diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 00000000..4ccacf75 --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1 @@ +* @sfc-gh-nsehrawat @sfc-gh-rehuang @sfc-gh-nlimtiaco @sfc-gh-jhilgart diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml new file mode 100644 index 00000000..1422a106 --- /dev/null +++ b/.github/workflows/lint.yaml @@ -0,0 +1,59 @@ +name: Semantic Model Format & Lint + +on: + pull_request: + paths: + - "semantic_model_generator/**" + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.10"] + + steps: + - name: Check out the code + uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + # Caching dependencies using Poetry + - name: Cache Poetry virtualenv + uses: actions/cache@v2 + with: + path: ~/.cache/pypoetry/virtualenvs + key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }} + restore-keys: | + ${{ runner.os }}-poetry- + + - name: Install Poetry + run: | + curl -sSL https://install.python-poetry.org | python3 - + + - name: Configure Poetry + run: | + $HOME/.local/bin/poetry config virtualenvs.create false + + - name: Install dependencies using Poetry + run: | + $HOME/.local/bin/poetry install --no-interaction + + - name: Run mypy + run: | + make run_mypy + + - name: Check with black + run: | + make check_black + + - name: Check with isort + run: | + make check_isort + + - name: Run flake8 + run: | + make run_flake8 diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml new file mode 100644 index 00000000..747e09e5 --- /dev/null +++ b/.github/workflows/release.yaml @@ -0,0 +1,47 @@ +name: Build and Attach Wheel to GitHub Release + +on: + push: + branches: + - 'main' + paths: + - '**.py' + - 'pyproject.toml' + - 'CHANGELOG.md' + +jobs: + build: + if: contains(github.ref, 'refs/heads/release/v') + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.10' + + - name: Install Poetry + run: pip install poetry + + - name: Get the version from pyproject.toml + run: echo "VERSION=$(poetry version -s)" >> $GITHUB_ENV + + - name: Build Wheel + run: poetry build -f wheel + + - name: Extract Changelog for the Version + run: | + VERSION=${{ env.VERSION }} + CHANGELOG=$(awk '/^## \['"${VERSION//./\\.}"'\]/ {flag=1; next} /^## \[/ {flag=0} flag' CHANGELOG.md) + echo "CHANGELOG<> $GITHUB_ENV + echo "$CHANGELOG" >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + - name: Upload Wheel to Release + uses: softprops/action-gh-release@v1 + with: + files: dist/*.whl + body: ${{ env.CHANGELOG }} + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml new file mode 100644 index 00000000..9d0807b7 --- /dev/null +++ b/.github/workflows/test.yaml @@ -0,0 +1,46 @@ +name: Semantic Model Generator Test + +on: + pull_request: + paths: + - "semantic_model_generator/**" + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.10"] + steps: + - name: Check out the code + uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + # Caching dependencies using Poetry + - name: Cache Poetry virtualenv + uses: actions/cache@v2 + with: + path: ~/.cache/pypoetry/virtualenvs + key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }} + restore-keys: | + ${{ runner.os }}-poetry- + + - name: Install Poetry + run: | + curl -sSL https://install.python-poetry.org | python3 - + + - name: Configure Poetry + run: | + $HOME/.local/bin/poetry config virtualenvs.create false + + - name: Install dependencies using Poetry + run: | + $HOME/.local/bin/poetry install --no-interaction + + - name: Test + run: | + make test_github_workflow diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..2d06207f --- /dev/null +++ b/.gitignore @@ -0,0 +1,37 @@ +# Local python environment +pyvenv + +# Notebook intermediate state +*.ipynb_checkpoints + +# Mypy +.mypy_cache + +# Pytest +.pytest_cache + +# pycache +**/__pycache__ + +# Python package builds +*.egg-info + +# VSCode +.vscode/settings.json +.vscode/.ropeproject +.vscode/*.log + +# Envs +.env +.venv +.direnv +.envrc + +# Output semantic models +semantic_model_generator/output_models/* + +# Whls +dist/ + +# test coverage +.coverage \ No newline at end of file diff --git a/.python-version b/.python-version new file mode 100644 index 00000000..c8cfe395 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.10 diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..8f334a17 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,7 @@ +# Changelog + +You must follow the format of `## [VERSION-NUMBER]` for the GitHub workflow to pick up the text. + +## [1.0.0] - 2024-04-08 +### Released +- Initial release of the project. \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 00000000..95854e4f --- /dev/null +++ b/Makefile @@ -0,0 +1,49 @@ + + + +check-deps: ## Check if poetry is installed on your system. + @command -v poetry >/dev/null 2>&1 || { echo >&2 "Poetry is required but it's not installed. Please install Poetry by following the instructions at: https://python-poetry.org/docs/#installation"; exit 1; } + @command -v pyenv >/dev/null 2>&1 || { echo >&2 "pyenv is recommended for managing Python versions but it's not installed. Install via `brew install pyenv`"; exit 1; } + @echo "Setting Python version to 3.10 using pyenv." + @pyenv local 3.10 + +shell: check-deps ## Get into a poetry shell + poetry shell + +setup: check-deps shell ## Install dependencies into your poetry environment. + poetry install + +# Linting and formatting below. +run_mypy: ## Run mypy + mypy --config-file=mypy.ini . + +run_flake8: ## Run flake8 + flake8 --ignore=E203,E501,W503 --exclude=pyvenv,tmp,*_pb2.py,*_pb2.pyi,images/*/src . + +check_black: ## Check to see if files would be updated with black. + # Exclude pyvenv and all generated protobuf code. + black --check --exclude="pyvenv|.*_pb2.py|.*_pb2.pyi" . + +run_black: ## Run black to format files. + # Exclude pyvenv, tmp, and all generated protobuf code. + black --exclude="pyvenv|tmp|.*_pb2.py|.*_pb2.pyi" . + +check_isort: ## Check if files would be updated with isort. + isort --profile black --check --skip=pyvenv --skip-glob='*_pb2.py*' . + +run_isort: ## Run isort to update imports. + isort --profile black --skip=pyvenv --skip=tmp --skip-glob='*_pb2.py*' . + + +fmt_lint: shell ## lint/fmt in current python environment + make run_mypy run_black run_isort run_flake8 + +# Test below +test: shell ## Run tests. + python -m pytest -vvs semantic_model_generator + +test_github_workflow: ## For use on github workflow. + python -m pytest -vvs semantic_model_generator + +help: ## Show this help. + @fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's \ No newline at end of file diff --git a/README.md b/README.md index 76c650e1..2f5b96db 100644 --- a/README.md +++ b/README.md @@ -1 +1,62 @@ -# copilot-semantic-model-generator \ No newline at end of file +# semantic-model-generator + +The `Semantic Model Generator` is used to generate a semantic model for use in your Snowflake account. + +## Setup + +This project uses `poetry` to manage dependencies and we recommend `pyenv` for python version management. + +1. `make setup` + +Next, export your credentials as environment variables. Note, `host` is optional depending on your Snowflake deployment. + +```bash +export SNOWFLAKE_ROLE = "" +export SNOWFLAKE_WAREHOUSE = "" +export SNOWFLAKE_USER = "" +export SNOWFLAKE_PASSWORD = "" +export SNOWFLAKE_HOST = "" +``` + +## Usage + +### Generation + +You may generate a semantic model for a given list of fully qualified tables following the `{database}.{schema}.{table}` format. Each table in this list should be a physical table or a view present in your database. + +All generated semantic models by default are saved under `semantic_model_generator/output_models`. + + +```bash +python -m semantic_model_generator.main \ + --fqn_tables "['..','..']" \ + --semantic_model_name "" \ + --snowflake_account="" +``` + +### Post-Generation + +**Important**: After generation, your YAML files will have a series of lines with `# `. Please take the time to fill these out with your business context. + +By default, the generated semantic model will contain all columns from the provided tables/views. However, it's highly encouraged to only keep relevant columns and drop any unwanted columns from the generated semantic model + +In addition, consider adding the following elements to your semantic model: + +1. Logical columns for a given table/view that are expressions over physical columns. + * Example: `col1 - col2` could be the `expr` for a logical column. +2. Synonyms. Any additional synonyms for column names. +3. Filters. Additional filters with their relevant `expr`. + +## Release + +In order to push a new build and release, follow the steps below. + +1. Checkout a new branch from main. You must name this branch `release/vYYYY-MM-DD`. The `release/v` prefix is used to trigger a github workflow post-merge. +2. Bump the poetry: + * `poetry version patch` + * `poetry version minor` + * `poetry version major` +3. 