From af572ef7f10c6c795a0d4882b2ee032e203f779f Mon Sep 17 00:00:00 2001 From: Pedro Castro <aspeddro@gmail.com> Date: Tue, 25 Jun 2024 16:51:06 -0300 Subject: [PATCH] Initial (#1) * initial * add TableArch class and add tests * fix type hint * fix type hint * add more methods * env: fix typo * remove link_directory_metadata.py * update README.md * update * update * add get_architecture_table_from_api * add new scripts * add more tests * add verbose param to upload_columns * add authors * create_yaml_file strip name and description --- .github/workflows/ci.yaml | 41 + .gitignore | 162 ++ CONTRIBUTING.md | 51 + LICENSE | 21 + README.md | 75 + poetry.lock | 1709 +++++++++++++++++ pyproject.toml | 36 + scripts/lint.py | 11 + src/databasers_utils/__init__.py | 9 + src/databasers_utils/constants.py | 12 + .../copy_models_from_dev_to_prod.py | 60 + src/databasers_utils/create_yaml_file.py | 209 ++ .../get_architecture_table_from_api.py | 152 ++ src/databasers_utils/table_architecture.py | 104 + src/databasers_utils/upload_columns.py | 351 ++++ src/databasers_utils/utils.py | 108 ++ tests/__init__.py | 0 .../models/br_ibge_pib/br_ibge_pib__gini.sql | 11 + tests/models/br_ibge_pib/br_ibge_pib__uf.sql | 13 + tests/models/br_ibge_pib/schema.yml | 87 + tests/test_get_architecture_table_from_api.py | 8 + tests/test_table_architecture.py | 63 + 22 files changed, 3293 insertions(+) create mode 100644 .github/workflows/ci.yaml create mode 100644 .gitignore create mode 100644 CONTRIBUTING.md create mode 100644 LICENSE create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 scripts/lint.py create mode 100644 src/databasers_utils/__init__.py create mode 100644 src/databasers_utils/constants.py create mode 100644 src/databasers_utils/copy_models_from_dev_to_prod.py create mode 100644 src/databasers_utils/create_yaml_file.py create mode 100644 src/databasers_utils/get_architecture_table_from_api.py create mode 100644 src/databasers_utils/table_architecture.py create mode 100644 src/databasers_utils/upload_columns.py create mode 100644 src/databasers_utils/utils.py create mode 100644 tests/__init__.py create mode 100644 tests/models/br_ibge_pib/br_ibge_pib__gini.sql create mode 100644 tests/models/br_ibge_pib/br_ibge_pib__uf.sql create mode 100644 tests/models/br_ibge_pib/schema.yml create mode 100644 tests/test_get_architecture_table_from_api.py create mode 100644 tests/test_table_architecture.py diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml new file mode 100644 index 0000000..6be485a --- /dev/null +++ b/.github/workflows/ci.yaml @@ -0,0 +1,41 @@ +--- +name: CI + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + test: + name: main + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup poetry + run: pipx install poetry + + - name: Setup python + uses: actions/setup-python@v4 + with: + cache: poetry + architecture: x64 + python-version: '3.9' + + - name: Poetry check + run: poetry check + + - name: Install requirements + run: poetry install + + - name: Test + run: poetry run pytest + + - name: Ruff check + run: poetry run ruff check . + + - name: Ruff format check + run: poetry run ruff format --check --diff diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..82f9275 --- /dev/null +++ b/.gitignore @@ -0,0 +1,162 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..545a2f7 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,51 @@ +# Contribuindo + +## Setup + +Requisitos: + +- [pyenv](https://github.com/pyenv/pyenv): Recomendado +- [poetry](https://python-poetry.org/) + +1. Clone o repositório e entre no diretório clonado + +```sh +git clone https://github.com/basedosdados/databasers-utils +``` + +1. Ative a versão 3.9 do Python + +```sh +pyenv shell 3.9 +``` + +2. Crie a env ou ativa se já existir + +```sh +poetry shell +``` + +3. Instale as dependências + +```sh +poetry install --with dev --no-root +``` + +## Code Style + +Após finalizar as alterações no código execute: + +```sh +poetry run lint +``` + +> [!IMPORTANT] +> O command vai executar o formatador `ruff format .` e o linter `ruff check --fix .`. Algumas regras do linter requer intervenção manual para ser resolvido + +## Testes + +Para rodar os testes: + +```sh +poetry run pytest +``` diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..31124c7 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Base dos Dados + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index 9371f11..62ffb33 100644 --- a/README.md +++ b/README.md @@ -1 +1,76 @@ # databasers-utils + +Pacote para uso da equipe de dados na Base dos Dados. + +- [CONTRIBUTING.md](./CONTRIBUTING.md) + +## Instalação + +```sh +poetry add git+https://github.com/basedosdados/databasers-utils.git +``` + +## Setup + +### Credenciais + +Para fazer o upload de colunas usando a arquitetura você deve configurar suas +credenciais. + +#### Linux/WSL + +Adicione no seu `.bashrc` ou `.zshrc` + +```sh +export BD_DJANGO_EMAIL="seuemail@basedosdados.org" +export BD_DJANGO_PASSWORD="password" +``` + +#### Windows + +- Em Pesquisar, procure e selecione: Sistema (Painel de Controle) +- Clique no link Configurações avançadas do sistema. +- Clique em Variáveis de Ambiente. +- Na seção 'Variáveis do Sistema' clique em 'Novo' e insira `BD_DJANGO_EMAIL` como nome da variável e o seu email como valor. Faça o mesmo para `BD_DJANGO_PASSWORD`. +- Clique em OK. Feche todas as janelas restantes clicando em OK. + +### Uso + +```python +from databasers_utils import ( + TableArchitecture, + copy_models_from_dev_to_prod, + get_architecture_table_from_api, +) + +arch = TableArchitecture( + dataset_id="br_ibge_pib", + tables={ + "uf": "https://docs.google.com/spreadsheets/d/12F5NzhOYlN_bi9flLBEdXDWpa5iVakSP4EKm9UoyWuo/edit?usp=drive_link", + "gini": "https://docs.google.com/spreadsheets/d/1K1svie4Gyqe6NnRjBgJbapU5sTsLqXWTQUmTRVIRwQc/edit?usp=drive_link", + }, +) + +# Retorna um dict[str, pd.DataFrame], cada chave o id da tabela e o dataframe +# da arquitetura +arch.tables() + +# Cria o yaml file +arch.create_yaml_file() + +# Cria os arquivos sql +arch.create_sql_files() + +# Atualiza o dbt_project.yml +arch.update_dbt_project() + +# Faz o upload das colunas para o DJango +arch.upload_columns() + +# Copia os modelos em dev para prod +copy_models_from_dev_to_prod(["br_ibge_ppm", "br_ibge_pam"]) + +# Retorna um DataFrame da arquitetura obtida na API +# Util para gerar arquitetura quando ela não está no Drive +get_architecture_table_from_api("br_ms_sinasc", "microdados") +``` diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..c12d15b --- /dev/null +++ b/poetry.lock @@ -0,0 +1,1709 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "anyio" +version = "4.4.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, + {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, +] + +[package.dependencies] +exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} +idna = ">=2.8" +sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", markers = 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= "2.0.0b15", extras = ["all"]} +pandas = "^2.2.2" +numpy = "^1.26.4" +requests = "^2.32.3" +ruamel-yaml = "^0.18.6" + + +[tool.poetry.group.dev.dependencies] +pytest = "^8.2.2" +ruff = "^0.4.8" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.ruff] +line-length = 80 diff --git a/scripts/lint.py b/scripts/lint.py new file mode 100644 index 0000000..f0d592f --- /dev/null +++ b/scripts/lint.py @@ -0,0 +1,11 @@ +from subprocess import run + + +def main(): + """Lint all python files in the project""" + ruff_check = run(["ruff", "check", "--fix", "."]) + ruff_format = run(["ruff", "format", "."]) + exit_code = ( + 0 if ruff_check.returncode == 0 and ruff_format.returncode == 0 else 1 + ) + exit(exit_code) diff --git a/src/databasers_utils/__init__.py b/src/databasers_utils/__init__.py new file mode 100644 index 0000000..15a2d62 --- /dev/null +++ b/src/databasers_utils/__init__.py @@ -0,0 +1,9 @@ +from .copy_models_from_dev_to_prod import copy_models_from_dev_to_prod +from .get_architecture_table_from_api import get_architecture_table_from_api +from .table_architecture import TableArchitecture + +__all__ = [ + "copy_models_from_dev_to_prod", + "get_architecture_table_from_api", + "TableArchitecture", +] diff --git a/src/databasers_utils/constants.py b/src/databasers_utils/constants.py new file mode 100644 index 0000000..a40a43d --- /dev/null +++ b/src/databasers_utils/constants.py @@ -0,0 +1,12 @@ +from enum import Enum + + +class constants(Enum): + API_URL = { + "staging": "https://staging.backend.basedosdados.org/api/v1/graphql", + "prod": "https://backend.basedosdados.org/api/v1/graphql", + } + ENV = { + "email": "BD_DJANGO_EMAIL", + "password": "BD_DJANGO_PASSWORD", + } diff --git a/src/databasers_utils/copy_models_from_dev_to_prod.py b/src/databasers_utils/copy_models_from_dev_to_prod.py new file mode 100644 index 0000000..d109023 --- /dev/null +++ b/src/databasers_utils/copy_models_from_dev_to_prod.py @@ -0,0 +1,60 @@ +import os +from typing import Optional +from distutils.dir_util import copy_tree +from .utils import update_dbt_project + + +def change_origin_from_dev_to_staging( + file: str, prod_models_dataset_dir: str +) -> None: + sql_file = f"{prod_models_dataset_dir}/{file}" + + with open(sql_file, "r") as io: + sql_content = io.read() + + new_sql_content = sql_content.replace( + "basedosdados-dev.", "basedosdados-staging." + ) + + with open(sql_file, "w") as io: + io.write(new_sql_content) + + +def search_model_directory_recursive(directory: str) -> Optional[str]: + # Check if 'model' is in the current directory + ls_dir = os.listdir(directory) + if "models" in ls_dir: + return os.path.join(directory, "models") + + if "queries-basedosdados-dev" in ls_dir: + return os.path.join(directory, "queries-basedosdados-dev", "models") + + # Get the parent directory + parent_directory = os.path.dirname(directory) + + # If we've reached the root directory without finding 'model', return None + if directory == parent_directory: + return None + + # Otherwise, continue searching recursively in parent directories + return search_model_directory_recursive(parent_directory) + + +def copy_models_from_dev_to_prod(datasets: list[str]) -> None: + dev_models_path = search_model_directory_recursive(os.getcwd()) + + if dev_models_path is None: + raise Exception("Failed to find model directory") + + prod_models_dir = dev_models_path.replace( + "queries-basedosdados-dev", "queries-basedosdados" + ) + for dataset_id in datasets: + prod_models_dataset_dir = f"{prod_models_dir}/{dataset_id}" + copy_tree(f"{dev_models_path}/{dataset_id}", prod_models_dataset_dir) + update_dbt_project(dataset_id, prod_models_dir) + [ + change_origin_from_dev_to_staging(file, prod_models_dataset_dir) + for file in os.listdir(prod_models_dataset_dir) + if file.endswith(".sql") + ] diff --git a/src/databasers_utils/create_yaml_file.py b/src/databasers_utils/create_yaml_file.py new file mode 100644 index 0000000..90507cd --- /dev/null +++ b/src/databasers_utils/create_yaml_file.py @@ -0,0 +1,209 @@ +import os +import re +import pandas as pd +import ruamel.yaml as yaml +from typing import Optional, Union +from .utils import get_model_directory, read_architecture_table + + +def extract_column_parts(input_string: str) -> str: + pattern_1 = re.compile(r"(\w+)\.