From d22e7bb4e7f68e70133c25a12c26a17cda8a985c Mon Sep 17 00:00:00 2001 From: oliverlaslett <11660098+owlas@users.noreply.github.com> Date: Wed, 4 Feb 2026 12:44:51 +0000 Subject: [PATCH] feat: query builder + async query api --- README.md | 4 +- docs/SDK_GUIDE.md | 364 +++++++ examples/getting_started.ipynb | 455 +++++++-- justfile | 2 +- lightdash/__init__.py | 26 +- lightdash/client.py | 35 +- lightdash/dimensions.py | 180 +++- lightdash/exceptions.py | 31 + lightdash/filter.py | 88 +- lightdash/metrics.py | 86 +- lightdash/models.py | 122 ++- lightdash/query.py | 707 ++++++++++--- lightdash/results.py | 99 ++ lightdash/sorting.py | 77 ++ lightdash/sql_runner.py | 235 +++++ pyproject.toml | 4 +- tests/test_acceptance.py | 8 +- tests/test_filter_operators.py | 249 +++++ tests/test_query_builder.py | 308 ++++++ tests/test_results.py | 121 +++ tests/test_validation.py | 248 +++++ uv.lock | 1754 ++++++++++++++++---------------- 22 files changed, 4070 insertions(+), 1133 deletions(-) create mode 100644 docs/SDK_GUIDE.md create mode 100644 lightdash/exceptions.py create mode 100644 lightdash/results.py create mode 100644 lightdash/sorting.py create mode 100644 lightdash/sql_runner.py create mode 100644 tests/test_filter_operators.py create mode 100644 tests/test_query_builder.py create mode 100644 tests/test_results.py create mode 100644 tests/test_validation.py diff --git a/README.md b/README.md index 63237a7..50f2481 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,9 @@ pip install lightdash ## Usage -See the [example notebook](examples/getting_started.ipynb) for a tutorial of how to use the client. +See the [SDK Guide](docs/SDK_GUIDE.md) for full documentation on the query builder, filters, and other features. + +For an interactive tutorial, check out the [example notebook](examples/getting_started.ipynb). ```python from lightdash import Client diff --git a/docs/SDK_GUIDE.md b/docs/SDK_GUIDE.md new file mode 100644 index 0000000..a1012ba --- /dev/null +++ b/docs/SDK_GUIDE.md @@ -0,0 +1,364 @@ +# Lightdash Python SDK Guide + +This guide covers the query builder, filtering system, and other features of the Lightdash Python SDK. + +## Quick Start + +```python +from lightdash import Client + +client = Client(base_url="https://app.lightdash.cloud", token="your-token") +model = client.get_model("your_project_uuid", "your_model_name") + +# Build and execute a query +result = ( + model.query() + .metrics(model.metrics.revenue, model.metrics.profit) + .dimensions(model.dimensions.country) + .filter(model.dimensions.status == "active") + .sort(model.metrics.revenue.desc()) + .limit(100) + .execute() +) + +df = result.to_df() +``` + +--- + +## Query Builder + +The SDK provides two patterns for building queries: **single-call** and **chainable builder**. + +### Single-Call Pattern + +Pass all parameters at once—useful for simple queries: + +```python +query = model.query( + metrics=[model.metrics.revenue, model.metrics.profit], + dimensions=[model.dimensions.country], + filters=model.dimensions.status == "active", + sort=model.metrics.revenue.desc(), + limit=100 +) +result = query.execute() +``` + +### Chainable Builder Pattern + +Build queries incrementally with method chaining—each method returns a new immutable `Query` object: + +```python +query = ( + model.query() + .metrics(model.metrics.revenue) + .dimensions(model.dimensions.country, model.dimensions.date) + .filter(model.dimensions.status == "active") + .sort(model.metrics.revenue.desc()) + .limit(100) +) +``` + +**Key characteristics:** + +- **Immutable**: Each method returns a new Query object (safe for reuse) +- **Lazy evaluation**: API calls only happen when `.execute()` is called +- **Order-independent**: Methods can be called in any order +- **Composable**: Create base queries and extend them + +```python +# Create a reusable base query +base = model.query().metrics(model.metrics.revenue).dimensions(model.dimensions.country) + +# Extend it for different use cases +by_active = base.filter(model.dimensions.status == "active") +by_inactive = base.filter(model.dimensions.status == "inactive") +``` + +--- + +## Filters + +Filters let you constrain your query results. The SDK provides an intuitive, Pythonic API for creating filters. + +### Creating Filters with Operators + +Use standard Python comparison operators on dimensions: + +```python +# Equality +f = model.dimensions.country == "USA" + +# Inequality +f = model.dimensions.country != "USA" + +# Numeric comparisons +f = model.dimensions.amount > 1000 +f = model.dimensions.amount >= 1000 +f = model.dimensions.amount < 500 +f = model.dimensions.amount <= 500 +``` + +### Filter Helper Methods + +For more complex filters, use helper methods: + +```python +# Check if value is in a list +f = model.dimensions.country.in_(["USA", "UK", "Canada"]) + +# String operations +f = model.dimensions.name.starts_with("John") +f = model.dimensions.name.ends_with("son") +f = model.dimensions.name.includes("Smith") + +# Null checks +f = model.dimensions.email.is_null() +f = model.dimensions.email.is_not_null() +``` + +### Supported Operators by Data Type + +| Operator | Numeric | String | Boolean | Date | +|----------|---------|--------|---------|------| +| `is null` | Yes | Yes | Yes | Yes | +| `is not null` | Yes | Yes | Yes | Yes | +| `equals` / `is` | Yes | Yes | Yes | Yes | +| `is not` | Yes | Yes | - | Yes | +| `is less than` | Yes | - | - | - | +| `is greater than` | Yes | - | - | - | +| `starts with` | - | Yes | - | - | +| `ends with` | - | Yes | - | - | +| `includes` | - | Yes | - | - | +| `in the last` | - | - | - | Yes | +| `in the next` | - | - | - | Yes | +| `in the current` | - | - | - | Yes | +| `is before` | - | - | - | Yes | +| `is after` | - | - | - | Yes | +| `is between` | - | - | - | Yes | + +### Combining Filters + +Use `&` (AND) and `|` (OR) operators to combine filters: + +```python +# AND: Both conditions must be true +f = (model.dimensions.country == "USA") & (model.dimensions.amount > 1000) + +# OR: Either condition must be true +f = (model.dimensions.status == "active") | (model.dimensions.status == "pending") + +# Complex combinations +f = ( + (model.dimensions.country == "USA") & + ((model.dimensions.amount > 1000) | (model.dimensions.priority == "high")) +) +``` + +Multiple `.filter()` calls on a query are combined with AND logic: + +```python +query = ( + model.query() + .filter(model.dimensions.country == "USA") + .filter(model.dimensions.amount > 1000) # AND-ed with above +) +``` + +--- + +## Dimensions and Metrics + +Access dimensions and metrics as attributes on the model: + +```python +# Access via attribute +country = model.dimensions.country +revenue = model.metrics.revenue + +# List all available +all_dimensions = model.dimensions.list() +all_metrics = model.metrics.list() +``` + +**Features:** + +- **Lazy loading**: Fetched from API on first access, then cached +- **Fuzzy matching**: Typos suggest closest matches +- **Tab completion**: Works in Jupyter/IPython for discovery +- **Rich display**: HTML rendering in notebooks + +--- + +## Sorting + +Sort results using the `.sort()` method or `Sort` class: + +```python +from lightdash import Sort + +# Using metric/dimension methods (recommended) +query = model.query().sort(model.metrics.revenue.desc()) +query = model.query().sort(model.dimensions.country.asc()) + +# Multiple sorts +query = model.query().sort( + model.metrics.revenue.desc(), + model.dimensions.country.asc() +) + +# Control null positioning +query = model.query().sort(model.dimensions.name.asc(nulls_first=True)) + +# Using Sort class directly +query = model.query().sort(Sort("orders_revenue", descending=True)) +``` + +--- + +## Results + +Query results implement a unified `ResultSet` interface: + +### Converting Results + +```python +result = query.execute() + +# To pandas DataFrame +df = result.to_df() # or result.to_df(backend="pandas") + +# To polars DataFrame +df = result.to_df(backend="polars") + +# To list of dictionaries +records = result.to_records() + +# To JSON string +json_str = result.to_json_str() +``` + +### Iterating Over Results + +```python +# Iterate over rows +for row in result: + print(row) + +# Get total count +total = len(result) +``` + +### Pagination + +For large result sets, results are paginated automatically: + +```python +result = query.execute() + +# Access specific page +page_2 = result.page(2) + +# Iterate through all pages +for page in result.iter_pages(): + process(page) + +# Lazy DataFrame loading (polars only) +lazy_df = result.to_df_lazy() +``` + +**Properties:** + +- `result.query_uuid` - Unique identifier for the query +- `result.total_results` - Total number of rows +- `result.total_pages` - Number of pages +- `result.fields` - Field metadata + +--- + +## SQL Runner + +Execute raw SQL queries directly against your data warehouse: + +```python +# Execute SQL +result = client.sql("SELECT * FROM orders WHERE status = 'active' LIMIT 100") +df = result.to_df() + +# With custom limit +result = client.sql("SELECT * FROM orders", limit=1000) + +# Introspection +tables = client.sql_runner.tables() +fields = client.sql_runner.fields("orders") +fields = client.sql_runner.fields("orders", schema="public") +``` + +--- + +## Exception Handling + +The SDK provides specific exceptions for different error conditions: + +```python +from lightdash import LightdashError, QueryError, QueryTimeout, QueryCancelled + +try: + result = query.execute() +except QueryTimeout as e: + print(f"Query timed out: {e.query_uuid}") +except QueryCancelled as e: + print(f"Query was cancelled: {e.query_uuid}") +except QueryError as e: + print(f"Query failed: {e.message}") +except LightdashError as e: + print(f"Lightdash error: {e.message} (status: {e.status_code})") +``` + +**Exception hierarchy:** + +- `LightdashError` - Base exception for all SDK errors + - `QueryError` - Query execution failed (HTTP 400) + - `QueryTimeout` - Query exceeded timeout (HTTP 408) + - `QueryCancelled` - Query was cancelled (HTTP 499) + +--- + +## Complete Example + +```python +from lightdash import Client, QueryError, QueryTimeout + +# Initialize client +client = Client( + base_url="https://app.lightdash.cloud", + token="your-api-token" +) + +# Get a model +model = client.get_model("project-uuid", "orders") + +# Build a query with filters +query = ( + model.query() + .metrics(model.metrics.total_revenue, model.metrics.order_count) + .dimensions(model.dimensions.country, model.dimensions.order_date) + .filter( + (model.dimensions.status == "completed") & + (model.dimensions.order_date >= "2024-01-01") + ) + .sort(model.metrics.total_revenue.desc()) + .limit(50) +) + +try: + result = query.execute() + df = result.to_df() + print(f"Fetched {len(result)} rows") + print(df.head()) +except QueryTimeout: + print("Query took too long - try adding more filters") +except QueryError as e: + print(f"Query failed: {e.message}") +``` diff --git a/examples/getting_started.ipynb b/examples/getting_started.ipynb index 9edbee9..4e0204c 100644 --- a/examples/getting_started.ipynb +++ b/examples/getting_started.ipynb @@ -22,7 +22,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Setup Lightdash Client\n", + "## 1. Setup Lightdash Client\n", "\n", "Make sure you have the following environment variables set:\n", "- `LIGHTDASH_ACCESS_TOKEN`\n", @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -56,150 +56,264 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run a query\n", + "## 2. Explore the semantic layer\n", "\n", - "### 1. Start with a model\n", + "### 2a. Find a model\n", "\n", - "You can use the client to list available models in your semantic layer:" + "You can use tab completion on `client.models` to find relevant models. If you print out the model in a notebook you'll get a rich preview including the model description and available metrics + dimensions" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# print all models\n", - "client.list_models()\n", - "\n", - "# get a specific model (try tab completion!)\n", "orders = client.models.dbt_orders" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "\n", + "
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This table contains information on all the confirmed orders and their status
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Average Profit per Order
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Email
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Average Profit per Order
Average profit earned per order. Track your margins as they grow!
Median Profit
Median of Profit
Profit
Total profit from all orders. The fruits of your labor!
Revenue
Total sales revenue from all orders.
Unique Customers
Number of unique customers who placed orders.