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", "Renee Huang ", "Nicole Limtiaco "] +license = "Apache Software License; BSD License" +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.10" +pandas = "^2.2.1" +loguru = "^0.7.2" +snowflake-connector-python = {extras = ["pandas"], version = "^3.7.1"} +protobuf = "^5.26.1" +pydantic = "^2.6.4" +PyYAML = "^6.0.1" +"ruamel.yaml" = "^0.18.6" +jsonargparse = "^4.27.7" +tqdm = "^4.66.2" + +[tool.poetry.group.dev.dependencies] +mypy = "^1.9.0" +black = "^24.3.0" +isort = "^5.13.2" +flake8 = "^7.0.0" +pytest = "^8.1.1" +types-pyyaml = "^6.0.12.20240311" +types-protobuf = "^4.24.0.20240311" +pip-licenses = "^4.4.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/semantic_model_generator/__init__.py b/semantic_model_generator/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/semantic_model_generator/data_processing/__init__.py b/semantic_model_generator/data_processing/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/semantic_model_generator/data_processing/data_types.py b/semantic_model_generator/data_processing/data_types.py new file mode 100644 index 00000000..111e96d5 --- /dev/null +++ b/semantic_model_generator/data_processing/data_types.py @@ -0,0 +1,47 @@ +from typing import Any, List, Optional + +from pydantic.dataclasses import dataclass + + +@dataclass +class FQNParts: + database: str + schema: str + table: str + + def __post_init__(self: Any) -> None: + """Uppercase table name""" + self.table = self.table.upper() + + +@dataclass +class Column: + id_: int + column_name: str + column_type: str + values: list[str] | None = None + comment: Optional[str] = ( + None # comment field's to save the column comment user specified on the column + ) + + def __post_init__(self: Any) -> None: + """ + Update column_type to cleaned up version, eg. NUMBER(38,0) -> NUMBER + """ + + self.column_type = self.column_type.split("(")[0].strip().upper() + + +@dataclass +class Table: + id_: int + name: str + columns: List[Column] + comment: Optional[str] = ( + None # comment field's to save the table comment user specified on the table + ) + + def __post_init__(self: Any) -> None: + for col in self.columns: + if col.column_name == "": + raise ValueError("column name in table must be nonempty") diff --git a/semantic_model_generator/data_processing/proto_utils.py b/semantic_model_generator/data_processing/proto_utils.py new file mode 100644 index 00000000..f33a8993 --- /dev/null +++ b/semantic_model_generator/data_processing/proto_utils.py @@ -0,0 +1,37 @@ +import io +import json +from typing import TypeVar + +import ruamel.yaml +from google.protobuf import json_format +from google.protobuf.message import Message + +ProtoMsg = TypeVar("ProtoMsg", bound=Message) + + +def proto_to_yaml(message: ProtoMsg) -> str: + """Serializes the input proto into a yaml message. + + Args: + message: Protobuf message to be serialized. + + Returns: + The serialized yaml string, or None if an error occurs. + """ + try: + json_data = json.loads( + json_format.MessageToJson(message, preserving_proto_field_name=True) + ) + + # Using ruamel.yaml package to preserve message order. + yaml = ruamel.yaml.YAML() + yaml.indent(mapping=2, sequence=4, offset=2) + yaml.preserve_quotes = True + + with io.StringIO() as stream: + yaml.dump(json_data, stream) + yaml_str = stream.getvalue() + assert isinstance(yaml_str, str) + return yaml_str + except Exception as e: + raise ValueError(f"Failed to convert protobuf message to YAML: {e}") diff --git a/semantic_model_generator/main.py b/semantic_model_generator/main.py new file mode 100644 index 00000000..008e7667 --- /dev/null +++ b/semantic_model_generator/main.py @@ -0,0 +1,318 @@ +from datetime import datetime + +import jsonargparse +from loguru import logger + +from semantic_model_generator.data_processing import data_types, proto_utils +from semantic_model_generator.protos import semantic_model_pb2 +from semantic_model_generator.snowflake_utils.snowflake_connector import ( + DIMENSION_DATATYPES, + MEASURE_DATATYPES, + TIME_MEASURE_DATATYPES, + SnowflakeConnector, + get_table_representation, + get_valid_schemas_tables_columns_df, +) +from semantic_model_generator.snowflake_utils.utils import create_fqn_table + +_PLACEHOLDER_COMMENT = " " +_FILL_OUT_TOKEN = " # " + + +def _get_placeholder_filter() -> list[semantic_model_pb2.NamedFilter]: + return [ + semantic_model_pb2.NamedFilter( + name=_PLACEHOLDER_COMMENT, + synonyms=[_PLACEHOLDER_COMMENT], + description=_PLACEHOLDER_COMMENT, + expr=_PLACEHOLDER_COMMENT, + ) + ] + + +def _raw_table_to_semantic_context_table( + database: str, schema: str, raw_table: data_types.Table +) -> semantic_model_pb2.Table: + """ + Converts a raw table representation to a semantic model table in protobuf format. + + Args: + database (str): The name of the database containing the table. + schema (str): The name of the schema containing the table. + raw_table (data_types.Table): The raw table object to be transformed. + + Returns: + semantic_model_pb2.Table: A protobuf representation of the semantic table. + + This function categorizes table columns into TimeDimensions, Dimensions, or Measures based on their data type, + populates them with sample values, and sets placeholders for descriptions and filters. + """ + + # For each column, decide if it is a TimeDimension, Measure, or Dimension column. + # For now, we decide this based on datatype. + # Any time datatype, is TimeDimension. + # Any varchar/text is Dimension. + # Any numerical column is Measure. + + time_dimensions = [] + dimensions = [] + measures = [] + + for col in raw_table.columns: + + if col.column_type in TIME_MEASURE_DATATYPES: + time_dimensions.append( + semantic_model_pb2.TimeDimension( + name=col.column_name, + expr=col.column_name, + data_type=col.column_type, + sample_values=col.values, + synonyms=[_PLACEHOLDER_COMMENT], + description=_PLACEHOLDER_COMMENT, + ) + ) + + elif col.column_type in DIMENSION_DATATYPES: + dimensions.append( + semantic_model_pb2.Dimension( + name=col.column_name, + expr=col.column_name, + data_type=col.column_type, + sample_values=col.values, + synonyms=[_PLACEHOLDER_COMMENT], + description=_PLACEHOLDER_COMMENT, + ) + ) + + elif col.column_type in MEASURE_DATATYPES: + measures.append( + semantic_model_pb2.Measure( + name=col.column_name, + expr=col.column_name, + data_type=col.column_type, + sample_values=col.values, + synonyms=[_PLACEHOLDER_COMMENT], + description=_PLACEHOLDER_COMMENT, + ) + ) + else: + raise ValueError( + f"Column datatype does not map to a known datatype. Input was = {col.column_type}. If this is a new datatype, please update the constants in snowflake_connector.py." + ) + + return semantic_model_pb2.Table( + name=raw_table.name, + base_table=semantic_model_pb2.FullyQualifiedTable( + database=database, schema=schema, table=raw_table.name + ), + # For fields we can not automatically infer, leave a comment for the user to fill out. + description=_PLACEHOLDER_COMMENT, + filters=_get_placeholder_filter(), + dimensions=dimensions, + time_dimensions=time_dimensions, + measures=measures, + ) + + +def raw_schema_to_semantic_context( + fqn_tables: list[str], snowflake_account: str, semantic_model_name: str +) -> semantic_model_pb2.SemanticModel: + """ + Converts a list of fully qualified Snowflake table names into a semantic model. + + Parameters: + - fqn_tables (list[str]): Fully qualified table names to include in the semantic model. + - snowflake_account (str): Snowflake account identifier. + - semantic_model_name (str): A meaningful semantic model name. + + Returns: + - The semantic model (semantic_model_pb2.SemanticModel). + + This function fetches metadata for the specified tables, performs schema validation, extracts key information, + enriches metadata from the Snowflake database, and constructs a semantic model in protobuf format. + It handles different databases and schemas within the same account by creating unique Snowflake connections as needed. + + Raises: + - AssertionError: If no valid tables are found in the specified schema. + """ + connector = SnowflakeConnector( + account_name=snowflake_account, + ndv_per_column=10, # number of sample values to pull per column. + max_workers=1, + ) + # For FQN tables, create a new snowflake connection per table in case the db/schema is different. + table_objects = [] + unique_database_schema: list[str] = [] + for table in fqn_tables: + # Verify this is a valid FQN table. For now, we check that the table follows the following format. + # {database}.{schema}.