SQLAlchemy Integration

# Dialect constants
dialect_name: str = 'clickhousedb'
"""SQLAlchemy dialect name for ClickHouse."""

ischema_names: dict[str, type]
"""Mapping of ClickHouse type names to SQLAlchemy types."""

class ClickHouseDialect:
    """
    SQLAlchemy dialect for ClickHouse database.
    
    Registered automatically as:
    - clickhousedb.connect
    - clickhousedb
    
    Connection URL format:
    clickhousedb://username:password@host:port/database
    """
    
    name: str = 'clickhousedb'
    supports_statement_cache: bool = True
    supports_native_boolean: bool = True
    supports_native_decimal: bool = True
    supports_unicode_statements: bool = True
    supports_unicode_binds: bool = True
    supports_default_values: bool = False
    supports_sequences: bool = False
    supports_native_enum: bool = True
    
    # Transaction support (ClickHouse auto-commits)
    supports_transactions: bool = False
    autocommit: bool = True
    
    # Schema reflection capabilities
    supports_reflection: bool = True
    supports_views: bool = True
    supports_indexes: bool = False  # ClickHouse uses different indexing

# ClickHouse to SQLAlchemy type mappings
from sqlalchemy import types as sqltypes

ischema_names = {
    # Integer types
    'Int8': sqltypes.SmallInteger,
    'Int16': sqltypes.SmallInteger,
    'Int32': sqltypes.Integer,
    'Int64': sqltypes.BigInteger,
    'UInt8': sqltypes.SmallInteger,
    'UInt16': sqltypes.Integer,
    'UInt32': sqltypes.BigInteger,
    'UInt64': sqltypes.Numeric,
    
    # Floating point types
    'Float32': sqltypes.Float,
    'Float64': sqltypes.Float,
    'Decimal': sqltypes.Numeric,
    
    # String types
    'String': sqltypes.String,
    'FixedString': sqltypes.CHAR,
    'LowCardinality(String)': sqltypes.String,
    
    # Date/time types
    'Date': sqltypes.Date,
    'DateTime': sqltypes.DateTime,
    'DateTime64': sqltypes.DateTime,
    
    # Boolean type
    'Bool': sqltypes.Boolean,
    
    # Array and complex types
    'Array': sqltypes.ARRAY,
    'Tuple': sqltypes.JSON,
    'Map': sqltypes.JSON,
    'Nested': sqltypes.JSON,
    
    # Special types
    'UUID': sqltypes.String,
    'IPv4': sqltypes.String,
    'IPv6': sqltypes.String,
    'Enum8': sqltypes.Enum,
    'Enum16': sqltypes.Enum,
    
    # Nullable wrapper
    'Nullable': sqltypes.TypeDecorator,
}

def create_connect_args(self, url):
    """
    Build connection arguments from SQLAlchemy URL.
    
    Parameters:
    - url: SQLAlchemy URL object
    
    Returns:
    Tuple of (args, kwargs) for connection creation
    
    URL format examples:
    clickhousedb://user:pass@host:port/database
    clickhousedb+http://user:pass@host:8123/database
    clickhousedb+https://user:pass@host:8443/database?secure=true
    """

def do_connect(self, cparams):
    """
    Create ClickHouse connection using clickhouse-connect.
    
    Parameters:
    - cparams: Connection parameters from create_connect_args()
    
    Returns:
    ClickHouse connection object wrapped for SQLAlchemy
    """

def do_close(self, dbapi_connection):
    """Close ClickHouse connection."""

def do_ping(self, dbapi_connection) -> bool:
    """Test connection health."""

def get_schema_names(self, connection, **kwargs) -> list[str]:
    """
    Get list of schema (database) names.
    
    Returns:
    List of database names available on the server
    """

def get_table_names(self, connection, schema=None, **kwargs) -> list[str]:
    """
    Get list of table names in specified schema.
    
    Parameters:
    - connection: SQLAlchemy connection
    - schema: Database name (None for default)
    
    Returns:
    List of table names in the schema
    """

def get_view_names(self, connection, schema=None, **kwargs) -> list[str]:
    """
    Get list of view names in specified schema.
    
