diff --git a/.gitignore b/.gitignore index 0fda688..339b7f6 100644 --- a/.gitignore +++ b/.gitignore @@ -130,6 +130,9 @@ ENV/ env.bak/ venv.bak/ +# Environments variables file +.mise.local.toml + # Spyder project settings .spyderproject .spyproject @@ -150,5 +153,8 @@ dmypy.json # generated *_spark/ +metastore_db/ +spark-warehouse/ *_pipeline/ .vscode/ + diff --git a/README.md b/README.md index e3bd4f2..05ea2fe 100644 --- a/README.md +++ b/README.md @@ -31,6 +31,7 @@ This repository contains different [Jupyter Notebooks](https://jupyter.org) to d - [Experimenting Locally](#experimenting-locally) - [Using Docker](#using-docker) - [On the Machine (Linux/x64 \& arm64)](#on-the-machine-linuxx64--arm64) + - [Optional: Spark and Databricks Support](#optional-spark-and-databricks-support) - [Notebooks](#notebooks) - [Overview](#overview) - [Descriptions](#descriptions) @@ -93,13 +94,29 @@ The following commands will set up a Python environment with necessary Python li ``` $ git clone https://github.com/getml/getml-demo.git $ cd getml-demo -$ pipx install hatch -$ hatch env create -$ hatch shell -$ pip install -r requirements.txt -$ jupyter-lab +$ pipx install uv +$ uv run jupyter-lab ``` +#### Optional: Spark and Databricks Support + +Some notebooks (e.g., `imdb.ipynb`, `online_retail.ipynb`) demonstrate exporting features to Spark SQL. For these, you need to install additional dependencies: + +> [!IMPORTANT] +> The `spark` and `databricks` dependency groups are **mutually exclusive** and cannot be installed together. The `--isolated` flag runs the command in a temporary environment without affecting your main installation. + +**For local Spark execution** (running Spark locally on your machine): +``` +$ uv run --group spark --isolated jupyter-lab +``` + +**For Databricks integration** (connecting to Databricks compute): +``` +$ uv run --group databricks jupyter-lab +``` + +See [integration/databricks/README.md](integration/databricks/README.md) for Databricks setup instructions. + > [!TIP] > Install the [Enterprise trial version](https://getml.com/latest/enterprise/request-trial) via the [Install getML on Linux guide](https://getml.com/latest/install/packages/linux#install-getml-on-linux) to try the Enterprise features. diff --git a/integration/__init__.py b/integration/__init__.py new file mode 100644 index 0000000..e2cf268 --- /dev/null +++ b/integration/__init__.py @@ -0,0 +1 @@ +"""Integration modules for connecting getML with external platforms.""" diff --git a/integration/databricks/README.md b/integration/databricks/README.md new file mode 100644 index 0000000..eb84b5b --- /dev/null +++ b/integration/databricks/README.md @@ -0,0 +1,124 @@ +# Databricks Data Integration + +This directory contains modules for ingesting data from GCS into Databricks Delta Lake and preparing population tables for getML feature engineering. + + +## Prerequisites + +- **Databricks Free Edition account** (or higher tier) +- **Databricks CLI** installed + +## Setup + +### 1. Install Databricks CLI + +```bash +# macOS +brew install databricks/tap/databricks + +# Linux & macOS & Windows +curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh +``` + +More: https://docs.databricks.com/gcp/en/dev-tools/cli/install + +### 2. Install Dependencies with uv + +> [!IMPORTANT] +> The `databricks` dependency group uses `databricks-connect`, which **cannot be installed alongside `pyspark`**. These packages are mutually exclusive. If you need local Spark execution (e.g., for notebooks like `imdb.ipynb`), use `uv run --group spark --isolated` instead to run in a temporary isolated environment. + +```bash +# From the repository root +cd getml-demo + +# Install uv if not already installed +pipx install uv + +# Run jupyter lab after install dependencies included in the databricks group +$ uv run --group databricks jupyter-lab +``` + +### 3. Authenticate with Databricks + +```bash +# Get your workspace URL from your