{table} + fqn_table = create_fqn_table(table) + fqn_databse_schema = f"{fqn_table.database}_{fqn_table.schema}" + if fqn_databse_schema not in unique_database_schema: + unique_database_schema.append(fqn_databse_schema) + + with connector.connect( + db_name=fqn_table.database, schema_name=fqn_table.schema + ) as conn: + logger.info(f"Pulling column information from {fqn_table}") + valid_schemas_tables_columns_df = get_valid_schemas_tables_columns_df( + conn=conn, table_schema=fqn_table.schema, table_names=[fqn_table.table] + ) + assert not valid_schemas_tables_columns_df.empty + + # get the valid columns for this table. + valid_columns_df_this_table = valid_schemas_tables_columns_df[ + valid_schemas_tables_columns_df["TABLE_NAME"] == fqn_table.table + ] + + raw_table = get_table_representation( + conn=conn, + schema_name=fqn_table.schema, + table_name=fqn_table.table, + table_index=0, + ndv_per_column=10, # number of sample values to pull per column. + columns_df=valid_columns_df_this_table, + max_workers=1, + ) + + table_object = _raw_table_to_semantic_context_table( + database=fqn_table.database, + schema=fqn_table.schema, + raw_table=raw_table, + ) + table_objects.append(table_object) + # TODO(jhilgart): Call cortex model to generate a semantically friendly name here. + context = semantic_model_pb2.SemanticModel( + name=semantic_model_name, tables=table_objects + ) + return context + + +def append_comment_to_placeholders(yaml_str: str) -> str: + """ + Finds all instances of a specified placeholder in a YAML string and appends a given text to these placeholders. + This is the homework to fill out after your yaml is generated. + + Parameters: + - yaml_str (str): The YAML string to process. + + Returns: + - str: The modified YAML string with appended text to placeholders. + """ + updated_yaml = [] + # Split the string into lines to process each line individually + lines = yaml_str.split("\n") + + for line in lines: + # Check if the placeholder is in the current line. + # Strip the last quote to match. + if line.rstrip("'").endswith(_PLACEHOLDER_COMMENT): + # Replace the _PLACEHOLDER_COMMENT with itself plus the append_text + updated_line = line + _FILL_OUT_TOKEN + updated_yaml.append(updated_line) + else: + updated_yaml.append(line) + + # Join the lines back together into a single string + return "\n".join(updated_yaml) + + +def _to_snake_case(s: str) -> str: + """ + Convert a string into snake case. + + Parameters: + s (str): The string to convert. + + Returns: + str: The snake case version of the string. + """ + # Replace common delimiters with spaces + s = s.replace("-", " ").replace("_", " ") + + words = s.split(" ") + + # Convert each word to lowercase and join with underscores + snake_case_str = "_".join([word.lower() for word in words if word]).strip() + + return snake_case_str + + +def generate_base_semantic_context_from_snowflake( + fqn_tables: list[str], + snowflake_account: str, + semantic_model_name: str, + output_yaml_path: str | None = None, +) -> None: + """ + Generates a base semantic context from specified Snowflake tables and exports it to a YAML file. + + Parameters: + fqn_tables: Fully qualified names of Snowflake tables to include in the semantic context. + snowflake_account: Identifier of the Snowflake account. + semantic_model_name: The human readable model name. This should be semantically meaningful to an organization. + output_yaml_path: Path for the output YAML file. If None, defaults to 'semantic_model_generator/output_models/YYYYMMDDHHMMSS_.yaml'. + + Returns: + None. Writes the semantic context to a YAML file. + """ + context = raw_schema_to_semantic_context( + fqn_tables=fqn_tables, + snowflake_account=snowflake_account, + semantic_model_name=semantic_model_name, + ) + + yaml_str = proto_utils.proto_to_yaml(context) + # Once we have the yaml, update to include to # tokens. + yaml_str = append_comment_to_placeholders(yaml_str) + if output_yaml_path: + write_path = output_yaml_path + else: + current_datetime = datetime.now() + + # Format the current date and time as "YYYY-MM-DD" + formatted_datetime = current_datetime.strftime("%Y%m%d%H%M%S") + write_path = f"semantic_model_generator/output_models/{formatted_datetime}_{_to_snake_case(semantic_model_name)}.yaml" + with open(write_path, "w") as f: + f.write(yaml_str) + return None + + +if __name__ == "__main__": + parser = jsonargparse.ArgumentParser( + description="CLI tool to generate semantic context models from Snowflake schemas." + ) + + parser.add_argument( + "--fqn_tables", + type=list, + required=True, + help="The list of fully qualified table names all following the format {database_name}.{schema_name}{table_name}", + ) + parser.add_argument( + "--snowflake_account", + type=str, + required=True, + help="Your Snowflake account ID.", + ) + parser.add_argument( + "--semantic_model_name", + type=str, + required=True, + help="What is the name of this semantic model? Examples could be (Churn Analysis, Marketing, Sales Prospects ...etc)", + ) + parser.add_argument( + "--output_yaml_path", + type=str, + required=False, + help="Custom path to save the YAML. Optional.", + ) + + args = parser.parse_args() + + generate_base_semantic_context_from_snowflake( + fqn_tables=args.fqn_tables, + snowflake_account=args.snowflake_account, + semantic_model_name=args.semantic_model_name, + output_yaml_path=args.output_yaml_path, + ) diff --git a/semantic_model_generator/protos/semantic_model.proto b/semantic_model_generator/protos/semantic_model.proto new file mode 100644 index 00000000..520bc0d4 --- /dev/null +++ b/semantic_model_generator/protos/semantic_model.proto @@ -0,0 +1,144 @@ +// If you make changes to this file, you'll need to run protoc to updated the +// generated files by running the following command: +// +// protoc -I=semantic_model_generator/protos/ --python_out=semantic_model_generator/protos/ --pyi_out=semantic_model_generator/protos/ semantic_model_generator/protos/semantic_model.proto + +syntax = "proto3"; + +package semantic_model_generator; + +// AggregationType defines a list of various aggregations. +enum AggregationType { + aggregation_type_unknown = 0; + sum = 1; + avg = 2; + median = 7; + min = 3; + max = 4; + count = 5; + count_distinct = 6; +} + +// Dimension columns contain categorical values (e.g. state, user_type, platform). +// NOTE: If modifying this protobuf, make appropriate changes in to_column_format(). +message Dimension { + // A descriptive name for this dimension. + string name = 1; + // A list of other terms/phrases used to refer to this dimension. + repeated string synonyms = 2; + // A brief description about this dimension, including things like + // what data this dimension has. + string description = 3; + // The SQL expression defining this dimension. Could simply be a physical column name + // or an arbitrary SQL expression over one or more columns of the physical table. + string expr = 4; + // The data type of this dimension. + string data_type = 5; + // If true, assume that this dimension has unique values. + bool unique = 6; + // Sample values of this column. + repeated string sample_values = 7; +} + +// Time dimension columns contain time values (e.g. sale_date, created_at, year). +// NOTE: If modifying this protobuf, make appropriate changes in to_column_format(). +message TimeDimension { + // A descriptive name for this time dimension. + string name = 1; + // A list of other terms/phrases used to refer to this time dimension. + repeated string synonyms = 2; + // A brief description about this time dimension, including things like + // what data it has, the timezone of values, etc. + string description = 3; + // The SQL expression defining this time dimension. Could simply be a physical + // column name or an arbitrary SQL expression over one or more columns of the + // physical table. + string expr = 4; + // The data type of this time dimension. + string data_type = 5; + // If true, assume that this time dimension has unique values. + bool unique = 6; + // Sample values of this time dimension. + repeated string sample_values = 7; +} + +// Measure columns contain numerical values (e.g. revenue, impressions, salary). +// NOTE: If modifying this protobuf, make appropriate changes in to_column_format(). +message Measure { + // A descriptive name for this measure. + string name = 1; + // A list of other terms/phrases used to refer to this measure. + repeated string synonyms = 2; + // A brief description about this measure, including things like what data + // it has. + string description = 3; + // The SQL expression defining this measure. Could simply be a physical column + // name or an arbitrary SQL expression over one or more physical columns of + // the underlying physical table. + string expr = 4; + // The data type of this measure. + string data_type = 5; + // If no aggregation is specified, then this is the default aggregation + // applied to this measure in contxt of a grouping. + AggregationType default_aggregation = 6; + // Sample values of this measure. + repeated string sample_values = 7; +} + +// Filter represents a named SQL expression that's used for filtering. +message NamedFilter { + // A descriptive name for this filter. + string name = 1; + // A list of other term/phrases used to refer to this column. + repeated string synonyms = 2; + // A brief description about this column, including details of what this filter + // is typically used for. + string description = 3; + // The SQL expression of this filter. + string expr = 4; +} + +// FullyQualifiedTable is used to represent three part table names - +// (database, schema, table). +message