(\w+):(\w+)") + pattern_2 = re.compile(r"\w+\.(\w+)\.(\w+):(\w+)") + + if pattern_1.match(input_string): + return pattern_1.findall(input_string)[0] + elif pattern_2.match(input_string): + return pattern_2.findall(input_string)[0] + else: + raise ValueError( + f"Invalid input format on `{input_string}`. Expected format: 'dataset.table:column'" + ) + + +def extract_relationship_info(input_string: str) -> Optional[tuple[str, str]]: + try: + dataset, table, column = extract_column_parts(input_string) + + if column == table: + column = f"{column}.{column}" + + field = column + + table_path = f"ref('{dataset}__{table}')" + + return table_path, field + + except Exception as e: + print(f"An unexpected error occurred: {e}") + return None + + +def create_relationships(directory_column: str) -> list[dict[str, str]]: + relationship_table, relationship_field = extract_relationship_info( + directory_column + ) # type: ignore + yaml_relationship = yaml.comments.CommentedMap() + yaml_relationship["relationships"] = { + "to": relationship_table, + "field": relationship_field, + } + return [yaml_relationship] + + +def create_unique_combination(unique_keys: list[str]) -> list: + combination = yaml.comments.CommentedMap() + combination["dbt_utils.unique_combination_of_columns"] = { + "combination_of_columns": unique_keys + } + return [combination] + + +def create_not_null_proportion(at_least: float) -> list: + not_null = yaml.comments.CommentedMap() + not_null["not_null_proportion_multiple_columns"] = { + "at_least": at_least, + } + return [not_null] + + +def create_dict_coverage( + dataset_id: str, list_covered_by_dict_columns: list[str] +) -> list: + dict_coverage = yaml.comments.CommentedMap() + dict_coverage["custom_dictionaries"] = { + "columns_covered_by_dictionary": list_covered_by_dict_columns, + "dictionary_model": f"ref('{dataset_id}__dicionario')", + } + return [dict_coverage] + + +def create_unique() -> list[str]: + return ["unique", "not_null"] + + +def create_yaml_file( + arch_url: Union[str, list[str]], + table_id: Union[str, list[str]], + dataset_id: str, + table_description: str = "Insert table description here", + at_least: float = 0.95, + unique_keys: list[str] = ["insert unique keys here"], + mkdir: bool = True, + dir: str = os.getcwd(), +) -> str: + """ + Creates dbt models and schema.yaml files based on the architecture table, including data quality tests automatically. + + Args: + arch_url (str or list): The URL(s) or file path(s) of the input file(s) containing the data. + table_id (str or list): The table ID(s) or name(s) to use as the YAML model name(s). + dataset_id (str): The ID or name of the dataset to be used in the dbt models. + at_least (float): The proportion of non-null values accepted in the columns. + unique_keys (list, optional): A list of column names for which the 'dbt_utils.unique_combination_of_columns' test should be applied. + Defaults to ["insert unique keys here"]. + mkdir (bool, optional): If True, creates a directory for the new model(s). Defaults to True. + preprocessed_staging_column_names (bool, optional): If True, builds SQL file renaming from 'original_name' to 'name' using the architecture file. Defaults to True. + + Raises: + TypeError: If the table_id is not a string or a list. + ValueError: If the number of URLs or file paths does not match the number of table IDs. + + Notes: + The function generates dbt models in YAML format based on the input data and saves them to the specified output file. + The generated YAML file includes information about the dataset, model names, descriptions, and column details. + + Example: + ```python + create_yaml_file(arch_url='input_data.csv', table_id='example_table', dataset_id='example_dataset') + ``` + + """ + if mkdir: + models_path = get_model_directory(dir) + output_path = f"{models_path}/{dataset_id}" + os.makedirs(output_path, exist_ok=True) + else: + print( + f"Directory for the new model has not been created, saving files in {os.getcwd()}" + ) + output_path = os.getcwd() + + schema_path = f"{output_path}/schema.yml" + + yaml_obj = yaml.YAML(typ="rt") + yaml_obj.indent(mapping=4, sequence=4, offset=2) + + if os.path.exists(schema_path): + with open(schema_path, "r") as file: + data = yaml_obj.load(file) + else: + data = yaml.comments.CommentedMap() + data["version"] = 2 + data.yaml_set_comment_before_after_key("models", before="\n\n") + data["models"] = [] + + exclude = ["(excluded)", "(erased)", "(deleted)", "(excluido)"] + + if isinstance(table_id, str): + table_id = [table_id] + arch_url = [arch_url] # type: ignore + + # If table_id is a list, assume multiple input files + if not isinstance(arch_url, list) or len(arch_url) != len(table_id): + raise ValueError( + "The number of URLs or file paths must match the number of table IDs." + ) + + for url, id in zip(arch_url, table_id): + unique_keys_copy = unique_keys.copy() + architecture_df = read_architecture_table(url) + architecture_df.dropna(subset=["bigquery_type"], inplace=True) + architecture_df: pd.DataFrame = architecture_df[ # type: ignore + ~architecture_df["bigquery_type"].apply( + lambda