    Parameters:
    - connection: SQLAlchemy connection
    - schema: Database name (None for default)
    
    Returns:
    List of view names in the schema
    """

def get_columns(self, connection, table_name, schema=None, **kwargs) -> list[dict]:
    """
    Get column information for specified table.
    
    Parameters:
    - connection: SQLAlchemy connection
    - table_name: Name of the table
    - schema: Database name (None for default)
    
    Returns:
    List of column dictionaries with keys:
    - name: Column name
    - type: SQLAlchemy type object
    - nullable: Boolean nullable flag
    - default: Default value (if any)
    - comment: Column comment (if any)
    """

def get_pk_constraint(self, connection, table_name, schema=None, **kwargs) -> dict:
    """
    Get primary key constraint information.
    
    Note: ClickHouse doesn't have traditional primary keys,
    returns empty constraint info for compatibility.
    """

def get_foreign_keys(self, connection, table_name, schema=None, **kwargs) -> list:
    """
    Get foreign key constraints.
    
    Note: ClickHouse doesn't support foreign keys,
    returns empty list for compatibility.
    """

def get_indexes(self, connection, table_name, schema=None, **kwargs) -> list:
    """
    Get index information.
    
    Note: ClickHouse uses different indexing mechanisms,
    returns empty list for standard indexes.
    """

def visit_create_table(self, create, **kwargs) -> str:
    """
    Compile CREATE TABLE statement for ClickHouse.
    
    Handles ClickHouse-specific table engines, partitioning,
    and other table creation options.
    """

def visit_drop_table(self, drop, **kwargs) -> str:
    """Compile DROP TABLE statement."""

def visit_create_index(self, create, **kwargs) -> str:
    """
    Handle index creation.
    
    Note: Translates to ClickHouse-appropriate indexing where applicable.
    """

from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

# Create engine with connection URL
engine = create_engine('clickhousedb://default@localhost:8123/default')

# Test connection
with engine.connect() as conn:
    result = conn.execute(text("SELECT version()"))
    version = result.scalar()
    print(f"ClickHouse version: {version}")

# Create session factory
Session = sessionmaker(bind=engine)
session = Session()

# Execute raw SQL
result = session.execute(text("""
    SELECT 
        database,
        count() as table_count
    FROM system.tables 
    GROUP BY database
"""))

for row in result:
    print(f"Database {row.database}: {row.table_count} tables")

session.close()

from sqlalchemy import create_engine, Column, Integer, String, DateTime, Float
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Base = declarative_base()

class Event(Base):
    """SQLAlchemy model for events table."""
    __tablename__ = 'events'
    
    id = Column(Integer, primary_key=True)
    event_type = Column(String(100), nullable=False)
    user_id = Column(Integer, nullable=False)
    timestamp = Column(DateTime, nullable=False)
    value = Column(Float)
    
    def __repr__(self):
        return f"<Event(id={self.id}, type='{self.event_type}', user={self.user_id})>"

# Create engine and tables
engine = create_engine('clickhousedb://default@localhost:8123/analytics')

# Note: ClickHouse table creation may require additional engine parameters
# This creates the table structure for SQLAlchemy to work with
Base.metadata.create_all(engine)

# Work with ORM
Session = sessionmaker(bind=engine)
session = Session()

# Query using ORM
events = session.query(Event).filter(
    Event.event_type == 'click'
).limit(100).all()

for event in events:
    print(f"{event.timestamp}: {event.event_type} by user {event.user_id}")

session.close()

from sqlalchemy import create_engine, MetaData, Table
from sqlalchemy.engine import reflection

engine = create_engine('clickhousedb://default@localhost:8123/system')

# Reflect schema information
inspector = reflection.Inspector.from_engine(engine)

# Get database names
schemas = inspector.get_schema_names()
print(f"Available databases: {schemas}")

# Get tables in system database
tables = inspector.get_table_names(schema='system')
print(f"System tables: {len(tables)}")

# Reflect specific table structure
table_columns = inspector.get_columns('tables', schema='system')
print("\nColumns in system.tables:")
for col in table_columns:
    print(f"  {col['name']}: {col['type']} ({'nullable' if col['nullable'] else 'not null'})")

