("span")); - Search.status = out.appendChild(searchSummary); - Search.output = out.appendChild(searchList); - - const searchProgress = document.getElementById("search-progress"); - // Some themes don't use the search progress node - if (searchProgress) { - searchProgress.innerText = _("Preparing search..."); - } - Search.startPulse(); - - // index already loaded, the browser was quick! - if (Search.hasIndex()) Search.query(query); - else Search.deferQuery(query); - }, - - /** - * execute search (requires search index to be loaded) - */ - query: (query) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - - // stem the search terms and add them to the correct list - const stemmer = new Stemmer(); - const searchTerms = new Set(); - const excludedTerms = new Set(); - const highlightTerms = new Set(); - const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); - splitQuery(query.trim()).forEach((queryTerm) => { - const queryTermLower = queryTerm.toLowerCase(); - - // maybe skip this "word" - // stopwords array is from language_data.js - if ( - stopwords.indexOf(queryTermLower) !== -1 || - queryTerm.match(/^\d+$/) - ) - return; - - // stem the word - let word = stemmer.stemWord(queryTermLower); - // select the correct list - if (word[0] === "-") excludedTerms.add(word.substr(1)); - else { - searchTerms.add(word); - highlightTerms.add(queryTermLower); - } - }); - - if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js - localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) - } - - // console.debug("SEARCH: searching for:"); - // console.info("required: ", [...searchTerms]); - // console.info("excluded: ", [...excludedTerms]); - - // array of [docname, title, anchor, descr, score, filename] - let results = []; - _removeChildren(document.getElementById("search-progress")); - - const queryLower = query.toLowerCase(); - for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { - for (const [file, id] of foundTitles) { - let score = Math.round(100 * queryLower.length / title.length) - results.push([ - docNames[file], - titles[file] !== title ? `${titles[file]} > ${title}` : title, - id !== null ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // search for explicit entries in index directives - for (const [entry, foundEntries] of Object.entries(indexEntries)) { - if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id] of foundEntries) { - let score = Math.round(100 * queryLower.length / entry.length) - results.push([ - docNames[file], - titles[file], - id ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // lookup as object - objectTerms.forEach((term) => - results.push(...Search.performObjectSearch(term, objectTerms)) - ); - - // lookup as search terms in fulltext - results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); - - // let the scorer override scores with a custom scoring function - if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); - - // now sort the results by score (in opposite order of appearance, since the - // display function below uses pop() to retrieve items) and then - // alphabetically - results.sort((a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; - }); - - // remove duplicate search results - // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept - let seen = new Set(); - results = results.reverse().reduce((acc, result) => { - let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); - if (!seen.has(resultStr)) { - acc.push(result); - seen.add(resultStr); - } - return acc; - }, []); - - results = results.reverse(); - - // for debugging - //Search.lastresults = results.slice(); // a copy - // console.info("search results:", Search.lastresults); - - // print the results - _displayNextItem(results, results.length, searchTerms, highlightTerms); - }, - - /** - * search for object names - */ - performObjectSearch: (object, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const objects = Search._index.objects; - const objNames = Search._index.objnames; - const titles = Search._index.titles; - - const results = []; - - const objectSearchCallback = (prefix, match) => { - const name = match[4] - const fullname = (prefix ? prefix + "." : "") + name; - const fullnameLower = fullname.toLowerCase(); - if (fullnameLower.indexOf(object) < 0) return; - - let score = 0; - const parts = fullnameLower.split("."); - - // check for different match types: exact matches of full name or - // "last name" (i.e. last dotted part) - if (fullnameLower === object || parts.slice(-1)[0] === object) - score += Scorer.objNameMatch; - else if (parts.slice(-1)[0].indexOf(object) > -1) - score += Scorer.objPartialMatch; // matches in last name - - const objName = objNames[match[1]][2]; - const title = titles[match[0]]; - - // If more than one term searched for, we require other words to be - // found in the name/title/description - const otherTerms = new Set(objectTerms); - otherTerms.delete(object); - if (otherTerms.size > 0) { - const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); - if ( - [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) - ) - return; - } - - let anchor = match[3]; - if (anchor === "") anchor = fullname; - else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; - - const descr = objName + _(", in ") + title; - - // add custom score for some objects according to scorer - if (Scorer.objPrio.hasOwnProperty(match[2])) - score += Scorer.objPrio[match[2]]; - else score += Scorer.objPrioDefault; - - results.push([ - docNames[match[0]], - fullname, - "#" + anchor, - descr, - score, - filenames[match[0]], - ]); - }; - Object.keys(objects).forEach((prefix) => - objects[prefix].forEach((array) => - objectSearchCallback(prefix, array) - ) - ); - return results; - }, - - /** - * search for full-text terms in the index - */ - performTermsSearch: (searchTerms, excludedTerms) => { - // prepare search - const terms = Search._index.terms; - const titleTerms = Search._index.titleterms; - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - - const scoreMap = new Map(); - const fileMap = new Map(); - - // perform the search on the required terms - searchTerms.forEach((word) => { - const files = []; - const arr = [ - { files: terms[word], score: Scorer.term }, - { files: titleTerms[word], score: Scorer.title }, - ]; - // add support for partial matches - if (word.length > 2) { - const escapedWord = _escapeRegExp(word); - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord) && !terms[word]) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord) && !titleTerms[word]) - arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); - }); - } - - // no match but word was a required one - if (arr.every((record) => record.files === undefined)) return; - - // found search word in contents - arr.forEach((record) => { - if (record.files === undefined) return; - - let recordFiles = record.files; - if (recordFiles.length === undefined) recordFiles = [recordFiles]; - files.push(...recordFiles); - - // set score for the word in each file - recordFiles.forEach((file) => { - if (!scoreMap.has(file)) scoreMap.set(file, {}); - scoreMap.get(file)[word] = record.score; - }); - }); - - // create the mapping - files.forEach((file) => { - if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) - fileMap.get(file).push(word); - else fileMap.set(file, [word]); - }); - }); - - // now check if the files don't contain excluded terms - const results = []; - for (const [file, wordList] of fileMap) { - // check if all requirements are matched - - // as search terms with length < 3 are discarded - const filteredTermCount = [...searchTerms].filter( - (term) => term.length > 2 - ).length; - if ( - wordList.length !== searchTerms.size && - wordList.length !== filteredTermCount - ) - continue; - - // ensure that none of the excluded terms is in the search result - if ( - [...excludedTerms].some( - (term) => - terms[term] === file || - titleTerms[term] === file || - (terms[term] || []).includes(file) || - (titleTerms[term] || []).includes(file) - ) - ) - break; - - // select one (max) score for the file. - const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); - // add result to the result list - results.push([ - docNames[file], - titles[file], - "", - null, - score, - filenames[file], - ]); - } - return results; - }, - - /** - * helper function to return a node containing the - * search summary for a given text. keywords is a list - * of stemmed words. - */ - makeSearchSummary: (htmlText, keywords) => { - const text = Search.htmlToText(htmlText); - if (text === "") return null; - - const textLower = text.toLowerCase(); - const actualStartPosition = [...keywords] - .map((k) => textLower.indexOf(k.toLowerCase())) - .filter((i) => i > -1) - .slice(-1)[0]; - const startWithContext = Math.max(actualStartPosition - 120, 0); - - const top = startWithContext === 0 ? "" : "..."; - const tail = startWithContext + 240 < text.length ? "..." : ""; - - let summary = document.createElement("p"); - summary.classList.add("context"); - summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; - - return summary; - }, -}; - -_ready(Search.init); diff --git a/docs/_static/sphinx_highlight.js b/docs/_static/sphinx_highlight.js deleted file mode 100644 index 8a96c69..0000000 --- a/docs/_static/sphinx_highlight.js +++ /dev/null @@ -1,154 +0,0 @@ -/* Highlighting utilities for Sphinx HTML documentation. */ -"use strict"; - -const SPHINX_HIGHLIGHT_ENABLED = true - -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - const rest = document.createTextNode(val.substr(pos + text.length)); - parent.insertBefore( - span, - parent.insertBefore( - rest, - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - /* There may be more occurrences of search term in this node. So call this - * function recursively on the remaining fragment. - */ - _highlight(rest, addItems, text, className); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - -/** - * Small JavaScript module for the documentation. - */ -const SphinxHighlight = { - - /** - * highlight the search words provided in localstorage in the text - */ - highlightSearchWords: () => { - if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight - - // get and clear terms from localstorage - const url = new URL(window.location); - const highlight = - localStorage.getItem("sphinx_highlight_terms") - || url.searchParams.get("highlight") - || ""; - localStorage.removeItem("sphinx_highlight_terms") - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - - // get individual terms from highlight string - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

' + - '' + - _("Hide Search Matches") + - "

" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - localStorage.removeItem("sphinx_highlight_terms") - }, - - initEscapeListener: () => { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; - if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { - SphinxHighlight.hideSearchWords(); - event.preventDefault(); - } - }); - }, -}; - -_ready(() => { - /* Do not call highlightSearchWords() when we are on the search page. - * It will highlight words from the *previous* search query. - */ - if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); - SphinxHighlight.initEscapeListener(); -}); diff --git a/docsource/usage/bigquery/examples.md b/docs/bigquery.md similarity index 79% rename from docsource/usage/bigquery/examples.md rename to docs/bigquery.md index f0c9c18..00772b7 100644 --- a/docsource/usage/bigquery/examples.md +++ b/docs/bigquery.md @@ -1,8 +1,12 @@ -```{toctree} -:maxdepth: 2 -``` +# Bigquery Docs + +## Settings + +In order to use sql-mock with BigQuery, ensure you have the `GOOGLE_APPLICATION_CREDENTIALS` environment variable correctly set to point to a service account file. + +You need to service account file in order to let SQL Mock connect to BigQuery while running the tests. -# Example: Testing Subscription Counts in BigQuery +## Example: Testing Subscription Counts in BigQuery ```python import datetime @@ -10,7 +14,7 @@ from sql_mock.bigquery import column_mocks as col from sql_mock.bigquery.table_mocks import BigQueryTableMock from sql_mock.table_mocks import table_meta -# Define mock tables for your data model that inherit from BigQueryTableMock +# Define table mocks for your data model that inherit from BigQueryTableMock @table_meta(table_ref='data.users') class UserTable(BigQueryTableMock): user_id = col.Int(default=1) diff --git a/docs/_sources/usage/clickhouse/examples.md.txt b/docs/clickhouse.md similarity index 70% rename from docs/_sources/usage/clickhouse/examples.md.txt rename to docs/clickhouse.md index d0a5c1b..73e21d2 100644 --- a/docs/_sources/usage/clickhouse/examples.md.txt +++ b/docs/clickhouse.md @@ -1,15 +1,24 @@ -```{toctree} -:maxdepth: 2 -``` +# Clickhouse Docs + +## Settings + +In order to use SQL Mock with Clickhouse, you need to provide the following environment variables when you run tests: + +* `SQL_MOCK_CLICKHOUSE_HOST`: Host of your Clickhouse instance +* `SQL_MOCK_CLICKHOUSE_USER`: User you want to use for the connection +* `SQL_MOCK_CLICKHOUSE_PASSWORD`: Password of your user +* `SQL_MOCK_CLICKHOUSE_PORT`: Port of your Clickhouse instance + +Having those environment variables enables SQL Mock to connect to your Clickhouse instance. -# Example: Testing Subscription Counts in ClickHouse +## Example: Testing Subscription Counts in ClickHouse ```python from sql_mock.clickhouse import column_mocks as col from sql_mock.clickhouse.table_mocks import ClickHouseTableMock from sql_mock.table_mocks import table_meta -# Define mock tables for your data model that inherit from ClickHouseTableMock +# Define table mocks for your data model that inherit from ClickHouseTableMock @table_meta(table_ref='data.users') class UserTable(ClickHouseTableMock): user_id = col.Int(default=1) @@ -47,7 +56,7 @@ subscriptions = SubscriptionTable.from_dicts([ # Define your expected results expected = [ - {'user_id': 1, 'subscription_count': 2}, + {'user_id': 1, 'subscription_count': 2}, {'user_id': 2, 'subscription_count': 1} ] diff --git a/docsource/usage/dbt.md b/docs/dbt.md similarity index 98% rename from docsource/usage/dbt.md rename to docs/dbt.md index e1dd090..bd56f0f 100644 --- a/docsource/usage/dbt.md +++ b/docs/dbt.md @@ -21,7 +21,7 @@ from sql_mock.config import SQLMockConfig SQLMockConfig.set_dbt_manifest_path('/path/to/your/dbt/manifest.json') ``` -## Creating Mock Tables +## Creating Table Mocks SQLMock offers specialized decorators for different dbt entities: models, sources, and seeds. @@ -71,7 +71,7 @@ class YourDBTSeedTable(BigQueryTableMock): Let’s consider a dbt model named `monthly_user_spend` that aggregates data from a source `user_transactions` and a seed `user_categories`. -### Step 1: Define Your Source and Seed Mock Tables +### Step 1: Define Your Source and Seed Table Mocks ```python @dbt_source_meta(source_name="transactions", table_name="user_transactions") diff --git a/docsource/usage/default_values.md b/docs/default_values.md similarity index 93% rename from docsource/usage/default_values.md rename to docs/default_values.md index 657d63a..09e6506 100644 --- a/docsource/usage/default_values.md +++ b/docs/default_values.md @@ -1,14 +1,10 @@ -```{toctree} -:maxdepth: 2 -``` - # Default values -Testing SQL queries can often involve repetitive setup for mock tables. In SQLMock, one effective way to streamline this process is by using default values. By setting reasonable defaults, you can significantly reduce the boilerplate code in your tests, especially when dealing with multiple input tables or complex queries. Let’s explore how you can efficiently implement this. +Testing SQL queries can often involve repetitive setup for table mocks. In SQLMock, one effective way to streamline this process is by using default values. By setting reasonable defaults, you can significantly reduce the boilerplate code in your tests, especially when dealing with multiple input tables or complex queries. Let’s explore how you can efficiently implement this. ## Utilizing Default Values in TableMock Fields -Defining default values at the field level in your mock tables is straightforward. +Defining default values at the field level in your table mocks is straightforward. The default argument in the field definition allows you to set default values consistency across all test scenarios in one step. They are particularly useful for ensuring that joins and other query functionalities operate correctly. diff --git a/docsource/usage/defining_table_mocks.md b/docs/defining_table_mocks.md similarity index 92% rename from docsource/usage/defining_table_mocks.md rename to docs/defining_table_mocks.md index 5506c97..8c6b85f 100644 --- a/docsource/usage/defining_table_mocks.md +++ b/docs/defining_table_mocks.md @@ -1,10 +1,6 @@ -```{toctree} -:maxdepth: 2 -``` - # Defining table mocks -When you want to provide mocked data to test your SQL model, you need to create TableMock classes for all upstream data that your model uses, as well as for the model you want to test. Those mock tables can be created by inheriting from a `BaseTableMock` class for the database provider you are using (e.g. `BigQueryTableMock`). +When you want to provide mocked data to test your SQL model, you need to create TableMock classes for all upstream data that your model uses, as well as for the model you want to test. Those table mocks can be created by inheriting from a `BaseTableMock` class for the database provider you are using (e.g. `BigQueryTableMock`). **We recommend to have a central `model.py` file where you create those models that you can easily reuse them across your tests** diff --git a/docs/faq.html b/docs/faq.html deleted file mode 100644 index ed6a5aa..0000000 --- a/docs/faq.html +++ /dev/null @@ -1,193 +0,0 @@ - - - - - - - FAQ — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