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...and 5 more
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" ], "text/plain": [ - " Partner name Sum of profit\n", - "0 Happy Harvesters 83031.18\n", - "1 Garden of Sweden 52685.50\n", - "2 Peat, Fruits and Leaves 120270.90\n", - "3 Redwood Ranch 708107.40\n", - "4 Potted Paradise 65746.48\n", - "5 Roots & Shoots 133449.58\n", - "6 Trowels R Us 94590.56\n", - "7 Sprout Society 114499.07\n", - "8 Plant Paradise 142884.72" + " Browser Partner name Revenue\n", + "0 chrome Redwood Ranch 1595232\n", + "1 safari Redwood Ranch 873290\n", + "2 edge Redwood Ranch 554889\n", + "3 internet_explorer Redwood Ranch 55904\n", + "4 netscape Redwood Ranch 24285\n", + "5 lynx Redwood Ranch 9886\n", + "6 firefox Redwood Ranch 105184" ] }, - "execution_count": 5, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# List available dimensions\n", - "orders.list_dimensions()\n", + "f = (orders.dimensions.partner_name == 'Redwood Ranch')\n", + "\n", + "query = (orders.query()\n", + " .metrics(orders.metrics.sum_of_basket_total)\n", + " .dimensions(orders.dimensions.browser)\n", + " .dimensions(orders.dimensions.partner_name)\n", + " .filter(f))\n", "\n", - "# Query profit broken down by partner\n", - "orders.query(\n", - " dimensions=[orders.dimensions.partner_name],\n", - " metrics=[orders.metrics.sum_of_profit]\n", - ").to_df()" + "# Execute the query and print as a dataframe. You can also use .to_records .to_json .to_json_string\n", + "query.to_df()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Run Raw SQL" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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01
\n", + "
" + ], + "text/plain": [ + " hello\n", + "0 1" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "client.sql('SELECT 1 as hello').to_df()" ] } ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -327,5 +602,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/justfile b/justfile index 6cb3eee..1813409 100644 --- a/justfile +++ b/justfile @@ -15,7 +15,7 @@ test: else echo "Warning: .env file not found. Make sure environment variables are set manually." fi - pytest tests/test_acceptance.py -v + uv run pytest tests/test_acceptance.py -v # Clean build artifacts clean: diff --git a/lightdash/__init__.py b/lightdash/__init__.py index a9382da..e876172 100644 --- a/lightdash/__init__.py +++ b/lightdash/__init__.py @@ -4,5 +4,29 @@ """ from lightdash.client import Client +from lightdash.exceptions import ( + LightdashError, + QueryError, + QueryTimeout, + QueryCancelled, +) +from lightdash.query import QueryResult +from lightdash.sorting import Sort +from lightdash.filter import DimensionFilter, CompositeFilter +from lightdash.sql_runner import SqlResult +from lightdash.results import ResultSet, BaseResult -__all__ = ['Client'] +__all__ = [ + 'Client', + 'LightdashError', + 'QueryError', + 'QueryTimeout', + 'QueryCancelled', + 'QueryResult', + 'Sort', + 'DimensionFilter', + 'CompositeFilter', + 'SqlResult', + 'ResultSet', + 'BaseResult', +] diff --git a/lightdash/client.py b/lightdash/client.py index f261c71..2e44768 100644 --- a/lightdash/client.py +++ b/lightdash/client.py @@ -9,6 +9,8 @@ from urllib.parse import urljoin from .models import Model, Models +from .exceptions import LightdashError +from .sql_runner import SqlRunner, SqlResult # Configure module logger @@ -16,15 +18,6 @@ logger.addHandler(logging.NullHandler()) -class LightdashError(Exception): - """Base exception for Lightdash API errors.""" - def __init__(self, message: str, name: str, status_code: int): - self.message = message - self.name = name - self.status_code = status_code - super().__init__(f"{name} ({status_code}): {message}") - - class Client: """ A client for interacting with the Lightdash API. @@ -177,3 +170,27 @@ def get_model(self, name: str) -> Model: A Model object representing the requested explore """ return self.models.get(name) + + @property + def sql_runner(self) -> SqlRunner: + """Access to SQL runner for raw SQL queries and table introspection.""" + if not hasattr(self, '_sql_runner'): + self._sql_runner = SqlRunner(self) + return self._sql_runner + + def sql(self, query: str, limit: int = 500) -> SqlResult: + """ + Execute a raw SQL query against the data warehouse. + + Args: + query: SQL query string + limit: Maximum rows to return (default 500) + + Returns: + SqlResult object with query results + + Example: + result = client.sql("SELECT * FROM orders LIMIT 10") + df = result.to_df() + """ + return self.sql_runner.execute(query, limit) diff --git a/lightdash/dimensions.py b/lightdash/dimensions.py index fa5929e..7f8efcf 100644 --- a/lightdash/dimensions.py +++ b/lightdash/dimensions.py @@ -1,11 +1,18 @@ """ Dimensions for Lightdash models. """ +from __future__ import annotations + +import html from dataclasses import dataclass -from typing import Any, Dict, List, Optional +from difflib import get_close_matches +from typing import Any, Dict, List, Optional, Union, TYPE_CHECKING from .types import Model, Dimension as DimensionProtocol +if TYPE_CHECKING: + from .filter import DimensionFilter + @dataclass class Dimension: @@ -15,6 +22,10 @@ class Dimension: label: Optional[str] = None description: Optional[str] = None + def __hash__(self) -> int: + """Make Dimension hashable for use in sets and dict keys.""" + return hash((self.name, self.model_name, self.label, self.description)) + def __str__(self) -> str: desc_part = f": {self.description}" if self.description else "" return f"Dimension({self.name}{desc_part})" @@ -25,6 +36,33 @@ def _repr_pretty_(self, p, cycle): else: p.text(str(self)) + def _repr_html_(self) -> str: + """Rich HTML display for Jupyter notebooks.""" + display_name = html.escape(self.label or self.name) + field_id = html.escape(self.field_id) + model_name = html.escape(self.model_name) + + desc_html = "" + if self.description: + escaped_desc = html.escape(self.description) + desc_html = f'
{escaped_desc}
' + + return f""" +
+
+ {display_name} +
+
+
Type: Dimension
+
Model: {model_name}
+
+
+ {field_id} +
+ {desc_html} +
+ """ + @property def field_id(self) -> str: """Get the field ID for this dimension in the format model_name.""" @@ -40,6 +78,89 @@ def from_api_response(cls, data: Dict[str, Any], model_name: str) -> "Dimension" description=data.get("description"), ) + # ------------------------------------------------------------------------- + # Filter operator overloading + # ------------------------------------------------------------------------- + + def __eq__(self, other: Any) -> Union[bool, "DimensionFilter"]: # type: ignore[override] + """Create equals filter: dim == 'value' or dim == ['a', 'b']""" + if isinstance(other, Dimension): + # Allow normal dataclass equality checks + return ( + self.name == other.name + and self.model_name == other.model_name + and self.label == other.label + and self.description == other.description + ) + from .filter import DimensionFilter + values = other if isinstance(other, list) else [other] + return DimensionFilter(field=self, operator="equals", values=values) + + def __ne__(self, other: Any) -> Union[bool, "DimensionFilter"]: # type: ignore[override] + """Create not equals filter: dim != 'value'""" + if isinstance(other, Dimension): + return not self.__eq__(other) + from .filter import DimensionFilter + values = other if isinstance(other, list) else [other] + return DimensionFilter(field=self, operator="is not", values=values) + + def __gt__(self, other: Any) -> "DimensionFilter": + """Create greater than filter: dim > value""" + from .filter import DimensionFilter + return DimensionFilter(field=self, operator="is greater than", values=[other]) + + def __lt__(self, other: Any) -> "DimensionFilter": + """Create less than filter: dim < value""" + from .filter import DimensionFilter + return DimensionFilter(field=self, operator="is less than", values=[other]) + + def __ge__(self, other: Any) -> "DimensionFilter": + """Create >= filter: dim >= value (uses 'is greater than' with adjusted value)""" + from .filter import DimensionFilter + # Note: Lightdash API doesn't have >= directly, this is an approximation + return DimensionFilter(field=self, operator="is greater than", values=[other]) + + def __le__(self, other: Any) -> "DimensionFilter": + """Create <= filter: dim <= value (uses 'is less than' with adjusted value)""" + from .filter import DimensionFilter + # Note: Lightdash API doesn't have <= directly, this is an approximation + return DimensionFilter(field=self, operator="is less than", values=[other]) + + def in_(self, values: List[Any]) -> "DimensionFilter": + """Create 'in' filter: dim.in_(['a', 'b'])""" + from .filter import DimensionFilter + return DimensionFilter(field=self, operator="equals", values=values) + + def not_in(self, values: List[Any]) -> "DimensionFilter": + """Create 'not in' filter: dim.not_in(['a', 'b'])""" + from .filter import DimensionFilter + return DimensionFilter(field=self, operator="is not", values=values) + + def contains(self, value: str) -> "DimensionFilter": + """Create contains filter: dim.contains('substring')""" + from .filter import DimensionFilter + return DimensionFilter(field=self, operator="includes", values=[value]) + + def starts_with(self, value: str) -> "DimensionFilter": + """Create starts with filter: dim.starts_with('prefix')""" + from .filter import DimensionFilter + return DimensionFilter(field=self, operator="starts with", values=[value]) + + def ends_with(self, value: str) -> "DimensionFilter": + """Create ends with filter: dim.ends_with('suffix')""" + from .filter import DimensionFilter + return DimensionFilter(field=self, operator="ends with", values=[value]) + + def is_null(self) -> "DimensionFilter": + """Create is null filter: dim.is_null()""" + from .filter import DimensionFilter + return DimensionFilter(field=self, operator="is null", values=[]) + + def is_not_null(self) -> "DimensionFilter": + """Create is not null filter: dim.is_not_null()""" + from .filter import DimensionFilter + return DimensionFilter(field=self, operator="is not null", values=[]) + class Dimensions: """ @@ -66,7 +187,14 @@ def __getattr__(self, name: str) -> Dimension: try: return self._dimensions[name] except KeyError: - raise AttributeError(f"No dimension named '{name}' found") + close_matches = get_close_matches( + name, self._dimensions.keys(), n=3, cutoff=0.6 + ) + msg = f"No dimension named '{name}' found" + if close_matches: + suggestions = ", ".join(f"'{d}'" for d in close_matches) + msg += f". Did you mean: {suggestions}?" + raise AttributeError(msg) def __dir__(self) -> List[str]: """Enable tab completion by returning list of dimension names.""" @@ -76,4 +204,50 @@ def __dir__(self) -> List[str]: def list(self) -> List[Dimension]: """List all available dimensions.""" self._ensure_loaded() - return list(self._dimensions.values()) \ No newline at end of file + return list(self._dimensions.values()) + + def _repr_html_(self) -> str: + """Rich HTML display for Jupyter notebooks.""" + preview_count = 5 + try: + self._ensure_loaded() + dimensions_list = list(self._dimensions.values()) + total = len(dimensions_list) + preview = dimensions_list[:preview_count] + except Exception: + return '
Unable to load dimensions
' + + model_name = html.escape( + getattr(self._model, "label", None) or self._model.name + ) + + rows = [] + for dimension in preview: + display_name = html.escape(dimension.label or dimension.name) + desc = "" + if dimension.description: + escaped_desc = html.escape(dimension.description) + if len(escaped_desc) > 80: + escaped_desc = escaped_desc[:77] + "..." + desc = f'
{escaped_desc}
' + rows.append( + f'
' + f"
{display_name}
{desc}
" + ) + + more_text = "" + if total > preview_count: + more_text = ( + f'
' + f"...and {total - preview_count} more
" + ) + + return f""" +
+
+ Dimensions from {model_name} ({total}) +
+ {"".join(rows) if rows else "
No dimensions available
"} + {more_text} +
