database-engine-implementation

name	database-engine-implementation
description	Guide for implementing database engines in IterableData. Use when adding support for new SQL or NoSQL databases, implementing DBDriver classes, or extending database capabilities. Use when this capability is needed.
metadata	{"author":"datenoio"}

Database Engine Implementation Guide

Overview

Database engines provide read-only access to SQL and NoSQL databases as iterable data sources. They wrap database drivers to provide a unified BaseIterable interface.

Architecture

• DBDriver (iterable/db/base.py) - Abstract base class for database drivers
• DatabaseIterable (iterable/db/iterable.py) - Wrapper that makes DBDriver work as BaseIterable
• Driver Registry (iterable/db/__init__.py) - Registry for mapping engine names to driver classes

Adding New Database Engines

Step-by-Step Process

1. Create driver file: iterable/db/<engine>.py
2. Implement DBDriver: Inherit from DBDriver in iterable/db/base.py
3. Required methods: connect(), iterate(), close() (close has default implementation)
4. Register driver: Call register_driver() in iterable/db/__init__.py
5. Add detection: Update iterable/helpers/detect.py to recognize database URLs
6. Create tests: tests/test_db_engines.py or add to existing test file
7. Update dependencies: Add optional dependency to pyproject.toml
8. Update documentation: Add engine to docs

Implementation Pattern

from collections.abc import Iterator
from typing import Any
from ..types import Row
from .base import DBDriver

class NewEngineDriver(DBDriver):
    """Database driver for NewEngine.
    
    Supports streaming queries using batch processing.
    """
    
    def __init__(self, source: str | Any, **kwargs: Any) -> None:
        """Initialize driver.
        
        Args:
            source: Connection string/URL or existing connection object
            **kwargs: Additional parameters:
                - query: Query string or table name
                - batch_size: Rows per batch (default: 10000)
                - on_error: Error handling policy ('raise', 'skip', 'warn')
        """
        super().__init__(source, **kwargs)
        self._cursor: Any = None
    
    def connect(self) -> None:
        """Establish database connection.
        
        Raises:
            ImportError: If required driver library is not installed
            ConnectionError: If connection fails
        """
        try:
            import database_library
        except ImportError:
            raise ImportError(
                "database-library is required. Install with: pip install database-library"
            ) from None
        
        # Handle existing connection object
        if (.source, ):
            .conn = .source
            ._connected = 
            
        
        
          (.source, ):
             ValueError()
        
        :
            .conn = database_library.connect(.source, **.kwargs.get(, {}))
            ._connected = 
         Exception  e:
            ._connected = 
             ConnectionError()  e
    
     () -> Iterator[Row]:
        
          ._connected:
             RuntimeError()
        
        ._start_metrics()
        batch_size = .kwargs.get(, )
        
        :
            query = ._build_query()
            cursor = .conn.cursor()
            
            
            cursor.execute(query)
            
             :
                rows = cursor.fetchmany(batch_size)
                  rows:
                    
                
                
                column_names = [desc[]  desc  cursor.description]
                 row  rows:
                    row_dict = ((column_names, row))
                    ._update_metrics(rows_read=)
                     row_dict
                    
         Exception  e:
            ._handle_error(e, )
             ._on_error == :
                

Key Implementation Details

Connection Handling

• Support both connection strings and existing connection objects
• Parse connection strings appropriately (URL format)
• Handle connection errors gracefully with clear messages
• Set self._connected = True on success

Query Building

• Accept query parameter (SQL query or table name)
• If table name provided, build SELECT query
• Support filtering, projection, sorting via kwargs
• Use parameterized queries to prevent SQL injection

Iteration

• Use batch processing (batch_size parameter)
• Convert database rows to dict objects
• Update metrics: self._update_metrics(rows_read=1)
• Handle errors according to on_error policy
• Start metrics: self._start_metrics() before iteration

Error Handling

Three policies (set via on_error kwarg):

• 'raise' - Raise exceptions (default)
• 'skip' - Skip problematic rows
• 'warn' - Warn and continue

Use self._handle_error(error, context) for consistent handling.

Metrics Tracking

Automatic metrics available via self.metrics:

• rows_read - Number of rows read
• bytes_read - Bytes read (may be None)
• elapsed_seconds - Time elapsed

Update during iteration: self._update_metrics(rows_read=count)

Registration

Register driver in iterable/db/__init__.py:

from .newengine import NewEngineDriver

register_driver("newengine", NewEngineDriver)

Format Detection

Update iterable/helpers/detect.py to recognize database URLs:

def detect_file_type(filename, content=None):
    # Database URL detection
    if filename.startswith(("postgresql://", "postgres://")):
        return "database"
    if filename.startswith("newengine://"):
        return "database"
    # ... existing detection

Testing Requirements

Test Structure

import pytest
from iterable.db.newengine import NewEngineDriver

class TestNewEngineDriver:
    def test_connect(self):
        driver = NewEngineDriver("newengine://localhost/db")
        driver.connect()
        assert driver.is_connected
        driver.close()
    
    def test_iterate(self):
        driver = NewEngineDriver("newengine://localhost/db", query="SELECT * FROM table")
        driver.connect()
        rows = list(driver.iterate())
        assert len(rows) > 0
        assert isinstance(rows[0], dict)
        driver.close()
    
    def test_batch_processing(self):
        driver = NewEngineDriver("newengine://localhost/db", query="SELECT * FROM table", batch_size=100)
        driver.connect()
        # Verify batching works correctly
        driver.close()
    
    def test_error_handling(self):
        driver = NewEngineDriver("newengine://invalid", query="SELECT * FROM table", on_error="skip")
        # Test error handling policies

Test Coverage

• Connection with string and object sources
• Query building (table name vs SQL)
• Batch processing
• Error handling policies
• Metrics tracking
• Context manager support (with statement)
• Missing dependencies (should raise ImportError)

Dependencies

Optional Dependencies

Add to pyproject.toml:

[project.optional-dependencies]
newengine = ["database-library>=1.0.0"]

Import Handling

Handle missing dependencies:

try:
    import database_library
except ImportError:
    raise ImportError(
        "newengine support requires 'database-library'. "
        "Install with: pip install iterabledata[newengine]"
    ) from None

Examples

Look at existing implementations:

• iterable/db/postgres.py - SQL database example (PostgreSQL)
• iterable/db/mongo.py - NoSQL database example (MongoDB)
• iterable/db/clickhouse.py - Columnar database example
• iterable/db/elasticsearch.py - Search engine example

Common Patterns

SQL Databases

• Use server-side cursors for memory efficiency
• Support read-only transactions
• Handle connection pooling
• Use parameterized queries

NoSQL Databases

• Support query filters and projections
• Handle pagination/cursors
• Support aggregation pipelines
• Batch document retrieval

Connection String Parsing

from urllib.parse import urlparse

def parse_connection_string(conn_str: str) -> dict:
    parsed = urlparse(conn_str)
    return {
        "host": parsed.hostname,
        "port": parsed.port,
        "database": parsed.path.lstrip("/"),
        "username": parsed.username,
        "password": parsed.password,
    }

Common Pitfalls

• Memory issues: Always use batch processing, don't load all rows at once
• Connection leaks: Always close connections, use context managers
• Error handling: Implement all three error policies consistently
• Metrics: Update metrics during iteration, not after
• Reset support: Most databases don't support reset (set _reset_supported = False)
• Read-only: Default to read-only access for safety

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Source: datenoio/iterabledata — distributed by TomeVault.