SQLAlchemy Integration

class ImpalaDialect:
    """
    SQLAlchemy dialect for Impala.
    
    Provides SQLAlchemy integration for Impala databases, supporting
    core SQLAlchemy functionality including table reflection, query
    construction, and result handling.
    
    Attributes:
        name (str): Dialect name 'impala'
        driver (str): Driver name 'impyla'
        supports_alter (bool): Whether ALTER statements are supported
        max_identifier_length (int): Maximum identifier length (128)
        supports_sane_rowcount (bool): Whether rowcount is reliable
        supports_sane_multi_rowcount (bool): Whether multi-statement rowcount works
        supports_native_decimal (bool): Whether native decimal is supported
        default_schema_name (str): Default schema name
        supports_default_values (bool): Whether DEFAULT values are supported
        supports_sequences (bool): Whether sequences are supported
        sequences_optional (bool): Whether sequences are optional
        preexecute_autoincrement_sequences (bool): Autoincrement sequence behavior
        postfetch_lastrowid (bool): Whether to fetch last row ID after insert
    """

class Impala4Dialect(ImpalaDialect):
    """
    SQLAlchemy dialect for Impala 4.x.
    
    Specialized dialect for Impala 4.x versions with enhanced
    features and optimizations specific to newer Impala releases.
    Inherits from ImpalaDialect with additional features.
    """

class TINYINT:
    """
    Impala TINYINT data type.
    
    Represents Impala's TINYINT type (8-bit signed integer) in SQLAlchemy.
    Maps to Python int with range validation.
    """

class INT:
    """
    Impala INT data type.
    
    Represents Impala's INT type (32-bit signed integer) in SQLAlchemy.
    Maps to Python int.
    """

class DOUBLE:
    """
    Impala DOUBLE data type.
    
    Represents Impala's DOUBLE type (64-bit floating point) in SQLAlchemy.
    Maps to Python float.
    """

class STRING:
    """
    Impala STRING data type.
    
    Represents Impala's STRING type (variable-length string) in SQLAlchemy.
    Maps to Python str.
    """

class ImpalaDDLCompiler:
    """
    DDL compiler for Impala dialect.
    
    Handles compilation of Data Definition Language (DDL) statements
    specific to Impala's SQL syntax and capabilities.
    """

class ImpalaTypeCompiler:
    """
    Type compiler for Impala dialect.
    
    Handles compilation of SQLAlchemy types to Impala-specific
    type representations in SQL statements.
    """

class Impala4TypeCompiler(ImpalaTypeCompiler):
    """
    Type compiler for Impala 4.x dialect.
    
    Enhanced type compiler with support for Impala 4.x specific
    data types and type representations.
    """

class ImpalaIdentifierPreparer:
    """
    Identifier preparer for Impala dialect.
    
    Handles proper quoting and escaping of identifiers (table names,
    column names, etc.) according to Impala's identifier rules.
    """

class ImpalaExecutionContext:
    """
    Execution context for Impala dialect.
    
    Manages the execution context for SQLAlchemy operations,
    including parameter binding and result processing.
    """

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

# Create engine using impyla dialect
engine = create_engine(
    'impala://impala-host:21050/default',
    echo=True  # Enable SQL logging
)

# Test connection
with engine.connect() as conn:
    result = conn.execute(text("SELECT version()"))
    version = result.fetchone()
    print(f"Connected to: {version[0]}")

from sqlalchemy import create_engine

# Kerberos authentication
engine = create_engine(
    'impala://impala-host:21050/default',
    connect_args={
        'auth_mechanism': 'GSSAPI',
        'kerberos_service_name': 'impala'
    }
)

# LDAP authentication
engine = create_engine(
    'impala://username:password@impala-host:21050/default',
    connect_args={
        'auth_mechanism': 'LDAP'
    }
)

# SSL connection
engine = create_engine(
    'impala://impala-host:21050/default',
    connect_args={
        'use_ssl': True,
        'ca_cert': '/path/to/ca-cert.pem'
    }
)

from sqlalchemy import create_engine, MetaData, Table
from sqlalchemy.orm import sessionmaker

engine = create_engine('impala://impala-host:21050/sales_db')

# Reflect existing tables
metadata = MetaData()
metadata.reflect(bind=engine)

# Access reflected tables
customers = metadata.tables['customers']
orders = metadata.tables['orders']

print("Customers table columns:")
for column in customers.columns:
    print(f"  {column.name}: {column.type}")

# Query using reflected table
with engine.connect() as conn:
    # Select all customers
    result = conn.execute(customers.select().limit(10))
    for row in result:
        print(row)

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

Base = declarative_base()

class Customer(Base):
    __tablename__ = 'customers'
    
    customer_id = Column(Integer, primary_key=True)
    name = Column(String)
    email = Column(String)
    created_at = Column(DateTime)

class Order(Base):
    __tablename__ = 'orders'
    
    order_id = Column(Integer, primary_key=True)
    customer_id = Column(Integer)
    order_date = Column(DateTime)
    total_amount = Column(Float)
    status = Column(String)

# Setup
engine = create_engine('impala://impala-host:21050/ecommerce')
Session = sessionmaker(bind=engine)
session = Session()

# Query using ORM
recent_customers = session.query(Customer).filter(
    Customer.created_at >= datetime(2023, 1, 1)
).limit(10).all()

for customer in recent_customers:
    print(f"Customer: {customer.name} ({customer.email})")