FullyQualifiedTable { + string database = 1; + string schema = 2; + string table = 3; +} + +// Table is analogous to a database table and provides a simple view over an +// existing database table. A table can leave out some columns from the base +// table and/or introduce new derived columns. +message Table { + // A descriptive name for this table. + string name = 1; + // A list of other term/phrases used to refer to this table. + repeated string synonyms = 2; + // A brief description of this table, including details of what kinds of + // analysis is it typically used for. + string description = 3; + // Fully qualified name of the underlying base table. + FullyQualifiedTable base_table = 4; + + // Dimension, TimeDimension, and Measure are all types of Columns for a table. + // These may be logical or physical columns. + repeated Dimension dimensions = 9; + repeated TimeDimension time_dimensions = 10; + repeated Measure measures = 11; + + + // Predefined filters on this table, if any. + repeated NamedFilter filters = 8; + // NEXT_TAG: 12. +} + +// The semantic context relevant to generating SQL for answering a data question. +message SemanticModel { + // A descriptive name of the project. + string name = 1; + // A brief description of this project, including details of what kind of + // analysis does this project enable. + string description = 2; + // List of tables in this project. + repeated Table tables = 3; +} \ No newline at end of file diff --git a/semantic_model_generator/protos/semantic_model_pb2.py b/semantic_model_generator/protos/semantic_model_pb2.py new file mode 100644 index 00000000..2677081c --- /dev/null +++ b/semantic_model_generator/protos/semantic_model_pb2.py @@ -0,0 +1,40 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: semantic_model.proto +# Protobuf Python Version: 5.26.1 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14semantic_model.proto\x12\x18semantic_model_generator\"\x88\x01\n\tDimension\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x10\n\x08synonyms\x18\x02 \x03(\t\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12\x0c\n\x04\x65xpr\x18\x04 \x01(\t\x12\x11\n\tdata_type\x18\x05 \x01(\t\x12\x0e\n\x06unique\x18\x06 \x01(\x08\x12\x15\n\rsample_values\x18\x07 \x03(\t\"\x8c\x01\n\rTimeDimension\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x10\n\x08synonyms\x18\x02 \x03(\t\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12\x0c\n\x04\x65xpr\x18\x04 \x01(\t\x12\x11\n\tdata_type\x18\x05 \x01(\t\x12\x0e\n\x06unique\x18\x06 \x01(\x08\x12\x15\n\rsample_values\x18\x07 \x03(\t\"\xbe\x01\n\x07Measure\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x10\n\x08synonyms\x18\x02 \x03(\t\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12\x0c\n\x04\x65xpr\x18\x04 \x01(\t\x12\x11\n\tdata_type\x18\x05 \x01(\t\x12\x46\n\x13\x64\x65\x66\x61ult_aggregation\x18\x06 \x01(\x0e\x32).semantic_model_generator.AggregationType\x12\x15\n\rsample_values\x18\x07 \x03(\t\"P\n\x0bNamedFilter\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x10\n\x08synonyms\x18\x02 \x03(\t\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12\x0c\n\x04\x65xpr\x18\x04 \x01(\t\"F\n\x13\x46ullyQualifiedTable\x12\x10\n\x08\x64\x61tabase\x18\x01 \x01(\t\x12\x0e\n\x06schema\x18\x02 \x01(\t\x12\r\n\x05table\x18\x03 \x01(\t\"\xe7\x02\n\x05Table\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x10\n\x08synonyms\x18\x02 \x03(\t\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12\x41\n\nbase_table\x18\x04 \x01(\x0b\x32-.semantic_model_generator.FullyQualifiedTable\x12\x37\n\ndimensions\x18\t \x03(\x0b\x32#.semantic_model_generator.Dimension\x12@\n\x0ftime_dimensions\x18\n \x03(\x0b\x32\'.semantic_model_generator.TimeDimension\x12\x33\n\x08measures\x18\x0b \x03(\x0b\x32!.semantic_model_generator.Measure\x12\x36\n\x07\x66ilters\x18\x08 \x03(\x0b\x32%.semantic_model_generator.NamedFilter\"c\n\rSemanticModel\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x02 \x01(\t\x12/\n\x06tables\x18\x03 \x03(\x0b\x32\x1f.semantic_model_generator.Table*~\n\x0f\x41ggregationType\x12\x1c\n\x18\x61ggregation_type_unknown\x10\x00\x12\x07\n\x03sum\x10\x01\x12\x07\n\x03\x61vg\x10\x02\x12\n\n\x06median\x10\x07\x12\x07\n\x03min\x10\x03\x12\x07\n\x03max\x10\x04\x12\t\n\x05\x63ount\x10\x05\x12\x12\n\x0e\x63ount_distinct\x10\x06\x62\x06proto3') + +_globals = globals() +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) +_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'semantic_model_pb2', _globals) +if not _descriptor._USE_C_DESCRIPTORS: + DESCRIPTOR._loaded_options = None + _globals['_AGGREGATIONTYPE']._serialized_start=1142 + _globals['_AGGREGATIONTYPE']._serialized_end=1268 + _globals['_DIMENSION']._serialized_start=51 + _globals['_DIMENSION']._serialized_end=187 + _globals['_TIMEDIMENSION']._serialized_start=190 + _globals['_TIMEDIMENSION']._serialized_end=330 + _globals['_MEASURE']._serialized_start=333 + _globals['_MEASURE']._serialized_end=523 + _globals['_NAMEDFILTER']._serialized_start=525 + _globals['_NAMEDFILTER']._serialized_end=605 + _globals['_FULLYQUALIFIEDTABLE']._serialized_start=607 + _globals['_FULLYQUALIFIEDTABLE']._serialized_end=677 + _globals['_TABLE']._serialized_start=680 + _globals['_TABLE']._serialized_end=1039 + _globals['_SEMANTICMODEL']._serialized_start=1041 + _globals['_SEMANTICMODEL']._serialized_end=1140 +# @@protoc_insertion_point(module_scope) diff --git a/semantic_model_generator/protos/semantic_model_pb2.pyi b/semantic_model_generator/protos/semantic_model_pb2.pyi new file mode 100644 index 00000000..ac767331 --- /dev/null +++ b/semantic_model_generator/protos/semantic_model_pb2.pyi @@ -0,0 +1,132 @@ +from google.protobuf.internal import containers as _containers +from google.protobuf.internal import enum_type_wrapper as _enum_type_wrapper +from google.protobuf import descriptor as _descriptor +from google.protobuf import message as _message +from typing import ClassVar as _ClassVar, Iterable as _Iterable, Mapping as _Mapping, Optional as _Optional, Union as _Union + +DESCRIPTOR: _descriptor.FileDescriptor + +class AggregationType(int, metaclass=_enum_type_wrapper.EnumTypeWrapper): + __slots__ = () + aggregation_type_unknown: _ClassVar[AggregationType] + sum: _ClassVar[AggregationType] + avg: _ClassVar[AggregationType] + median: _ClassVar[AggregationType] + min: _ClassVar[AggregationType] + max: _ClassVar[AggregationType] + count: _ClassVar[AggregationType] + count_distinct: _ClassVar[AggregationType] +aggregation_type_unknown: AggregationType +sum: AggregationType +avg: AggregationType +median: AggregationType +min: AggregationType +max: AggregationType +count: AggregationType +count_distinct: AggregationType + +class Dimension(_message.Message): + __slots__ = ("name", "synonyms", "description", "expr", "data_type", "unique", "sample_values") + NAME_FIELD_NUMBER: _ClassVar[int] + SYNONYMS_FIELD_NUMBER: _ClassVar[int] + DESCRIPTION_FIELD_NUMBER: _ClassVar[int] + EXPR_FIELD_NUMBER: _ClassVar[int] + DATA_TYPE_FIELD_NUMBER: _ClassVar[int] + UNIQUE_FIELD_NUMBER: _ClassVar[int] + SAMPLE_VALUES_FIELD_NUMBER: _ClassVar[int] + name: str + synonyms: _containers.RepeatedScalarFieldContainer[str] + description: str + expr: str + data_type: str + unique: bool + sample_values: _containers.RepeatedScalarFieldContainer[str] + def __init__(self, name: _Optional[str] = ..., synonyms: _Optional[_Iterable[str]] = ..., description: _Optional[str] = ..., expr: _Optional[str] = ..., data_type: _Optional[str] = ..., unique: bool = ..., sample_values: _Optional[_Iterable[str]] = ...) -> None: ... + +class TimeDimension(_message.Message): + __slots__ = ("name", "synonyms", "description", "expr", "data_type", "unique", "sample_values") + NAME_FIELD_NUMBER: _ClassVar[int] + SYNONYMS_FIELD_NUMBER: _ClassVar[int] + DESCRIPTION_FIELD_NUMBER: _ClassVar[int] + EXPR_FIELD_NUMBER: _ClassVar[int] + DATA_TYPE_FIELD_NUMBER: _ClassVar[int] + UNIQUE_FIELD_NUMBER: _ClassVar[int] + SAMPLE_VALUES_FIELD_NUMBER: _ClassVar[int] + name: str + synonyms: _containers.RepeatedScalarFieldContainer[str] + description: str + expr: str + data_type: str + unique: bool + sample_values: _containers.RepeatedScalarFieldContainer[str] + def __init__(self, name: _Optional[str] = ..., synonyms: _Optional[_Iterable[str]] = ..., description: _Optional[str] = ..., expr: _Optional[str] = ..., data_type: _Optional[str] = ..., unique: bool = ..., sample_values: _Optional[_Iterable[str]] = ...) -> None: ... + +class Measure(_message.Message): + __slots__ = ("name", "synonyms", "description", "expr", "data_type", "default_aggregation", "sample_values") + NAME_FIELD_NUMBER: _ClassVar[int] + SYNONYMS_FIELD_NUMBER: _ClassVar[int] + DESCRIPTION_FIELD_NUMBER: _ClassVar[int] + EXPR_FIELD_NUMBER: _ClassVar[int] + DATA_TYPE_FIELD_NUMBER: _ClassVar[int] + DEFAULT_AGGREGATION_FIELD_NUMBER: _ClassVar[int] + SAMPLE_VALUES_FIELD_NUMBER: _ClassVar[int] + name: str + synonyms: _containers.RepeatedScalarFieldContainer[str] + description: str + expr: str + data_type: str + default_aggregation: AggregationType + sample_values: _containers.RepeatedScalarFieldContainer[str] + def __init__(self, name: _Optional[str] = ..., synonyms: _Optional[_Iterable[str]] = ..., description: _Optional[str] = ..., expr: _Optional[str] = ..., data_type: _Optional[str] = ..., default_aggregation: _Optional[_Union[AggregationType, str]] = ..., sample_values: _Optional[_Iterable[str]] = ...) -> None: ... + +class NamedFilter(_message.Message): + __slots__ = ("name", "synonyms", "description", "expr") + NAME_FIELD_NUMBER: _ClassVar[int] + SYNONYMS_FIELD_NUMBER: _ClassVar[int] + DESCRIPTION_FIELD_NUMBER: _ClassVar[int] + EXPR_FIELD_NUMBER: _ClassVar[int] + name: str + synonyms: _containers.RepeatedScalarFieldContainer[str] + description: str + expr: str + def __init__(self, name: _Optional[str] = ..., synonyms: _Optional[_Iterable[str]] = ..., description: _Optional[str] = ..., expr: _Optional[str] = ...) -> None: ... + +class FullyQualifiedTable(_message.Message): + __slots__ = ("database", "schema", "table") + DATABASE_FIELD_NUMBER: _ClassVar[int] + SCHEMA_FIELD_NUMBER: _ClassVar[int] + TABLE_FIELD_NUMBER: _ClassVar[int] + database: str + schema: str + table: str + def __init__(self, database: _Optional[str] = ..., schema: _Optional[str] = ..., table: _Optional[str] = ...) -> None: ... + +class Table(_message.Message): + __slots__ = ("name", "synonyms", "description", "base_table", "dimensions", "time_dimensions", "measures", "filters") + NAME_FIELD_NUMBER: _ClassVar[int] + SYNONYMS_FIELD_NUMBER: _ClassVar[int] + DESCRIPTION_FIELD_NUMBER: _ClassVar[int] + BASE_TABLE_FIELD_NUMBER: _ClassVar[int] + DIMENSIONS_FIELD_NUMBER: _ClassVar[int] + TIME_DIMENSIONS_FIELD_NUMBER: _ClassVar[int] + MEASURES_FIELD_NUMBER: _ClassVar[int] + FILTERS_FIELD_NUMBER: _ClassVar[int] + name: str + synonyms: _containers.RepeatedScalarFieldContainer[str] + description: str + base_table: FullyQualifiedTable + dimensions: _containers.RepeatedCompositeFieldContainer[Dimension] + time_dimensions: _containers.RepeatedCompositeFieldContainer[TimeDimension] + measures: _containers.RepeatedCompositeFieldContainer[Measure] + filters: _containers.RepeatedCompositeFieldContainer[NamedFilter] + def __init__(self, name: _Optional[str] = ..., synonyms: _Optional[_Iterable[str]] = ..., description: _Optional[str] = ..., base_table: _Optional[_Union[FullyQualifiedTable, _Mapping]] = ..., dimensions: _Optional[_Iterable[_Union[Dimension, _Mapping]]] = ..., time_dimensions: _Optional[_Iterable[_Union[TimeDimension, _Mapping]]] = ..., measures: _Optional[_Iterable[_Union[Measure, _Mapping]]] = ..., filters: _Optional[_Iterable[_Union[NamedFilter, _Mapping]]] = ...) -> None: ... + +class SemanticModel(_message.Message): + __slots__ = ("name", "description", "tables") + NAME_FIELD_NUMBER: _ClassVar[int] + DESCRIPTION_FIELD_NUMBER: _ClassVar[int] + TABLES_FIELD_NUMBER: _ClassVar[int] + name: str + description: str + tables: _containers.RepeatedCompositeFieldContainer[Table] + def __init__(self, name: _Optional[str] = ..., description: _Optional[str] = ..., tables: _Optional[_Iterable[_Union[Table, _Mapping]]] = ...) -> None: ... diff --git a/semantic_model_generator/snowflake_utils/env_vars.py b/semantic_model_generator/snowflake_utils/env_vars.py new file mode 100644 index 00000000..a71339bd --- /dev/null +++ b/semantic_model_generator/snowflake_utils/env_vars.py @@ -0,0 +1,8 @@ +import os + +DEFAULT_SESSION_TIMEOUT_SEC = int(os.environ.get("SNOWFLAKE_SESSION_TIMEOUT_SEC", 120)) +SNOWFLAKE_ROLE = os.getenv("SNOWFLAKE_ROLE") +SNOWFLAKE_WAREHOUSE = os.getenv("SNOWFLAKE_WAREHOUSE") +SNOWFLAKE_USER = os.getenv("SNOWFLAKE_USER") +SNOWFLAKE_PASSWORD = os.getenv("SNOWFLAKE_PASSWORD") +SNOWFLAKE_HOST = os.getenv("SNOWFLAKE_HOST") diff --git a/semantic_model_generator/snowflake_utils/snowflake_connector.py b/semantic_model_generator/snowflake_utils/snowflake_connector.py new file mode 100644 index 00000000..408fffd7 --- /dev/null +++ b/semantic_model_generator/snowflake_utils/snowflake_connector.py @@ -0,0 +1,362 @@ +import concurrent.futures +from collections import defaultdict +from contextlib import contextmanager +from typing import Any, Generator, Optional, TypeVar + +import pandas as pd +from loguru import logger +from snowflake.connector import DictCursor +from snowflake.connector.connection import SnowflakeConnection +from snowflake.connector.errors import ProgrammingError + +from semantic_model_generator.data_processing.data_types import Column, Table +from semantic_model_generator.snowflake_utils import env_vars +from semantic_model_generator.snowflake_utils.utils import snowflake_connection + +ConnectionType = TypeVar("ConnectionType") +# This is the raw column name from snowflake information schema or desc table +_COMMENT_COL = "COMMENT" +_COLUMN_NAME_COL = "COLUMN_NAME" +_DATATYPE_COL = "DATA_TYPE" +_TABLE_SCHEMA_COL = "TABLE_SCHEMA" +_TABLE_NAME_COL = "TABLE_NAME" +# Below are the renamed column names when we fetch into dataframe, to differentiate between table/column comments +_COLUMN_COMMENT_ALIAS = "COLUMN_COMMENT" +_TABLE_COMMENT_COL = "TABLE_COMMENT" + +# https://docs.snowflake.com/en/sql-reference/data-types-datetime +TIME_MEASURE_DATATYPES = [ + "DATE", + "DATETIME", + "TIMESTAMP_LTZ", + "TIMESTAMP_NTZ", + "TIMESTAMP_TZ", + "TIMESTAMP", + "TIME", +] +# https://docs.snowflake.com/en/sql-reference/data-types-text +DIMENSION_DATATYPES = [ + "VARCHAR", + "CHAR", + "CHARACTER", + "NCHAR", + "STRING", + "TEXT", + "NVARCHAR", + "NVARCHAR2", + "CHAR VARYING", + "NCHAR VARYING", + "BINARY", + "VARBINARY", +] +# https://docs.snowflake.com/en/sql-reference/data-types-numeric +MEASURE_DATATYPES = [ + "NUMBER", + "DECIMAL", + "DEC", + "NUMERIC", + "INT", + "INTEGER", + "BIGINT", + "SMALLINT", + "TINYINT", + "BYTEINT", + "FLOAT", + "FLOAT4", + "FLOAT8", + "DOUBLE", + "DOUBLE PRECISION", + "REAL", +] + + +_QUERY_TAG = "SEMANTIC_MODEL_GENERATOR" + + +def get_table_representation( + conn: SnowflakeConnection, + schema_name: str, + table_name: str, + table_index: int, + ndv_per_column: int, + columns_df: pd.DataFrame, + max_workers: int, +) -> Table: + table_comment = columns_df[_TABLE_COMMENT_COL].iloc[0] + + def _get_col(col_index: int, column_row: pd.Series) -> Column: + return _get_column_representation( + conn=conn, + schema_name=schema_name, + table_name=table_name, + column_name=column_row[_COLUMN_NAME_COL], + column_comment=column_row[_COLUMN_COMMENT_ALIAS], + column_index=col_index, + column_datatype=column_row[_DATATYPE_COL], + ndv=ndv_per_column, + ) + + with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor: + future_to_col_index = { + executor.submit(_get_col, col_index, column_row): col_index + for col_index, (_, column_row) in enumerate(columns_df.iterrows()) + } + index_and_column = [] + for future in concurrent.futures.as_completed(future_to_col_index): + col_index = future_to_col_index[future] + column = future.result() + index_and_column.append((col_index, column)) + columns = [c for _, c in sorted(index_and_column, key=lambda x: x[0])] + + return Table( + id_=table_index, name=table_name, comment=table_comment, columns=columns + ) + + +def _get_column_representation( + conn: SnowflakeConnection, + schema_name: str, + table_name: str, + column_name: str, + column_comment: str, + column_index: int, + column_datatype: str, + ndv: int, +) -> Column: + + column_values = None + if ndv > 0: + # Pull sample values. + try: + cursor = conn.cursor(DictCursor) + assert cursor is not None, "Cursor is unexpectedly None" + cursor_execute = cursor.execute( + f'select distinct "{column_name}" from "{schema_name}"."{table_name}" limit {ndv}' + ) + assert cursor_execute is not None, "cursor_execute should not be none " + res = cursor_execute.fetchall() + # Cast all values to string to ensure the list is json serializable. + # A better solution would be to identify the possible types that are not + # json serializable (e.g. datetime objects) and apply the appropriate casting + # in just those cases. + if len(res) > 0: + if isinstance(res[0], dict): + col_key = [k for k in res[0].keys()][0] + column_values = [str(r[col_key]) for r in res] + else: + raise ValueError( + f"Expected the first item of res to be a dict. Instead passed {res}" + ) + except Exception as e: + logger.error(f"unable to get values: {e}") + + column = Column( + id_=column_index, + column_name=column_name, + comment=column_comment, + column_type=column_datatype, + values=column_values, + ) + return column + + +def _fetch_valid_tables_and_views(conn: SnowflakeConnection) -> pd.DataFrame: + def _get_df(query: str) -> pd.DataFrame: + cursor = conn.cursor().execute(query) + assert cursor is not None, "cursor should not be none here." + + df = pd.DataFrame( + cursor.fetchall(), columns=[c.name for c in cursor.description] + ) + return df[["name", "schema_name", "comment"]].rename( + columns=dict( + name=_TABLE_NAME_COL, + schema_name=_TABLE_SCHEMA_COL, + comment=_TABLE_COMMENT_COL, + ) + ) + + tables = _get_df("show tables in database") + views = _get_df("show views in database") + return pd.concat([tables, views], axis=0) + + +def get_valid_schemas_tables_columns_df( + conn: SnowflakeConnection, + table_schema: str | None = None, + table_names: list[str] | None = None, +) -> pd.DataFrame: + if table_names and not table_schema: + logger.warning( + "Provided table_name without table_schema, cannot filter to fetch the specific table" + ) + + where_clause = "" + if table_schema: + where_clause += f" where t.table_schema ilike '{table_schema}' " + if table_names: + table_names_str = ", ".join([f"'{t.lower()}'" for t in table_names]) + where_clause += f"AND LOWER(t.table_name) in ({table_names_str}) " + query = f"""select t.