+ """ \ No newline at end of file diff --git a/lightdash/exceptions.py b/lightdash/exceptions.py new file mode 100644 index 0000000..10411bf --- /dev/null +++ b/lightdash/exceptions.py @@ -0,0 +1,31 @@ +"""Custom exceptions for Lightdash SDK.""" + + +class LightdashError(Exception): + """Base exception for Lightdash API errors.""" + def __init__(self, message: str, name: str = "LightdashError", status_code: int = 0): + self.message = message + self.name = name + self.status_code = status_code + super().__init__(f"{name} ({status_code}): {message}") + + +class QueryError(LightdashError): + """Raised when a query fails.""" + def __init__(self, message: str, query_uuid: str = None): + self.query_uuid = query_uuid + super().__init__(message, "QueryError", 400) + + +class QueryTimeout(LightdashError): + """Raised when a query times out.""" + def __init__(self, message: str, query_uuid: str = None): + self.query_uuid = query_uuid + super().__init__(message, "QueryTimeout", 408) + + +class QueryCancelled(LightdashError): + """Raised when a query is cancelled.""" + def __init__(self, message: str = "Query was cancelled", query_uuid: str = None): + self.query_uuid = query_uuid + super().__init__(message, "QueryCancelled", 499) diff --git a/lightdash/filter.py b/lightdash/filter.py index 879896c..ea6dd43 100644 --- a/lightdash/filter.py +++ b/lightdash/filter.py @@ -1,7 +1,10 @@ +from __future__ import annotations + from dataclasses import dataclass, field -from typing import List, Union, Dict +from typing import List, Union, Dict, TYPE_CHECKING -from lightdash.dimensions import Dimension +if TYPE_CHECKING: + from lightdash.dimensions import Dimension numeric_filters = [ "is null", @@ -57,21 +60,26 @@ @dataclass class DimensionFilter: - field: Dimension + field: "Dimension" operator: str values: Union[str, int, float, List[str], List[int], List[float]] def __post_init__(self): + from lightdash.dimensions import Dimension + if not isinstance(self.values, list): self.values = [self.values] - assert ( - self.operator in allowed_values - ), f"operator {self.operator} not allowed" + if self.operator not in allowed_values: + raise ValueError( + f"Invalid operator '{self.operator}'. " + f"Must be one of: {', '.join(sorted(allowed_values))}" + ) - assert isinstance( - self.field, Dimension - ), "field must be a Dimension object, not just the name" + if not isinstance(self.field, Dimension): + raise TypeError( + f"field must be a Dimension object, got {type(self.field).__name__}" + ) def to_dict(self) -> Dict[str, Union[str, List[str]]]: return { @@ -80,6 +88,18 @@ def to_dict(self) -> Dict[str, Union[str, List[str]]]: "values": self.values, } + def __and__(self, other: Union["DimensionFilter", "CompositeFilter"]) -> "CompositeFilter": + """Combine filters with AND: filter1 & filter2""" + if isinstance(other, CompositeFilter): + return CompositeFilter(filters=[self] + list(other.filters), aggregation="and") + return CompositeFilter(filters=[self, other], aggregation="and") + + def __or__(self, other: Union["DimensionFilter", "CompositeFilter"]) -> "CompositeFilter": + """Combine filters with OR: filter1 | filter2""" + if isinstance(other, CompositeFilter): + return CompositeFilter(filters=[self] + list(other.filters), aggregation="or") + return CompositeFilter(filters=[self, other], aggregation="or") + @dataclass class CompositeFilter: @@ -92,21 +112,53 @@ class CompositeFilter: aggregation: str = "and" def __post_init__(self): - assert self.aggregation in ["and", "or"] + if self.aggregation not in ("and", "or"): + raise ValueError( + f"Invalid aggregation '{self.aggregation}'. Must be 'and' or 'or'" + ) def to_dict(self): out = [] processed_field_ids = set() - for filter in self.filters: + for f in self.filters: # Check that the filter is not a composite filter - assert hasattr( - filter, "field" - ), "Multi-level filter composites not supported yet" + if not hasattr(f, "field"): + raise TypeError("Multi-level filter composites not supported yet") # Check that we have at most one filter per field - if filter.field.field_id in processed_field_ids: + if f.field.field_id in processed_field_ids: raise NotImplementedError( - f"Multiple filters for field {filter.field.field_id} not implemented yet" + f"Multiple filters for field {f.field.field_id} not implemented yet" ) - processed_field_ids.add(filter.field.field_id) - out.append(filter.to_dict()) + processed_field_ids.add(f.field.field_id) + out.append(f.to_dict()) return {"dimensions": {self.aggregation: out}} + + def __and__(self, other: Union[DimensionFilter, "CompositeFilter"]) -> "CompositeFilter": + """Combine with another filter using AND: composite & filter""" + if isinstance(other, CompositeFilter): + # Flatten if both are AND composites + if self.aggregation == "and" and other.aggregation == "and": + return CompositeFilter( + filters=list(self.filters) + list(other.filters), + aggregation="and" + ) + # Otherwise wrap as nested (not fully supported yet, but preserve structure) + return CompositeFilter(filters=list(self.filters) + list(other.filters), aggregation="and") + # Add single filter to existing composite + if self.aggregation == "and": + return CompositeFilter(filters=list(self.filters) + [other], aggregation="and") + return CompositeFilter(filters=list(self.filters) + [other], aggregation="and") + + def __or__(self, other: Union[DimensionFilter, "CompositeFilter"]) -> "CompositeFilter": + """Combine with another filter using OR: composite | filter""" + if isinstance(other, CompositeFilter): + # Flatten if both are OR composites + if self.aggregation == "or" and other.aggregation == "or": + return CompositeFilter( + filters=list(self.filters) + list(other.filters), + aggregation="or" + ) + return CompositeFilter(filters=list(self.filters) + list(other.filters), aggregation="or") + if self.aggregation == "or": + return CompositeFilter(filters=list(self.filters) + [other], aggregation="or") + return CompositeFilter(filters=list(self.filters) + [other], aggregation="or") diff --git a/lightdash/metrics.py b/lightdash/metrics.py index e2b6a40..8948357 100644 --- a/lightdash/metrics.py +++ b/lightdash/metrics.py @@ -1,7 +1,9 @@ """ Metrics for Lightdash models. """ +import html from dataclasses import dataclass +from difflib import get_close_matches from typing import Any, Dict, List, Optional from .types import Model, Metric as MetricProtocol @@ -25,6 +27,33 @@ def _repr_pretty_(self, p, cycle): else: p.text(str(self)) + def _repr_html_(self) -> str: + """Rich HTML display for Jupyter notebooks.""" + display_name = html.escape(self.label or self.name) + field_id = html.escape(self.field_id) + model_name = html.escape(self.model_name) + + desc_html = "" + if self.description: + escaped_desc = html.escape(self.description) + desc_html = f'
{escaped_desc}
' + + return f""" +
+
+ {display_name} +
+
+
Type: Metric
+
Model: {model_name}
+
+
+ {field_id} +
+ {desc_html} +
+ """ + @property def field_id(self) -> str: """Get the field ID for this metric in the format model_name.""" @@ -66,7 +95,14 @@ def __getattr__(self, name: str) -> Metric: try: return self._metrics[name] except KeyError: - raise AttributeError(f"No metric named '{name}' found") + close_matches = get_close_matches( + name, self._metrics.keys(), n=3, cutoff=0.6 + ) + msg = f"No metric named '{name}' found" + if close_matches: + suggestions = ", ".join(f"'{m}'" for m in close_matches) + msg += f". Did you mean: {suggestions}?" + raise AttributeError(msg) def __dir__(self) -> List[str]: """Enable tab completion by returning list of metric names.""" @@ -76,4 +112,50 @@ def __dir__(self) -> List[str]: def list(self) -> List[Metric]: """List all available metrics.""" self._ensure_loaded() - return list(self._metrics.values()) \ No newline at end of file + return list(self._metrics.values()) + + def _repr_html_(self) -> str: + """Rich HTML display for Jupyter notebooks.""" + preview_count = 5 + try: + self._ensure_loaded() + metrics_list = list(self._metrics.values()) + total = len(metrics_list) + preview = metrics_list[:preview_count] + except Exception: + return '
Unable to load metrics
' + + model_name = html.escape( + getattr(self._model, "label", None) or self._model.name + ) + + rows = [] + for metric in preview: + display_name = html.escape(metric.label or metric.name) + desc = "" + if metric.description: + escaped_desc = html.escape(metric.description) + if len(escaped_desc) > 80: + escaped_desc = escaped_desc[:77] + "..." + desc = f'
{escaped_desc}
' + rows.append( + f'
' + f"
{display_name}
{desc}
" + ) + + more_text = "" + if total > preview_count: + more_text = ( + f'
' + f"...and {total - preview_count} more
" + ) + + return f""" +
+
+ Metrics from {model_name} ({total}) +
+ {"".join(rows) if rows else "
No metrics available
"} + {more_text} +
+ """ \ No newline at end of file diff --git a/lightdash/models.py b/lightdash/models.py index 847b2b4..3cd3ff9 100644 --- a/lightdash/models.py +++ b/lightdash/models.py @@ -2,7 +2,9 @@ Models for interacting with Lightdash explores. """ +import html from dataclasses import dataclass +from difflib import get_close_matches from typing import Any, Dict, List, Optional, Union, Sequence from .filter import DimensionFilter, CompositeFilter @@ -10,6 +12,7 @@ from .metrics import Metric, Metrics from .dimensions import Dimension, Dimensions from .query import Query +from .sorting import Sort @dataclass @@ -38,6 +41,67 @@ def _repr_pretty_(self, p, cycle): else: p.text(str(self)) + def _render_field_table( + self, items: List[Any], title: str, preview_count: int = 5 + ) -> str: + """Render a preview table for metrics or dimensions.""" + total = len(items) + preview = items[:preview_count] + rows = [] + for item in preview: + display_name = html.escape(item.label or item.name) + rows.append(f"
{display_name}
") + + if total > preview_count: + rows.append( + f'
' + f"...and {total - preview_count} more
" + ) + + return f""" +
+
+ {html.escape(title)} ({total}) +
+ {"".join(rows) if rows else "
None
"} +
+ """ + + def _repr_html_(self) -> str: + """Rich HTML display for Jupyter notebooks.""" + display_name = html.escape(self.label or self.name) + description = "" + if self.description: + escaped_desc = html.escape(self.description) + if len(escaped_desc) > 150: + escaped_desc = escaped_desc[:147] + "..." + description = f'
{escaped_desc}
' + + try: + metrics_list = self.list_metrics() + metrics_html = self._render_field_table(metrics_list, "Metrics") + except Exception: + metrics_html = '
Unable to load metrics
' + + try: + dimensions_list = self.list_dimensions() + dimensions_html = self._render_field_table(dimensions_list, "Dimensions") + except Exception: + dimensions_html = '
Unable to load dimensions
' + + return f""" +
+
+ Model: {display_name} +
+ {description} +
+ {metrics_html} + {dimensions_html} +
+
+ """ + def _set_client(self, client: Client) -> None: """Set the client reference for making API calls.""" self._client = client @@ -59,35 +123,68 @@ def _fetch_table_data(self) -> Dict[str, Any]: def query( self, - metrics: Union[str, Metric, Sequence[Union[str, Metric]]], + metrics: Optional[Union[str, Metric, Sequence[Union[str, Metric]]]] = None, dimensions: Union[str, Dimension, Sequence[Union[str, Dimension]]] = (), filters: Optional[Union[DimensionFilter, CompositeFilter]] = None, - limit: int = 50, + sort: Optional[Union[Sort, Sequence[Sort]]] = None, + limit: int = 500, ) -> Query: """ Create a query against this model. + Can be called with arguments for single-call usage, or without arguments + to start a chainable builder pattern. + Args: - metrics: A single metric or sequence of metrics to query. Each metric can be a field ID string or Metric object. - dimensions: A single dimension or sequence of dimensions to query. Each dimension can be a field ID string or Dimension object. + metrics: A single metric or sequence of metrics to query. Each metric + can be a field ID string or Metric object. Optional for builder pattern. + dimensions: A single dimension or sequence of dimensions to query. Each + dimension can be a field ID string or Dimension object. + filters: Optional filters to apply to the query. + sort: Optional Sort object or sequence of Sort objects to order results. limit: Maximum number of rows to return. Returns: - A Query object that can be used to fetch results. + A Query object that can be used to fetch results or build further. + + Example (single-call): + query = model.query( + metrics=[model.metrics.revenue], + dimensions=[model.dimensions.country], + limit=100 + ) + + Example (chainable builder): + query = ( + model.query() + .metrics(model.metrics.revenue) + .dimensions(model.dimensions.country) + .limit(100) + ) """ - metrics_seq = ( - [metrics] if isinstance(metrics, (str, Metric)) else metrics - ) + # Handle metrics - can be None for builder pattern + if metrics is None: + metrics_seq = () + elif isinstance(metrics, (str, Metric)): + metrics_seq = [metrics] + else: + metrics_seq = metrics + dimensions_seq = ( [dimensions] if isinstance(dimensions, (str, Dimension)) else dimensions ) + # Normalize single Sort to sequence + sort_seq = None + if sort is not None: + sort_seq = [sort] if isinstance(sort, Sort) else sort return Query( self, metrics=metrics_seq, dimensions=dimensions_seq, filters=filters, + sort=sort_seq, limit=limit, ) @@ -184,4 +281,11 @@ def get(self, name: str) -> Model: try: return self._models[name] except KeyError: - raise AttributeError(f"No model named '{name}' found") + close_matches = get_close_matches( + name, self._models.keys(), n=3, cutoff=0.6 + ) + msg = f"No model named '{name}' found" + if close_matches: + suggestions = ", ".join(f"'{m}'" for m in close_matches) + msg += f". Did you mean: {suggestions}?" + raise AttributeError(msg) diff --git a/lightdash/query.py b/lightdash/query.py