FAQ

-
-

My database system is not supported yet but I want to use SQL Mock. What should I do?

-

We are planning to add more and more supported database systems. However, if your system is not supported yet, you can still use SQL Mock. There are only 2 things you need to do:

-
-

Create your MockTable class

-

First, you need to create a MockTable class for your database system that inherits from sql_mock.table_mocks.BaseMockTable.

-

That class needs to implement the _get_results method which should make sure to fetch the results of a query (e.g. produced by self._generate_query()) and return it as list of dictionaries.

-

Look at one of the existing client libraries to see how this could work (e.g. BigQueryMockTable).

-

You might want to create a settings class as well in case you need some specific connection settings to be available within the _get_results method.

-
-
-

Create your ColumnMocks

-

Your database system might support specific database types. In order to make them available as column types, you can use the sql_mock.column_mocks.ColumnMock class as a base and inherit your specific column types from it. -For most of your column mocks you might only need to specify the dtype that should be used to parse the inputs.

-

A good practise is to create a ColumnMock class that is specific to your database and inherit all your column types from it, e.g.:

-
from sql_mock.column_mocks import ColumnMock
-
-class MyFanceDatabaseColumnMock(ColumnMock):
-    # In case you need some specific logic that overwrites the default behavior, you can do so here
-    pass
-
-class Int(MyFanceDatabaseColumnMock):
-    dtype = "Integer"
-
-class String(MyFanceDatabaseColumnMock):
-    dtype = "String"
-
-
-
-
-

Contribute your database setup

-

There will definitely be folks in the community that are in the need of support for the database you just created all the setup for. -Feel free to create a PR on this repository that we can start supporting your database system!

-
-
-
-

I am missing a specific ColumnMock type for my model fields

-

We implemented some basic column types but it could happen that you don’t find the one you need. -Luckily, you can easily create those with the tools provided. -The only thing you need to do is to inherit from the ColumnMock that is specific to your database system (e.g. BigQueryColumnMock) and write classes for the column mocks you are missing. Usually you only need to set the correct dtype. This would later be used in the cast(col to <dtype>) expression.

-
# Replace the import with the database system you are using
-from sql_mock.bigquery.column_mock import BigQueryColumnMock
-
-class MyFancyMissingColType(BigQueryColumnMock):
-    dtype = "FancyMissingColType"
-
-    # In case you need to implement additional logic for casting, you can do so here
-    ...
-
-
-

Don’t forget to create a PR in case you feel that your column mock type could be useful for the community!

-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/genindex.html b/docs/genindex.html deleted file mode 100644 index 981ed35..0000000 --- a/docs/genindex.html +++ /dev/null @@ -1,865 +0,0 @@ - - - - - - Index — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- - -

Index

- -
- _ - | A - | B - | C - | D - | F - | G - | H - | I - | L - | M - | N - | O - | P - | Q - | R - | S - | T - | U - | V - -
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- - - - \ No newline at end of file diff --git a/docs/getting_started/installation.html b/docs/getting_started/installation.html deleted file mode 100644 index fc06c34..0000000 --- a/docs/getting_started/installation.html +++ /dev/null @@ -1,166 +0,0 @@ - - - - - - - Installation — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
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Installation

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The library can be installed from PyPI using pip:

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# BigQuery
-pip install --upgrade "sql-mock[bigquery]"
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-# Clickhouse
-pip install --upgrade "sql-mock[clickhouse]"
-
-# Redshift
-pip install --upgrade "sql-mock[redshift]"
-
-# Snowflake
-pip install --upgrade "sql-mock[snowflake]"
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If you need to modify this source code, install the dependencies using poetry:

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poetry install --all-extras
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Quickstart

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Before diving into specific database scenarios, let’s start with a simplified example of how SQL Mock works behind the scenes.

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  1. You have an original SQL query, for instance:

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    -- path/to/query_for_result_table.sql
-SELECT id FROM data.table1
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    2. -

  2. Using SQL Mock, you define mock tables. You can use the built-in column types provided by SQL Mock. Available column types include Int, String, Date, and more. Each database type has their own column types. Define your tables by subclassing a mock table class that fits your database (e.g. BigQueryMockTable) and specifying the column types along with default values. In our example we use the ClickHouseTableMock class

    -
    from sql_mock.clickhouse import column_mocks as col
-from sql_mock.clickhouse.table_mocks import ClickHouseTableMock
-from sql_mock.table_mocks import table_meta
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-@table_meta(table_ref='data.table1')
-class Table(ClickHouseTableMock):
-    id = col.Int(default=1)
-    name = col.String(default='Peter')
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-@table_meta(table_ref='data.result_table', query_path='path/to/query_for_result_table.sql')
-class ResultTable(ClickHouseTableMock):
-    id = col.Int(default=1)
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  3. Creating mock data: Define mock data for your tables using dictionaries. Each dictionary represents a row in the table, with keys corresponding to column names. Table column keys that don’t get a value will use the default.

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    user_data = [
-    {}, # This will use the defaults for both id and name
-    {'id': 2, 'name': 'Martin'},
-    {'id': 3}, # This will use defaults for the name
-]
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-input_table_mock = Table.from_dicts(user_data)
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  4. Getting results for a table mock: Use the from_mocks method of the table mock object to generate mock query results based on your mock data.

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  5. Behind the scene SQL Mock replaces table references (e.g. data.table1) in your query with Common Table Expressions (CTEs) filled with dummy data. It can roughly be compared to something like this:

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    WITH data__table1 AS (
-    -- Mocked inputs
-    SELECT 
-        cast('1' AS 'String') AS id, 
-        cast('Peter' AS 'String') AS name
-    UNION ALL 
-    SELECT 
-        cast('2' AS 'String') AS id, 
-        cast('Martin' AS 'String') AS name
-    UNION ALL 
-    SELECT 
-        cast('3' AS 'String') AS id, 
-        cast('Peter' AS 'String') AS name
-)
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-result AS (
-    -- Original query with replaced references
-    SELECT id FROM data__table1 
-)
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-SELECT 
-    cast(id AS 'String') AS id
-FROM result
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    -
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  6. Finally, you can compare your results to some expected results using the assert_equal method.

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-res.assert_equal(expected)
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Welcome to SQL Mock’s documentation!

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The primary purpose of this library is to simplify the testing of SQL data models and queries by allowing users to mock input data and create tests for various scenarios. -It provides a consistent and convenient way to test the execution of your query without the need to process a massive amount of data.

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Basic Usage

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Database Specifics

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API Reference

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Indices and tables

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sql_mock

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© Copyright 2023, DeepL, Thomas Schmidt.