{_TABLE_SCHEMA_COL}, t.{_TABLE_NAME_COL}, c.{_COLUMN_NAME_COL}, c.{_DATATYPE_COL}, c.{_COMMENT_COL} as {_COLUMN_COMMENT_ALIAS} +from information_schema.tables as t +join information_schema.columns as c on t.table_schema = c.table_schema and t.table_name = c.table_name{where_clause} +order by 1, 2, c.ordinal_position""" + cursor_execute = conn.cursor().execute(query) + assert cursor_execute, "cursor_execute should not be None here" + schemas_tables_columns_df = cursor_execute.fetch_pandas_all() + + valid_tables_and_views_df = _fetch_valid_tables_and_views(conn=conn) + + valid_schemas_tables_columns_df = valid_tables_and_views_df.merge( + schemas_tables_columns_df, how="inner", on=(_TABLE_SCHEMA_COL, _TABLE_NAME_COL) + ) + return valid_schemas_tables_columns_df + + +class SnowflakeConnector: + def __init__( + self, + account_name: str, + ndv_per_column: int = 0, + max_workers: int = 1, + ): + self.account_name: str = account_name + self._ndv_per_column = ndv_per_column + self._max_workers = max_workers + + # Required env vars below + def _get_role(self) -> str: + role = env_vars.SNOWFLAKE_ROLE + if not role: + raise ValueError( + "You need to set an env var for the snowflake role. export SNOWFLAKE_ROLE=" + ) + return role + + def _get_user(self) -> str: + user = env_vars.SNOWFLAKE_USER + if not user: + raise ValueError( + "You need to set an env var for the snowflake user. export SNOWFLAKE_USER=" + ) + return user + + def _get_password(self) -> str: + password = env_vars.SNOWFLAKE_PASSWORD + if not password: + raise ValueError( + "You need to set an env var for the snowflake password. export SNOWFLAKE_PASSWORD=" + ) + return password + + def _get_warehouse(self) -> str: + warehouse = env_vars.SNOWFLAKE_WAREHOUSE + if not warehouse: + raise ValueError( + "You need to set an env var for the snowflake warehouse. export SNOWFLAKE_WAREHOUSE=" + ) + return warehouse + + def _get_host(self) -> str | None: + host = env_vars.SNOWFLAKE_HOST + if not host: + logger.info( + "No host set. Attempting to connect without. To set export SNOWFLAKE_HOST=" + ) + return host + + @contextmanager + def connect( + self, db_name: str, schema_name: Optional[str] = None + ) -> Generator[SnowflakeConnection, None, None]: + """Opens a connection to the database and optional schema. + + This function is a context manager for a connection that can be used to execute queries. + Example usage: + + with connector.connect(db_name="my_db", schema_name="my_schema") as conn: + connector.execute(conn=conn, query="select * from table") + + Args: + db_name: The name of the database to connect to. + schema_name: The name of the schema to connect to. Primarily needed for Snowflake databases. + """ + conn = None + try: + conn = self._open_connection(db_name, schema_name=schema_name) + yield conn + finally: + if conn is not None: + self._close_connection(connection=conn) + + def _open_connection( + self, db_name: str, schema_name: Optional[str] = None + ) -> SnowflakeConnection: + connection = snowflake_connection( + user=self._get_user(), + password=self._get_password(), + account=str(self.account_name), + role=self._get_role(), + warehouse=self._get_warehouse(), + host=self._get_host(), + ) + if db_name: + try: + connection.cursor().execute(f"USE DATABASE {db_name}") + except Exception as e: + raise ValueError( + f"Could not connect to database {db_name}. Does the database exist in {self.account_name}?" + ) from e + if schema_name: + try: + connection.cursor().execute(f"USE SCHEMA {schema_name}") + except Exception as e: + raise ValueError( + f"Could not connect to schema {schema_name}. Does the schema exist in the {db_name} database?" + ) from e + if _QUERY_TAG: + connection.cursor().execute(f"ALTER SESSION SET QUERY_TAG = '{_QUERY_TAG}'") + connection.cursor().execute( + f"ALTER SESSION SET STATEMENT_TIMEOUT_IN_SECONDS = {env_vars.DEFAULT_SESSION_TIMEOUT_SEC}" + ) + return connection + + def _close_connection(self, connection: SnowflakeConnection) -> None: + connection.close() + + def execute( + self, + connection: SnowflakeConnection, + query: str, + ) -> dict[str, list[Any]]: + try: + if connection.warehouse is None: + warehouse = self._get_warehouse() + logger.debug( + f"There is no Warehouse assigned to Connection, setting it to config default ({warehouse})" + ) + # TODO(jhilgart): Do we need to replace - with _? + # Snowflake docs suggest we need identifiers with _, https://docs.snowflake.com/en/sql-reference/identifiers-syntax, + # but unclear if we need this here. + connection.cursor().execute( + f'use warehouse {warehouse.replace("-", "_")}' + ) + cursor = connection.cursor(DictCursor) + logger.info(f"Executing query = {query}") + cursor_execute = cursor.execute(query) + # assert below for MyPy. Should always be true. + assert cursor_execute, "cursor_execute should not be None here" + result = cursor_execute.fetchall() + except ProgrammingError as e: + raise ValueError(f"Query Error: {e}") + + out_dict = defaultdict(list) + for row in result: + if isinstance(row, dict): + for k, v in row.items(): + out_dict[k].append(v) + else: + raise ValueError( + f"Expected a dict for row object. Instead passed {row}" + ) + return out_dict diff --git a/semantic_model_generator/snowflake_utils/utils.py b/semantic_model_generator/snowflake_utils/utils.py new file mode 100644 index 00000000..a760c272 --- /dev/null +++ b/semantic_model_generator/snowflake_utils/utils.py @@ -0,0 +1,73 @@ +from typing import Dict + +from snowflake.connector import connect +from snowflake.connector.connection import SnowflakeConnection + +from semantic_model_generator.data_processing.data_types import FQNParts + + +def create_fqn_table(fqn_str: str) -> FQNParts: + if fqn_str.count(".") != 2: + raise ValueError( + "Expected to have a table fully qualified name following the {database}.{schema}.{table} format." + + f"Instead found {fqn_str}" + ) + database, schema, table = fqn_str.split(".") + return FQNParts(database=database, schema=schema, table=table) + + +def create_connection_parameters( + user: str, + password: str, + account: str, + host: str | None = None, + role: str | None = None, + warehouse: str | None = None, + database: str | None = None, + schema: str | None = None, + authenticator: str | None = None, +) -> Dict[str, str]: + connection_parameters: dict[str, str] = dict( + user=user, password=password, account=account + ) + if role: + connection_parameters["role"] = role + if warehouse: + connection_parameters["warehouse"] = warehouse + if database: + connection_parameters["database"] = database + if schema: + connection_parameters["schema"] = schema + if authenticator: + connection_parameters["authenticator"] = authenticator + if host: + connection_parameters["host"] = host + return connection_parameters + + +def _connection(connection_parameters: dict[str, str]) -> SnowflakeConnection: + # https://docs.snowflake.com/en/developer-guide/python-connector/python-connector-connect + return connect(**connection_parameters) + + +def snowflake_connection( + user: str, + password: str, + account: str, + role: str, + warehouse: str, + host: str | None = None, +) -> SnowflakeConnection: + """ + Returns a Snowflake Connection to the specified account. + """ + return _connection( + create_connection_parameters( + user=user, + password=password, + host=host, + account=account, + role=role, + warehouse=warehouse, + ) + ) diff --git a/semantic_model_generator/tests/main_test.py b/semantic_model_generator/tests/main_test.py new file mode 100644 index 00000000..c674dfa2 --- /dev/null +++ b/semantic_model_generator/tests/main_test.py @@ -0,0 +1,285 @@ +from unittest.mock import MagicMock, mock_open, patch + +import pandas as pd +import pytest +import yaml + +from semantic_model_generator.data_processing import proto_utils +from semantic_model_generator.data_processing.data_types import Column, Table +from semantic_model_generator.main import ( + _to_snake_case, + generate_base_semantic_context_from_snowflake, + raw_schema_to_semantic_context, +) +from semantic_model_generator.protos import semantic_model_pb2 +from semantic_model_generator.snowflake_utils.snowflake_connector import ( + SnowflakeConnector, +) + + +def test_to_snake_case(): + text = "Hello World-How are_you" + + assert "hello_world_how_are_you" == _to_snake_case(text) + + +@pytest.fixture +def mock_snowflake_connection(): + """Fixture