Databricks Free Edition account +# It looks like: https://.cloud.databricks.com + +databricks auth login --host https://.cloud.databricks.com --profile DEFAULT +``` + +This will open a browser for OAuth authentication. After successful login, `DEFAULT` profile is stored in ~/.databrickscfg. + +### 4. Verify Authentication + +```bash +databricks auth profiles +``` + +You should see your workspace listed. + +## Usage + +### Python API (Recommended) + +Use the modules directly in notebooks or scripts: + +```python +from integration.databricks.data import ingestion + +# Load raw data from GCS to Databricks +loaded_tables = ingestion.load_from_gcs( + bucket="https://static.getml.com/datasets/jaffle_shop/", + destination_schema="jaffle_shop" +) +print(f"Loaded {len(loaded_tables)} tables") +``` + +### Load Specific Tables + +```python +from integration.databricks.data import ingestion + +# Load only the tables you need +ingestion.load_from_gcs( + destination_schema="RAW", + tables=["raw_customers", "raw_orders", "raw_items", "raw_products"] +) +``` + +### Configure the Databricks profile (optional) + +The above steps created `DEFAULT` profile for Databricks authentication. The ingestion +module also defaults to `DEFAULT` profile. The authentication should work smoothly for +a single profile. + +If you have multiple profiles (e.g for different Databricks hosts), you can set +`DATABRICKS_CONFIG_PROFILE` environment variable in `.mise.local.toml` (gitignored) to +pin a specific profile to be used for this project: + +```toml +[env] +DATABRICKS_CONFIG_PROFILE = "Code17" +``` + +In this example, `Code17` profile will be used instead of `DEFAULT` one. + +## Troubleshooting + +### Authentication Errors + +```bash +# Re-authenticate +databricks auth login --host https://.cloud.databricks.com + +# Check your profile +databricks auth env +``` + +### Connection Timeout + +Free Edition has limited compute resources. If you see timeouts: +- Wait a few minutes and retry (serverless cold start can take few seconds or minutes) +- Check your quota in the Databricks workspace + + diff --git a/integration/databricks/__init__.py b/integration/databricks/__init__.py new file mode 100644 index 0000000..cdd62df --- /dev/null +++ b/integration/databricks/__init__.py @@ -0,0 +1 @@ +"""Databricks integration for getML demos.""" diff --git a/integration/databricks/data/__init__.py b/integration/databricks/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/integration/databricks/data/ingestion.py b/integration/databricks/data/ingestion.py new file mode 100644 index 0000000..69ec1c1 --- /dev/null +++ b/integration/databricks/data/ingestion.py @@ -0,0 +1,364 @@ +""" +GCS to Databricks Delta Lake ingestion module. + +This module provides functions to load parquet files from GCS +and write them as Delta tables in Databricks. + +Example: + # Assuming running from root of repository + from integration.databricks.data import ingestion + + # Load all jaffle_shop tables + ingestion.load_from_gcs( + bucket="https://static.getml.com/datasets/jaffle_shop/", + destination_schema="jaffle_shop" + ) + + # Or load specific tables + ingestion.load_from_gcs( + bucket="https://static.getml.com/datasets/jaffle_shop/", + destination_schema="jaffle_shop", + tables=["raw_customers", "raw_orders"] + ) +""" + +from __future__ import annotations + +from io import BytesIO +import logging +import os +import re +from collections.abc import Sequence +from typing import Annotated, ClassVar, Final + +import requests +from databricks.connect import DatabricksSession +from databricks.sdk import WorkspaceClient +from databricks.sdk.service.catalog import VolumeType +from pydantic import AfterValidator, BaseModel, ConfigDict, Field +from pyspark.sql import SparkSession + +logger = logging.getLogger(__name__) + + +# Default configuration +DEFAULT_BUCKET: Final[str] = "https://static.getml.com/datasets/jaffle_shop" +DEFAULT_CATALOG: Final[str] = "workspace" +DEFAULT_SCHEMA: Final[str] = "jaffle_shop" +DEFAULT_PROFILE: Final[str] = "DEFAULT" +STAGING_VOLUME: Final[str] = "ingestion_staging" + +JAFFLE_SHOP_TABLES: Final[tuple[str, ...]] = ( + "raw_customers", + "raw_items", + "raw_orders", + "raw_products", + "raw_stores", + "raw_supplies", + "raw_tweets", +) + +_IDENTIFIER_PATTERN: Final[re.Pattern[str]] = re.compile(r"^[a-zA-Z_][a-zA-Z0-9_]*$") + + +def _validate_sql_identifier(value: str) -> str: + """ + Validate SQL identifier to prevent injection attacks. + + Args: + value: Identifier to validate. + + Returns: + The validated identifier. + + Raises: + ValueError: If identifier contains invalid characters. + """ + if not _IDENTIFIER_PATTERN.fullmatch(value): + msg = ( + f"Invalid SQL identifier {value!r}. " + f"Must match pattern: {_IDENTIFIER_PATTERN.pattern!r}" + ) + raise ValueError(msg) + + return value + + +# Type alias for validated SQL identifiers (parse-don't-validate) +SqlIdentifier = Annotated[str, AfterValidator(_validate_sql_identifier)] + + +class SchemaLocation(BaseModel): + """Location identifier for a Databricks schema.""" + + model_config: ClassVar[ConfigDict] = ConfigDict(frozen=True) + + catalog: SqlIdentifier + schema_: SqlIdentifier = Field(alias="schema") + + @property + def qualified_name(self) -> str: + """Return fully qualified schema name.""" + return f"{self.catalog}.{self.schema_}" + + +class TableConfig(BaseModel): + """Configuration for a Delta table to be created.""" + + model_config: ClassVar[ConfigDict] = ConfigDict(frozen=True) # type: ignore[assignment] + + source_url: str + table_name: SqlIdentifier + location: SchemaLocation + + @property + def full_table_name(self) -> str: + """Return fully qualified table name.""" + return f"{self.location.catalog}.{self.location.schema_}.{self.table_name}" + + +def _stream_from_url_to_volume( + workspace: WorkspaceClient, url: str, volume_path: str +) -> None: + """Stream file from URL directly to Databricks Volume.""" + logger.info(f"Streaming from {url} to volume: {volume_path}") + with requests.get(url, stream=True, timeout=120) as response: + response.raise_for_status() + _ = workspace.files.upload( + volume_path, BytesIO(response.raw.read()), overwrite=True + ) + + +def _ensure_volume_exists( + workspace: WorkspaceClient, schema_location: SchemaLocation, volume_name: str +) -> None: + """Ensure the staging volume exists.""" + full_name = f"{schema_location.qualified_name}.{volume_name}" + logger.info(f"Ensuring volume exists: {full_name}") + try: + _ = workspace.volumes.read(full_name) + except Exception: + logger.info(f"Volume {full_name} not found, creating...") + _ = workspace.volumes.create( + catalog_name=schema_location.catalog, + schema_name=schema_location.schema_, + name=volume_name, + volume_type=VolumeType.MANAGED, + ) + + +def _create_spark_session(profile: str | None = None) -> SparkSession: + """ + Create a Databricks Spark session using serverless compute. + + Uses Databricks CLI authentication. If profile is not specified, + uses DATABRICKS_CONFIG_PROFILE env var or defaults to 'DEFAULT'. + + Args: + profile: Databricks CLI profile name (optional). + + Returns: + SparkSession connected to Databricks serverless compute. + + Raises: + RuntimeError: If connection fails. + """ + logger.info("Creating Databricks Spark session (serverless)...") + try: + profile_name = profile or os.environ.get( + "DATABRICKS_CONFIG_PROFILE", DEFAULT_PROFILE + ) + + if profile_name: + logger.info(f"Using Databricks profile: {profile_name}") + os.environ["DATABRICKS_CONFIG_PROFILE"] = profile_name + + spark = DatabricksSession.builder.serverless().getOrCreate() + + logger.info("Successfully connected to Databricks serverless compute") + return spark + except Exception as e: + logger.error(f"Failed to create Databricks session: {e}") + logger.error( + "Make sure you've authenticated with: databricks auth login --host " + + " --profile " + ) + logger.error( + "If using a named profile, set DATABRICKS_CONFIG_PROFILE=" + ) + raise RuntimeError(f"Failed to connect to Databricks: {e}") from e + + +def _write_to_delta( + spark: SparkSession, + source_path: str, + config: TableConfig, +) -> None: + """Write a parquet file from Volume to Delta Lake as a managed table.""" + logger.info(f"Writing to Delta table: {config.full_table_name} from {source_path}") + + spark_dataframe = spark.read.parquet(source_path) + + spark_dataframe.write.format("delta").mode("overwrite").option( + "overwriteSchema", "true" + ).saveAsTable(config.full_table_name) + + logger.info(f"Successfully wrote to {config.full_table_name}") + + +def _ensure_schema_exists(spark: SparkSession, schema_location: SchemaLocation) -> None: + """Ensure the target schema exists, create if necessary.""" + logger.info(f"Ensuring schema exists: {schema_location.qualified_name}") + _ = spark.sql( # pyright: ignore[reportUnknownMemberType] + "CREATE SCHEMA IF NOT EXISTS IDENTIFIER(:full_schema_name)", + args={"full_schema_name": schema_location.qualified_name}, + ) + + +def _build_table_configs( + bucket: str, + table_names: Sequence[str], + location: SchemaLocation, +) -> list[TableConfig]: + """Build TableConfig objects for all tables to be loaded.""" + return [ + TableConfig( + source_url=f"{bucket.rstrip('/')}/{name}.parquet", + table_name=name, + location=location, + ) + for name in table_names + ] + + +def _cleanup_volume_file(workspace: WorkspaceClient, volume_path: str) -> None: + """Clean up temporary file from volume, suppressing errors.""" + try: + workspace.files.delete(volume_path) + except Exception as e: + logger.warning(f"Failed to clean up volume file {volume_path}: {e}") + + +def _process_single_table( + workspace: WorkspaceClient, + spark: SparkSession, + config: TableConfig, + volume_path: str, +) -> bool: + """Process a single table: download, write to Delta, and cleanup. + + Returns: + True if successful, False otherwise. + """ + try: + _stream_from_url_to_volume(workspace, config.source_url, volume_path) + _write_to_delta(spark, volume_path, config) + return True + + except requests.RequestException as e: + logger.error(f"Failed to download {config.source_url}: {e}") + return False + + except Exception as e: + logger.error(f"Failed to process {config.table_name}: {e}") + return False + + finally: + _cleanup_volume_file(workspace, volume_path) + + +def load_from_gcs( + bucket: str = DEFAULT_BUCKET, + destination_schema: str = DEFAULT_SCHEMA, + destination_catalog: str = DEFAULT_CATALOG, + tables: Sequence[str] | None = None, + spark: SparkSession | None = None, + profile: str = DEFAULT_PROFILE, + workspace: WorkspaceClient | None = None, +) -> list[str]: + """ + Load parquet files from GCS bucket to Databricks Delta Lake. + + Streams parquet files from the specified GCS bucket to Databricks volume and then + writes them from the volume to Delta Lake as managed tables in Databricks. + + Args: + bucket: GCS bucket URL (default: jaffle_shop dataset). + destination_schema: Target schema name in Databricks. + destination_catalog: Target catalog name in Databricks. + tables: List of table names to load. If None, loads all jaffle_shop tables. + spark: Optional existing SparkSession. If None, creates a new one. + profile: Databricks CLI profile name (optional). + + Returns: + List of successfully loaded table names. + + Example: + >>> from integration.databricks.data import ingestion + >>> loaded = ingestion.load_from_gcs( + ... bucket="https://static.getml.com/datasets/jaffle_shop/", + ... destination_schema="RAW" + ... ) + >>> print(f"Loaded {len(loaded)} tables") + """ + table_names = list(tables) if tables else list(JAFFLE_SHOP_TABLES) + + location = SchemaLocation(catalog=destination_catalog, schema=destination_schema) + table_configs = _build_table_configs(bucket, table_names, location) + + logger.info(f"Loading {len(table_names)} tables from {bucket}") + logger.info(f"Destination: {location.qualified_name}") + + if spark is None: + spark = _create_spark_session(profile=profile) + + _ensure_schema_exists(spark, location) + + if workspace is None: + workspace = WorkspaceClient() + + _ensure_volume_exists(workspace, location, STAGING_VOLUME) + + loaded_tables: list[str] = [] + for config in table_configs: + volume_path = f"/Volumes/{location.catalog}/{location.schema_}/{STAGING_VOLUME}" + f"/{config.table_name}.parquet" + + if _process_single_table(workspace, spark, config, volume_path): + loaded_tables.append(config.table_name) + + logger.info(f"Successfully loaded {len(loaded_tables)}/{len(table_names)} tables") + return loaded_tables + + +def list_tables( + schema: str = DEFAULT_SCHEMA, + catalog: str = DEFAULT_CATALOG, + spark: SparkSession | None = None, + profile: str | None = None, +) -> list[str]: + """ + List all tables in the specified schema. + + Args: + schema: Schema name. + catalog: Catalog name. + spark: Optional existing SparkSession. + profile: Databricks CLI profile name (optional). + + Returns: + List of table names. + """ + schema_location = SchemaLocation( + catalog=catalog, + schema=schema, + ) + + if spark is None: + spark = _create_spark_session(profile=profile) + + rows = spark.sql( # pyright: ignore[reportUnknownMemberType] + "SHOW TABLES IN IDENTIFIER(:full_schema_name)", + args={"full_schema_name": schema_location.qualified_name}, + ).collect() + return [row.tableName for row in rows] # pyright: ignore[reportAny] diff --git a/integration/databricks/ingest_to_databricks.py b/integration/databricks/ingest_to_databricks.py new file mode 100644 index 0000000..0762b38 --- /dev/null +++ b/integration/databricks/ingest_to_databricks.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python3 +""" +Ingest parquet files from GCS to Databricks Delta Lake. + +This is a CLI wrapper around the ingestion module. For programmatic use, +import the module directly: + + from integration.databricks.data import ingestion + +Usage: + 1. Authenticate with Databricks CLI: databricks auth login --host + 2. Run: python ingest_to_databricks.py + +Environment variables (optional): + DATABRICKS_CONFIG_PROFILE: Databricks CLI profile name +""" + +from __future__ import annotations + +import logging +import sys + +from integration.databricks.data import ingestion + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + + +def main() -> int: + """Main entry point for the ingestion script.""" + + logger.info("Starting parquet ingestion to Databricks Delta Lake") + + try: + loaded = ingestion.load_from_gcs() + logger.info(f"Completed: {len(loaded)} tables ingested") + return 0 if len(loaded) == len(ingestion.JAFFLE_SHOP_TABLES) else 1 + except Exception as e: + logger.error(f"Ingestion failed: {e}") + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..05237cf --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,32 @@ +[project] +name = "getml-demo" +version = "0.1.0" +description = "Jupyter Notebooks demonstrating getML capabilities for machine learning on relational datasets" +readme = "README.md" +requires-python = "==3.12.*" + +dependencies = [ + "jupyterlab==4.1.1", + "getml==1.5.0", + "featuretools==1.31.0", + "tsfresh==0.20.3", + "seaborn==0.13.2", + "ipywidgets==8.1.2", + "plotly==5.18.0", + "prophet==1.1.5", + "matplotlib==3.8.2", +] + +[dependency-groups] +spark = [ + "pyspark==4.0.1", +] + +databricks = [ + "databricks-connect~=17.3.2", + "requests~=2.32.5", + "pydantic~=2.12.5", +] + + + diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 2a961aa..0000000 --- a/requirements.txt +++ /dev/null @@ -1,10 +0,0 @@ -jupyterlab==4.1.1 -getml==1.5.0 -featuretools==1.31.0 -tsfresh==0.20.3 -pyspark==3.5.0 -seaborn==0.13.2 -ipywidgets==8.1.2 -plotly==5.18.0 -prophet==1.1.5 -matplotlib==3.8.2 diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..dbb0a8d --- /dev/null +++ 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