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- - - - \ No newline at end of file diff --git a/docsource/usage/redshift/examples.md b/docs/redshift.md similarity index 72% rename from docsource/usage/redshift/examples.md rename to docs/redshift.md index 4ce4c12..87a01fd 100644 --- a/docsource/usage/redshift/examples.md +++ b/docs/redshift.md @@ -1,8 +1,17 @@ -```{toctree} -:maxdepth: 2 -``` +# Redshift Docs + +## Settings + +In order to use SQL Mock with Redshift, you need to provide the following environment variables when you run tests: + +* `SQL_MOCK_REDSHIFT_HOST`: The host of your Redshift instance +* `SQL_MOCK_REDSHIFT_USER`: The user of your Redshift instance +* `SQL_MOCK_REDSHIFT_PASSWORD`: The password of your Redshift instance +* `SQL_MOCK_REDSHIFT_PORT`: The port of your Redshift instance + +Having those environment variables enables SQL Mock to connect to your Redshift instance. -# Example: Testing Subscription Counts in Redshift +## Example: Testing Subscription Counts in Redshift ```python import datetime @@ -10,7 +19,7 @@ from sql_mock.redshift import column_mocks as col from sql_mock.redshift.table_mocks import RedshiftTableMock from sql_mock.table_mocks import table_meta -# Define mock tables for your data model that inherit from RedshiftTableMock +# Define table mocks for your data model that inherit from RedshiftTableMock @table_meta(table_ref="data.users") class UserTable(RedshiftTableMock): user_id = col.INTEGER(default=1) diff --git a/docsource/usage/result_assertion.md b/docs/result_assertion.md similarity index 97% rename from docsource/usage/result_assertion.md rename to docs/result_assertion.md index 2aca22d..80bf81f 100644 --- a/docsource/usage/result_assertion.md +++ b/docs/result_assertion.md @@ -1,7 +1,3 @@ -```{toctree} -:maxdepth: 2 -``` - # Result assertion There are 2 ways how you can check the output of your query given the mocked input data on your Table Mock instance: @@ -31,7 +27,7 @@ users_with_multiple_subs AS ( SELECT user_id FROM users_with_multiple_subs ``` -... and we define the following mock tables: +... and we define the following table mocks: ```python import datetime diff --git a/docs/robots.html b/docs/robots.html deleted file mode 100644 index d20502d..0000000 --- a/docs/robots.html +++ /dev/null @@ -1,125 +0,0 @@ - - - - - - - <no title> — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - -
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-https://deeplcom.github.io/sql-mock/en/faq.htmlhttps://deeplcom.github.io/sql-mock/en/getting_started/installation.htmlhttps://deeplcom.github.io/sql-mock/en/index.htmlhttps://deeplcom.github.io/sql-mock/en/modules.htmlhttps://deeplcom.github.io/sql-mock/en/sql_mock.htmlhttps://deeplcom.github.io/sql-mock/en/sql_mock.bigquery.htmlhttps://deeplcom.github.io/sql-mock/en/sql_mock.clickhouse.htmlhttps://deeplcom.github.io/sql-mock/en/sql_mock.redshift.htmlhttps://deeplcom.github.io/sql-mock/en/sql_mock.snowflake.htmlhttps://deeplcom.github.io/sql-mock/en/usage/redshift/examples.htmlhttps://deeplcom.github.io/sql-mock/en/usage/redshift/index.htmlhttps://deeplcom.github.io/sql-mock/en/usage/redshift/settings.htmlhttps://deeplcom.github.io/sql-mock/en/usage/snowflake/examples.htmlhttps://deeplcom.github.io/sql-mock/en/usage/snowflake/index.htmlhttps://deeplcom.github.io/sql-mock/en/usage/snowflake/settings.htmlhttps://deeplcom.github.io/sql-mock/en/genindex.htmlhttps://deeplcom.github.io/sql-mock/en/py-modindex.htmlhttps://deeplcom.github.io/sql-mock/en/search.html \ No newline at end of file diff --git a/docsource/usage/snowflake/examples.md b/docs/snowflake.md similarity index 75% rename from docsource/usage/snowflake/examples.md rename to docs/snowflake.md index 9979f5f..a294d4d 100644 --- a/docsource/usage/snowflake/examples.md +++ b/docs/snowflake.md @@ -1,8 +1,16 @@ -```{toctree} -:maxdepth: 2 -``` +# Snowflake Docs + +## Settings + +In order to use SQL Mock with Snowflake, you need to provide the following environment variables when you run tests: + +* `SQL_MOCK_SNOWFLAKE_ACCOUNT`: The name of your Snowflake account +* `SQL_MOCK_SNOWFLAKE_USER`: The name of your Snowflake user +* `SQL_MOCK_SNOWFLAKE_PASSWORD`: The password for your Snowflake user + +Having those environment variables enables SQL Mock to connect to your Snowflake instance. -# Example: Testing Subscription Counts in Snowflake +## Example: Testing Subscription Counts in Snowflake ```python import datetime @@ -10,7 +18,7 @@ from sql_mock.snowflake import column_mocks as col from sql_mock.snowflake.table_mocks import SnowflakeTableMock from sql_mock.table_mocks import table_meta -# Define mock tables for your data model that inherit from SnowflakeTableMock +# Define table mocks for your data model that inherit from SnowflakeTableMock @table_meta(table_ref="data.users") class UserTable(SnowflakeTableMock): user_id = col.INTEGER(default=1) diff --git a/docs/sql_mock.bigquery.html b/docs/sql_mock.bigquery.html deleted file mode 100644 index d736b7f..0000000 --- a/docs/sql_mock.bigquery.html +++ /dev/null @@ -1,263 +0,0 @@ - - - - - - - sql_mock.bigquery package — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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sql_mock.bigquery package

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Submodules

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sql_mock.bigquery.column_mocks module

-
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-class sql_mock.bigquery.column_mocks.Array(inner_dtype, default, nullable=False)
-

Bases: BigQueryColumnMock

-
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-use_quotes_for_casting = False
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- -
- -
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-class sql_mock.bigquery.column_mocks.BigQueryColumnMock(default=None, nullable=False)
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Bases: ColumnMock

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- -
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-class sql_mock.bigquery.column_mocks.Date(default=None, nullable=False)
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Bases: BigQueryColumnMock

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-dtype = 'Date'
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- -
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-class sql_mock.bigquery.column_mocks.Decimal(default, precision, scale, nullable=False)
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Bases: BigQueryColumnMock

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-class sql_mock.bigquery.column_mocks.Float(default=None, nullable=False)
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Bases: BigQueryColumnMock

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-dtype = 'Float'
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-class sql_mock.bigquery.column_mocks.Int(default=None, nullable=False)
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Bases: BigQueryColumnMock

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-dtype = 'Integer'
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-class sql_mock.bigquery.column_mocks.String(default=None, nullable=False)
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Bases: BigQueryColumnMock

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-dtype = 'String'
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sql_mock.bigquery.settings module

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-class sql_mock.bigquery.settings.BigQuerySettings(_case_sensitive: bool | None = None, _env_prefix: str | None = None, _env_file: DotenvType | None = PosixPath('.'), _env_file_encoding: str | None = None, _env_nested_delimiter: str | None = None, _secrets_dir: str | Path | None = None, *, google_application_credentials: str)
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Bases: BaseSettings

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-google_application_credentials: str
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- -
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-model_config: ClassVar[SettingsConfigDict] = {'arbitrary_types_allowed': True, 'case_sensitive': False, 'env_file': None, 'env_file_encoding': None, 'env_nested_delimiter': None, 'env_prefix': '', 'extra': 'forbid', 'protected_namespaces': ('model_', 'settings_'), 'secrets_dir': None, 'validate_default': True}
-

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

-
- -
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-model_fields: ClassVar[dict[str, FieldInfo]] = {'google_application_credentials': FieldInfo(annotation=str, required=True)}
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Metadata about the fields defined on the model, -mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

-

This replaces Model.__fields__ from Pydantic V1.

-
- -
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sql_mock.bigquery.table_mocks module

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-class sql_mock.bigquery.table_mocks.BigQueryMockTable(*args, **kwargs)
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Bases: BaseMockTable

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- -
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Module contents

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- - - - \ No newline at end of file diff --git a/docs/sql_mock.clickhouse.html b/docs/sql_mock.clickhouse.html deleted file mode 100644 index 65f5811..0000000 --- a/docs/sql_mock.clickhouse.html +++ /dev/null @@ -1,309 +0,0 @@ - - - - - - - sql_mock.clickhouse package — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - -
- - -
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sql_mock.clickhouse package

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Submodules

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sql_mock.clickhouse.column_mocks module

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-class sql_mock.clickhouse.column_mocks.Array(inner_dtype, default, nullable=False)
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Bases: ClickhouseColumnMock

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-use_quotes_for_casting = False
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- -
- -
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-class sql_mock.clickhouse.column_mocks.Boolean(default, nullable=False)
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Bases: ClickhouseColumnMock

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-dtype = 'Boolean'
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-class sql_mock.clickhouse.column_mocks.ClickhouseColumnMock(default, nullable=False)
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Bases: ColumnMock

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-class sql_mock.clickhouse.column_mocks.Date(default, nullable=False)
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Bases: ClickhouseColumnMock

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-dtype = 'Date'
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- -
- -
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-class sql_mock.clickhouse.column_mocks.Datetime(default, nullable=False)
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Bases: ClickhouseColumnMock

-
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-dtype = 'Datetime'
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- -
- -
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-class sql_mock.clickhouse.column_mocks.Datetime64(default, nullable=False)
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Bases: ClickhouseColumnMock

-
-
-dtype = 'Datetime64'
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- -
- -
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-class sql_mock.clickhouse.column_mocks.Decimal(default, precision, scale, nullable=False)
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Bases: ClickhouseColumnMock

-
- -
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-class sql_mock.clickhouse.column_mocks.Float(default, nullable=False)
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Bases: ClickhouseColumnMock

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-dtype = 'Float'
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- -
- -
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-class sql_mock.clickhouse.column_mocks.Int(default, nullable=False)
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Bases: ClickhouseColumnMock

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-dtype = 'Integer'
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- -
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-class sql_mock.clickhouse.column_mocks.String(default, nullable=False)
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Bases: ClickhouseColumnMock

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-dtype = 'String'
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- -
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sql_mock.clickhouse.settings module

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-class sql_mock.clickhouse.settings.ClickHouseSettings(_case_sensitive: bool | None = None, _env_prefix: str | None = None, _env_file: DotenvType | None = PosixPath('.'), _env_file_encoding: str | None = None, _env_nested_delimiter: str | None = None, _secrets_dir: str | Path | None = None, *, host: str, user: str, password: str, port: str)
-

Bases: BaseSettings

-
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-host: str
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- -
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-model_config: ClassVar[SettingsConfigDict] = {'arbitrary_types_allowed': True, 'case_sensitive': False, 'env_file': None, 'env_file_encoding': None, 'env_nested_delimiter': None, 'env_prefix': 'SQL_MOCK_CLICKHOUSE_', 'extra': 'forbid', 'protected_namespaces': ('model_', 'settings_'), 'secrets_dir': None, 'validate_default': True}
-

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

-
- -
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-model_fields: ClassVar[dict[str, FieldInfo]] = {'host': FieldInfo(annotation=str, required=True), 'password': FieldInfo(annotation=str, required=True), 'port': FieldInfo(annotation=str, required=True), 'user': FieldInfo(annotation=str, required=True)}
-

Metadata about the fields defined on the model, -mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

-

This replaces Model.__fields__ from Pydantic V1.

-
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-password: str
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-port: str
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-user: str
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-
-

sql_mock.clickhouse.table_mocks module

-
-
-class sql_mock.clickhouse.table_mocks.ClickHouseTableMock(*args, **kwargs)
-

Bases: BaseMockTable

-
- -
-
-

Module contents

-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/sql_mock.html b/docs/sql_mock.html deleted file mode 100644 index 4d51fed..0000000 --- a/docs/sql_mock.html +++ /dev/null @@ -1,600 +0,0 @@ - - - - - - - sql_mock package — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
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- -
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- -
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sql_mock package

-
-

Subpackages

-
- -
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Submodules

-
-
-

sql_mock.column_mocks module

-
-
-class sql_mock.column_mocks.ColumnMock(default=None, nullable=False)
-

Bases: object

-

Represents a mock column in a database table.

-
-
-dtype
-

The data type of the column.

-
-
Type:
-

str

-
-
-
- -
-
-nullable
-

Indicator whether the column can be null

-
- -
-
-default
-

The default value for the column.

-
- -
-
-use_quotes_for_casting
-

Indicator whether the value needs to be quoted (e.g. in the final cast)

-
-
Type:
-

bool

-
-
-
- -
-
-cast_field(column_name)
-
- -
-
-default = None
-
- -
-
-dtype = None
-
- -
-
-nullable = False
-
- -
-
-to_sql(column_name: str, value=<sql_mock.constants.NoInput object>) → str
-
- -
-
-use_quotes_for_casting = True
-
- -
- -
-
-

sql_mock.constants module

-
-
-class sql_mock.constants.NoInput
-

Bases: object

-
- -
-
-

sql_mock.exceptions module

-
-
-exception sql_mock.exceptions.ValidationError
-

Bases: Exception

-
- -
-
-

sql_mock.table_mocks module

-
-
-class sql_mock.table_mocks.BaseMockTable(data: list[dict] = None, sql_mock_data: SQLMockData = None)
-

Bases: object

-

Represents a base class for creating mock database tables for testing. -When inheriting from this class you need to add column attributes for the table you would like to mock - e.g.:

-

col1 = Int(default=1)

-
-
-_sql_mock_data
-

A class that stores data which is for processing. This is automatcially created on instantiation.

-
-
Type:
-

SQLMockData

-
-
-
- -
-
-_sql_dialect
-

The sql dialect that the mock model uses. It will be leveraged by sqlglot.

-
-
Type:
-

str

-
-
-
- -
-
-as_sql_input()
-

Generate a UNION ALL SQL CTE that combines data from all rows.

-
-
Returns:
-

A SQL query that combines data from all rows.

-
-
Return type:
-

str

-
-
-
- -
-
-assert_cte_equal(cte_name, expected: [<class 'dict'>], ignore_missing_keys: bool = False, ignore_order: bool = True, print_query_on_fail: bool = True)
-

Assert that a CTE within the table mock’s query equals the provided expected data.

-
-
Parameters:
-
    -

  • cte_name (str) – Name of the CTE that should be compared against the expected results

    • -

  • expected (list of dicts) – Expected data to compare the class data against

    • -

  • ignore_missing_keys (bool) – If true, the comparison will only happen for the fields that are present in the -list of dictionaries of the expected argument.

    • -

  • ignore_order (bool) – If true, the order of dicts / rows will be ignored for comparison.