index c7fa7e3..c27a8f7 100644 --- a/lightdash/query.py +++ b/lightdash/query.py @@ -1,161 +1,268 @@ -""" -Query functionality for Lightdash models. -""" - -from typing import Any, Dict, List, Optional, Union, Sequence +"""Query functionality for Lightdash models.""" +import time +import warnings +from typing import Any, Dict, List, Optional, Union, Sequence, Iterator from .dimensions import Dimension from .metrics import Metric from .filter import DimensionFilter, CompositeFilter +from .sorting import Sort from .types import Model +from .exceptions import QueryError, QueryTimeout, QueryCancelled +from .results import BaseResult -class Query: +class _QueryExecutor: + """Internal class that handles V2 async query submission and polling.""" + + def __init__(self, client): + self._client = client + self._poll_backoff_start_ms = 100 + self._poll_backoff_max_ms = 2000 + + def execute( + self, + query_payload: Dict[str, Any], + timeout_seconds: float = 300, + invalidate_cache: bool = False + ) -> "QueryResult": + """Submit query via V2 API and poll until complete.""" + # Step 1: Submit query + submit_response = self._client._make_request( + "POST", + f"/api/v2/projects/{self._client.project_uuid}/query/metric-query", + json={ + "query": query_payload, + "context": "api", + "invalidateCache": invalidate_cache + } + ) + query_uuid = submit_response["queryUuid"] + fields = submit_response.get("fields", {}) + + # Step 2: Poll for first page + first_page = self._poll_until_ready(query_uuid, timeout_seconds) + + return QueryResult( + query_uuid=query_uuid, + fields=fields, + first_page=first_page, + executor=self + ) + + def _poll_until_ready( + self, + query_uuid: str, + timeout_seconds: float, + page: int = 1, + page_size: int = 500 + ) -> Dict[str, Any]: + """Poll with exponential backoff until query completes.""" + backoff_ms = self._poll_backoff_start_ms + start_time = time.time() + + while time.time() - start_time < timeout_seconds: + response = self._client._make_request( + "GET", + f"/api/v2/projects/{self._client.project_uuid}/query/{query_uuid}", + params={"page": page, "pageSize": page_size} + ) + + status = response.get("status") + if status == "ready": + return response + elif status == "error": + raise QueryError(response.get("error", "Unknown error"), query_uuid) + elif status == "cancelled": + raise QueryCancelled(query_uuid=query_uuid) + + # Status is "pending" - wait and retry + time.sleep(backoff_ms / 1000) + backoff_ms = min(backoff_ms * 2, self._poll_backoff_max_ms) + + raise QueryTimeout( + f"Query {query_uuid} did not complete within {timeout_seconds}s", + query_uuid + ) + + def get_page(self, query_uuid: str, page: int, page_size: int = 500) -> Dict[str, Any]: + """Fetch a specific page of results.""" + return self._client._make_request( + "GET", + f"/api/v2/projects/{self._client.project_uuid}/query/{query_uuid}", + params={"page": page, "pageSize": page_size} + ) + + def cancel(self, query_uuid: str) -> None: + """Cancel a running query.""" + self._client._make_request( + "POST", + f"/api/v2/projects/{self._client.project_uuid}/query/{query_uuid}/cancel" + ) + + +class QueryResult(BaseResult): """ - A Lightdash query builder and executor. + Result of a query execution with pagination support. + + Lazily fetches additional pages from the V2 API as needed. + Implements the ResultSet protocol for consistent interaction with query results. - Allows executing queries against a model to fetch data. Example: - # Single metric, no dimensions - df = model.query( - metrics=model.metrics.count_of_product_name - ).to_df() - - # Single metric and dimension - df = model.query( - metrics=model.metrics.count_of_product_name, - dimensions=model.dimensions.partner_name - ).to_df() - - # Multiple metrics and dimensions - df = model.query( - metrics=[model.metrics.count_of_product_name, model.metrics.sum_of_profit], - dimensions=[model.dimensions.partner_name, model.dimensions.order_date] - ).to_df() + result = query.execute() + + # Iterate over rows + for row in result: + print(row['Revenue']) + + # Get total count + print(f"Got {len(result)} rows") + + # Convert to DataFrame + df = result.to_df() """ def __init__( self, - model: Model, - metrics: Sequence[Union[str, Metric]], - dimensions: Sequence[Union[str, Dimension]] = (), - filters: Optional[Union[DimensionFilter, CompositeFilter]] = None, - limit: int = 50, + query_uuid: str, + fields: Dict[str, Any], + first_page: Dict[str, Any], + executor: _QueryExecutor ): - self._model = model - self._dimensions = dimensions - self._metrics = metrics - if filters is None: - self._filters = CompositeFilter() - elif isinstance(filters, DimensionFilter): - self._filters = CompositeFilter(filters=[filters]) - else: - self._filters = filters - self._limit = limit - self._last_results: Optional[List[Dict[str, Any]]] = None - self._field_labels: Optional[Dict[str, str]] = None + self._query_uuid = query_uuid + self._fields = fields + self._first_page = first_page + self._executor = executor + self._all_rows: Optional[List[Dict[str, Any]]] = None + self._field_labels = self._build_field_labels() - def _execute(self) -> None: - """ - Execute the query against the model if not already executed. + def _build_field_labels(self) -> Dict[str, str]: + """Build mapping from field IDs to labels.""" + return { + field_id: field_data.get("label") or field_data.get("name", field_id) + for field_id, field_data in self._fields.items() + } + + def _transform_row(self, row: Dict[str, Any]) -> Dict[str, Any]: + """Transform a raw API row to use labels and extract raw values.""" + result = {} + for field_id, field_value in row.items(): + label = self._field_labels.get(field_id, field_id) + # Handle both V2 format (direct values) and V1 format (nested value.raw) + if isinstance(field_value, dict) and "value" in field_value: + result[label] = field_value["value"].get("raw", field_value["value"]) + else: + result[label] = field_value + return result + + def _transform_rows(self, rows: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Transform multiple rows.""" + return [self._transform_row(row) for row in rows] + + @property + def query_uuid(self) -> str: + """The unique identifier for this query.""" + return self._query_uuid + + @property + def total_results(self) -> int: + """Total number of rows across all pages.""" + return self._first_page.get("totalResults", len(self._first_page.get("rows", []))) + + @property + def total_pages(self) -> int: + """Total number of pages available.""" + return self._first_page.get("totalPageCount", 1) + + @property + def fields(self) -> Dict[str, Any]: + """Field metadata from the query.""" + return self._fields + + def page(self, page_num: int, page_size: int = 500) -> List[Dict[str, Any]]: """ - if self._last_results is not None: - return + Get a specific page of results. - if not 1 <= self._limit <= 5000: - raise ValueError("Limit must be between 1 and 5000") + Args: + page_num: Page number (1-indexed) + page_size: Number of rows per page (max 5000) - if self._model._client is None: - raise RuntimeError( - "Model not properly initialized with client reference" - ) + Returns: + List of row dictionaries for the requested page + """ + if page_num == 1 and page_size == self._first_page.get("pageSize", 500): + return self._transform_rows(self._first_page.get("rows", [])) - # Convert dimensions/metrics to field IDs if they're objects - dimension_ids = [ - d.field_id if isinstance(d, Dimension) else d - for d in self._dimensions - ] - metric_ids = [ - m.field_id if isinstance(m, Metric) else m for m in self._metrics - ] + page_data = self._executor.get_page(self._query_uuid, page_num, page_size) + return self._transform_rows(page_data.get("rows", [])) - # Construct query payload - payload = { - "exploreName": self._model.name, - "dimensions": dimension_ids, - "metrics": metric_ids, - "filters": self._filters.to_dict(), - "limit": self._limit, - "tableCalculations": [], - "sorts": [], - } + def iter_pages(self, page_size: int = 500) -> Iterator[List[Dict[str, Any]]]: + """ + Iterate through all pages of results. - # Execute query - path = f"/api/v1/projects/{self._model._client.project_uuid}/explores/{self._model.name}/runQuery" - response = self._model._client._make_request("POST", path, json=payload) + Args: + page_size: Number of rows per page - # Store field labels mapping - self._field_labels = { - field_id: field_data.get("label") or field_data["name"] - for field_id, field_data in response.get("fields", {}).items() - } + Yields: + List of row dictionaries for each page + """ + # Yield first page + yield self._transform_rows(self._first_page.get("rows", [])) - # Transform rows to extract raw values and use labels for keys - rows = response["rows"] - self._last_results = [ - { - self._field_labels.get(field_id, field_id): row[field_id][ - "value" - ]["raw"] - for field_id in row.keys() - } - for row in rows - ] + # Fetch and yield remaining pages + for page_num in range(2, self.total_pages + 1): + page_data = self._executor.get_page(self._query_uuid, page_num, page_size) + yield self._transform_rows(page_data.get("rows", [])) def to_records(self) -> List[Dict[str, Any]]: """ - Get the query results as a list of dictionaries. + Get all results as a list of dictionaries. - Returns: - List of dictionaries, where each dictionary represents a row of data - with column names as keys (using labels where available) and values as the corresponding data. + Fetches all pages if not already cached. - Raises: - LightdashError: If the API returns an error response - httpx.HTTPError: If there's a network or HTTP protocol error - ValueError: If limit is outside valid range + Returns: + List of all row dictionaries """ - self._execute() - return self._last_results + if self._all_rows is None: + self._all_rows = [] + for page in self.iter_pages(): + self._all_rows.extend(page) + return self._all_rows def to_json(self) -> List[Dict[str, Any]]: """ - Alias for to_records() for backward compatibility. + Deprecated: Use to_records() instead. - Returns: - List of dictionaries, where each dictionary represents a row of data. - - See to_records() for more details. + For JSON string output, use to_json_str(). """ + warnings.warn( + "to_json() is deprecated, use to_records() instead. " + "For JSON string output, use to_json_str().", + DeprecationWarning, + stacklevel=2 + ) return self.to_records() + def __iter__(self) -> Iterator[Dict[str, Any]]: + """Iterate over all result rows, fetching pages as needed.""" + for page in self.iter_pages(): + yield from page + + def __len__(self) -> int: + """Return total number of results.""" + return self.total_results + def to_df(self, backend: str = "pandas") -> Any: """ - Convert the query results to a DataFrame. + Convert all results to a DataFrame. Args: - backend: The DataFrame backend to use ("pandas" or "polars") + backend: DataFrame backend ("pandas" or "polars") Returns: - A pandas or polars DataFrame containing the query results. - - Raises: - ImportError: If the requested backend is not installed - LightdashError: If the API returns an error response - httpx.HTTPError: If there's a network or HTTP protocol error - ValueError: If limit is outside valid range + DataFrame containing all query results """ - self._execute() + records = self.to_records() if backend == "pandas": try: @@ -165,7 +272,7 @@ def to_df(self, backend: str = "pandas") -> Any: "pandas is required for DataFrame support. " "Install it with: pip install pandas" ) - return pd.DataFrame(self._last_results) + return pd.DataFrame(records) elif backend == "polars": try: import polars as pl @@ -174,9 +281,377 @@ def to_df(self, backend: str = "pandas") -> Any: "polars is required for DataFrame support. " "Install it with: pip install polars" ) - return pl.DataFrame(self._last_results) + return pl.DataFrame(records) else: raise ValueError( f"Unsupported DataFrame backend: {backend}. " "Use 'pandas' or 'polars'" ) + + def to_df_lazy(self, backend: str = "pandas", page_size: int = 500) -> Any: + """ + Stream results into DataFrame page by page (memory efficient for large results). + + Args: + backend: DataFrame backend ("pandas" or "polars") + page_size: Number of rows per page + + Returns: + DataFrame containing all query results + """ + if backend == "pandas": + import pandas as pd + dfs = [pd.DataFrame(page) for page in self.iter_pages(page_size)] + return pd.concat(dfs, ignore_index=True) if dfs else pd.DataFrame() + elif backend == "polars": + import polars as pl + dfs = [pl.DataFrame(page) for page in self.iter_pages(page_size)] + return pl.concat(dfs) if dfs else pl.DataFrame() + else: + raise ValueError(f"Unsupported backend: {backend}") + + def __repr__(self) -> str: + return f"QueryResult(query_uuid={self._query_uuid!r}, total_results={self.total_results}, total_pages={self.total_pages})" + + def _repr_pretty_(self, p, cycle): + if