# Aggregate queries
from sqlalchemy import func

monthly_sales = session.query(
    func.date_trunc('month', Order.order_date).label('month'),
    func.sum(Order.total_amount).label('total_sales'),
    func.count(Order.order_id).label('order_count')
).group_by(
    func.date_trunc('month', Order.order_date)
).order_by('month').all()

for month, sales, count in monthly_sales:
    print(f"{month}: ${sales:,.2f} ({count} orders)")

session.close()

from sqlalchemy import create_engine, text, select, func, and_, or_
from sqlalchemy import MetaData, Table

engine = create_engine('impala://impala-host:21050/analytics')

# Reflect tables
metadata = MetaData()
metadata.reflect(bind=engine)
sales = metadata.tables['sales']
products = metadata.tables['products']

with engine.connect() as conn:
    # Complex query with joins
    query = select([
        products.c.category,
        func.sum(sales.c.amount).label('total_sales'),
        func.count(sales.c.sale_id).label('sale_count'),
        func.avg(sales.c.amount).label('avg_sale')
    ]).select_from(
        sales.join(products, sales.c.product_id == products.c.product_id)
    ).where(
        and_(
            sales.c.sale_date >= '2023-01-01',
            products.c.category.in_(['Electronics', 'Clothing', 'Books'])
        )
    ).group_by(
        products.c.category
    ).order_by(
        func.sum(sales.c.amount).desc()
    )
    
    result = conn.execute(query)
    
    print("Sales by Category:")
    for row in result:
        print(f"{row.category}: ${row.total_sales:,.2f} "
              f"({row.sale_count} sales, avg: ${row.avg_sale:.2f})")

from sqlalchemy import create_engine, Column, Table, MetaData
from impala.sqlalchemy import TINYINT, INT, DOUBLE, STRING

engine = create_engine('impala://impala-host:21050/test_db')
metadata = MetaData()

# Define table with Impala-specific types
sensor_data = Table('sensor_readings', metadata,
    Column('sensor_id', INT, primary_key=True),
    Column('reading_type', TINYINT),  # 0-255 range
    Column('temperature', DOUBLE),
    Column('location', STRING),
    Column('notes', STRING)
)

# Create table (if it doesn't exist)
with engine.connect() as conn:
    # Note: CREATE TABLE IF NOT EXISTS may need to be done manually
    # as Impala has specific syntax requirements
    
    # Insert data using the defined types
    conn.execute(sensor_data.insert().values([
        {'sensor_id': 1, 'reading_type': 1, 'temperature': 23.5, 
         'location': 'Building A', 'notes': 'Normal reading'},
        {'sensor_id': 2, 'reading_type': 2, 'temperature': 25.1, 
         'location': 'Building B', 'notes': 'Slightly elevated'}
    ]))
    
    # Query back the data
    result = conn.execute(sensor_data.select())
    for row in result:
        print(f"Sensor {row.sensor_id}: {row.temperature}°C at {row.location}")

from sqlalchemy import create_engine, text
from sqlalchemy.pool import StaticPool

# Connection pooling configuration
engine = create_engine(
    'impala://impala-host:21050/warehouse',
    poolclass=StaticPool,
    pool_size=10,
    max_overflow=20,
    pool_pre_ping=True,  # Validate connections
    connect_args={
        'timeout': 60,
        'retries': 3
    }
)

# Partition-aware queries
with engine.connect() as conn:
    # Query with partition pruning
    partitioned_query = text("""
        SELECT 
            product_category,
            SUM(sales_amount) as total_sales
        FROM sales_partitioned 
        WHERE 
            year = :year 
            AND month = :month
        GROUP BY product_category
    """)
    
    result = conn.execute(partitioned_query, year=2023, month=6)
    
    for row in result:
        print(f"{row.product_category}: ${row.total_sales:,.2f}")

# Bulk operations
def bulk_insert_with_sqlalchemy(engine, table_name, data_rows):
    """Efficient bulk insert using SQLAlchemy."""
    
    metadata = MetaData()
    metadata.reflect(bind=engine)
    table = metadata.tables[table_name]
    
    with engine.connect() as conn:
        # Use bulk insert for better performance
        conn.execute(table.insert(), data_rows)
        print(f"Inserted {len(data_rows)} rows into {table_name}")

# Usage
sample_data = [
    {'product_id': 1, 'name': 'Widget A', 'price': 19.99},
    {'product_id': 2, 'name': 'Widget B', 'price': 24.99},
    {'product_id': 3, 'name': 'Widget C', 'price': 29.99}
]

bulk_insert_with_sqlalchemy(engine, 'products', sample_data)

# Basic connection
engine = create_engine('impala://hostname:21050/database')

# With authentication
engine = create_engine('impala://user:pass@hostname:21050/database')

# With SSL
engine = create_engine('impala://hostname:21050/database?use_ssl=true')

# HTTP transport
engine = create_engine(
    'impala://hostname:28000/database',
    connect_args={
        'use_http_transport': True,
        'http_path': 'cliservice'
    }
)

# Multiple connection parameters
engine = create_engine(
    'impala://hostname:21050/database',
    connect_args={
        'auth_mechanism': 'GSSAPI',
        'kerberos_service_name': 'impala',
        'use_ssl': True,
        'timeout': 120
    }
)