    • -

  • print_query_on_fail (bool) – If true, the tested query will be printed to the console output when the test fails.

    • -
-
-
-
- -
-
-assert_equal(expected: [<class 'dict'>], ignore_missing_keys: bool = False, ignore_order: bool = True, print_query_on_fail: bool = True)
-

Assert that the result of the table mock’s query equals the provided expected data.

-
-
Parameters:
-
    -

  • expected (list of dicts) – Expected data to compare the class data against

    • -

  • ignore_missing_keys (bool) – If true, the comparison will only happen for the fields that are present in the -list of dictionaries of the expected argument.

    • -

  • ignore_order (bool) – If true, the order of dicts / rows will be ignored for comparison.

    • -

  • print_query_on_fail (bool) – If true, the tested query will be printed to the console output when the test fails.

    • -
-
-
-
- -
-
-classmethod from_dicts(data: list[dict] = None)
-
- -
-
-classmethod from_mocks(input_data: list[BaseMockTable] = None, query_template_kwargs: dict = None, query: str = None)
-

Instantiate the mock table from input mocks. This runs the tables query with static data provided by the input mocks.

-
-
Parameters:
-
    -

  • input_data – List of MockTable instances that hold static data that should be used as inputs.

    • -

  • query_template_kwargs – Dictionary of Jinja template key-value pairs that should be used to render the query.

    • -

  • query – String of the SQL query that is used to generate the model. Can be a Jinja template. If provided, it overwrites the query on cls._sql_mock_meta.query.

    • -
-
-
-
- -
- -
-
-class sql_mock.table_mocks.MockTableMeta(*, default_inputs: List[BaseMockTable] = None, table_ref: str = None, query: str = None)
-

Bases: BaseModel

-

Class to store static metadata of BaseMockTable instances which is used during processing. -We use this class to avoid collision with field names of the table we want to mock.

-
-
-table_ref
-

String that represents the table reference to the original table.

-
-
Type:
-

string

-
-
-
- -
-
-query
-

Srting of the SQL query (can be in Jinja format).

-
-
Type:
-

string

-
-
-
- -
-
-property cte_name
-
- -
-
-default_inputs: List[BaseMockTable]
-
- -
-
-model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
-

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

-
- -
-
-model_fields: ClassVar[dict[str, FieldInfo]] = {'default_inputs': FieldInfo(annotation=List[Annotated[BaseMockTable, SkipValidation]], required=False), 'query': FieldInfo(annotation=str, required=False), 'table_ref': FieldInfo(annotation=str, required=False)}
-

Metadata about the fields defined on the model, -mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

-

This replaces Model.__fields__ from Pydantic V1.

-
- -
-
-query: str
-
- -
-
-table_ref: str
-
- -
- -
-
-class sql_mock.table_mocks.SQLMockData(*, columns: dict[str, Type[ColumnMock]] = None, data: list[dict] = None, input_data: list[dict] = None, rendered_query: str = None, last_query: str = None)
-

Bases: BaseModel

-

Class to store data on BaseMockTable instances which is used during processing. -We use this class to avoid collision with field names of the table we want to mock.

-
-
-columns: dict[str, Type[ColumnMock]]
-
- -
-
-data: list[dict]
-
- -
-
-input_data: list[dict]
-
- -
-
-last_query: str
-
- -
-
-model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
-

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

-
- -
-
-model_fields: ClassVar[dict[str, FieldInfo]] = {'columns': FieldInfo(annotation=dict[str, Type[ColumnMock]], required=False), 'data': FieldInfo(annotation=list[dict], required=False), 'input_data': FieldInfo(annotation=list[dict], required=False), 'last_query': FieldInfo(annotation=str, required=False), 'rendered_query': FieldInfo(annotation=str, required=False)}
-

Metadata about the fields defined on the model, -mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

-

This replaces Model.__fields__ from Pydantic V1.

-
- -
-
-rendered_query: str
-
- -
- -
-
-sql_mock.table_mocks.table_meta(table_ref: str = '', query_path: str = None, query: str = None, default_inputs: ['BaseMockTable'] = None)
-

Decorator that is used to define MockTable metadata

-
-
Parameters:
-
    -

  • table_ref (string) – String that represents the table reference to the original table.

    • -

  • query_path (string) – Path to a SQL query file that should be used to generate the model. Can be a Jinja template. Note only one of query_path or query can be provided.

    • -

  • query (string) – Srting of the SQL query (can be in Jinja format). Note only one of query_path or query can be provided.

    • -

  • default_inputs – List of default input mock instances that serve as default input if no other instance of that class is provided.

    • -
-
-
-
- -
-
-

Module contents

-
-
- - -
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- -
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-
- - - - \ No newline at end of file diff --git a/docs/sql_mock.redshift.html b/docs/sql_mock.redshift.html deleted file mode 100644 index bc32e3a..0000000 --- a/docs/sql_mock.redshift.html +++ /dev/null @@ -1,403 +0,0 @@ - - - - - - - sql_mock.redshift package — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - -
- - -
- -
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-
- -
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-
- -
-

sql_mock.redshift package

-
-

Submodules

-
-
-

sql_mock.redshift.column_mocks module

-
-
-class sql_mock.redshift.column_mocks.BIGINT(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'BIGINT'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.BOOLEAN(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'BOOLEAN'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.CHAR(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'CHAR'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.DATE(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'DATE'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.DECIMAL(default, precision, scale, nullable=False)
-

Bases: RedshiftColumnMock

-
- -
-
-class sql_mock.redshift.column_mocks.DOUBLE_PRECISION(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'DOUBLE PRECISION'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.GEOGRAPHY(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'GEOGRAPHY'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.GEOMETRY(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'GEOMETRY'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.HLLSKETCH(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'HLLSKETCH'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.INTEGER(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'INTEGER'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.REAL(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'REAL'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.RedshiftColumnMock(default=None, nullable=False)
-

Bases: ColumnMock

-
- -
-
-class sql_mock.redshift.column_mocks.SMALLINT(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'SMALLINT'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.SUPER(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'SUPER'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.TIME(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'TIME'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.TIMESTAMP(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'TIMESTAMP'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.TIMESTAMPTZ(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'TIMESTAMPTZ'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.TIMETZ(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'TIMETZ'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.VARBYTE(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'VARBYTE'
-
- -
- -
-
-class sql_mock.redshift.column_mocks.VARCHAR(default=None, nullable=False)
-

Bases: RedshiftColumnMock

-
-
-dtype = 'VARCHAR'
-
- -
- -
-
-

sql_mock.redshift.settings module

-
-
-class sql_mock.redshift.settings.RedshiftSettings(_case_sensitive: bool | None = None, _env_prefix: str | None = None, _env_file: DotenvType | None = PosixPath('.'), _env_file_encoding: str | None = None, _env_nested_delimiter: str | None = None, _secrets_dir: str | Path | None = None, *, host: str, database: str, user: str, password: str = None, port: int = 5439)
-

Bases: BaseSettings

-
-
-database: str
-
- -
-
-host: str
-
- -
-
-model_config: ClassVar[SettingsConfigDict] = {'arbitrary_types_allowed': True, 'case_sensitive': False, 'env_file': None, 'env_file_encoding': None, 'env_nested_delimiter': None, 'env_prefix': 'SQL_MOCK_REDSHIFT_', 'extra': 'forbid', 'protected_namespaces': ('model_', 'settings_'), 'secrets_dir': None, 'validate_default': True}
-

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

-
- -
-
-model_fields: ClassVar[dict[str, FieldInfo]] = {'database': FieldInfo(annotation=str, required=True), 'host': FieldInfo(annotation=str, required=True), 'password': FieldInfo(annotation=str, required=False), 'port': FieldInfo(annotation=int, required=False, default=5439), 'user': FieldInfo(annotation=str, required=True)}
-

Metadata about the fields defined on the model, -mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

-

This replaces Model.__fields__ from Pydantic V1.

-
- -
-
-password: str
-
- -
-
-port: int
-
- -
-
-user: str
-
- -
- -
-
-

sql_mock.redshift.table_mocks module

-
-
-class sql_mock.redshift.table_mocks.RedshiftMockTable(*args, **kwargs)
-

Bases: BaseMockTable

-
- -
-
-

Module contents

-
-
- - -
-
-
- -
- -
-

© Copyright 2023, DeepL, Thomas Schmidt.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/sql_mock.snowflake.html b/docs/sql_mock.snowflake.html deleted file mode 100644 index f4e8713..0000000 --- a/docs/sql_mock.snowflake.html +++ /dev/null @@ -1,376 +0,0 @@ - - - - - - - sql_mock.snowflake package — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

sql_mock.snowflake package

-
-

Submodules

-
-
-

sql_mock.snowflake.column_mocks module

-
-
-class sql_mock.snowflake.column_mocks.ARRAY(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'ARRAY'
-
- -
-
-use_quotes_for_casting = False
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.BINARY(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'BINARY'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.BOOLEAN(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'BOOLEAN'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.DATE(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'DATE'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.DECIMAL(default, precision, scale, nullable=False)
-

Bases: SnowflakeColumnMock

-
- -
-
-class sql_mock.snowflake.column_mocks.FLOAT(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'FLOAT'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.GEOGRAPHY(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'GEOGRAPHY'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.INTEGER(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'INTEGER'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.OBJECT(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'OBJECT'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.STRING(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'STRING'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.SnowflakeColumnMock(default=None, nullable=False)
-

Bases: ColumnMock

-
- -
-
-class sql_mock.snowflake.column_mocks.TEXT(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'TEXT'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.TIME(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'TIME'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.TIMESTAMP(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'TIMESTAMP'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.TIMESTAMP_LTZ(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'TIMESTAMP_LTZ'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.TIMESTAMP_NTZ(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'TIMESTAMP_NTZ'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.TIMESTAMP_TZ(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'TIMESTAMP_TZ'
-
- -
- -
-
-class sql_mock.snowflake.column_mocks.VARIANT(default=None, nullable=False)
-

Bases: SnowflakeColumnMock

-
-
-dtype = 'VARIANT'
-
- -
- -
-
-

sql_mock.snowflake.settings module

-
-
-class sql_mock.snowflake.settings.SnowflakeSettings(_case_sensitive: bool | None = None, _env_prefix: str | None = None, _env_file: DotenvType | None = PosixPath('.'), _env_file_encoding: str | None = None, _env_nested_delimiter: str | None = None, _secrets_dir: str | Path | None = None, *, account: str, user: str, password: str)
-

Bases: BaseSettings

-
-
-account: str
-
- -
-
-model_config: ClassVar[SettingsConfigDict] = {'arbitrary_types_allowed': True, 'case_sensitive': False, 'env_file': None, 'env_file_encoding': None, 'env_nested_delimiter': None, 'env_prefix': 'SQL_MOCK_SNOWFLAKE_', 'extra': 'forbid', 'protected_namespaces': ('model_', 'settings_'), 'secrets_dir': None, 'validate_default': True}
-

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

-
- -
-
-model_fields: ClassVar[dict[str, FieldInfo]] = {'account': FieldInfo(annotation=str, required=True), 'password': FieldInfo(annotation=str, required=True), 'user': FieldInfo(annotation=str, required=True)}
-

Metadata about the fields defined on the model, -mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

-

This replaces Model.__fields__ from Pydantic V1.