# Use reflected table
metadata = MetaData()
tables_table = Table('tables', metadata, autoload_with=engine, schema='system')

with engine.connect() as conn:
    # Query using reflected table
    result = conn.execute(tables_table.select().where(
        tables_table.c.database == 'default'
    ))
    
    for row in result:
        print(f"Table: {row.name}, Engine: {row.engine}")

from sqlalchemy import create_engine, text, select, func, and_, or_
from sqlalchemy.sql import column, table

engine = create_engine('clickhousedb://default@localhost:8123/analytics')

# Define table structure for queries (without ORM)
events = table('events',
    column('id'),
    column('event_type'),
    column('user_id'),
    column('timestamp'),
    column('value')
)

with engine.connect() as conn:
    # SQLAlchemy Core query building
    query = select([
        events.c.event_type,
        func.count().label('event_count'),
        func.avg(events.c.value).label('avg_value')
    ]).where(
        and_(
            events.c.timestamp >= '2023-01-01',
            events.c.value.isnot(None)
        )
    ).group_by(events.c.event_type).order_by('event_count DESC')
    
    result = conn.execute(query)
    
    for row in result:
        print(f"{row.event_type}: {row.event_count} events, avg value: {row.avg_value:.2f}")
    
    # Raw SQL with parameters
    complex_query = text("""
        SELECT 
            toYYYYMM(timestamp) as month,
            event_type,
            uniq(user_id) as unique_users,
            count() as total_events
        FROM events 
        WHERE timestamp >= :start_date 
          AND timestamp < :end_date
        GROUP BY month, event_type
        ORDER BY month, total_events DESC
    """)
    
    result = conn.execute(complex_query, {
        'start_date': '2023-01-01',
        'end_date': '2023-07-01'
    })
    
    for row in result:
        print(f"{row.month}: {row.event_type} - {row.unique_users} users, {row.total_events} events")

from sqlalchemy import create_engine
from sqlalchemy.pool import QueuePool

# Engine with custom connection pool
engine = create_engine(
    'clickhousedb://analytics_user:password@clickhouse.example.com:8123/analytics',
    poolclass=QueuePool,
    pool_size=10,
    max_overflow=20,
    pool_pre_ping=True,  # Validate connections before use
    pool_recycle=3600,   # Recycle connections after 1 hour
    connect_args={
        'compress': 'lz4',
        'settings': {
            'max_threads': 4,
            'max_memory_usage': '2G'
        }
    }
)

# Connection health check
def check_connection_health():
    try:
        with engine.connect() as conn:
            result = conn.execute(text("SELECT 1"))
            return result.scalar() == 1
    except Exception as e:
        print(f"Connection health check failed: {e}")
        return False

if check_connection_health():
    print("Database connection is healthy")
else:
    print("Database connection issues detected")

import pandas as pd
from sqlalchemy import create_engine

# Create engine for pandas integration
engine = create_engine('clickhousedb://default@localhost:8123/analytics')

# Pandas DataFrame from SQLAlchemy query
df = pd.read_sql_query("""
    SELECT 
        user_id,
        event_type,
        COUNT(*) as event_count,
        AVG(value) as avg_value,
        MAX(timestamp) as last_event
    FROM events
    WHERE timestamp >= '2023-01-01'
    GROUP BY user_id, event_type
    ORDER BY event_count DESC
""", engine)

print(f"Loaded {len(df)} rows into DataFrame")
print(df.head())

# Data analysis with pandas
top_users = df.groupby('user_id')['event_count'].sum().nlargest(10)
print(f"\nTop 10 users by event count:")
print(top_users)

# Write DataFrame back to ClickHouse via SQLAlchemy
processed_df = df.groupby('event_type').agg({
    'event_count': 'sum',
    'avg_value': 'mean',
    'user_id': 'nunique'
}).reset_index()

processed_df.columns = ['event_type', 'total_events', 'mean_value', 'unique_users']

# Insert processed data
processed_df.to_sql(
    'event_summary',
    engine,
    if_exists='replace',
    index=False,
    method='multi'  # Batch insert
)

print("Processed data written to event_summary table")