x: any(word in x.lower() for word in exclude) + ) + ] + + table = yaml.comments.CommentedMap() + table["name"] = f"{dataset_id}__{id}" + + # If model is already in the schema.yaml, delete old model from schema and create a new one + for model in data["models"]: + if id == model["name"] or table["name"] == model["name"]: + data["models"].remove(model) + break + + table["description"] = table_description + table["tests"] = create_unique_combination(unique_keys_copy) + table["tests"] += create_not_null_proportion(at_least) + + covered_by_dict_columns = ( + architecture_df["covered_by_dictionary"] == "yes" + ) + if covered_by_dict_columns.sum(): + list_covered_by_dict_columns = architecture_df[ + covered_by_dict_columns + ]["name"].tolist() + table["tests"] += create_dict_coverage( + dataset_id, list_covered_by_dict_columns + ) + + table["columns"] = [] + + for _, row in architecture_df.iterrows(): + column = yaml.comments.CommentedMap() + column["name"] = row["name"].strip() + column["description"] = row["description"].strip() + directory_column = row["directory_column"].strip() + if len(directory_column) != 0: + tests = [] + tests = create_relationships(directory_column) + column["tests"] = tests + table["columns"].append(column) + + data["models"].append(table) + + with open(schema_path, "w") as file: + yaml_obj.dump(data, file) + + return schema_path diff --git a/src/databasers_utils/get_architecture_table_from_api.py b/src/databasers_utils/get_architecture_table_from_api.py new file mode 100644 index 0000000..0c2fe03 --- /dev/null +++ b/src/databasers_utils/get_architecture_table_from_api.py @@ -0,0 +1,152 @@ +import pandas as pd +from basedosdados import backend as b +from typing import Optional +from .constants import constants + + +def get_columns_info_from_api( + table_name: str, dataset_name: str, url_api: str +) -> pd.DataFrame: + backend = b.Backend(graphql_url=url_api) + + id = backend._get_table_id_from_name( + gcp_dataset_id=dataset_name, gcp_table_id=table_name + ) + + variables = {"table_Id": id} + + query = """query($table_Id: ID){ + allColumn(table_Id: $table_Id){ + edges{ + node{ + name + order + bigqueryType{ + name + } + description + coverages{ + edges{ + node{ + datetimeRanges{ + edges{ + node{ + startYear + endYear + } + } + } + } + } + } + coveredByDictionary + directoryPrimaryKey{ + cloudTables{ + edges{ + node{ + gcpDatasetId + gcpTableId + } + } + } + name + } + measurementUnit + containsSensitiveData + observations + } + } + } + }""" + + query_data = backend._execute_query(query=query, variables=variables) # type: ignore + + query_data = backend._simplify_graphql_response(query_data)["allColumn"] + + df = pd.json_normalize(query_data, max_level=3) + df.index = df.order + df.sort_index(inplace=True) + return df + + +def format_temporal_coverage_column(df: pd.DataFrame) -> pd.DataFrame: + try: + start_year = ( + df["coverages"] + .map(lambda x: x[0]["datetimeRanges"][0]["startYear"]) + .astype("Int64") + ) + end_year = ( + df["coverages"] + .map(lambda x: x[0]["datetimeRanges"][0]["endYear"]) + .astype("Int64") + ) + df["temporal_coverage"] = ( + start_year.astype(str) + "(1)" + end_year.astype(str) + ) + df["temporal_coverage"] = df["temporal_coverage"].str.replace( + "<NA>", "" + ) + except Exception: + df["temporal_coverage"] = "(1)" + + return df["temporal_coverage"] # type: ignore + + +def boolean_to_yes_no() -> dict[bool, str]: + return {True: "yes", False: "no"} + + +def get_directory_column(row) -> Optional[str]: + if "directoryPrimaryKey.cloudTables" in row.index: + if isinstance(row["directoryPrimaryKey.cloudTables"], list): + directory_dataset = row["directoryPrimaryKey.cloudTables"][0][ + "gcpDatasetId" + ] + directory_table = row["directoryPrimaryKey.cloudTables"][0][ + "gcpTableId" + ] + directory_column_name = row["directoryPrimaryKey.name"] + return ( + directory_dataset + + "." + + directory_table + + ":" + + directory_column_name + ) + return None + + +def get_architecture_table_from_api( + dataset_id: str, + table_id: str, + url_api: str = constants.API_URL.value["prod"], +) -> pd.DataFrame: + df = get_columns_info_from_api(table_id, dataset_id, url_api) + + df["bigquery_type"] = df["bigqueryType.name"].str.lower() + df["temporal_coverage"] = format_temporal_coverage_column(df) + df["covered_by_dictionary"] = df["coveredByDictionary"].map( + boolean_to_yes_no() # type: ignore + ) + df["has_sensitive_data"] = df["containsSensitiveData"].map( + boolean_to_yes_no() # type: ignore + ) + df["directory_column"] = df.apply( + lambda row: get_directory_column(row), axis=1 + ) + df["measurement_unit"] = df["measurementUnit"] + + columns = [ + "name", + "bigquery_type", + "description", + "temporal_coverage", + "covered_by_dictionary", + "directory_column", + "measurement_unit", + "has_sensitive_data", + "observations", + ] + + return df[columns] # type: ignore diff --git a/src/databasers_utils/table_architecture.py b/src/databasers_utils/table_architecture.py new file mode 100644 index 0000000..3cc6764 --- /dev/null +++ b/src/databasers_utils/table_architecture.py @@ -0,0 +1,104 @@ +import pandas as pd +import os +from basedosdados import Backend +from .utils import ( + read_architecture_table, + get_model_directory, + update_dbt_project, +) +from .create_yaml_file import create_yaml_file +from .upload_columns import upload_columns_from_architecture +from .constants import constants + + +class TableArchitecture: + def __init__(self, dataset_id: str, tables: dict[str, str]): + self.dataset_id = dataset_id + self.