-
- -
-
-password: str
-
- -
-
-user: str
-
- -
- -
-
-

sql_mock.snowflake.table_mocks module

-
-
-class sql_mock.snowflake.table_mocks.SnowflakeMockTable(*args, **kwargs)
-

Bases: BaseMockTable

-
- -
-
-

Module contents

-
-
- - -
-
-
- -
- -
-

© Copyright 2023, DeepL, Thomas Schmidt.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/usage/bigquery/examples.html b/docs/usage/bigquery/examples.html deleted file mode 100644 index e31adc3..0000000 --- a/docs/usage/bigquery/examples.html +++ /dev/null @@ -1,197 +0,0 @@ - - - - - - - Example: Testing Subscription Counts in BigQuery — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-
-
-

Example: Testing Subscription Counts in BigQuery

-
import datetime
-from sql_mock.bigquery import column_mocks as col
-from sql_mock.bigquery.table_mocks import BigQueryMockTable
-from sql_mock.table_mocks import table_meta
-
-# Define mock tables for your data model that inherit from BigQueryMockTable
-@table_meta(table_ref='data.users')
-class UserTable(BigQueryMockTable):
-    user_id = col.Int(default=1)
-    user_name = col.String(default='Mr. T')
-
-
-@table_meta(table_ref='data.subscriptions')
-class SubscriptionTable(BigQueryMockTable):
-    subscription_id = col.Int(default=1)
-    period_start_date = col.Date(default=datetime.date(2023, 9, 5))
-    period_end_date = col.Date(default=datetime.date(2023, 9, 5))
-    user_id = col.Int(default=1)
-
-
-# Define a mock table for your expected results
-class SubscriptionCountTable(BigQueryMockTable):
-    subscription_count = col.Int(default=1)
-    user_id = col.Int(default=1)
-
-# Your original SQL query
-query = """
-SELECT
-    count(*) AS subscription_count,
-    user_id
-FROM data.users
-LEFT JOIN data.subscriptions USING(user_id)
-GROUP BY user_id
-"""
-
-# Create mock data for the 'data.users' and 'data.subscriptions' tables
-users = UserTable.from_dicts([{'user_id': 1}, {'user_id': 2}])
-subscriptions = SubscriptionTable.from_dicts([
-    {'subscription_id': 1, 'user_id': 1},
-    {'subscription_id': 2, 'user_id': 1},
-    {'subscription_id': 2, 'user_id': 2},
-])
-
-# Define your expected results
-expected = [
-    {'user_id': 1, 'subscription_count': 2}, 
-    {'user_id': 2, 'subscription_count': 1}
-]
-
-# Simulate the SQL query using SQL Mock
-res = SubscriptionCountTable.from_mocks(
-    query=query, 
-    input_data=[users, subscriptions]
-)
-
-# Assert the results
-res.assert_equal(expected)
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/usage/bigquery/index.html b/docs/usage/bigquery/index.html deleted file mode 100644 index 2c56674..0000000 --- a/docs/usage/bigquery/index.html +++ /dev/null @@ -1,142 +0,0 @@ - - - - - - - BigQuery — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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BigQuery

-

This section documents the specifics on how to use SQL Mock with BigQuery

-
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- - - - \ No newline at end of file diff --git a/docs/usage/bigquery/settings.html b/docs/usage/bigquery/settings.html deleted file mode 100644 index 842441f..0000000 --- a/docs/usage/bigquery/settings.html +++ /dev/null @@ -1,140 +0,0 @@ - - - - - - - Settings — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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- -
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Settings

-

In order to use sql-mock with BigQuery, ensure you have the GOOGLE_APPLICATION_CREDENTIALS environment variable correctly set to point to a service account file.

-

You need to service account file in order to let SQL Mock connect to BigQuery while running the tests.

-
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Example: Testing Subscription Counts in ClickHouse

-
from sql_mock.clickhouse import column_mocks as col
-from sql_mock.clickhouse.table_mocks import ClickHouseTableMock
-from sql_mock.table_mocks import table_meta
-
-# Define mock tables for your data model that inherit from ClickHouseTableMock
-@table_meta(table_ref='data.users')
-class UserTable(ClickHouseTableMock):
-    user_id = col.Int(default=1)
-    user_name = col.String(default="Mr. T")
-
-@table_meta(table_ref='data.subscriptions')
-class SubscriptionTable(ClickHouseTableMock):
-    subscription_id = col.Int(default=1)
-    period_start_date = col.Date(default=datetime.date(2023, 9, 5))
-    period_end_date = col.Date(default=datetime.date(2023, 9, 5))
-    user_id = col.Int(default=1)
-
-# Define a mock table for your expected results
-class SubscriptionCountTable(ClickHouseTableMock):
-    subscription_count = col.Int(default=1)
-    user_id = col.Int(default=1)
-
-# Your original SQL query
-query = """
-SELECT
-    count(*) AS subscription_count,
-    user_id
-FROM data.users
-LEFT JOIN data.subscriptions USING(user_id)
-GROUP BY user_id
-"""
-
-# Create mock data for the 'data.users' and 'data.subscriptions' tables
-users = UserTable.from_dicts([{'user_id': 1}, {'user_id': 2}])
-subscriptions = SubscriptionTable.from_dicts([
-    {'subscription_id': 1, 'user_id': 1},
-    {'subscription_id': 2, 'user_id': 1},
-    {'subscription_id': 2, 'user_id': 2},
-])
-
-# Define your expected results
-expected = [
-    {'user_id': 1, 'subscription_count': 2}, 
-    {'user_id': 2, 'subscription_count': 1}
-]
-
-# Simulate the SQL query using SQL Mock
-res = SubscriptionCountTable.from_mocks(query=query, input_data=[users, subscriptions])
-
-# Assert the results
-res.assert_equal(expected)
-
-
-
- - -
-
- -
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- - - - \ No newline at end of file diff --git a/docs/usage/clickhouse/index.html b/docs/usage/clickhouse/index.html deleted file mode 100644 index 0ab1138..0000000 --- a/docs/usage/clickhouse/index.html +++ /dev/null @@ -1,142 +0,0 @@ - - - - - - - Clickhouse — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
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Clickhouse

-

This section documents the specifics on how to use SQL Mock with Clickhouse

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Settings

-

In order to use SQL Mock with Clickhouse, you need to provide the following environment variables when you run tests:

-
    -

  • SQL_MOCK_CLICKHOUSE_HOST: Host of your Clickhouse instance

    • -

  • SQL_MOCK_CLICKHOUSE_USER: User you want to use for the connection

    • -

  • SQL_MOCK_CLICKHOUSE_PASSWORD: Password of your user

    • -

  • SQL_MOCK_CLICKHOUSE_PORT: Port of your Clickhouse instance

    • -
-

Having those environment variables enables SQL Mock to connect to your Clickhouse instance.

-
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Use with dbt

-
-

Introduction

-

This guide will provide a quick start on how to use SQLMock with dbt (data build tool). You can use it to mock dbt models, sources, and seed models. We’ll cover how to use these features effectively in your unit tests.

-
-
-

Prerequisites

-
    -

  • A working dbt project with a manifest.json file that has the latest compiled run. (make sure to run dbt compile).

    • -

  • The SQLMock library installed in your Python environment.

    • -
-
-
-

Configuration

-
-

Setting the dbt Manifest Path

-

Initialize your testing environment by setting the global path to your dbt manifest file:

-
from sql_mock.config import SQLMockConfig
-
-SQLMockConfig.set_dbt_manifest_path('/path/to/your/dbt/manifest.json')
-
-
-
-
-
-

Creating Mock Tables

-

SQLMock offers specialized decorators for different dbt entities: models, sources, and seeds.

-
-

dbt Model Mock Table

-

For dbt models, use the dbt_model_meta decorator from sql_mock.dbt. This decorator is suited for mocking the transformed data produced by dbt models.

-
from sql_mock.dbt import dbt_model_meta
-from sql_mock.bigquery.table_mocks import BigQueryMockTable
-
-@dbt_model_meta(model_name="your_dbt_model_name")
-class YourDBTModelTable(BigQueryMockTable):
-    # Define your table columns and other necessary attributes here
-    ...
-
-
-
-
-

dbt Source Mock Table

-

For dbt sources, use the dbt_source_meta decorator from sql_mock.dbt. This is ideal for mocking the raw data sources that dbt models consume.

-
from sql_mock.dbt import dbt_source_meta
-from sql_mock.bigquery.table_mocks import BigQueryMockTable
-
-@dbt_source_meta(source_name="your_source_name", table_name="your_source_table")
-class YourDBTSourceTable(BigQueryMockTable):
-    # Define your table columns and other necessary attributes here
-    ...
-
-
-
-
-

dbt Seed Mock Table

-

For dbt seeds, which are static data sets loaded into the database, use the dbt_seed_meta decorator from sql_mock.dbt.

-
from sql_mock.dbt import dbt_seed_meta
-from sql_mock.bigquery.table_mocks import BigQueryMockTable
-
-@dbt_seed_meta(seed_name="your_dbt_seed_name")
-class YourDBTSeedTable(BigQueryMockTable):
-    # Define your table columns and other necessary attributes here
-    ...
-
-
-
-
-
-

Example: Testing a dbt Model with Upstream Source and Seed Data

-

Let’s consider a dbt model named monthly_user_spend that aggregates data from a source user_transactions and a seed user_categories.

-
-

Step 1: Define Your Source and Seed Mock Tables

-
@dbt_source_meta(source_name="transactions", table_name="user_transactions")
-class UserTransactionsTable(BigQueryMockTable):
-    transaction_id = col.Int(default=1)
-    user_id = col.Int(default=1)
-    amount = col.Float(default=1.0)
-    transaction_date = col.Date(default='2023-12-24')
-
-@dbt_seed_meta(seed_name="user_categories")
-class UserCategoriesTable(BigQueryMockTable):
-    user_id = col.Int(default=1)
-    category = col.String(default='foo')
-
-
-
-
-

Step 2: Define Your Model Mock Table

-
@dbt_model_meta(model_name="monthly_user_spend")
-class MonthlyUserSpendTable(BigQueryMockTable):
-    user_id = col.Int(default=1)
-    month = col.String(default='foo')
-    total_spend = col.Float(default=1.0)
-    category = col.String(default='foo')
-
-
-
-
-

Step 3: Write Your Test Case

-
import datetime
-
-def test_monthly_user_spend_model():
-    # Mock input data for UserTransactionsTable and UserCategoriesTable
-    transactions_data = [
-        {"transaction_id": 1, "user_id": 1, "amount": 120.0, "transaction_date": datetime.date(2023, 1, 10)},
-        {"transaction_id": 2, "user_id": 2, "amount": 150.0, "transaction_date": datetime.date(2023, 1, 20)},
-    ]
-
-    categories_data = [
-        {"user_id": 1, "category": "Premium"},
-        {"user_id": 2, "category": "Standard"}
-    ]
-
-    transactions_table = UserTransactionsTable.from_dicts(transactions_data)
-    categories_table = UserCategoriesTable.from_dicts(categories_data)
-
-    # Expected result
-    expected_output = [
-        {"user_id": 1, "month": "2023-01", "total_spend": 120.0, "category": "Premium"},
-        {"user_id": 2, "month": "2023-01", "total_spend": 150.0, "category": "Standard"},
-    ]
-
-    monthly_spend_table = MonthlyUserSpendTable.from_mocks(input_data=[transactions_table, categories_table])
-
-    monthly_spend_table.assert_equal(expected_output)
-
-
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/usage/default_values.html b/docs/usage/default_values.html deleted file mode 100644 index 178edab..0000000 --- a/docs/usage/default_values.html +++ /dev/null @@ -1,200 +0,0 @@ - - - - - - - Default values — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
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- -
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Default values

-

Testing SQL queries can often involve repetitive setup for mock tables. In SQLMock, one effective way to streamline this process is by using default values. By setting reasonable defaults, you can significantly reduce the boilerplate code in your tests, especially when dealing with multiple input tables or complex queries. Let’s explore how you can efficiently implement this.

-
-

Utilizing Default Values in MockTable Fields

-

Defining default values at the field level in your mock tables is straightforward. -The default argument in the field definition allows you to set default values consistency across all test scenarios in one step. -They are particularly useful for ensuring that joins and other query functionalities operate correctly.

-

Here’s an example:

-
@table_meta(table_ref="data.users")
-class UserTable(BigQueryMockTable):
-    user_id = col.Int(default=1)
-    user_name = col.String(default="Mr. T")
-
-# Create instances of the UserTable with various combinations of defaults and specified values
-users = UserTable.from_dicts([
-    {}, # Left empty {} uses default values --> {"user_id": 1, "user_name": "Mr. T"}
-    {"user_id": 2}, # Overrides user_id but uses default for user_name
-    {"user_id": 3, "user_name": "Nala"} # No defaults used here
-])
-
-
-
-
-

Setting Mock Defaults with table_meta

-

When defining your MockTable classes, the table_meta decorator accepts a default_inputs argument. -The Mock instances passed here, will be used as defaults in the from_mocks method.