to mock the snowflake_connection function.""" + with patch( + "semantic_model_generator.snowflake_utils.snowflake_connector.snowflake_connection" + ) as mock: + mock.return_value = MagicMock() + yield mock + + +_CONVERTED_TABLE_ALIAS = Table( + id_=0, + name="ALIAS", + columns=[ + Column( + id_=0, + column_name="ZIP_CODE", + column_type="TEXT", + values=None, + comment=None, + ), + Column( + id_=1, + column_name="AREA_CODE", + column_type="NUMBER", + values=None, + comment=None, + ), + Column( + id_=2, + column_name="BAD_ALIAS", + column_type="TIMESTAMP", + values=None, + comment=None, + ), + Column( + id_=3, + column_name="CBSA", + column_type="NUMBER", + values=None, + comment=None, + ), + ], + comment=None, +) + +_CONVERTED_TABLE_ZIP_CODE = Table( + id_=0, + name="PRODUCTS", + columns=[ + Column( + id_=0, + column_name="SKU", + column_type="NUMBER", + values=["1", "2", "3"], + comment=None, + ), + ], + comment=None, +) + + +@pytest.fixture +def mock_snowflake_connection_env(monkeypatch): + # Mock environment variable + monkeypatch.setenv("SNOWFLAKE_HOST", "test_host") + + # Use this fixture to also patch instance methods if needed + with patch.object( + SnowflakeConnector, "_get_user", return_value="test_user" + ), patch.object( + SnowflakeConnector, "_get_password", return_value="test_password" + ), patch.object( + SnowflakeConnector, "_get_role", return_value="test_role" + ), patch.object( + SnowflakeConnector, "_get_warehouse", return_value="test_warehouse" + ), patch.object( + SnowflakeConnector, "_get_host", return_value="test_host" + ): + yield + + +@pytest.fixture +def mock_dependencies(mock_snowflake_connection): + valid_schemas_tables_columns_df_alias = pd.DataFrame( + { + "TABLE_NAME": ["ALIAS"] * 4, + "COLUMN_NAME": ["ZIP_CODE", "AREA_CODE", "BAD_ALIAS", "CBSA"], + "DATA_TYPE": ["VARCHAR", "INTEGER", "DATETIME", "DECIMAL"], + } + ) + valid_schemas_tables_columns_df_zip_code = pd.DataFrame( + { + "TABLE_NAME": ["PRODUCTS"], + "COLUMN_NAME": ["SKU"], + "DATA_TYPE": ["NUMBER"], + } + ) + valid_schemas_tables_representations = [ + valid_schemas_tables_columns_df_alias, + valid_schemas_tables_columns_df_zip_code, + ] + table_representations = [ + _CONVERTED_TABLE_ALIAS, # Value returned on the first call. + _CONVERTED_TABLE_ZIP_CODE, # Value returned on the second call. + ] + + with patch( + "semantic_model_generator.main.get_valid_schemas_tables_columns_df", + side_effect=valid_schemas_tables_representations, + ), patch( + "semantic_model_generator.main.get_table_representation", + side_effect=table_representations, + ): + yield + + +def test_raw_schema_to_semantic_context( + mock_dependencies, mock_snowflake_connection, mock_snowflake_connection_env +): + want_yaml = "name: this is the best semantic model ever\ntables:\n - name: ALIAS\n description: ' '\n base_table:\n database: test_db\n schema: schema_test\n table: ALIAS\n filters:\n - name: ' '\n synonyms:\n - ' '\n description: ' '\n expr: ' '\n dimensions:\n - name: ZIP_CODE\n synonyms:\n - ' '\n description: ' '\n expr: ZIP_CODE\n data_type: TEXT\n time_dimensions:\n - name: BAD_ALIAS\n synonyms:\n - ' '\n description: ' '\n expr: BAD_ALIAS\n data_type: TIMESTAMP\n measures:\n - name: AREA_CODE\n synonyms:\n - ' '\n description: ' '\n expr: AREA_CODE\n data_type: NUMBER\n - name: CBSA\n synonyms:\n - ' '\n description: ' '\n expr: CBSA\n data_type: NUMBER\n" + + snowflake_account = "test_account" + fqn_tables = ["test_db.schema_test.ALIAS"] + semantic_model_name = "this is the best semantic model ever" + + semantic_model = raw_schema_to_semantic_context( + fqn_tables=fqn_tables, + snowflake_account=snowflake_account, + semantic_model_name=semantic_model_name, + ) + + # Assert the result as expected + assert isinstance(semantic_model, semantic_model_pb2.SemanticModel) + assert len(semantic_model.tables) > 0 + + result_yaml = proto_utils.proto_to_yaml(semantic_model) + assert result_yaml == want_yaml + + mock_snowflake_connection.assert_called_once_with( + user="test_user", + password="test_password", + account="test_account", + role="test_role", + warehouse="test_warehouse", + host="test_host", + ) + + +@patch("builtins.open", new_callable=mock_open) +def test_generate_base_context_with_placeholder_comments( + mock_file, + mock_dependencies, + mock_snowflake_connection, + mock_snowflake_connection_env, +): + + fqn_tables = ["test_db.schema_test.ALIAS"] + snowflake_account = "test_account" + output_path = "output_model_path.yaml" + semantic_model_name = "my awesome semantic model" + + generate_base_semantic_context_from_snowflake( + fqn_tables=fqn_tables, + snowflake_account=snowflake_account, + output_yaml_path=output_path, + semantic_model_name=semantic_model_name, + ) + + mock_file.assert_called_once_with(output_path, "w") + # Assert file save called with placeholder comments added. + mock_file().write.assert_called_once_with( + "name: my awesome semantic model\ntables:\n - name: ALIAS\n description: ' ' # \n base_table:\n database: test_db\n schema: schema_test\n table: ALIAS\n filters:\n - name: ' ' # \n synonyms:\n - ' ' # \n description: ' ' # \n expr: ' ' # \n dimensions:\n - name: ZIP_CODE\n synonyms:\n - ' ' # \n description: ' ' # \n expr: ZIP_CODE\n data_type: TEXT\n time_dimensions:\n - name: BAD_ALIAS\n synonyms:\n - ' ' # \n description: ' ' # \n expr: BAD_ALIAS\n data_type: TIMESTAMP\n measures:\n - name: AREA_CODE\n synonyms:\n - ' ' # \n description: ' ' # \n expr: AREA_CODE\n data_type: NUMBER\n - name: CBSA\n synonyms:\n - ' ' # \n description: ' ' # \n expr: CBSA\n data_type: NUMBER\n" + ) + + +@patch("builtins.open", new_callable=mock_open) +def test_generate_base_context_with_placeholder_comments_cross_database_cross_schema( + mock_file, + mock_dependencies, + mock_snowflake_connection, + mock_snowflake_connection_env, +): + + fqn_tables = [ + "test_db.schema_test.ALIAS", + "a_different_database.a_different_schema.PRODUCTS", + ] + snowflake_account = "test_account" + output_path = "output_model_path.yaml" + semantic_model_name = "Another Incredible Semantic Model" + + generate_base_semantic_context_from_snowflake( + fqn_tables=fqn_tables, + snowflake_account=snowflake_account, + output_yaml_path=output_path, + semantic_model_name=semantic_model_name, + ) + + mock_file.assert_called_once_with(output_path, "w") + # Assert file save called with placeholder comments added along with sample values and cross-database + mock_file().write.assert_called_once_with( + "name: Another Incredible Semantic Model\ntables:\n - name: ALIAS\n description: ' ' # \n base_table:\n database: test_db\n schema: schema_test\n table: ALIAS\n filters:\n - name: ' ' # \n synonyms:\n - ' ' # \n description: ' ' # \n expr: ' ' # \n dimensions:\n - name: ZIP_CODE\n synonyms:\n - ' ' # \n description: ' ' # \n expr: ZIP_CODE\n data_type: TEXT\n time_dimensions:\n - name: BAD_ALIAS\n synonyms:\n - ' ' # \n description: ' ' # \n expr: BAD_ALIAS\n data_type: TIMESTAMP\n measures:\n - name: AREA_CODE\n synonyms:\n - ' ' # \n description: ' ' # \n expr: AREA_CODE\n data_type: NUMBER\n - name: CBSA\n synonyms:\n - ' ' # \n description: ' ' # \n expr: CBSA\n data_type: NUMBER\n - name: PRODUCTS\n description: ' ' # \n base_table:\n database: a_different_database\n schema: a_different_schema\n table: PRODUCTS\n filters:\n - name: ' ' # \n synonyms:\n - ' ' # \n description: ' ' # \n expr: ' ' # \n measures:\n - name: SKU\n synonyms:\n - ' ' # \n description: ' ' # \n expr: SKU\n data_type: NUMBER\n sample_values:\n - '1'\n - '2'\n - '3'\n" + ) + + +def test_semantic_model_to_yaml() -> None: + want_yaml = "name: transaction_ctx\ntables:\n - name: transactions\n description: A table containing data about financial transactions. Each row contains\n details of a financial transaction.\n base_table:\n database: my_database\n schema: my_schema\n table: transactions\n dimensions:\n - name: transaction_id\n description: A unique id for this transaction.\n expr: transaction_id\n data_type: BIGINT\n unique: true\n time_dimensions:\n - name: initiation_date\n description: Timestamp when the transaction was initiated. In UTC.\n expr: initiation_date\n data_type: DATETIME\n measures:\n - name: amount\n description: The amount of this transaction.