__tables = tables + + def __str__(self) -> str: + return f"TableArchitecture({self.dataset_id}, {str(self.__tables)})" + + def tables(self) -> dict[str, pd.DataFrame]: + return {k: read_architecture_table(v) for k, v in self.__tables.items()} + + def create_yaml_file( + self, + table_description: str = "Insert table description here", + at_least: float = 0.95, + unique_keys: list[str] = ["insert unique keys here"], + mkdir: bool = True, + dir: str = os.getcwd(), + ) -> None: + path = create_yaml_file( + arch_url=list(self.__tables.values()), + table_id=list(self.__tables.keys()), + dataset_id=self.dataset_id, + table_description=table_description, + at_least=at_least, + unique_keys=unique_keys, + mkdir=mkdir, + dir=dir, + ) + print(f"Files successfully created for {self.dataset_id} at {path}") + return None + + def create_sql_files( + self, + preprocessed_staging_column_names: bool = True, + dir: str = os.getcwd(), + ) -> None: + models_dir = get_model_directory(dir) + + output_path = f"{models_dir}/{self.dataset_id}" + + for table_id, url in self.__tables.items(): + architecture_df = read_architecture_table(url) + + if preprocessed_staging_column_names: + architecture_df["original_name"] = architecture_df["name"] + + header = f'{{{{ config(alias="{table_id}", schema="{self.dataset_id}") }}}}' + + with open( + f"{output_path}/{self.dataset_id}__{table_id}.sql", "w" + ) as file: + file.write(header + "\n") + file.write("select\n") + + for _, column in architecture_df.iterrows(): + original_name = column["original_name"] + column_name = column["name"] + bq_type = column["bigquery_type"].lower() # type: ignore + + sql_line = f" safe_cast({original_name} as {bq_type}) {column_name},\n" + file.write(sql_line) + + sql_last_line = f"from `basedosdados-dev.{self.dataset_id}_staging.{table_id}` as t\n" + file.write(sql_last_line) + + print("SQL files created!") + return None + + def update_dbt_project(self) -> None: + return update_dbt_project(self.dataset_id, dir=os.getcwd()) + + def upload_columns( + self, + if_column_exists: str = "pass", + replace_all_schema: bool = True, + verbose: bool = False, + ) -> None: + backend = Backend(graphql_url=constants.API_URL.value["prod"]) + for table_id, url in self.__tables.items(): + upload_columns_from_architecture( + dataset_id=self.dataset_id, + table_id=table_id, + url_architecture=url, + backend=backend, + if_column_exists=if_column_exists, + replace_all_schema=replace_all_schema, + verbose=verbose, + ) + + return None diff --git a/src/databasers_utils/upload_columns.py b/src/databasers_utils/upload_columns.py new file mode 100644 index 0000000..98e7e20 --- /dev/null +++ b/src/databasers_utils/upload_columns.py @@ -0,0 +1,351 @@ +import json +import pandas as pd +from basedosdados import backend as b +from .utils import get_headers, read_architecture_table +from typing import Optional + + +def get_directory_column_id( + directory_column_name: str, + directory_table_name: str, + backend: b.Backend, + verbose: bool = False, +) -> str: + """ + Get the directory id from that column + """ + query = """ query($column_name: String) + {allColumn(name: $column_name) { + edges { + node { + name + _id + table { + dataset { + fullSlug + } + slug + } + } + } + } + } + """ + + variables = {"column_name": directory_column_name} + response = backend._execute_query(query=query, variables=variables) + response = backend._simplify_graphql_response(response)["allColumn"] + df = pd.json_normalize(response) + + colunas_de_diretorio = df["table.dataset.fullSlug"].str.contains( + "diretorios" + ) + for _, coluna in df[colunas_de_diretorio].iterrows(): + if coluna["table.slug"] == directory_table_name: + if verbose: + print( + f"Connecting to the directory column: {coluna['table.dataset.fullSlug']}.{coluna['table.slug']}:{coluna['name']}" + ) + + return coluna["_id"] # type: ignore + + raise ( + ValueError( + f"WARNING - Unable to find the directory column with the following information: Column name: {directory_column_name}. Table: {directory_table_name}" + ) + ) + + +def create_column( + backend: b.Backend, + mutation_parameters: Optional[dict[str, str]] = None, + verbose: bool = False, +) -> bool: + ## tinha que ser create or replace, por enquanto ele duplica + ## os dados se rodar duas vezes por isso atenção na hora de rodar! + + # GraphQL mutation to create or update a column + mutation = """ + mutation($input: CreateUpdateColumnInput!) { + CreateUpdateColumn(input: $input) { + errors { + field, + messages + }, + clientMutationId, + column { + id, + } + } + } + """ + + # Set headers for the GraphQL request, including the token for authentication + headers = get_headers(backend) + + # Print the mutation parameters for debugging purposes + pretty_json = json.dumps(mutation_parameters, indent=4) + + if verbose: + print(pretty_json) + + # Execute the GraphQL query with the provided mutation parameters and headers + response = backend._execute_query( + query=mutation, + variables={"input": mutation_parameters}, # type: ignore + headers=headers, + ) + + # Print the response for debugging purposes + if response["CreateUpdateColumn"]["errors"] != []: + pretty_json = json.dumps(response, indent=4) + + if verbose: + print(pretty_json) + + return False + + return True + + +def get_column_id( + table_id: str, column_name: str, backend: b.Backend +) -> Optional[str]: + query = f"""{{ + allColumn(table_Id:"{table_id}", name:"{column_name}"){{ + edges{{ + node{{ + _id + }} + }} + }} + }}""" + + data = backend._execute_query(query=query) + data = backend._simplify_graphql_response(response=data)["allColumn"] + if data: + return data[0]["_id"] + else: + return None + + +def get_n_columns(table_id, backend: b.Backend): + query = f"""query get_n_columns{{ + allTable(id:"{table_id}"){{ + edges{{ + node{{ + columns{{ + edgeCount + }} + }} + }} + }} + }}""" + + data = backend._execute_query(query=query) + data = backend._simplify_graphql_response(response=data)["allTable"] + + return data[0]["columns"]["edgeCount"] + + +def get_bqtype_dict(backend: b.Backend): + # GraphQL query to fetch all BigQuery types + query = """{ + allBigquerytype{ + edges{ + node{ + name + _id + } + } + } + }""" + + # Execute the GraphQL query to retrieve the data + data = backend._execute_query(query=query) + + # Simplify the GraphQL response to extract the relevant data + data = backend._simplify_graphql_response(response=data)["allBigquerytype"] + + # Create a dictionary where the 'name' part is the key and the '_id' is the value + bqtype_dict = {item["name"]: item["_id"] for item in data} + + # Return the resulting dictionary + return bqtype_dict + + +def check_metadata_columns( + dataset_id: str, + table_slug: str, + backend: b.Backend, + architecture: pd.DataFrame, +) -> None: + # Get the table ID using the dataset ID and table ID + table_id = backend._get_table_id_from_name( + gcp_dataset_id=dataset_id, gcp_table_id=table_slug + ) + + n_columns_metadata = get_n_columns(table_id=table_id, backend=backend) + n_columns_architecture = architecture.shape[0] + + if n_columns_metadata == n_columns_architecture: + print("Upload done!!. Columns metadata equal to columns architecture!!") + else: + print( + f"Something wrong!!. Number of metadata columns not equal to architecture, {n_columns_metadata} != {n_columns_architecture}" + ) + + +def get_all_columns_id(table_id: str, backend: b.Backend): + query = f"""{{ + allColumn(table_Id:"{table_id}"){{ + edges{{ + node{{ + _id + }} + }} + }} + }}""" + + data = backend._execute_query(query=query) + columns_json = backend._simplify_graphql_response(response=data)[ + "allColumn" + ] + + if data: + columns_list = [col["_id"] for col in columns_json] + return columns_list + else: + print("There is no column in this table to be deleted") + + +def delete_column_by_id(column_id: str, backend: b.Backend): + mutation = """ + mutation($input: UUID!) { + DeleteColumn(id: $input) { + errors, + ok + } + } + """ + + # Set headers for the GraphQL request, including the token for authentication + headers = get_headers(backend) + # Execute the GraphQL query with the provided mutation parameters and headers + response = backend._execute_query( + query=mutation, variables={"input": column_id}, headers=headers + ) + + # Print the response for debugging purposes + if response["DeleteColumn"]["errors"] != []: + pretty_json = json.dumps(response, indent=4) + print(pretty_json) + return False + + return True + + +def delete_all_columns(table_id: str, backend: b.Backend): + columns = get_all_columns_id(table_id, backend) + + for col in columns: + delete_column_by_id(col, backend) + + +def upload_columns_from_architecture( + dataset_id: str, + table_id: str, + url_architecture: str, + backend: b.Backend, + if_column_exists: str = "pass", + replace_all_schema: bool = True, + verbose: bool = False, +): + """ + Uploads columns from an architecture table to the specified dataset and table in platform. + + Notes: + - This function assumes a specific structure/format for the architecture table. + - It interacts with the Base dos Dados GraphQL API to create or update columns. + - Columns from the architecture table are uploaded to the specified dataset and table. + - It prints information about the existing columns + and performs metadata checks after uploading columns. + """ + accepted_if_exists_values = ["pass", "replace"] + + if if_column_exists not in accepted_if_exists_values: + raise ValueError( + f"`if_exists` only accepts {accepted_if_exists_values}" + ) + + # Get the table ID using the dataset ID and table ID + table_slug = backend._get_table_id_from_name( + gcp_dataset_id=dataset_id, gcp_table_id=table_id + ) + + # Read the architecture table + architecture = read_architecture_table(url=url_architecture) + + # Get the id of BigQueryTypes in a dict + bqtype_dict = get_bqtype_dict(backend) + + if replace_all_schema: + delete_all_columns(table_slug, backend) + + # Iterate over each row in the 'architecture' DataFrame + for _, row in architecture.iterrows(): + column_name = row["name"] + + if verbose: + print(f"\nColumn: {column_name}") + + column_id = get_column_id( + table_id=table_slug, + column_name=column_name, + backend=backend, + ) + + if column_id is None and verbose: + print(f"{column_name} dont exists") + + if column_id is not None and if_column_exists == "pass": + if verbose: + print("row already exists") + continue + + # Define the mutation parameters for creating a new column + directory_column_id = None + if row["directory_column"]: + directory_table_slug = ( + row["directory_column"].split(":")[0].split(".")