-

Consider this example:

-
@table_meta(
-    query_path="./examples/test_query.sql",
-    default_inputs=[UserTable([]), SubscriptionTable([])] # We can provide defaults for the class if needed.
-)
-class MultipleSubscriptionUsersTable(BigQueryMockTable):
-    user_id = col.Int(default=1)
-
-# Setting up different scenarios to demonstrate the use of defaults
-users = UserTable.from_dicts([
-    {"user_id": 1}, 
-    {"user_id": 2}
-])
-subscriptions = SubscriptionTable.from_dicts(
-    [
-        {"subscription_id": 1, "user_id": 1},
-        {"subscription_id": 2, "user_id": 1},
-        {"subscription_id": 2, "user_id": 2},
-    ]
-)
-
-# Utilizing the default inputs set in the table_meta
-res = MultipleSubscriptionUsersTable.from_mocks(input_data=[])
-res = MultipleSubscriptionUsersTable.from_mocks(input_data=[users]) # Using only users, defaults for others
-res = MultipleSubscriptionUsersTable.from_mocks(input_data=[users, subscriptions]) # Overriding defaults
-
-
-
-
-

When is this useful?

-
    -

  • Safe time and code by changing only the data you need for your test case: You can only change single columns for the data you provide for a test case. The rest will be filled by defaults.

    • -

  • Simplifying Happy Path Testing: Validate basic functionality and syntax correctness of your SQL queries with minimal setup.

    • -

  • Testing Subset Logic: When certain tables in your query don’t require data, default values can help focus on specific test scenarios.

    • -

  • Provide reasonable defaults for joins: In tests with numerous input tables you can specify inputs that joins between tables work. For frequent addition of new tables, defaults can prevent the need for extensive refactoring.

    • -
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Defining table mocks

-

When you want to provide mocked data to test your SQL model, you need to create MockTable classes for all upstream data that your model uses, as well as for the model you want to test. Those mock tables can be created by inheriting from a BaseMockTable class for the database provider you are using (e.g. BigQueryMockTable).

-

We recommend to have a central model.py file where you create those models that you can easily reuse them across your tests

-
# models.py
-
-from sql_mock.bigquery import column_mocks as col
-from sql_mock.bigquery.table_mocks import BigQueryMockTable, table_meta
-
-# The models you are goign to use as inputs need to have a `table_ref` specified
-@table_meta(table_ref='data.table1')
-class Table(BigQueryMockTable):
-    id = col.Int(default=1)
-    name = col.String(default='Peter')
-
-@table_meta(
-    table_ref='data.result_table', 
-    query_path='path/to/query_for_result_table.sql', 
-    default_inputs=[Table()] # You can provide default inputs on a class level
-)
-class ResultTable(BigQueryMockTable):
-    id = col.Int(default=1)
-
-
-

Some important things to mention:

-

The models you are goign to use as inputs need to have a table_ref specified. -The table_ref is how the table will be referenced in your production database (usually some pattern like <schema>.<table>)

-

The result model needs to have a query. -There are currently 2 ways to provide a query to the model:

-
    -

  1. Pass a path to your query file in the class definition using the table_meta decorator. This allows us to only specify it once.

    -
    @table_meta(table_ref='data.result_table', query_path='path/to/query_for_result_table.sql')
-class ResultTable(BigQueryMockTable):
-    ...
-
    -
    -
    2. -

  2. Pass it as query argument to the from_mocks method when you are using the model in your test. This will also overwrite whatever query was read from the query_path in the table_meta decorator.

    -
    res = ResultTable.from_mocks(query='SELECT 1', input_data=[<your-input-mocks-table-instances>])
-
    -
    -
    3. -
-

More details on how to handle queries can be found in the “Your SQL query to test” section

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Examples

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You can find some examples in the examples folder.

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Example: Testing Subscription Counts in Redshift

-
import datetime
-from sql_mock.redshift import column_mocks as col
-from sql_mock.redshift.table_mocks import RedshiftMockTable
-from sql_mock.table_mocks import table_meta
-
-# Define mock tables for your data model that inherit from RedshiftMockTable
-@table_meta(table_ref="data.users")
-class UserTable(RedshiftMockTable):
-    user_id = col.INTEGER(default=1)
-    user_name = col.VARCHAR(default="Mr. T")
-
-
-@table_meta(table_ref="data.subscriptions")
-class SubscriptionTable(RedshiftMockTable):
-    subscription_id = col.INTEGER(default=1)
-    period_start_date = col.DATE(default=datetime.date(2023, 9, 5))
-    period_end_date = col.DATE(default=datetime.date(2023, 9, 5))
-    user_id = col.INTEGER(default=1)
-
-# Define a mock table for your expected results
-class SubscriptionCountTable(RedshiftMockTable):
-    subscription_count = col.INTEGER(default=1)
-    user_id = col.INTEGER(default=1)
-
-# Your original SQL query
-query = """
-SELECT
-    count(*) AS subscription_count,
-    user_id
-FROM data.users
-LEFT JOIN data.subscriptions USING(user_id)
-GROUP BY user_id
-"""
-
-# Create mock data for the 'data.users' and 'data.subscriptions' tables
-users = UserTable.from_dicts([{'user_id': 1}, {'user_id': 2}])
-subscriptions = SubscriptionTable.from_dicts([
-    {'subscription_id': 1, 'user_id': 1},
-    {'subscription_id': 2, 'user_id': 1},
-    {'subscription_id': 2, 'user_id': 2},
-])
-
-# Define your expected results
-expected = [
-    {'user_id': 1, 'subscription_count': 2},
-    {'user_id': 2, 'subscription_count': 1}
-]
-
-# Simulate the SQL query using SQL Mock
-res = SubscriptionCountTable.from_mocks(query=query, input_data=[users, subscriptions])
-
-# Assert the results
-res.assert_equal(expected)
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/usage/redshift/index.html b/docs/usage/redshift/index.html deleted file mode 100644 index 31ec563..0000000 --- a/docs/usage/redshift/index.html +++ /dev/null @@ -1,143 +0,0 @@ - - - - - - - Redshift — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
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Redshift

-

This section documents the specifics on how to use SQL Mock with Redshift

-
- -
-
- - -
-
- -
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-
-
- - - - \ No newline at end of file diff --git a/docs/usage/redshift/settings.html b/docs/usage/redshift/settings.html deleted file mode 100644 index b75d5a3..0000000 --- a/docs/usage/redshift/settings.html +++ /dev/null @@ -1,147 +0,0 @@ - - - - - - - Settings — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-
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-

Settings

-

In order to use SQL Mock with Redshift, you need to provide the following environment variables when you run tests:

-
    -

  • SQL_MOCK_REDSHIFT_HOST: The host of your Redshift instance

    • -

  • SQL_MOCK_REDSHIFT_USER: The user of your Redshift instance

    • -

  • SQL_MOCK_REDSHIFT_PASSWORD: The password of your Redshift instance

    • -

  • SQL_MOCK_REDSHIFT_PORT: The port of your Redshift instance

    • -
-

Having those environment variables enables SQL Mock to connect to your Redshift instance.

-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/usage/result_assertion.html b/docs/usage/result_assertion.html deleted file mode 100644 index 4c2e7f0..0000000 --- a/docs/usage/result_assertion.html +++ /dev/null @@ -1,209 +0,0 @@ - - - - - - - Result assertion — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
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- -
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- -
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Result assertion

-

There are 2 ways how you can check the output of your query given the mocked input data on your Table Mock instance:

-
    -

  1. assert_equal method: Assert the normal result of the query (running the full query with your input mocks)

    2. -

  2. assert_cte_equal method: Assert the result of a CTE in your query. A lot of times when we built more complicated data models, they include a bunch of CTEs that map to separate logical steps. In those cases, when we unit test our models, we want to be able to not only check the final result but also the single steps. To do this, you can use the assert_cte_equal method.

    3. -
-

Let’s assume we have the following query:

-
WITH subscriptions_per_user AS (
-    SELECT
-        count(sub.user_id) AS subscription_count,
-        users.user_id
-    FROM data.users AS users
-    LEFT JOIN data.subscriptions AS sub ON sub.user_id = users.user_id
-    GROUP BY user_id
-),
-
-users_with_multiple_subs AS (
-    SELECT 
-        *
-    FROM subscriptions_per_user
-    WHERE subscription_count >= 2
-)
-
-SELECT user_id FROM users_with_multiple_subs
-
-
-

… and we define the following mock tables:

-
import datetime
-
-from sql_mock.bigquery import column_mocks as col
-from sql_mock.bigquery.table_mocks import BigQueryMockTable
-from sql_mock.table_mocks import table_meta
-
-@table_meta(table_ref="data.users")
-class UserTable(BigQueryMockTable):
-    user_id = col.Int(default=1)
-    user_name = col.String(default="Mr. T")
-
-
-@table_meta(table_ref="data.subscriptions")
-class SubscriptionTable(BigQueryMockTable):
-    subscription_id = col.Int(default=1)
-    period_start_date = col.Date(default=datetime.date(2023, 9, 5))
-    period_end_date = col.Date(default=datetime.date(2023, 9, 5))
-    user_id = col.Int(default=1)
-
-
-@table_meta(query_path="./examples/test_query.sql")
-class MultipleSubscriptionUsersTable(BigQueryMockTable):
-    user_id = col.Int(default=1)
-
-
-

Now we can use the different testing methods the following way:

-
def test_model():
-    users = UserTable.from_dicts([{"user_id": 1}, {"user_id": 2}])
-    subscriptions = SubscriptionTable.from_dicts(
-        [
-            {"subscription_id": 1, "user_id": 1},
-            {"subscription_id": 2, "user_id": 1},
-            {"subscription_id": 2, "user_id": 2},
-        ]
-    )
-
-    subscriptions_per_user__expected = [{"user_id": 1, "subscription_count": 2}, {"user_id": 2, "subscription_count": 1}]
-    users_with_multiple_subs__expected = [{"user_id": 1, "subscription_count": 2}]
-    end_result__expected = [{"user_id": 1}]
-
-    res = MultipleSubscriptionUsersTable.from_mocks(input_data=[users, subscriptions])
-
-    # Check the results of the subscriptions_per_user CTE
-    res.assert_cte_equal('subscriptions_per_user', subscriptions_per_user__expected)
-    # Check the results of the users_with_multiple_subs CTE
-    res.assert_cte_equal('users_with_multiple_subs', users_with_multiple_subs__expected)
-    # Check the end result
-    res.assert_equal(end_result__expected)
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/usage/snowflake/examples.html b/docs/usage/snowflake/examples.html deleted file mode 100644 index 467b936..0000000 --- a/docs/usage/snowflake/examples.html +++ /dev/null @@ -1,196 +0,0 @@ - - - - - - - Example: Testing Subscription Counts in Snowflake — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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- -
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Example: Testing Subscription Counts in Snowflake

-
import datetime
-from sql_mock.snowflake import column_mocks as col
-from sql_mock.snowflake.table_mocks import SnowflakeMockTable
-from sql_mock.table_mocks import table_meta
-
-# Define mock tables for your data model that inherit from SnowflakeMockTable
-@table_meta(table_ref="data.users")
-class UserTable(SnowflakeMockTable):
-    user_id = col.INTEGER(default=1)
-    user_name = col.STRING(default="Mr. T")
-
-
-@table_meta(table_ref="data.subscriptions")
-class SubscriptionTable(SnowflakeMockTable):
-    subscription_id = col.INTEGER(default=1)
-    period_start_date = col.DATE(default=datetime.date(2023, 9, 5))
-    period_end_date = col.DATE(default=datetime.date(2023, 9, 5))
-    user_id = col.INTEGER(default=1)
-
-
-# Define a mock table for your expected results
-class SubscriptionCountTable(SnowflakeMockTable):
-    subscription_count = col.INTEGER(default=1)
-    user_id = col.INTEGER(default=1)
-
-# Your original SQL query
-query = """
-SELECT
-    count(*) AS subscription_count,
-    user_id
-FROM data.users
-LEFT JOIN data.subscriptions USING(user_id)
-GROUP BY user_id
-"""
-
-def test_something():
-  # Create mock data for the 'data.users' and 'data.subscriptions' tables
-  users = UserTable.from_dicts([{'user_id': 1}, {'user_id': 2}])
-  subscriptions = SubscriptionTable.from_dicts([
-      {'subscription_id': 1, 'user_id': 1},
-      {'subscription_id': 2, 'user_id': 1},
-      {'subscription_id': 2, 'user_id': 2},
-  ])
-
-  # Define your expected results
-  expected = [
-      {"USER_ID": 1, "SUBSCRIPTION_COUNT": 2},
-      {"USER_ID": 2, "SUBSCRIPTION_COUNT": 1},
-  ]
-
-  # Simulate the SQL query using SQL Mock
-  res = SubscriptionCountTable.from_mocks(query=query, input_data=[users, subscriptions])
-
-  # Assert the results
-  res.assert_equal(expected)
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/usage/snowflake/index.html b/docs/usage/snowflake/index.html deleted file mode 100644 index 726bda3..0000000 --- a/docs/usage/snowflake/index.html +++ /dev/null @@ -1,143 +0,0 @@ - - - - - - - Snowflake — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

Snowflake

-

This section documents the specifics on how to use SQL Mock with Snowflake

-
- -
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- - -
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- -
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- - - - \ No newline at end of file diff --git a/docs/usage/snowflake/settings.html b/docs/usage/snowflake/settings.html deleted file mode 100644 index 3cfee40..0000000 --- a/docs/usage/snowflake/settings.html +++ /dev/null @@ -1,146 +0,0 @@ - - - - - - - Settings — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-
-
-

Settings

-

In order to use SQL Mock with Snowflake, you need to provide the following environment variables when you run tests:

-
    -

  • SQL_MOCK_SNOWFLAKE_ACCOUNT: The name of your Snowflake account

    • -

  • SQL_MOCK_SNOWFLAKE_USER: The name of your Snowflake user

    • -

  • SQL_MOCK_SNOWFLAKE_PASSWORD: The password for your Snowflake user

    • -
-

Having those environment variables enables SQL Mock to connect to your Snowflake instance.

