# Capability: DuckDB Parquet Handler

## ADDED Requirements

### Requirement: DuckDB Parquet Handler Initialization

The system SHALL provide a `DuckDBParquetHandler` class that can be initialized with either a storage options object or an existing filesystem instance to enable parquet operations with DuckDB.

#### Scenario: Initialize with storage options

- **WHEN** user creates `DuckDBParquetHandler(storage_options=AwsStorageOptions(...))`
- **THEN** handler creates filesystem from storage options and registers it in DuckDB connection

#### Scenario: Initialize with filesystem instance

- **WHEN** user creates `DuckDBParquetHandler(filesystem=fs)`
- **THEN** handler uses provided filesystem and registers it in DuckDB connection

#### Scenario: Initialize with default filesystem

- **WHEN** user creates `DuckDBParquetHandler()` without parameters
- **THEN** handler creates default local filesystem for operations

### Requirement: Filesystem Registration in DuckDB

The system SHALL register fsspec filesystem instances in DuckDB connections using `.register_filesystem(fs)` to enable operations on remote storage systems.

#### Scenario: Register S3 filesystem

- **WHEN** handler is initialized with S3 storage options
- **THEN** S3 filesystem is registered in DuckDB connection via `.register_filesystem(fs)`
- **AND** DuckDB can access S3 paths using the registered filesystem

#### Scenario: Register local filesystem

- **WHEN** handler is initialized with local storage options or no options
- **THEN** local filesystem is registered in DuckDB connection
- **AND** DuckDB can access local paths

### Requirement: Read Parquet Files and Datasets

The system SHALL provide a `read_parquet` method that reads parquet files or directories containing parquet files and returns PyArrow tables.

#### Scenario: Read single parquet file

- **WHEN** user calls `handler.read_parquet("/path/to/file.parquet")`
- **THEN** method returns PyArrow table with all data from the file

#### Scenario: Read parquet dataset directory

- **WHEN** user calls `handler.read_parquet("/path/to/dataset/")`
- **THEN** method reads all parquet files in directory and subdirectories
- **AND** returns combined PyArrow table with all data

#### Scenario: Read with column selection

- **WHEN** user calls `handler.read_parquet(path, columns=["col1", "col2"])`
- **THEN** method returns PyArrow table containing only specified columns
- **AND** improves performance by reading only required columns

#### Scenario: Read from remote storage

- **WHEN** user provides remote path like "s3://bucket/data.parquet"
- **AND** handler has appropriate storage options configured
- **THEN** method reads parquet data from remote location using registered filesystem

### Requirement: Write Parquet Files

The system SHALL provide a `write_parquet` method that writes PyArrow tables to parquet format with configurable compression.

#### Scenario: Write parquet file with default compression

- **WHEN** user calls `handler.write_parquet(table, "/path/to/output.parquet")`
- **THEN** method writes PyArrow table to parquet file
- **AND** creates parent directories if they don't exist

#### Scenario: Write with custom compression

- **WHEN** user calls `handler.write_parquet(table, path, compression="gzip")`
- **THEN** method writes parquet file with specified compression codec
- **AND** supports codecs: "snappy", "gzip", "lz4", "zstd", "brotli"

#### Scenario: Write to remote storage

- **WHEN** user provides remote path like "s3://bucket/output.parquet"
- **AND** handler has appropriate storage options configured
- **THEN** method writes parquet data to remote location using registered filesystem

#### Scenario: Write to nested directory

- **WHEN** user provides path with multiple nested directories
- **AND** parent directories don't exist
- **THEN** method creates all necessary parent directories
- **AND** writes parquet file successfully

### Requirement: SQL Query Execution

The system SHALL provide an `execute_sql` method that executes SQL queries on parquet files using DuckDB and returns results as PyArrow tables.

#### Scenario: Execute SQL query on parquet file

- **WHEN** user calls `handler.execute_sql("SELECT * FROM parquet_scan('file.parquet') WHERE col > 10")`
- **THEN** method executes query using DuckDB
- **AND** returns PyArrow table with query results

#### Scenario: Execute parameterized query

- **WHEN** user calls `handler.execute_sql(query, parameters=[value1, value2])`
- **AND** query contains parameter placeholders (`?`)
- **THEN** method safely binds parameters to query
- **AND** executes parameterized query
- **AND** returns PyArrow table with results

#### Scenario: Execute aggregation query

- **WHEN** user executes SQL with GROUP BY, aggregate functions, or window functions
- **THEN** method returns PyArrow table with aggregated results
- **AND** leverages DuckDB's analytical query capabilities

#### Scenario: Execute query on remote parquet

- **WHEN** query references remote parquet path (s3://, gs://, etc.)
- **AND** filesystem is registered
- **THEN** method executes query on remote data
- **AND** returns PyArrow table with results

### Requirement: Context Manager Support

The system SHALL implement context manager protocol for automatic resource cleanup and connection management.

#### Scenario: Use with statement

- **WHEN** user creates handler with `with DuckDBParquetHandler() as handler:`
- **THEN** handler initializes DuckDB connection on enter
- **AND** automatically closes connection on exit
- **AND** resources are properly cleaned up even if exceptions occur

#### Scenario: Manual resource management

- **WHEN** user creates handler without context manager
- **THEN** handler still functions correctly
- **AND** user can manually close connection if needed

### Requirement: Type Safety and Documentation

The system SHALL provide complete type hints for all public methods and comprehensive Google-style docstrings with usage examples.

#### Scenario: Type hints for all methods

- **WHEN** developer uses handler in type-checked code
- **THEN** all method signatures have complete type annotations
- **AND** mypy validates types correctly

#### Scenario: Comprehensive docstrings

- **WHEN** developer reads method documentation
- **THEN** each method has Google-style docstring
- **AND** docstring includes description, arguments, returns, and usage examples

### Requirement: Error Handling

The system SHALL provide clear error messages for common failure scenarios.

#### Scenario: Invalid path error

- **WHEN** user provides non-existent path to read_parquet
- **THEN** method raises clear exception indicating file not found

#### Scenario: Invalid storage options error

- **WHEN** user provides storage options with missing credentials for remote storage
- **THEN** method raises clear exception indicating authentication failure

#### Scenario: SQL execution error

- **WHEN** SQL query has syntax error or references invalid columns
- **THEN** execute_sql raises exception with DuckDB error message