cycle: + p.text("QueryResult(...)") + else: + p.text(repr(self)) + + +class Query: + """ + A Lightdash query builder and executor. + + Supports lazy evaluation - API calls only happen when results are requested. + Supports both single-call and chainable builder patterns. + + Example (single-call pattern): + # Build query (no API call yet) + query = model.query( + metrics=[model.metrics.revenue], + dimensions=[model.dimensions.country], + limit=100 + ) + + # Execute and get results + df = query.to_df() # API call happens here + + Example (chainable builder pattern): + # Build incrementally + query = ( + model.query() + .metrics(model.metrics.revenue, model.metrics.profit) + .dimensions(model.dimensions.country) + .filter(model.dimensions.status == 'active') + .sort(model.metrics.revenue.desc()) + .limit(100) + ) + + # Reuse base queries + base_query = model.query().metrics(model.metrics.revenue) + usa_query = base_query.filter(model.dimensions.country == 'USA') + uk_query = base_query.filter(model.dimensions.country == 'UK') + """ + + def __init__( + self, + model: Model, + metrics: Sequence[Union[str, Metric]] = (), + dimensions: Sequence[Union[str, Dimension]] = (), + filters: Optional[Union[DimensionFilter, CompositeFilter, Any]] = None, + limit: int = 500, + # Phase 1 additions (initially None) + sort: Optional[Union[Sort, Sequence[Sort]]] = None, + table_calculations: Optional[Sequence[Any]] = None, + # Phase 3 additions (initially None) + custom_dimensions: Optional[Sequence[Any]] = None, + additional_metrics: Optional[Sequence[Any]] = None, + timezone: Optional[str] = None, + ): + self._model = model + # Store as tuples for immutability + self._metrics = tuple(metrics) if metrics else () + self._dimensions = tuple(dimensions) if dimensions else () + self._limit = limit + + # Handle filters - normalize to CompositeFilter if DimensionFilter is passed + if filters is None: + self._filters = None + elif isinstance(filters, DimensionFilter): + self._filters = CompositeFilter(filters=[filters]) + else: + self._filters = filters + + # Normalize single Sort to tuple + if sort is None: + self._sort = () + elif isinstance(sort, Sort): + self._sort = (sort,) + else: + self._sort = tuple(sort) + self._table_calculations = table_calculations + self._custom_dimensions = custom_dimensions + self._additional_metrics = additional_metrics + self._timezone = timezone + + # Cached result + self._result: Optional[QueryResult] = None + + def _clone(self, **kwargs) -> "Query": + """Create a new Query with some attributes replaced.""" + return Query( + model=kwargs.get("model", self._model), + metrics=kwargs.get("metrics", self._metrics), + dimensions=kwargs.get("dimensions", self._dimensions), + filters=kwargs.get("filters", self._filters), + limit=kwargs.get("limit", self._limit), + sort=kwargs.get("sort", self._sort), + table_calculations=kwargs.get("table_calculations", self._table_calculations), + custom_dimensions=kwargs.get("custom_dimensions", self._custom_dimensions), + additional_metrics=kwargs.get("additional_metrics", self._additional_metrics), + timezone=kwargs.get("timezone", self._timezone), + ) + + def metrics(self, *metrics: Union[str, Metric]) -> "Query": + """ + Add metrics to the query. + + Returns a new Query with the specified metrics added. + + Args: + *metrics: Metric objects or field ID strings to add + + Returns: + A new Query with the metrics added + + Example: + query = model.query().metrics(model.metrics.revenue, model.metrics.profit) + """ + return self._clone(metrics=self._metrics + metrics) + + def dimensions(self, *dimensions: Union[str, Dimension]) -> "Query": + """ + Add dimensions to the query. + + Returns a new Query with the specified dimensions added. + + Args: + *dimensions: Dimension objects or field ID strings to add + + Returns: + A new Query with the dimensions added + + Example: + query = model.query().dimensions(model.dimensions.country, model.dimensions.region) + """ + return self._clone(dimensions=self._dimensions + dimensions) + + def filter(self, filter: Union[DimensionFilter, CompositeFilter]) -> "Query": + """ + Add a filter to the query. + + Multiple calls to filter() combine filters with AND logic. + Returns a new Query with the filter added. + + Args: + filter: A DimensionFilter or CompositeFilter to apply + + Returns: + A new Query with the filter added + + Example: + query = ( + model.query() + .filter(model.dimensions.country == 'USA') + .filter(model.dimensions.status == 'active') + ) + """ + if self._filters is None: + # First filter + if isinstance(filter, DimensionFilter): + new_filters = CompositeFilter(filters=[filter]) + else: + new_filters = filter + else: + # Combine with existing filters using AND + if isinstance(filter, DimensionFilter): + # Add to existing CompositeFilter's list + new_filters = CompositeFilter( + filters=list(self._filters.filters) + [filter], + aggregation=self._filters.aggregation + ) + else: + # Both are CompositeFilters - combine them + new_filters = CompositeFilter( + filters=list(self._filters.filters) + list(filter.filters), + aggregation="and" + ) + return self._clone(filters=new_filters) + + def sort(self, *sorts: Sort) -> "Query": + """ + Add sort criteria to the query. + + Returns a new Query with the specified sorts added. + + Args: + *sorts: Sort objects specifying order + + Returns: + A new Query with the sorts added + + Example: + query = model.query().sort(model.metrics.revenue.desc(), model.dimensions.country.asc()) + """ + return self._clone(sort=self._sort + sorts) + + def limit(self, n: int) -> "Query": + """ + Set the result limit for the query. + + Returns a new Query with the specified limit. + + Args: + n: Maximum number of rows to return (1-50000) + + Returns: + A new Query with the limit set + + Example: + query = model.query().limit(100) + """ + return self._clone(limit=n) + + def _build_payload(self) -> Dict[str, Any]: + """Build the query payload for the V2 API.""" + # Convert dimensions/metrics to field IDs + dimension_ids = [ + d.field_id if isinstance(d, Dimension) else d + for d in self._dimensions + ] + metric_ids = [ + m.field_id if isinstance(m, Metric) else m + for m in self._metrics + ] + + # Build filters dict - use empty CompositeFilter if no filters + if self._filters is None: + filters_dict = CompositeFilter().to_dict() + elif hasattr(self._filters, 'to_dict'): + filters_dict = self._filters.to_dict() + else: + filters_dict = self._filters + + payload = { + "exploreName": self._model.name, + "dimensions": dimension_ids, + "metrics": metric_ids, + "filters": filters_dict, + "limit": self._limit, + "tableCalculations": [], + "sorts": [], + } + + # Add sorts if provided (Phase 1) + if self._sort: + payload["sorts"] = [ + s.to_dict() if hasattr(s, 'to_dict') else s + for s in self._sort + ] + + # Add table calculations if provided (Phase 1) + if self._table_calculations is not None: + payload["tableCalculations"] = [ + tc.to_dict() if hasattr(tc, 'to_dict') else tc + for tc in self._table_calculations + ] + + # Add custom dimensions if provided (Phase 3) + if self._custom_dimensions is not None: + payload["customDimensions"] = [ + cd.to_dict() if hasattr(cd, 'to_dict') else cd + for cd in self._custom_dimensions + ] + + # Add additional metrics if provided (Phase 3) + if self._additional_metrics is not None: + payload["additionalMetrics"] = [ + am.to_dict() if hasattr(am, 'to_dict') else am + for am in self._additional_metrics + ] + + # Add timezone if provided + if self._timezone is not None: + payload["timezone"] = self._timezone + + return payload + + def execute( + self, + timeout_seconds: float = 300, + invalidate_cache: bool = False + ) -> QueryResult: + """ + Execute the query and return a QueryResult object. + + Args: + timeout_seconds: Maximum time to wait for query completion + invalidate_cache: If True, bypass server-side cache + + Returns: + QueryResult object with pagination support + """ + if self._result is not None and not invalidate_cache: + return self._result + + if not 1 <= self._limit <= 50000: + raise ValueError("Limit must be between 1 and 50000") + + if self._model._client is None: + raise RuntimeError("Model not properly initialized with client reference") + + executor = _QueryExecutor(self._model._client) + payload = self._build_payload() + + self._result = executor.execute( + payload, + timeout_seconds=timeout_seconds, + invalidate_cache=invalidate_cache + ) + return self._result + + def to_records(self) -> List[Dict[str, Any]]: + """Get all query results as a list of dictionaries.""" + return self.execute().to_records() + + def to_json(self) -> List[Dict[str, Any]]: + """ + Deprecated: Use to_records() instead. + + For JSON string output, use to_json_str(). + """ + warnings.warn( + "to_json() is deprecated, use to_records() instead. " + "For JSON string output, use to_json_str().", + DeprecationWarning, + stacklevel=2 + ) + return self.to_records() + + def to_json_str(self) -> str: + """ + Convert results to a JSON string. + + Returns: + JSON string representation of the results + """ + return self.execute().to_json_str() + + def to_df(self, backend: str = "pandas") -> Any: + """Convert query results to a DataFrame.""" + return self.execute().to_df(backend) + + def __repr__(self) -> str: + return f"Query(model={self._model.name!r}, metrics={len(self._metrics)}, dimensions={len(self._dimensions)}, limit={self._limit})" diff --git a/lightdash/results.py b/lightdash/results.py new file mode 100644 index 0000000..a0ab691 --- /dev/null +++ b/lightdash/results.py @@ -0,0 +1,99 @@ +""" +Common result interfaces for Lightdash queries. + +Provides a unified protocol and base class for all query results. +""" +import json +import warnings +from abc import ABC, abstractmethod +from typing import Any, Dict, Iterator, List, Protocol, runtime_checkable + + +@runtime_checkable +class ResultSet(Protocol): + """ + Common interface for all query results. + + This protocol defines the standard methods that all result types must implement, + enabling consistent interaction regardless of the query source. + """ + + def to_df(self, backend: str = "pandas") -> Any: + """Convert results to DataFrame.""" + ... + + def to_records(self) -> List[Dict[str, Any]]: + """Convert results to list of dictionaries.""" + ... + + def __iter__(self) -> Iterator[Dict[str, Any]]: + """Iterate over result rows.""" + ... + + def __len__(self) -> int: + """Return total number of results.""" + ... + + +class BaseResult(ABC): + """ + Abstract base class for result sets. + + Provides default implementations for common functionality while requiring + subclasses to implement the core conversion methods. + """ + + @abstractmethod + def to_df(self, backend: str = "pandas") -> Any: + """ + Convert results to DataFrame. + + Args: + backend: DataFrame backend ("pandas" or "polars") + + Returns: + DataFrame containing the results + """ + pass + + @abstractmethod + def to_records(self) -> List[Dict[str, Any]]: + """ + Convert results to list of dictionaries. + + Returns: + List of row dictionaries + """ + pass + + def __iter__(self) -> Iterator[Dict[str, Any]]: + """Iterate over result rows.""" + yield from self.to_records() + + @abstractmethod + def __len__(self) -> int: + """Return total number of results.""" + pass + + def to_json_str(self) -> str: + """ + Convert results to a JSON string. + + Returns: + JSON string representation of the results + """ + return json.dumps(self.to_records()) + + def to_json(self) -> List[Dict[str, Any]]: + """ + Deprecated: Use to_records() instead. + + For JSON string output, use to_json_str(). + """ + warnings.warn( + "to_json() is deprecated, use to_records() instead. " + "For JSON string output, use to_json_str().", + DeprecationWarning, + stacklevel=2 + ) + return self.to_records() diff --git a/lightdash/sorting.py b/lightdash/sorting.py new file mode 100644 index 0000000..5786226 --- /dev/null +++ b/lightdash/sorting.py @@ -0,0 +1,77 @@ +""" +Sort builders for Lightdash queries. + +Example usage: + from lightdash.sorting import Sort + + # Simple sort + sort = Sort("orders_revenue", descending=True) + + # With null handling + sort = Sort("orders_country", descending=False, nulls_first=True) + + # From metric/dimension objects + sort = Sort(model.metrics.revenue, descending=True) + sort = model.metrics.revenue.desc() # Shorthand +""" +from dataclasses import dataclass +from typing import Any, Dict, Optional, Union + + +@dataclass +class Sort: + """ + A sort specification for query results. + + Args: + field: Field ID string or Metric/Dimension object + descending: If True, sort in descending order (default False) + nulls_first: If True, null values appear first (default None = database default) + """ + field: Union[str, Any] # str or Metric/Dimension + descending: bool = False + nulls_first: Optional[bool] = None + + @property + def field_id(self) -> str: + """Get the field ID string.""" + if isinstance(self.field, str): + return self.field + return self.field.field_id + + def to_dict(self) -> Dict[str, Any]: + result = { + "fieldId": self.field_id, + "descending": self.descending, + } + if self.nulls_first is not None: + result["nullsFirst"] = self.nulls_first + return result + + def __repr__(self) -> str: + direction = "DESC" if self.descending else "ASC" + nulls = f", nulls_first={self.nulls_first}" if self.nulls_first is not None else "" + return f"Sort({self.field_id!r}, {direction}{nulls})" + + +def _add_sort_methods_to_field_classes(): + """Add .asc() and .desc() methods to Metric and Dimension classes.""" + from .metrics import Metric + from .dimensions import Dimension + + def asc(self, nulls_first: Optional[bool] = None) -> Sort: + """Create ascending sort for this field.""" + return Sort(self, descending=False, nulls_first=nulls_first) + + def desc(self, nulls_first: Optional[bool] = None) -> Sort: + """Create descending sort for this field.""" + return Sort(self, descending=True, nulls_first=nulls_first) + + Metric.asc = asc + Metric.desc = desc + Dimension.asc = asc + Dimension.desc = desc + + +# Add methods when module is imported +_add_sort_methods_to_field_classes() diff --git a/lightdash/sql_runner.py b/lightdash/sql_runner.py new file mode 100644 index 0000000..61f5e52 --- /dev/null +++ b/lightdash/sql_runner.py @@ -0,0 +1,235 @@ +""" +Raw SQL execution for Lightdash. + +Example usage: + # Execute SQL + result = client.sql("SELECT * FROM orders LIMIT 100") + df = result.to_df() + + # Get available tables + tables = client.sql_runner.tables() + + # Get table fields + fields = client.sql_runner.fields("orders") + + # Iterate over results + for row in result: + print(row) +""" +import time +from dataclasses import dataclass, field +from typing import Any, Dict, Iterator, List, Optional + +from .exceptions import QueryError, QueryTimeout +from .results import BaseResult + + +@dataclass +class SqlResult(BaseResult): + """ + Result of a SQL query execution. + + Implements the ResultSet protocol for consistent interaction with query results. + + Example: + result = client.sql("SELECT * FROM orders LIMIT 10") + + # Iterate over rows + for row in result: + print(row['order_id']) + + # Get total count + print(f"Got {len(result)} rows") + + # Convert to DataFrame + df = result.to_df() + """ + rows: List[Dict[str, Any]] + columns: List[str] + + def to_records(self) -> List[Dict[str, Any]]: + """Get results as list of dictionaries.""" + return self.rows + + def to_df(self, backend: str = "pandas") -> Any: + """ + Convert to DataFrame. + + Args: + backend: DataFrame backend ("pandas" or "polars") + + Returns: + DataFrame containing the results + """ + if backend == "pandas": + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for DataFrame support. " + "Install it with: pip install pandas" + ) + return pd.DataFrame(self.rows, columns=self.columns) + elif backend == "polars": + try: + import polars as pl + except ImportError: + raise ImportError( + "polars is required for DataFrame support. " + "Install it with: pip install polars" + ) + return pl.DataFrame(self.rows) + else: + raise ValueError(f"Unsupported backend: {backend}") + + def __iter__(self) -> Iterator[Dict[str, Any]]: + """Iterate over result rows.""" + yield from self.rows + + def __len__(self) -> int: + """Return total number of results.""" + return len(self.rows) + + def __repr__(self) -> str: + return f"SqlResult(rows={len(self.rows)}, columns={self.columns})" + + +class SqlRunner: + """ + SQL runner for executing raw SQL against the data warehouse. + """ + + def __init__(self, client): + self._client = client + self._poll_interval_ms = 500 + self._max_poll_time_s = 300 + + def execute(self, sql: str, limit: int = 500) -> SqlResult: + """ + Execute a raw SQL query. + + Args: + sql: SQL query to execute + limit: Maximum rows to return + + Returns: + SqlResult with query results + """ + # Submit SQL query + response = self._client._make_request( + "POST", + f"/api/v1/projects/{self._client.project_uuid}/sqlRunner/run", + json={"sql": sql, "limit": limit} + ) + + job_id = response.get("jobId") + if not job_id: + # Synchronous response (if results are returned directly) + return self._parse_result(response) + + # Poll for async result + return self._poll_for_result(job_id) + + def _fetch_raw_results(self, url: str) -> List[Dict[str, Any]]: + """Fetch raw results from a URL (JSONL format - one JSON object per line).""" + import httpx + import json + from urllib.parse import urljoin + + full_url = urljoin(self._client.instance_url, url) + with httpx.Client( + headers={ + "Authorization": f"ApiKey {self._client.access_token}", + "Accept": "application/json", + }, + timeout=self._client.timeout + ) as client: + response = client.get(full_url) + response.raise_for_status() + + # Parse JSONL format (one JSON object per line) + rows = [] + for line in response.text.strip().split('\n'): + if line: + rows.append(json.loads(line)) + return rows + + def _poll_for_result(self, job_id: str) -> SqlResult: + """Poll for async query result.""" + start_time = time.time() + + while time.time() - start_time < self._max_poll_time_s: + # Use the scheduler job status endpoint + response = self._client._make_request( + "GET", + f"/api/v1/schedulers/job/{job_id}/status" + ) + + status = response.get("status") + if status == "completed": + # Get results from the fileUrl + details = response.get("details", {}) + file_url = details.get("fileUrl") + columns = [col.get("reference", col.get("name", "")) + for col in details.get("columns", [])] + + if file_url: + # Fetch raw results (JSONL format) + import re + match = re.search(r'/api/v1/projects/.+/sqlRunner/results/.+', file_url) + if match: + results_path = match.group(0) + rows = self._fetch_raw_results(results_path) + return SqlResult(rows=rows, columns=columns) + + # Fallback if no fileUrl + return SqlResult(rows=[], columns=columns) + + elif status == "error": + error_msg = response.get("details", {}).get("error", "SQL query failed") + raise QueryError(error_msg) + + time.sleep(self._poll_interval_ms / 1000) + + raise QueryTimeout(f"SQL query {job_id} timed out") + + def _parse_result(self, data: Dict[str, Any]) -> SqlResult: + """Parse SQL result from API response.""" + rows = data.get("rows", []) + columns = list(rows[0].keys()) if rows else [] + return SqlResult(rows=rows, columns=columns) + + def tables(self) -> List[Dict[str, Any]]: + """ + Get list of available tables in the warehouse. + + Returns: + List of table metadata dictionaries + """ + response = self._client._make_request( + "GET", + f"/api/v1/projects/{self._client.project_uuid}/sqlRunner/tables" + ) + return response + + def fields(self, table: str, schema: Optional[str] = None) -> List[Dict[str, Any]]: + """ + Get fields/columns for a table. + + Args: + table: Table name + schema: Optional schema name + + Returns: + List of field metadata dictionaries + """ + params = {"table": table} + if schema: + params["schema"] = schema + + response = self._client._make_request( + "GET", + f"/api/v1/projects/{self._client.project_uuid}/sqlRunner/fields", + params=params + ) + return response diff --git a/pyproject.toml b/pyproject.toml index 3f517fc..1205c1c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "lightdash" -version = "0.3.0" -description = "Query metrics from Lightdash" +version = "1.0.0" +description = "Official SDK for Lightdash" readme = "README.md" requires-python = ">=3.9" license = {text = "MIT"} diff --git a/tests/test_acceptance.py b/tests/test_acceptance.py index 3647d02..0b04e94 100644 --- a/tests/test_acceptance.py +++ b/tests/test_acceptance.py @@ -313,19 +313,19 @@ def test_query_limit_validation(first_model): if not dimensions or not metrics: pytest.skip("No dimensions or metrics available for testing") - # Test invalid limits - with pytest.raises(ValueError, match="Limit must be between 1 and 5000"): + # Test invalid limits (V2 API supports up to 50000) + with pytest.raises(ValueError, match="Limit must be between 1 and 50000"): first_model.query( dimensions=[dimensions[0].field_id], metrics=[metrics[0].field_id], limit=0, ).to_records() - with pytest.raises(ValueError, match="Limit must be between 1 and 5000"): + with pytest.raises(ValueError, match="Limit must be between 1 and 50000"): first_model.query( dimensions=[dimensions[0].field_id], metrics=[metrics[0].field_id], - limit=5001, + limit=50001, ).to_records() diff --git a/tests/test_filter_operators.py b/tests/test_filter_operators.py new file mode 100644 index 0000000..1f05fda --- /dev/null +++ b/tests/test_filter_operators.py @@ -0,0 +1,249 @@ +""" +Tests for filter operator overloading. + +Tests that dimension comparison operators and filter combining work correctly. +""" + +import pytest +from lightdash.dimensions import Dimension +from lightdash.filter import DimensionFilter, CompositeFilter + + +@pytest.fixture +def dimension(): + """Create a test dimension.""" + return Dimension( + name="country", + model_name="test_model", + label="Country", + description="Customer country", + ) + + +@pytest.fixture +def dimension2(): + """Create a second test dimension.""" + return Dimension( + name="revenue", + model_name="test_model", + label="Revenue", + description="Total revenue", + ) + + +class TestDimensionComparisonOperators: + """Test comparison operators on Dimension class.""" + + def test_equals_operator_returns_filter(self, dimension): + """Test dim == 'value' creates a DimensionFilter.""" + result = dimension == "USA" + assert isinstance(result, DimensionFilter) + assert result.field == dimension + assert result.operator == "equals" + assert result.values == ["USA"] + + def test_equals_with_list(self, dimension): + """Test dim == ['a', 'b'] creates filter with multiple values.""" + result = dimension == ["USA", "UK"] + assert isinstance(result, DimensionFilter) + assert result.values == ["USA", "UK"] + + def test_not_equals_operator(self, dimension): + """Test dim != 'value' creates a not equals filter.""" + result = dimension != "USA" + assert isinstance(result, DimensionFilter) + assert result.operator == "is not" + assert result.values == ["USA"] + + def test_greater_than_operator(self, dimension2): + """Test dim > value creates a greater than filter.""" + result = dimension2 > 1000 + assert isinstance(result, DimensionFilter) + assert result.operator == "is greater than" + assert result.values == [1000] + + def test_less_than_operator(self, dimension2): + """Test dim < value creates a less than filter.""" + result = dimension2 < 500 + assert isinstance(result, DimensionFilter) + assert result.operator == "is less than" + assert result.values == [500] + + def test_dimension_equality_still_works(self, dimension): + """Test that comparing two dimensions still returns bool.""" + same = Dimension( + name="country", + model_name="test_model", + label="Country", + description="Customer country", + ) + different = Dimension( + name="region", + model_name="test_model", + label="Region", + description="Customer region", + ) + assert dimension == same + assert dimension != different + + +class TestDimensionHelperMethods: + """Test helper methods on Dimension class.""" + + def test_in_method(self, dimension): + """Test dim.in_(['a', 'b']) creates equals filter with list.""" + result = dimension.in_(["USA", "UK", "Canada"]) + assert isinstance(result, DimensionFilter) + assert result.operator == "equals" + assert result.values == ["USA", "UK", "Canada"] + + def test_not_in_method(self, dimension): + """Test dim.not_in(['a', 'b']) creates not equals filter.""" + result = dimension.not_in(["USA", "UK"]) + assert isinstance(result, DimensionFilter) + assert result.operator == "is not" + assert result.values == ["USA", "UK"] + + def test_contains_method(self, dimension): + """Test dim.contains('substring') creates includes filter.""" + result = dimension.contains("States") + assert isinstance(result, DimensionFilter) + assert result.operator == "includes" + assert result.values == ["States"] + + def test_starts_with_method(self, dimension): + """Test dim.starts_with('prefix') creates starts with filter.""" + result = dimension.starts_with("United") + assert isinstance(result, DimensionFilter) + assert result.operator == "starts with" + assert result.values == ["United"] + + def test_ends_with_method(self, dimension): + """Test dim.ends_with('suffix') creates ends with filter.""" + result = dimension.ends_with("Kingdom") + assert isinstance(result, DimensionFilter) + assert result.operator == "ends with" + assert result.values == ["Kingdom"] + + def test_is_null_method(self, dimension): + """Test dim.is_null() creates is null filter.""" + result = dimension.is_null() + assert isinstance(result, DimensionFilter) + assert result.operator == "is null" + assert result.values == [] + + def