-
- - -
-
- -
-
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-
- - - - \ No newline at end of file diff --git a/docs/usage/your_sql_query_to_test.html b/docs/usage/your_sql_query_to_test.html deleted file mode 100644 index 6bb84d3..0000000 --- a/docs/usage/your_sql_query_to_test.html +++ /dev/null @@ -1,184 +0,0 @@ - - - - - - - Your SQL query to test — SQL Mock 0.3.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

Your SQL query to test

-

There are multiple ways on how you can provide the SQL query that you want to test. Let’s walk through them and also cover some specifics.

-
-

Ways to provide your SQL query to be tested

- -
-

Option 2: Pass the query in the .from_mocks call

-

You can also pass your query in your test case when you call the from_mocks method.

-
res = ResultTable.from_mocks(query='SELECT 1', input_data=[<your-input-mocks-table-instances>])
-
-
-

Note that this will overwride whatever was specified by using the table_meta decorator.

-
-
-
-

Queries with Jinja templates

-

Sometimes we need to test queries that use jinja templates (e.g. for dbt). -In those cases, you can provide the necessary context for rendering your query using the from_mocks call.

-

Example:

-

Let’s assume the following jinja template query:

-
SELECT * FROM data.users 
-WHERE created_at > {{ creation_date }}
-
-
-

We can provide the creation_date variable in a dictionary using the query_template_kwargs argument:

-
res = ResultTable.from_mocks(input_data=[your_input_mock_instances], query_template_kwargs={'creation_date': '2023-09-05'})
-
-
-

This will automatically render your query using the given input.