\n expr: amount\n data_type: DECIMAL\n default_aggregation: sum\n" + got = semantic_model_pb2.SemanticModel( + name="transaction_ctx", + tables=[ + semantic_model_pb2.Table( + name="transactions", + description="A table containing data about financial transactions. Each row contains details of a financial transaction.", + base_table=semantic_model_pb2.FullyQualifiedTable( + database="my_database", + schema="my_schema", + table="transactions", + ), + time_dimensions=[ + semantic_model_pb2.TimeDimension( + name="initiation_date", + description="Timestamp when the transaction was initiated. In UTC.", + expr="initiation_date", + data_type="DATETIME", + unique=False, + ) + ], + measures=[ + semantic_model_pb2.Measure( + name="amount", + description="The amount of this transaction.", + expr="amount", + data_type="DECIMAL", + default_aggregation=semantic_model_pb2.AggregationType.sum, + ), + ], + dimensions=[ + semantic_model_pb2.Dimension( + name="transaction_id", + description="A unique id for this transaction.", + expr="transaction_id", + data_type="BIGINT", + unique=True, + ) + ], + ) + ], + ) + got_yaml = proto_utils.proto_to_yaml(got) + assert got_yaml == want_yaml + + # Parse the YAML strings into Python data structures + want_data = yaml.safe_load(want_yaml) + got_data = yaml.safe_load(got_yaml) + + # Now compare the data structures + assert ( + want_data == got_data + ), "The generated YAML does not match the expected structure." diff --git a/semantic_model_generator/tests/snowflake_connector_test.py b/semantic_model_generator/tests/snowflake_connector_test.py new file mode 100644 index 00000000..702c5b19 --- /dev/null +++ b/semantic_model_generator/tests/snowflake_connector_test.py @@ -0,0 +1,247 @@ +from unittest import mock +from unittest.mock import MagicMock, call, patch + +import pandas as pd +import pytest +from pandas.testing import assert_frame_equal + +from semantic_model_generator.data_processing.data_types import Column, Table +from semantic_model_generator.snowflake_utils import snowflake_connector + + +@pytest.fixture +def mock_snowflake_connection_env(monkeypatch): + # Mock environment variable + monkeypatch.setenv("SNOWFLAKE_HOST", "test_host") + + # Use this fixture to also patch instance methods if needed + with patch.object( + snowflake_connector.SnowflakeConnector, "_get_user", return_value="test_user" + ), patch.object( + snowflake_connector.SnowflakeConnector, + "_get_password", + return_value="test_password", + ), patch.object( + snowflake_connector.SnowflakeConnector, "_get_role", return_value="test_role" + ), patch.object( + snowflake_connector.SnowflakeConnector, + "_get_warehouse", + return_value="test_warehouse", + ), patch.object( + snowflake_connector.SnowflakeConnector, "_get_host", return_value="test_host" + ): + yield + + +@pytest.fixture +def schemas_tables_columns() -> pd.DataFrame: + return pd.DataFrame( + columns=[ + "TABLE_SCHEMA", + "TABLE_NAME", + "COLUMN_NAME", + "DATA_TYPE", + "COLUMN_COMMENT", + ], + data=[ + ["TEST_SCHEMA_1", "table_1", "col_1", "VARCHAR", None], + ["TEST_SCHEMA_1", "table_1", "col_2", "NUMBER", None], + ["TEST_SCHEMA_1", "table_2", "col_1", "NUMBER", "table_2_col_1_comment"], + [ + "TEST_SCHEMA_1", + "table_2", + "col_2", + "TIMESTAMP_NTZ", + "table_2_col_2_comment", + ], + ["TEST_SCHEMA_2", "table_3", "col_1", "VARIANT", None], + [ + "TEST_SCHEMA_2", + "invalid_table", + "col_1", + "VARIANT", + "invalid_table_col_1_comment", + ], + ], + ) + + +@pytest.fixture +def valid_tables() -> pd.DataFrame: + return pd.DataFrame( + columns=["TABLE_SCHEMA", "TABLE_NAME", "TABLE_COMMENT"], + data=[ + ["TEST_SCHEMA_1", "table_1", None], + ["TEST_SCHEMA_1", "table_2", "table_2_comment"], + ["TEST_SCHEMA_2", "table_3", "table_3_comment"], + ], + ) + + +_TEST_TABLE_ONE = Table( + id_=0, + name="table_1", + columns=[ + Column( + id_=0, + column_name="col_1", + column_type="text", + is_primary_key=True, + is_foreign_key=False, + ), + Column( + id_=1, + column_name="col_2", + column_type="number", + is_primary_key=False, + is_foreign_key=False, + ), + ], +) + + +@mock.patch( + "semantic_model_generator.snowflake_utils.snowflake_connector.snowflake_connection" +) +def test_connect( + mock_snowflake_connection: mock.MagicMock, mock_snowflake_connection_env +): + mock_snowflake_connection.return_value = mock.MagicMock() + + connector = snowflake_connector.SnowflakeConnector(account_name="test_account") + with connector.connect(db_name="test") as conn: + pass + + conn.cursor().execute.assert_has_calls( + [ + call("USE DATABASE test"), + call("ALTER SESSION SET QUERY_TAG = 'SEMANTIC_MODEL_GENERATOR'"), + call("ALTER SESSION SET STATEMENT_TIMEOUT_IN_SECONDS = 120"), + ] + ) + conn.close.assert_called_with() + + +@mock.patch( + "semantic_model_generator.snowflake_utils.snowflake_connector.snowflake_connection" +) +def test_connect_with_schema( + mock_snowflake_connection: mock.MagicMock, mock_snowflake_connection_env +): + mock_snowflake_connection.return_value = mock.MagicMock() + + connector = snowflake_connector.SnowflakeConnector( + account_name="test_account", + ) + with connector.connect(db_name="test_db", schema_name="test_schema") as conn: + pass + + conn.cursor().execute.assert_has_calls( + [ + call("USE DATABASE test_db"), + call("USE SCHEMA test_schema"), + call("ALTER SESSION SET QUERY_TAG = 'SEMANTIC_MODEL_GENERATOR'"), + call("ALTER SESSION SET STATEMENT_TIMEOUT_IN_SECONDS = 120"), + ] + ) + conn.close.assert_called_with() + + +@mock.patch( + "semantic_model_generator.snowflake_utils.snowflake_connector._fetch_valid_tables_and_views" +) +@mock.patch( + "semantic_model_generator.snowflake_utils.snowflake_connector.snowflake_connection" +) +def test_get_valid_schema_table_columns_df( + mock_snowflake_connection: mock.MagicMock, + mock_valid_tables: mock.MagicMock, + valid_tables: pd.DataFrame, + schemas_tables_columns: pd.DataFrame, +): + mock_conn = mock.MagicMock() + # We expect get_database_representation() to execute queries in this order: + # - select from information_schema.tables + # - select from information_schema.columns for each table. + mock_conn.cursor().execute().fetch_pandas_all.side_effect = [ + schemas_tables_columns[schemas_tables_columns["TABLE_NAME"] == "table_1"] + ] + mock_snowflake_connection.return_value = mock_conn + mock_valid_tables.return_value = valid_tables + + got = snowflake_connector.get_valid_schemas_tables_columns_df( + mock_conn, "TEST_SCHEMA_1", ["table_1"] + ) + + want_data = { + "TABLE_SCHEMA": ["TEST_SCHEMA_1", "TEST_SCHEMA_1"], + "TABLE_NAME": ["table_1", "table_1"], + "TABLE_COMMENT": [None, None], + "COLUMN_NAME": ["col_1", "col_2"], + "DATA_TYPE": ["VARCHAR", "NUMBER"], + "COLUMN_COMMENT": [None, None], + } + + # Create a DataFrame + want = pd.DataFrame(want_data) + + assert_frame_equal(want, got) + + # Assert that the connection executed the expected queries. + query = "select t.TABLE_SCHEMA, t.TABLE_NAME, c.COLUMN_NAME, c.DATA_TYPE, c.COMMENT as COLUMN_COMMENT\nfrom information_schema.tables as t\njoin information_schema.columns as c on t.table_schema = c.table_schema and t.table_name = c.table_name where t.table_schema ilike 'TEST_SCHEMA_1' AND LOWER(t.table_name) in ('table_1') \norder by 1, 2, c.ordinal_position" + mock_conn.cursor().execute.assert_any_call(query) + + +@pytest.fixture +def snowflake_data(): + return [ + # This mimics the return value of cursor.fetchall() for tables and views + ([("table1", "schema1", "A table comment")], [("column1", "dtype")]), + ([("view1", "schema1", "A view comment")], [("column1", "dtype")]), + ] + + +@pytest.fixture +def expected_df(): + # Expected DataFrame structure based on mocked fetchall data + return pd.DataFrame( + { + snowflake_connector._TABLE_NAME_COL: ["table1", "view1"], + snowflake_connector._TABLE_SCHEMA_COL: ["schema1", "schema1"], + snowflake_connector._TABLE_COMMENT_COL: [ + "A table comment", + "A view comment", + ], + } + ) + + +def test_fetch_valid_tables_and_views(snowflake_data, expected_df): + # Mock SnowflakeConnection and cursor + mock_conn = mock.MagicMock() + mock_cursor = mock_conn.cursor.return_value + mock_cursor.execute.return_value = mock_cursor + # Set side effects for fetchall and description based on snowflake_data fixture + mock_cursor.fetchall.side_effect = [snowflake_data[0][0], snowflake_data[1][0]] + + mock_name_one = MagicMock() + mock_name_one.name = "name" + mock_name_two = MagicMock() + mock_name_two.name = "schema_name" + mock_name_three = MagicMock() + mock_name_three.name = "comment" + + mocked_descriptions = [mock_name_one, mock_name_two, mock_name_three] + mock_cursor.description = mocked_descriptions + + # Call the function to test + result_df = snowflake_connector._fetch_valid_tables_and_views(mock_conn) + + # Assert the result is as expected + pd.testing.assert_frame_equal( + result_df.reset_index(drop=True), expected_df.reset_index(drop=True) + ) + + # Verify execute was called with correct queries + mock_cursor.execute.assert_any_call("show tables in database") + mock_cursor.execute.assert_any_call("show views in database") diff --git a/semantic_model_generator/tests/utils_test.py b/semantic_model_generator/tests/utils_test.py new file mode 100644 index 00000000..dc826ef2 --- /dev/null +++ b/semantic_model_generator/tests/utils_test.py @@ -0,0 +1,18 @@ +import pytest + +from semantic_model_generator.data_processing.data_types import FQNParts +from semantic_model_generator.snowflake_utils.utils import create_fqn_table + + +def test_fqn_creation(): + input_name = "database.schema.table" + + fqn_parts = create_fqn_table(input_name) + + assert fqn_parts == FQNParts(database="database", schema="schema", table="table") + + +def test_fqn_creation_invalid_name(): + input_name = "database.schema table" + with pytest.raises(ValueError): + create_fqn_table(input_name)