[1] + ) + directory_column_name = row["directory_column"].split(":")[1] + directory_column_id = get_directory_column_id( + directory_column_name, directory_table_slug, backend, verbose + ) + + mutation_parameters = { + "table": table_slug, + "bigqueryType": bqtype_dict[row["bigquery_type"].upper()], + "name": row["name"], + "description": row["description"], + "coveredByDictionary": row["covered_by_dictionary"] == "yes", + "measurementUnit": row["measurement_unit"], + "containsSensitiveData": row["has_sensitive_data"] == "yes", + "observations": row["observations"], + "directoryPrimaryKey": directory_column_id, + } + + if column_id is not None: + mutation_parameters["id"] = column_id + + create_column( + backend, mutation_parameters=mutation_parameters, verbose=verbose + ) + + check_metadata_columns( + dataset_id=dataset_id, + table_slug=table_id, + backend=backend, + architecture=architecture, + ) diff --git a/src/databasers_utils/utils.py b/src/databasers_utils/utils.py new file mode 100644 index 0000000..60776fe --- /dev/null +++ b/src/databasers_utils/utils.py @@ -0,0 +1,108 @@ +import io +import os +import pandas as pd +import numpy as np +import requests +from basedosdados import Backend +from urllib.parse import urlparse, urlunparse, urlencode +from .constants import constants +import ruamel.yaml as yaml + + +def read_architecture_table(url: str) -> pd.DataFrame: + parsed_url = urlparse(url) + + # Path Format: /spreadsheets/d/<ID>/edit + # Drop /edit and add "/export" + new_path = "/".join(parsed_url.path.split("/")[0:-1]) + "/export" + + new_query = urlencode({"format": "csv"}, doseq=True) + new_url = parsed_url._replace(path=new_path, query=new_query) + + export_url = urlunparse(new_url) + + df_architecture = pd.read_csv( + io.StringIO( + requests.get(export_url, timeout=10).content.decode("utf-8") + ) + ) + + df_architecture = df_architecture.loc[ + df_architecture["name"] != "(excluido)" + ] + + return df_architecture.replace(np.nan, "", regex=True) + + +def get_credentials_from_env() -> dict[str, str]: + return { + "email": os.getenv(constants.ENV.value["email"]).strip(), # type: ignore + "password": os.getenv(constants.ENV.value["password"]).strip(), # type: ignore + } + + +def get_headers(backend: Backend) -> dict[str, str]: + """ + Get headers to be able to do mutations in backend api + """ + + credentials = get_credentials_from_env() + + mutation = """ + mutation ($email: String!, $password: String!) { + tokenAuth(email: $email, password: $password) { + token + } + } + """ + + variables = { + "email": credentials["email"], + "password": credentials["password"], + } + + response = backend._execute_query(query=mutation, variables=variables) + token = response["tokenAuth"]["token"] + + header_for_mutation_query = {"Authorization": f"Bearer {token}"} + + return header_for_mutation_query + + +def get_model_directory(dir: str) -> str: + model_dir = f"{dir}/models" + if os.path.exists(model_dir): + return model_dir + else: + raise Exception(f"Failed to find models directory under {dir}") + + +def update_dbt_project(dataset_id: str, dir: str) -> None: + if ["dbt_project.yml", "dbt_project.yaml"] not in os.listdir(dir): + raise Exception("Failed to find root directory with dbt_project file") + + dbt_project_yaml = f"{dir}/dbt_project.yml" + + if not os.path.exists(dbt_project_yaml): + dbt_project_yaml = f"{dir}/dbt_project.yaml" + + yaml_obj = yaml.YAML(typ="rt") + yaml_obj.explicit_start = True + yaml_obj.indent(mapping=2, sequence=2, offset=2) + + with open(dbt_project_yaml, "r") as file: + data = yaml_obj.load(file) + + models = data["models"]["basedosdados"] + models.update( + {dataset_id: {"+materialized": "table", "+schema": dataset_id}} + ) + + data["models"]["basedosdados"] = { + key: models[key] for key in sorted(models) + } + + with open(dbt_project_yaml, "w") as file: + yaml_obj.dump(data, file) + + print(f"dbt_project successfully updated with {dataset_id}!") diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/models/br_ibge_pib/br_ibge_pib__gini.sql b/tests/models/br_ibge_pib/br_ibge_pib__gini.sql new file mode 100644 index 0000000..4518a0a --- /dev/null +++ b/tests/models/br_ibge_pib/br_ibge_pib__gini.sql @@ -0,0 +1,11 @@ +{{ config(alias="gini", schema="br_ibge_pib") }} +select + safe_cast(id_uf as string) id_uf, + safe_cast(ano as int64) ano, + safe_cast(gini_pib as int64) gini_pib, + safe_cast(gini_va as int64) gini_va, + safe_cast(gini_va_agro as int64) gini_va_agro, + safe_cast(gini_va_industria as int64) gini_va_industria, + safe_cast(gini_va_servicos as int64) gini_va_servicos, + safe_cast(gini_va_adespss as int64) gini_va_adespss, +from `basedosdados-dev.br_ibge_pib_staging.gini` as t diff --git a/tests/models/br_ibge_pib/br_ibge_pib__uf.sql b/tests/models/br_ibge_pib/br_ibge_pib__uf.sql new file mode 100644 index 0000000..d89b5c6 --- /dev/null +++ b/tests/models/br_ibge_pib/br_ibge_pib__uf.sql @@ -0,0 +1,13 @@ +{{ config(alias="uf", schema="br_ibge_pib") }} +select + safe_cast(ano as int64) ano, + safe_cast(id_uf as string) id_uf, + safe_cast(sigla_uf as string) sigla_uf, + safe_cast(pib as int64) pib, + safe_cast(impostos_liquidos as int64) impostos_liquidos, + safe_cast(va as int64) va, + safe_cast(va_agropecuaria as int64) va_agropecuaria, + safe_cast(va_industria as int64) va_industria, + safe_cast(va_servicos as int64) va_servicos, + safe_cast(va_adespss as int64) va_adespss, +from `basedosdados-dev.br_ibge_pib_staging.uf` as t diff --git a/tests/models/br_ibge_pib/schema.yml b/tests/models/br_ibge_pib/schema.yml new file mode 100644 index 0000000..3a056b1 --- /dev/null +++ b/tests/models/br_ibge_pib/schema.yml @@ -0,0 +1,87 @@ +version: 2 + +models: + - name: br_ibge_pib__uf + description: Insert table description here + tests: + - dbt_utils.unique_combination_of_columns: + combination_of_columns: + - insert unique keys here + - not_null_proportion_multiple_columns: + at_least: 0.95 + columns: + - name: ano + description: Ano + tests: + - relationships: + to: ref('br_bd_diretorios_data_tempo__ano') + field: ano.ano + - name: id_uf + description: ID UF - IBGE 2 Dígitos + tests: + - relationships: + to: ref('br_bd_diretorios_brasil__uf') + field: id_uf + - name: sigla_uf + description: Sigla da Unidade da Federação + tests: + - relationships: + to: ref('br_bd_diretorios_brasil__uf') + field: sigla + - name: pib + description: Produto Interno Bruto a preços correntes + - name: impostos_liquidos + description: Impostos, líquidos de subsídios, sobre produtos a preços correntes + - name: va + description: Valor adicionado bruto a preços correntes total + - name: va_agropecuaria + description: Valor adicionado bruto a preços correntes da agropecuária + - name: va_industria + description: Valor adicionado bruto a preços correntes da indústria + - name: va_servicos + description: Valor adicionado bruto a preços correntes dos serviços, exclusive + administração, defesa, educação e saúde públicas e seguridade social + - name: va_adespss + description: Valor adicionado bruto a preços correntes da administração, defesa, + educação e saúde públicas e seguridade social + - name: br_ibge_pib__gini + description: Insert table description here + tests: + - dbt_utils.unique_combination_of_columns: + combination_of_columns: + - insert unique keys here + - not_null_proportion_multiple_columns: + at_least: 0.95 + columns: + - name: id_uf + description: ID UF - IBGE 2 Dígitos + tests: + - relationships: + to: ref('br_bd_diretorios_brasil__uf') + field: id_uf + - name: ano + description: Ano + tests: + - relationships: + to: ref('br_bd_diretorios_data_tempo__ano') + field: ano.ano + - name: gini_pib + description: Índice de Gini da distribuição do produto interno bruto a preços + correntes + - name: gini_va + description: Índice de Gini da distribuição do valor adicionado bruto a preços + correntes da agropecuária + - name: gini_va_agro + description: Índice de Gini da distribuição do valor adicionado bruto a preços + correntes da agropecuária + - name: gini_va_industria + description: Índice de Gini da distribuição do valor adicionado bruto a preços + correntes da indústria + - name: gini_va_servicos + description: Índice de Gini da distribuição do valor adicionado bruto a preços + correntes dos serviços, exclusive administração, defesa, educação e saúde + públicas e seguridade social + - name: gini_va_adespss + description: Índice de Gini da distribuição do valor adicionado bruto a preços + correntes da administração, defesa, educação e saúde públicas e seguridade + social diff --git a/tests/test_get_architecture_table_from_api.py b/tests/test_get_architecture_table_from_api.py new file mode 100644 index 0000000..f484a45 --- /dev/null +++ b/tests/test_get_architecture_table_from_api.py @@ -0,0 +1,8 @@ +import pandas as pd +from databasers_utils import get_architecture_table_from_api + + +def test_get_architecture_table_from_api(): + arch = get_architecture_table_from_api("br_ms_sinasc", "microdados") + assert isinstance(arch, pd.DataFrame) + assert len(arch) > 0 diff --git a/tests/test_table_architecture.py b/tests/test_table_architecture.py new file mode 100644 index 0000000..6e46a60 --- /dev/null +++ b/tests/test_table_architecture.py @@ -0,0 +1,63 @@ +import os +import pandas as pd +from databasers_utils import TableArchitecture + +TESTS_DIR = os.path.join(os.getcwd(), "tests") + + +def test_architecture(): + table_arch = TableArchitecture( + dataset_id="br_ibge_pib", + tables={ + "uf": "https://docs.google.com/spreadsheets/d/12F5NzhOYlN_bi9flLBEdXDWpa5iVakSP4EKm9UoyWuo/edit?usp=drive_link", + "gini": "https://docs.google.com/spreadsheets/d/1K1svie4Gyqe6NnRjBgJbapU5sTsLqXWTQUmTRVIRwQc/edit?usp=drive_link", + }, + ) + + tables = table_arch.tables() + assert "uf" and "gini" in tables.keys() + assert isinstance(tables["uf"], pd.DataFrame) + assert isinstance(tables["gini"], pd.DataFrame) + assert len(tables) == 2 + + +def test_create_yaml_file(): + table_arch = TableArchitecture( + dataset_id="br_ibge_pib", + tables={ + "uf": "https://docs.google.com/spreadsheets/d/12F5NzhOYlN_bi9flLBEdXDWpa5iVakSP4EKm9UoyWuo/edit?usp=drive_link", + "gini": "https://docs.google.com/spreadsheets/d/1K1svie4Gyqe6NnRjBgJbapU5sTsLqXWTQUmTRVIRwQc/edit?usp=drive_link", + }, + ) + + os.makedirs(os.path.join(TESTS_DIR, "models"), exist_ok=True) + + table_arch.create_yaml_file(dir=TESTS_DIR) + + assert os.path.exists( + os.path.join(TESTS_DIR, "models", "br_ibge_pib", "schema.yml") + ) + + +def test_create_sql_files(): + table_arch = TableArchitecture( + dataset_id="br_ibge_pib", + tables={ + "uf": "https://docs.google.com/spreadsheets/d/12F5NzhOYlN_bi9flLBEdXDWpa5iVakSP4EKm9UoyWuo/edit?usp=drive_link", + "gini": "https://docs.google.com/spreadsheets/d/1K1svie4Gyqe6NnRjBgJbapU5sTsLqXWTQUmTRVIRwQc/edit?usp=drive_link", + }, + ) + + os.makedirs(os.path.join(TESTS_DIR, "models"), exist_ok=True) + + table_arch.create_sql_files(dir=TESTS_DIR) + + assert os.path.exists( + os.path.join( + TESTS_DIR, "models", "br_ibge_pib", "br_ibge_pib__gini.sql" + ) + ) + + assert os.path.exists( + os.path.join(TESTS_DIR, "models", "br_ibge_pib", "br_ibge_pib__uf.sql") + )