-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docsource/usage/your_sql_query_to_test.md b/docs/your_sql_query_to_test.md similarity index 98% rename from docsource/usage/your_sql_query_to_test.md rename to docs/your_sql_query_to_test.md index e662f95..2fc6f18 100644 --- a/docsource/usage/your_sql_query_to_test.md +++ b/docs/your_sql_query_to_test.md @@ -27,7 +27,7 @@ The advantage of that option is that you only need to define your Table Mock cla You can also pass your query in your test case when you call the `from_mocks` method. -```python +```python res = ResultTable.from_mocks(query='SELECT 1', input_data=[]) ``` @@ -43,7 +43,7 @@ In those cases, you can provide the necessary context for rendering your query u Let's assume the following jinja template query: ```jinja -SELECT * FROM data.users +SELECT * FROM data.users WHERE created_at > {{ creation_date }} ``` diff --git a/docsource/.nojekyll b/docsource/.nojekyll deleted file mode 100644 index e69de29..0000000 diff --git a/docsource/conf.py b/docsource/conf.py deleted file mode 100644 index 999482f..0000000 --- a/docsource/conf.py +++ /dev/null @@ -1,57 +0,0 @@ -import os -import sys - -sys.path.insert(0, os.path.abspath("../src")) - -# Configuration file for the Sphinx documentation builder. -# -# For the full list of built-in configuration values, see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Project information ----------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information - -project = "SQL Mock" -copyright = "2023, DeepL, Thomas Schmidt" -author = "DeepL, Thomas Schmidt" -release = "0.3.0" - -# -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration - -extensions = ["sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.napoleon", "myst_parser", "sphinx_sitemap"] - -templates_path = ["_templates"] -exclude_patterns = [] -source_suffix = { - ".rst": "restructuredtext", - ".txt": "markdown", - ".md": "markdown", -} - - -autodoc_inherit_docstrings = True -autosummary_generate = True - -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output - -html_theme = "sphinx_rtd_theme" -html_static_path = ["_static"] -html_extra_path = ["_static"] -html_sidebars = { - "**": [ - "globaltoc.html", - "relations.html", # needs 'show_related': True option to display - "sourcelink.html", - "searchbox.html", - ] -} -html_context = { - "display_github": True, # Integrate GitHub - "github_user": "DeepLcom", # Username - "github_repo": "sql-mock", # Repo name - "github_version": "master", # Version - "conf_py_path": "/docsource/", # Path in the checkout to the docs root -} -html_baseurl = "https://deeplcom.github.io/sql-mock/" diff --git a/docsource/faq.md b/docsource/faq.md deleted file mode 100644 index b306d16..0000000 --- a/docsource/faq.md +++ /dev/null @@ -1,61 +0,0 @@ - -# FAQ - -## My database system is not supported yet but I want to use SQL Mock. What should I do? - -We are planning to add more and more supported database systems. However, if your system is not supported yet, you can still use SQL Mock. There are only 2 things you need to do: - -### Create your `TableMock` class - -First, you need to create a `TableMock` class for your database system that inherits from `sql_mock.table_mocks.BaseTableMock`. - -That class needs to implement the `_get_results` method which should make sure to fetch the results of a query (e.g. produced by `self._generate_query()`) and return it as list of dictionaries. - -Look at one of the existing client libraries to see how this could work (e.g. [BigQueryTableMock](https://github.com/DeepLcom/sql-mock/blob/main/src/sql_mock/bigquery/table_mocks.py)). - -You might want to create a settings class as well in case you need some specific connection settings to be available within the `_get_results` method. - -### Create your `ColumnMocks` - -Your database system might support specific database types. In order to make them available as column types, you can use the `sql_mock.column_mocks.BaseColumnMock` class as a base and inherit your specific column types from it. -For most of your column mocks you might only need to specify the `dtype` that should be used to parse the inputs. - -A good practise is to create a `BaseColumnMock` class that is specific to your database and inherit all your column types from it, e.g.: - -```python -from sql_mock.column_mocks import BaseColumnMock - -class MyFanceDatabaseColumnMock(BaseColumnMock): - # In case you need some specific logic that overwrites the default behavior, you can do so here - pass - -class Int(MyFanceDatabaseColumnMock): - dtype = "Integer" - -class String(MyFanceDatabaseColumnMock): - dtype = "String" -``` - -### Contribute your database setup - -There will definitely be folks in the community that are in the need of support for the database you just created all the setup for. -Feel free to create a PR on this repository that we can start supporting your database system! - -## I am missing a specific BaseColumnMock type for my model fields - -We implemented some basic column types but it could happen that you don't find the one you need. -Luckily, you can easily create those with the tools provided. -The only thing you need to do is to inherit from the `BaseColumnMock` that is specific to your database system (e.g. `BigQueryColumnMock`) and write classes for the column mocks you are missing. Usually you only need to set the correct `dtype`. This would later be used in the `cast(col to )` expression. - -```python -# Replace the import with the database system you are using -from sql_mock.bigquery.column_mock import BigQueryColumnMock - -class MyFancyMissingColType(BigQueryColumnMock): - dtype = "FancyMissingColType" - - # In case you need to implement additional logic for casting, you can do so here - ... -``` - -**Don't forget to create a PR in case you feel that your column mock type could be useful for the community**! diff --git a/docsource/getting_started/installation.md b/docsource/getting_started/installation.md deleted file mode 100644 index 0a36650..0000000 --- a/docsource/getting_started/installation.md +++ /dev/null @@ -1,39 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Installation - -The library can be installed from [PyPI](https://pypi.org/project/sql-mock/) using pip: - -```shell -# BigQuery -pip install --upgrade "sql-mock[bigquery]" - -# Clickhouse -pip install --upgrade "sql-mock[clickhouse]" - -# Redshift -pip install --upgrade "sql-mock[redshift]" - -# Snowflake -pip install --upgrade "sql-mock[snowflake]" -``` - -If you need to modify this source code, install the dependencies using poetry: - -```shell -poetry install --all-extras -``` - - -## Recommended Setup for Pytest -If you are using pytest, make sure to add a `conftest.py` file to the root of your project. -In the file add the following lines: -```python -import pytest -pytest.register_assert_rewrite('sql_mock') -``` -This allows you to get a rich comparison when using the `.assert_equal` method on the table mock instances. - -We also recommend using [pytest-icdiff](https://github.com/hjwp/pytest-icdiff) for better visibility on diffs of failed tests. diff --git a/docsource/getting_started/quickstart.md b/docsource/getting_started/quickstart.md deleted file mode 100644 index 34d692e..0000000 --- a/docsource/getting_started/quickstart.md +++ /dev/null @@ -1,84 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Quickstart - -Before diving into specific database scenarios, let's start with a simplified example of how SQL Mock works behind the scenes. - -1. You have an original SQL query, for instance: - - ```sql - -- path/to/query_for_result_table.sql - SELECT id FROM data.table1 - ``` - -2. Using SQL Mock, you define mock tables. You can use the built-in column types provided by SQL Mock. Available column types include `Int`, `String`, `Date`, and more. Each database type has their own column types. Define your tables by subclassing a mock table class that fits your database (e.g. `BigQueryTableMock`) and specifying the column types along with default values. In our example we use the `ClickHouseTableMock` class - - ```python - from sql_mock.clickhouse import column_mocks as col - from sql_mock.clickhouse.table_mocks import ClickHouseTableMock - from sql_mock.table_mocks import table_meta - - @table_meta(table_ref='data.table1') - class Table(ClickHouseTableMock): - id = col.Int(default=1) - name = col.String(default='Peter') - - @table_meta(table_ref='data.result_table', query_path='path/to/query_for_result_table.sql') - class ResultTable(ClickHouseTableMock): - id = col.Int(default=1) - ``` - -3. **Creating mock data:** Define mock data for your tables using dictionaries. Each dictionary represents a row in the table, with keys corresponding to column names. Table column keys that don't get a value will use the default. - - ```python - user_data = [ - {}, # This will use the defaults for both id and name - {'id': 2, 'name': 'Martin'}, - {'id': 3}, # This will use defaults for the name - ] - - input_table_mock = Table.from_dicts(user_data) - ``` - -4. **Getting results for a table mock:** Use the `from_mocks` method of the table mock object to generate mock query results based on your mock data. - - ```python - res = ResultTable.from_mocks(input_data=[input_table_mock]) - ``` - -5. Behind the scene SQL Mock replaces table references (e.g. `data.table1`) in your query with Common Table Expressions (CTEs) filled with dummy data. It can roughly be compared to something like this: - - ```sql - WITH data__table1 AS ( - -- Mocked inputs - SELECT - cast('1' AS 'String') AS id, - cast('Peter' AS 'String') AS name - UNION ALL - SELECT - cast('2' AS 'String') AS id, - cast('Martin' AS 'String') AS name - UNION ALL - SELECT - cast('3' AS 'String') AS id, - cast('Peter' AS 'String') AS name - ) - - result AS ( - -- Original query with replaced references - SELECT id FROM data__table1 - ) - - SELECT - cast(id AS 'String') AS id - FROM result - ``` - -6. Finally, you can compare your results to some expected results using the `assert_equal` method. - - ```python - expected = [{'id': '1'},{'id': '2'},{'id': '3'}] - res.assert_equal(expected) - ``` diff --git a/docsource/index.rst b/docsource/index.rst deleted file mode 100644 index b78bacb..0000000 --- a/docsource/index.rst +++ /dev/null @@ -1,60 +0,0 @@ -.. SQL Mock documentation master file, created by - sphinx-quickstart on Wed Nov 1 07:36:03 2023. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to SQL Mock's documentation! -==================================== - -The primary purpose of this library is to simplify the testing of SQL data models and queries by allowing users to mock input data and create tests for various scenarios. -It provides a consistent and convenient way to test the execution of your query without the need to process a massive amount of data. - -.. meta:: - :google-site-verification: ohDOHPn1YMLYMaWQpFmqQfPW_KRZXNK9Gq47pP57VQM - -.. automodule:: sql_mock - :members: - :undoc-members: - :show-inheritance: - -.. toctree:: - :maxdepth: 3 - :caption: Getting Started - - getting_started/installation - getting_started/quickstart - faq - -.. toctree:: - :maxdepth: 3 - :caption: Basic Usage - - usage/defining_table_mocks - usage/dbt - usage/your_sql_query_to_test - usage/result_assertion - usage/default_values - usage/examples - -.. toctree:: - :maxdepth: 3 - :caption: Database Specifics - - usage/bigquery/index - usage/clickhouse/index - usage/redshift/index - usage/snowflake/index - -.. toctree:: - :maxdepth: 3 - :caption: API Reference - - API Reference - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docsource/modules.rst b/docsource/modules.rst deleted file mode 100644 index f4baeab..0000000 --- a/docsource/modules.rst +++ /dev/null @@ -1,8 +0,0 @@ -sql_mock -======== - -.. toctree:: - :maxdepth: 4 - :hidden: - - sql_mock diff --git a/docsource/robots.txt b/docsource/robots.txt deleted file mode 100644 index 7557cc5..0000000 --- a/docsource/robots.txt +++ /dev/null @@ -1,3 +0,0 @@ -User-agent: * - -Sitemap: https://deeplcom.github.io/sql-mock/sitemap.xml diff --git a/docsource/sql_mock.bigquery.rst b/docsource/sql_mock.bigquery.rst deleted file mode 100644 index 0ec2aea..0000000 --- a/docsource/sql_mock.bigquery.rst +++ /dev/null @@ -1,37 +0,0 @@ -sql\_mock.bigquery package -========================== - -Submodules ----------- - -sql\_mock.bigquery.column\_mocks module ---------------------------------------- - -.. automodule:: sql_mock.bigquery.column_mocks - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.bigquery.settings module ----------------------------------- - -.. automodule:: sql_mock.bigquery.settings - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.bigquery.table\_mocks module --------------------------------------- - -.. automodule:: sql_mock.bigquery.table_mocks - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: sql_mock.bigquery - :members: - :undoc-members: - :show-inheritance: diff --git a/docsource/sql_mock.clickhouse.rst b/docsource/sql_mock.clickhouse.rst deleted file mode 100644 index d8577e0..0000000 --- a/docsource/sql_mock.clickhouse.rst +++ /dev/null @@ -1,37 +0,0 @@ -sql\_mock.clickhouse package -============================ - -Submodules ----------- - -sql\_mock.clickhouse.column\_mocks module ------------------------------------------ - -.. automodule:: sql_mock.clickhouse.column_mocks - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.clickhouse.settings module ------------------------------------- - -.. automodule:: sql_mock.clickhouse.settings - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.clickhouse.table\_mocks module ----------------------------------------- - -.. automodule:: sql_mock.clickhouse.table_mocks - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: sql_mock.clickhouse - :members: - :undoc-members: - :show-inheritance: diff --git a/docsource/sql_mock.redshift.rst b/docsource/sql_mock.redshift.rst deleted file mode 100644 index 1a3a286..0000000 --- a/docsource/sql_mock.redshift.rst +++ /dev/null @@ -1,37 +0,0 @@ -sql\_mock.redshift package -========================== - -Submodules ----------- - -sql\_mock.redshift.column\_mocks module ---------------------------------------- - -.. automodule:: sql_mock.redshift.column_mocks - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.redshift.settings module ----------------------------------- - -.. automodule:: sql_mock.redshift.settings - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.redshift.table\_mocks module --------------------------------------- - -.. automodule:: sql_mock.redshift.table_mocks - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: sql_mock.redshift - :members: - :undoc-members: - :show-inheritance: diff --git a/docsource/sql_mock.rst b/docsource/sql_mock.rst deleted file mode 100644 index 407d078..0000000 --- a/docsource/sql_mock.rst +++ /dev/null @@ -1,54 +0,0 @@ -sql\_mock package -================= - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - sql_mock.bigquery - sql_mock.clickhouse - -Submodules ----------- - -sql\_mock.column\_mocks module ------------------------------- - -.. automodule:: sql_mock.column_mocks - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.constants module --------------------------- - -.. automodule:: sql_mock.constants - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.exceptions module ---------------------------- - -.. automodule:: sql_mock.exceptions - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.table\_mocks module ------------------------------ - -.. automodule:: sql_mock.table_mocks - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: sql_mock - :members: - :undoc-members: - :show-inheritance: diff --git a/docsource/sql_mock.snowflake.rst b/docsource/sql_mock.snowflake.rst deleted file mode 100644 index ba3ca51..0000000 --- a/docsource/sql_mock.snowflake.rst +++ /dev/null @@ -1,37 +0,0 @@ -sql\_mock.snowflake package -=========================== - -Submodules ----------- - -sql\_mock.snowflake.column\_mocks module ----------------------------------------- - -.. automodule:: sql_mock.snowflake.column_mocks - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.snowflake.settings module ------------------------------------ - -.. automodule:: sql_mock.snowflake.settings - :members: - :undoc-members: - :show-inheritance: - -sql\_mock.snowflake.table\_mocks module ---------------------------------------- - -.. automodule:: sql_mock.snowflake.table_mocks - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: sql_mock.snowflake - :members: - :undoc-members: - :show-inheritance: diff --git a/docsource/usage/bigquery/index.rst b/docsource/usage/bigquery/index.rst deleted file mode 100644 index 5af047d..0000000 --- a/docsource/usage/bigquery/index.rst +++ /dev/null @@ -1,10 +0,0 @@ -BigQuery -===================== - -This section documents the specifics on how to use SQL Mock with BigQuery - -.. toctree:: - :maxdepth: 4 - - ./settings.md - ./examples.md diff --git a/docsource/usage/bigquery/settings.md b/docsource/usage/bigquery/settings.md deleted file mode 100644 index 9e261ad..0000000 --- a/docsource/usage/bigquery/settings.md +++ /dev/null @@ -1,9 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Settings - -In order to use sql-mock with BigQuery, ensure you have the `GOOGLE_APPLICATION_CREDENTIALS` environment variable correctly set to point to a service account file. - -You need to service account file in order to let SQL Mock connect to BigQuery while running the tests. diff --git a/docsource/usage/clickhouse/examples.md b/docsource/usage/clickhouse/examples.md deleted file mode 100644 index d0a5c1b..0000000 --- a/docsource/usage/clickhouse/examples.md +++ /dev/null @@ -1,59 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Example: Testing Subscription Counts in ClickHouse - -```python -from sql_mock.clickhouse import column_mocks as col -from sql_mock.clickhouse.table_mocks import ClickHouseTableMock -from sql_mock.table_mocks import table_meta - -# Define mock tables for your data model that inherit from ClickHouseTableMock -@table_meta(table_ref='data.users') -class UserTable(ClickHouseTableMock): - user_id = col.Int(default=1) - user_name = col.String(default="Mr. T") - -@table_meta(table_ref='data.subscriptions') -class SubscriptionTable(ClickHouseTableMock): - subscription_id = col.Int(default=1) - period_start_date = col.Date(default=datetime.date(2023, 9, 5)) - period_end_date = col.Date(default=datetime.date(2023, 9, 5)) - user_id = col.Int(default=1) - -# Define a mock table for your expected results -class SubscriptionCountTable(ClickHouseTableMock): - subscription_count = col.Int(default=1) - user_id = col.Int(default=1) - -# Your original SQL query -query = """ -SELECT - count(*) AS subscription_count, - user_id -FROM data.users -LEFT JOIN data.subscriptions USING(user_id) -GROUP BY user_id -""" - -# Create mock data for the 'data.users' and 'data.subscriptions' tables -users = UserTable.from_dicts([{'user_id': 1}, {'user_id': 2}]) -subscriptions = SubscriptionTable.from_dicts([ - {'subscription_id': 1, 'user_id': 1}, - {'subscription_id': 2, 'user_id': 1}, - {'subscription_id': 2, 'user_id': 2}, -]) - -# Define your expected results -expected = [ - {'user_id': 1, 'subscription_count': 2}, - {'user_id': 2, 'subscription_count': 1} -] - -# Simulate the SQL query using SQL Mock -res = SubscriptionCountTable.from_mocks(query=query, input_data=[users, subscriptions]) - -# Assert the results -res.assert_equal(expected) -``` diff --git a/docsource/usage/clickhouse/index.rst b/docsource/usage/clickhouse/index.rst deleted file mode 100644 index 4bca7b1..0000000 --- a/docsource/usage/clickhouse/index.rst +++ /dev/null @@ -1,10 +0,0 @@ -Clickhouse -===================== - -This section documents the specifics on how to use SQL Mock with Clickhouse - -.. toctree:: - :maxdepth: 4 - - ./settings.md - ./examples.md diff --git a/docsource/usage/clickhouse/settings.md b/docsource/usage/clickhouse/settings.md deleted file mode 100644 index 8e9d6fb..0000000 --- a/docsource/usage/clickhouse/settings.md +++ /dev/null @@ -1,13 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Settings - -In order to use SQL Mock with Clickhouse, you need to provide the following environment variables when you run tests: -* `SQL_MOCK_CLICKHOUSE_HOST`: Host of your Clickhouse instance -* `SQL_MOCK_CLICKHOUSE_USER`: User you want to use for the connection -* `SQL_MOCK_CLICKHOUSE_PASSWORD`: Password of your user -* `SQL_MOCK_CLICKHOUSE_PORT`: Port of your Clickhouse instance - -Having those environment variables enables SQL Mock to connect to your Clickhouse instance. diff --git a/docsource/usage/examples.md b/docsource/usage/examples.md deleted file mode 100644 index 39763e6..0000000 --- a/docsource/usage/examples.md +++ /dev/null @@ -1,7 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Examples - -You can find some examples in the [examples folder](https://github.com/DeepLcom/sql-mock/tree/main/examples). diff --git a/docsource/usage/redshift/index.rst b/docsource/usage/redshift/index.rst deleted file mode 100644 index b2f7caa..0000000 --- a/docsource/usage/redshift/index.rst +++ /dev/null @@ -1,10 +0,0 @@ -Redshift -===================== - -This section documents the specifics on how to use SQL Mock with Redshift - -.. toctree:: - :maxdepth: 4 - - ./settings.md - ./examples.md diff --git a/docsource/usage/redshift/settings.md b/docsource/usage/redshift/settings.md deleted file mode 100644 index fdf1ba0..0000000 --- a/docsource/usage/redshift/settings.md +++ /dev/null @@ -1,14 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Settings - -In order to use SQL Mock with Redshift, you need to provide the following environment variables when you run tests: - -* `SQL_MOCK_REDSHIFT_HOST`: The host of your Redshift instance -* `SQL_MOCK_REDSHIFT_USER`: The user of your Redshift instance -* `SQL_MOCK_REDSHIFT_PASSWORD`: The password of your Redshift instance -* `SQL_MOCK_REDSHIFT_PORT`: The port of your Redshift instance - -Having those environment variables enables SQL Mock to connect to your Redshift instance. diff --git a/docsource/usage/snowflake/index.rst b/docsource/usage/snowflake/index.rst deleted file mode 100644 index 6c143df..0000000 --- a/docsource/usage/snowflake/index.rst +++ /dev/null @@ -1,10 +0,0 @@ -Snowflake -===================== - -This section documents the specifics on how to use SQL Mock with Snowflake - -.. toctree:: - :maxdepth: 4 - - ./settings.md - ./examples.md diff --git a/docsource/usage/snowflake/settings.md b/docsource/usage/snowflake/settings.md deleted file mode 100644 index 2c9143d..0000000 --- a/docsource/usage/snowflake/settings.md +++ /dev/null @@ -1,13 +0,0 @@ -```{toctree} -:maxdepth: 2 -``` - -# Settings 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