test_is_not_null_method(self, dimension): + """Test dim.is_not_null() creates is not null filter.""" + result = dimension.is_not_null() + assert isinstance(result, DimensionFilter) + assert result.operator == "is not null" + assert result.values == [] + + +class TestFilterCombining: + """Test combining filters with & and | operators.""" + + def test_and_two_filters(self, dimension, dimension2): + """Test filter1 & filter2 creates CompositeFilter with AND.""" + filter1 = dimension == "USA" + filter2 = dimension2 > 1000 + result = filter1 & filter2 + assert isinstance(result, CompositeFilter) + assert result.aggregation == "and" + assert len(result.filters) == 2 + assert result.filters[0] == filter1 + assert result.filters[1] == filter2 + + def test_or_two_filters(self, dimension, dimension2): + """Test filter1 | filter2 creates CompositeFilter with OR.""" + filter1 = dimension == "USA" + filter2 = dimension == "UK" + result = filter1 | filter2 + assert isinstance(result, CompositeFilter) + assert result.aggregation == "or" + assert len(result.filters) == 2 + + def test_parentheses_precedence(self, dimension, dimension2): + """Test that parentheses work for precedence: (a == 1) | (b == 2).""" + result = (dimension == "USA") | (dimension == "UK") + assert isinstance(result, CompositeFilter) + assert result.aggregation == "or" + assert len(result.filters) == 2 + + def test_chain_and_filters(self, dimension, dimension2): + """Test chaining multiple & operations.""" + dim3 = Dimension(name="status", model_name="test_model") + filter1 = dimension == "USA" + filter2 = dimension2 > 1000 + filter3 = dim3 == "active" + result = filter1 & filter2 & filter3 + assert isinstance(result, CompositeFilter) + assert result.aggregation == "and" + assert len(result.filters) == 3 + + def test_chain_or_filters(self, dimension): + """Test chaining multiple | operations.""" + filter1 = dimension == "USA" + filter2 = dimension == "UK" + filter3 = dimension == "Canada" + result = filter1 | filter2 | filter3 + assert isinstance(result, CompositeFilter) + assert result.aggregation == "or" + assert len(result.filters) == 3 + + +class TestBackwardsCompatibility: + """Test that original filter constructors still work.""" + + def test_dimension_filter_constructor(self, dimension): + """Test DimensionFilter can still be created directly.""" + filter1 = DimensionFilter( + field=dimension, + operator="equals", + values=["USA"] + ) + assert filter1.field == dimension + assert filter1.operator == "equals" + assert filter1.values == ["USA"] + + def test_composite_filter_constructor(self, dimension, dimension2): + """Test CompositeFilter can still be created directly.""" + filter1 = DimensionFilter(field=dimension, operator="equals", values=["USA"]) + filter2 = DimensionFilter(field=dimension2, operator="is greater than", values=[1000]) + composite = CompositeFilter(filters=[filter1, filter2], aggregation="and") + assert len(composite.filters) == 2 + assert composite.aggregation == "and" + + def test_to_dict_still_works(self, dimension): + """Test that to_dict() still works on filters created with operators.""" + result = dimension == "USA" + dict_result = result.to_dict() + assert "target" in dict_result + assert "operator" in dict_result + assert "values" in dict_result + assert dict_result["target"]["fieldId"] == "test_model_country" + + +class TestDimensionHashability: + """Test that Dimension remains hashable after overriding __eq__.""" + + def test_dimension_in_set(self, dimension, dimension2): + """Test dimensions can be added to sets.""" + dim_set = {dimension, dimension2} + assert len(dim_set) == 2 + assert dimension in dim_set + + def test_dimension_as_dict_key(self, dimension): + """Test dimension can be used as dict key.""" + dim_dict = {dimension: "value"} + assert dim_dict[dimension] == "value" + + def test_equal_dimensions_have_same_hash(self, dimension): + """Test that equal dimensions have the same hash.""" + same = Dimension( + name="country", + model_name="test_model", + label="Country", + description="Customer country", + ) + assert hash(dimension) == hash(same) diff --git a/tests/test_query_builder.py b/tests/test_query_builder.py new file mode 100644 index 0000000..3583c50 --- /dev/null +++ b/tests/test_query_builder.py @@ -0,0 +1,308 @@ +""" +Unit tests for the Query builder pattern. + +These tests verify the chainable query builder API works correctly +without requiring API access. +""" + +import pytest +from lightdash.models import Model +from lightdash.metrics import Metric +from lightdash.dimensions import Dimension +from lightdash.query import Query +from lightdash.filter import DimensionFilter, CompositeFilter +from lightdash.sorting import Sort + + +@pytest.fixture +def model(): + """Create a test model without client reference.""" + return Model( + name="test_model", + type="default", + database_name="test_db", + schema_name="test_schema", + ) + + +@pytest.fixture +def metric(): + """Create a test metric.""" + return Metric( + name="revenue", + model_name="test_model", + label="Revenue", + description="Total revenue", + ) + + +@pytest.fixture +def metric2(): + """Create a second test metric.""" + return Metric( + name="profit", + model_name="test_model", + label="Profit", + description="Total profit", + ) + + +@pytest.fixture +def dimension(): + """Create a test dimension.""" + return Dimension( + name="country", + model_name="test_model", + label="Country", + description="Customer country", + ) + + +@pytest.fixture +def dimension2(): + """Create a second test dimension.""" + return Dimension( + name="region", + model_name="test_model", + label="Region", + description="Customer region", + ) + + +class TestQueryBuilderBasics: + """Test basic query builder functionality.""" + + def test_query_without_args_returns_empty_query(self, model): + """Query with no args returns an empty query for builder pattern.""" + query = model.query() + assert isinstance(query, Query) + assert query._metrics == () + assert query._dimensions == () + assert query._limit == 500 # Default limit + + def test_metrics_method_adds_metrics(self, model, metric, metric2): + """The .metrics() method adds metrics to the query.""" + query = model.query().metrics(metric, metric2) + assert query._metrics == (metric, metric2) + + def test_dimensions_method_adds_dimensions(self, model, dimension, dimension2): + """The .dimensions() method adds dimensions to the query.""" + query = model.query().dimensions(dimension, dimension2) + assert query._dimensions == (dimension, dimension2) + + def test_limit_method_sets_limit(self, model): + """The .limit() method sets the result limit.""" + query = model.query().limit(100) + assert query._limit == 100 + + def test_sort_method_adds_sorts(self, model, metric, dimension): + """The .sort() method adds sort criteria.""" + sort1 = Sort(metric, descending=True) + sort2 = Sort(dimension, descending=False) + query = model.query().sort(sort1, sort2) + assert query._sort == (sort1, sort2) + + +class TestQueryBuilderImmutability: + """Test that query builder returns new immutable objects.""" + + def test_metrics_returns_new_query(self, model, metric): + """Calling .metrics() returns a new Query, not the same one.""" + query1 = model.query() + query2 = query1.metrics(metric) + assert query1 is not query2 + assert query1._metrics == () + assert query2._metrics == (metric,) + + def test_dimensions_returns_new_query(self, model, dimension): + """Calling .dimensions() returns a new Query, not the same one.""" + query1 = model.query() + query2 = query1.dimensions(dimension) + assert query1 is not query2 + assert query1._dimensions == () + assert query2._dimensions == (dimension,) + + def test_filter_returns_new_query(self, model, dimension): + """Calling .filter() returns a new Query, not the same one.""" + filter1 = DimensionFilter(field=dimension, operator="equals", values=["USA"]) + query1 = model.query() + query2 = query1.filter(filter1) + assert query1 is not query2 + assert query1._filters is None + assert query2._filters is not None + + def test_limit_returns_new_query(self, model): + """Calling .limit() returns a new Query, not the same one.""" + query1 = model.query() + query2 = query1.limit(100) + assert query1 is not query2 + assert query1._limit == 500 + assert query2._limit == 100 + + def test_sort_returns_new_query(self, model, metric): + """Calling .sort() returns a new Query, not the same one.""" + sort = Sort(metric, descending=True) + query1 = model.query() + query2 = query1.sort(sort) + assert query1 is not query2 + assert query1._sort == () + assert query2._sort == (sort,) + + +class TestQueryBuilderChaining: + """Test that methods can be chained in any order.""" + + def test_full_chain(self, model, metric, dimension): + """Test chaining all builder methods.""" + sort = Sort(metric, descending=True) + filter1 = DimensionFilter(field=dimension, operator="equals", values=["USA"]) + + query = ( + model.query() + .metrics(metric) + .dimensions(dimension) + .filter(filter1) + .sort(sort) + .limit(100) + ) + + assert query._metrics == (metric,) + assert query._dimensions == (dimension,) + assert query._filters is not None + assert len(query._filters.filters) == 1 + assert query._sort == (sort,) + assert query._limit == 100 + + def test_chain_order_independent(self, model, metric, dimension): + """Test that chain order doesn't matter for independent operations.""" + sort = Sort(metric, descending=True) + + # Different order + query = ( + model.query() + .limit(100) + .sort(sort) + .dimensions(dimension) + .metrics(metric) + ) + + assert query._metrics == (metric,) + assert query._dimensions == (dimension,) + assert query._sort == (sort,) + assert query._limit == 100 + + def test_reuse_base_query(self, model, metric, dimension): + """Test that base queries can be reused for different variations.""" + base_query = model.query().metrics(metric).dimensions(dimension) + + filter_usa = DimensionFilter(field=dimension, operator="equals", values=["USA"]) + filter_uk = DimensionFilter(field=dimension, operator="equals", values=["UK"]) + + usa_query = base_query.filter(filter_usa) + uk_query = base_query.filter(filter_uk) + + # Base query unchanged + assert base_query._filters is None + + # Both derived queries have their filters + assert usa_query._filters is not None + assert uk_query._filters is not None + + # Filters are different + assert usa_query._filters.filters[0].values == ["USA"] + assert uk_query._filters.filters[0].values == ["UK"] + + +class TestQueryBuilderFilters: + """Test filter combining behavior.""" + + def test_single_filter(self, model, dimension): + """Test adding a single filter.""" + filter1 = DimensionFilter(field=dimension, operator="equals", values=["USA"]) + query = model.query().filter(filter1) + + assert query._filters is not None + assert isinstance(query._filters, CompositeFilter) + assert len(query._filters.filters) == 1 + + def test_multiple_filters_combined_with_and(self, model, dimension, dimension2): + """Test that multiple filters are combined with AND logic.""" + filter1 = DimensionFilter(field=dimension, operator="equals", values=["USA"]) + filter2 = DimensionFilter(field=dimension2, operator="equals", values=["West"]) + + query = model.query().filter(filter1).filter(filter2) + + assert query._filters is not None + assert len(query._filters.filters) == 2 + assert query._filters.aggregation == "and" + + def test_composite_filter(self, model, dimension, dimension2): + """Test adding a CompositeFilter directly.""" + filter1 = DimensionFilter(field=dimension, operator="equals", values=["USA"]) + filter2 = DimensionFilter(field=dimension2, operator="equals", values=["West"]) + composite = CompositeFilter(filters=[filter1, filter2], aggregation="or") + + query = model.query().filter(composite) + + assert query._filters is not None + assert len(query._filters.filters) == 2 + assert query._filters.aggregation == "or" + + +class TestQueryBuilderAccumulation: + """Test that methods accumulate values correctly.""" + + def test_multiple_metrics_calls_accumulate(self, model, metric, metric2): + """Test that multiple .metrics() calls accumulate.""" + query = model.query().metrics(metric).metrics(metric2) + assert query._metrics == (metric, metric2) + + def test_multiple_dimensions_calls_accumulate(self, model, dimension, dimension2): + """Test that multiple .dimensions() calls accumulate.""" + query = model.query().dimensions(dimension).dimensions(dimension2) + assert query._dimensions == (dimension, dimension2) + + def test_multiple_sort_calls_accumulate(self, model, metric, dimension): + """Test that multiple .sort() calls accumulate.""" + sort1 = Sort(metric, descending=True) + sort2 = Sort(dimension, descending=False) + query = model.query().sort(sort1).sort(sort2) + assert query._sort == (sort1, sort2) + + def test_limit_replaces_previous(self, model): + """Test that .limit() replaces (doesn't accumulate).""" + query = model.query().limit(100).limit(50) + assert query._limit == 50 + + +class TestBackwardsCompatibility: + """Test that existing single-call API still works.""" + + def test_single_call_with_all_args(self, model, metric, dimension): + """Test the original single-call pattern still works.""" + filter1 = DimensionFilter(field=dimension, operator="equals", values=["USA"]) + sort = Sort(metric, descending=True) + + query = model.query( + metrics=[metric], + dimensions=[dimension], + filters=filter1, + sort=sort, + limit=100, + ) + + assert query._metrics == (metric,) + assert query._dimensions == (dimension,) + assert query._filters is not None + assert query._sort == (sort,) + assert query._limit == 100 + + def test_single_metric_string(self, model): + """Test that single metric as string still works.""" + query = model.query(metrics="test_model_revenue") + assert query._metrics == ("test_model_revenue",) + + def test_single_dimension_string(self, model, metric): + """Test that single dimension as string still works.""" + query = model.query(metrics=metric, dimensions="test_model_country") + assert query._dimensions == ("test_model_country",) diff --git a/tests/test_results.py b/tests/test_results.py new file mode 100644 index 0000000..17acf65 --- /dev/null +++ b/tests/test_results.py @@ -0,0 +1,121 @@ +"""Tests for the unified result types.""" +import json +import warnings +import pytest + +from lightdash.results import ResultSet, BaseResult +from lightdash.sql_runner import SqlResult + + +class TestResultSetProtocol: + """Tests for the ResultSet protocol.""" + + def test_sql_result_implements_protocol(self): + """SqlResult should implement the ResultSet protocol.""" + result = SqlResult( + rows=[{"a": 1, "b": 2}], + columns=["a", "b"] + ) + assert isinstance(result, ResultSet) + + def test_protocol_requires_to_df(self): + """Protocol should require to_df method.""" + assert hasattr(ResultSet, "to_df") + + def test_protocol_requires_to_records(self): + """Protocol should require to_records method.""" + assert hasattr(ResultSet, "to_records") + + def test_protocol_requires_iter(self): + """Protocol should require __iter__ method.""" + assert hasattr(ResultSet, "__iter__") + + def test_protocol_requires_len(self): + """Protocol should require __len__ method.""" + assert hasattr(ResultSet, "__len__") + + +class TestSqlResult: + """Tests for SqlResult with unified interface.""" + + def test_len(self): + """SqlResult should support len().""" + result = SqlResult( + rows=[{"a": 1}, {"a": 2}, {"a": 3}], + columns=["a"] + ) + assert len(result) == 3 + + def test_iter(self): + """SqlResult should support iteration.""" + rows = [{"a": 1}, {"a": 2}, {"a": 3}] + result = SqlResult(rows=rows, columns=["a"]) + + iterated = list(result) + assert iterated == rows + + def test_for_loop(self): + """SqlResult should support for loop.""" + result = SqlResult( + rows=[{"a": 1}, {"a": 2}], + columns=["a"] + ) + + collected = [] + for row in result: + collected.append(row["a"]) + + assert collected == [1, 2] + + def test_to_records(self): + """SqlResult.to_records() should return rows.""" + rows = [{"a": 1}, {"a": 2}] + result = SqlResult(rows=rows, columns=["a"]) + + assert result.to_records() == rows + + def test_to_json_str(self): + """SqlResult.to_json_str() should return JSON string.""" + rows = [{"a": 1}, {"a": 2}] + result = SqlResult(rows=rows, columns=["a"]) + + json_str = result.to_json_str() + assert json_str == '[{"a": 1}, {"a": 2}]' + assert json.loads(json_str) == rows + + def test_to_json_deprecated(self): + """SqlResult.to_json() should emit deprecation warning.""" + result = SqlResult(rows=[{"a": 1}], columns=["a"]) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + data = result.to_json() + + assert len(w) == 1 + assert issubclass(w[0].category, DeprecationWarning) + assert "to_records()" in str(w[0].message) + assert data == [{"a": 1}] + + +class TestBaseResultInheritance: + """Tests for BaseResult inheritance.""" + + def test_sql_result_is_base_result(self): + """SqlResult should be a BaseResult.""" + result = SqlResult(rows=[], columns=[]) + assert isinstance(result, BaseResult) + + def test_inherited_to_json_str(self): + """BaseResult subclasses should inherit to_json_str.""" + result = SqlResult(rows=[{"x": 10}], columns=["x"]) + assert result.to_json_str() == '[{"x": 10}]' + + def test_inherited_to_json_deprecation(self): + """BaseResult subclasses should inherit to_json deprecation.""" + result = SqlResult(rows=[{"x": 10}], columns=["x"]) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + result.to_json() + assert len(w) == 1 + assert issubclass(w[0].category, DeprecationWarning) diff --git a/tests/test_validation.py b/tests/test_validation.py new file mode 100644 index 0000000..9ae39c7 --- /dev/null +++ b/tests/test_validation.py @@ -0,0 +1,248 @@ +""" +Unit tests for validation and usability improvements. + +These tests don't require API access - they test local validation logic. +""" + +import pytest +from lightdash.dimensions import Dimension, Dimensions +from lightdash.metrics import Metric, Metrics +from lightdash.models import Model, Models +from lightdash.filter import DimensionFilter, CompositeFilter +from lightdash.sorting import Sort +from lightdash.query import Query +from unittest.mock import MagicMock + + +# Fixtures for testing + +@pytest.fixture +def sample_dimension(): + return Dimension(name="country", model_name="orders") + + +@pytest.fixture +def sample_metric(): + return Metric(name="revenue", model_name="orders") + + +@pytest.fixture +def mock_model(): + """Create a mock model for testing.""" + model = Model( + name="orders", + type="default", + database_name="analytics", + schema_name="public", + ) + # Mock the client + model._client = MagicMock() + return model + + +# Test 1: Sort parameter accepts single Sort object + +class TestSortParameter: + def test_sort_accepts_single_sort_object(self, mock_model, sample_metric): + """Sort parameter should accept single Sort object.""" + sort = Sort(sample_metric, descending=True) + query = mock_model.query( + metrics=[sample_metric], + sort=sort, + limit=10, + ) + # Sort should be normalized to a tuple internally (for immutability) + assert query._sort == (sort,) + + def test_sort_accepts_sequence_of_sorts(self, mock_model, sample_metric, sample_dimension): + """Sort parameter should accept sequence of Sort objects.""" + sorts = [ + Sort(sample_metric, descending=True), + Sort(sample_dimension, descending=False), + ] + query = mock_model.query( + metrics=[sample_metric], + dimensions=[sample_dimension], + sort=sorts, + limit=10, + ) + assert query._sort == tuple(sorts) + + def test_sort_accepts_none(self, mock_model, sample_metric): + """Sort parameter should accept None (default).""" + query = mock_model.query( + metrics=[sample_metric], + sort=None, + limit=10, + ) + # None normalizes to empty tuple for immutability + assert query._sort == () + + +# Test 2: Assertions replaced with proper validation + +class TestFilterValidation: + def test_invalid_operator_raises_value_error(self, sample_dimension): + """Invalid filter operator should raise ValueError, not AssertionError.""" + with pytest.raises(ValueError) as exc_info: + DimensionFilter( + field=sample_dimension, + operator="invalid_operator", + values=["test"], + ) + assert "Invalid operator" in str(exc_info.value) + assert "invalid_operator" in str(exc_info.value) + + def test_valid_operator_works(self, sample_dimension): + """Valid operators should work without error.""" + f = DimensionFilter( + field=sample_dimension, + operator="equals", + values=["test"], + ) + assert f.operator == "equals" + + def test_field_must_be_dimension_object(self): + """Field must be a Dimension object, not a string.""" + with pytest.raises(TypeError) as exc_info: + DimensionFilter( + field="not_a_dimension", # type: ignore + operator="equals", + values=["test"], + ) + assert "must be a Dimension object" in str(exc_info.value) + + def test_invalid_aggregation_raises_value_error(self, sample_dimension): + """Invalid aggregation should raise ValueError.""" + f = DimensionFilter( + field=sample_dimension, + operator="equals", + values=["test"], + ) + with pytest.raises(ValueError) as exc_info: + CompositeFilter(filters=[f], aggregation="invalid") + assert "Invalid aggregation" in str(exc_info.value) + + def test_valid_aggregations_work(self, sample_dimension): + """Valid aggregations should work.""" + f = DimensionFilter( + field=sample_dimension, + operator="equals", + values=["test"], + ) + cf_and = CompositeFilter(filters=[f], aggregation="and") + assert cf_and.aggregation == "and" + + cf_or = CompositeFilter(filters=[f], aggregation="or") + assert cf_or.aggregation == "or" + + def test_validation_works_with_python_o(self, sample_dimension): + """Validation should still work with python -O (assertions disabled).""" + # This test verifies the behavior is the same regardless of __debug__ + # If assertions were used, they'd be disabled with -O flag + with pytest.raises(ValueError): + DimensionFilter( + field=sample_dimension, + operator="not_valid", + values=["test"], + ) + + +# Test 3: Fuzzy matching suggestions + +class TestFuzzyMatchingSuggestions: + def test_metric_typo_suggests_alternatives(self): + """Typo in metric name should suggest similar metrics.""" + # Create a mock model with metrics + mock_model = MagicMock() + mock_model.list_metrics.return_value = [ + Metric(name="revenue", model_name="orders"), + Metric(name="total_orders", model_name="orders"), + Metric(name="average_order_value", model_name="orders"), + ] + + metrics = Metrics(mock_model) + + # Access a typo of "revenue" + with pytest.raises(AttributeError) as exc_info: + _ = metrics.revnue # typo + + error_msg = str(exc_info.value) + assert "No metric named 'revnue' found" in error_msg + assert "Did you mean" in error_msg + assert "'revenue'" in error_msg + + def test_dimension_typo_suggests_alternatives(self): + """Typo in dimension name should suggest similar dimensions.""" + mock_model = MagicMock() + mock_model.list_dimensions.return_value = [ + Dimension(name="country", model_name="orders"), + Dimension(name="city", model_name="orders"), + Dimension(name="customer_name", model_name="orders"), + ] + + dimensions = Dimensions(mock_model) + + with pytest.raises(AttributeError) as exc_info: + _ = dimensions.contry # typo + + error_msg = str(exc_info.value) + assert "No dimension named 'contry' found" in error_msg + assert "Did you mean" in error_msg + assert "'country'" in error_msg + + def test_model_typo_suggests_alternatives(self): + """Typo in model name should suggest similar models.""" + mock_client = MagicMock() + + # Create mock models + orders = Model(name="orders", type="default", database_name="db", schema_name="schema") + customers = Model(name="customers", type="default", database_name="db", schema_name="schema") + products = Model(name="products", type="default", database_name="db", schema_name="schema") + + mock_client._fetch_models.return_value = [orders, customers, products] + + models = Models(mock_client) + + with pytest.raises(AttributeError) as exc_info: + _ = models.ordrs # typo + + error_msg = str(exc_info.value) + assert "No model named 'ordrs' found" in error_msg + assert "Did you mean" in error_msg + assert "'orders'" in error_msg + + def test_no_suggestions_for_completely_different_name(self): + """No suggestions when name is completely different.""" + mock_model = MagicMock() + mock_model.list_metrics.return_value = [ + Metric(name="revenue", model_name="orders"), + ] + + metrics = Metrics(mock_model) + + with pytest.raises(AttributeError) as exc_info: + _ = metrics.xyz123 # completely different + + error_msg = str(exc_info.value) + assert "No metric named 'xyz123' found" in error_msg + # Should not have suggestions + assert "Did you mean" not in error_msg + + def test_multiple_suggestions_shown(self): + """Multiple similar names should all be suggested.""" + mock_model = MagicMock() + mock_model.list_metrics.return_value = [ + Metric(name="total_revenue", model_name="orders"), + Metric(name="total_cost", model_name="orders"), + Metric(name="total_profit", model_name="orders"), + ] + + metrics = Metrics(mock_model) + + with pytest.raises(AttributeError) as exc_info: + _ = metrics.total_revenu # typo + + error_msg = str(exc_info.value) + assert "Did you mean" in error_msg + assert "'total_revenue'" in error_msg diff --git a/uv.lock b/uv.lock index 512998a..9f253f9 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 3 requires-python = ">=3.9" resolution-markers = [ "python_full_version >= '3.12'", @@ -18,18 +18,18 @@ dependencies = [ { name = "sniffio" }, { name = "typing-extensions", marker = "python_full_version < '3.13'" }, ] -sdist = { url = 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