SQL Dialects

class Dialect:
    """Base dialect class providing core SQL processing functionality."""
    
    def parse(self, sql: str, **opts) -> List[Optional[Expression]]:
        """Parse SQL string using dialect-specific grammar rules."""
    
    def generate(self, expression: Expression, **opts) -> str:
        """Generate SQL string from expression using dialect-specific syntax."""
    
    def tokenize(self, sql: str) -> List[Token]:
        """Tokenize SQL string using dialect-specific keywords and operators."""
    
    @classmethod
    def get_or_raise(cls, dialect: str) -> Dialect:
        """Get dialect instance by name or raise error if not found."""

    @classmethod  
    def get(cls, dialect: str) -> Optional[Dialect]:
        """Get dialect by name, returns None if not found."""

class BigQuery(Dialect):
    """Google BigQuery dialect with standard SQL and legacy SQL support."""

class Snowflake(Dialect):
    """Snowflake dialect with cloud data warehouse features."""

class Redshift(Dialect):
    """Amazon Redshift dialect with PostgreSQL-based syntax."""

class Databricks(Dialect):
    """Databricks dialect based on Apache Spark SQL."""

class Fabric(Dialect):
    """Microsoft Fabric dialect for data analytics."""

class Spark(Dialect):
    """Apache Spark SQL dialect for distributed data processing."""

class Spark2(Dialect):
    """Apache Spark 2.x dialect with legacy compatibility."""

class DuckDB(Dialect):
    """DuckDB dialect for embedded analytical processing."""

class ClickHouse(Dialect):
    """ClickHouse dialect for real-time analytics."""

class Presto(Dialect):
    """Presto dialect for distributed query processing."""

class Trino(Dialect):
    """Trino dialect (formerly PrestoSQL) for federated queries."""

class Hive(Dialect):
    """Apache Hive dialect for Hadoop data warehouse."""

class Drill(Dialect):
    """Apache Drill dialect for schema-free SQL queries."""

class Doris(Dialect):
    """Apache Doris dialect for modern data warehouse."""

class StarRocks(Dialect):
    """StarRocks dialect for real-time analytics."""

class MySQL(Dialect):
    """MySQL dialect with MySQL-specific syntax and functions."""

class Postgres(Dialect):
    """PostgreSQL dialect with advanced SQL features."""

class Oracle(Dialect):
    """Oracle Database dialect with enterprise features."""

class SQLite(Dialect):
    """SQLite dialect for embedded databases."""

class TSQL(Dialect):
    """Microsoft SQL Server T-SQL dialect."""

class Teradata(Dialect):
    """Teradata dialect for enterprise data warehousing."""

class Athena(Dialect):
    """Amazon Athena dialect for serverless queries."""

class Dremio(Dialect):
    """Dremio dialect for data lake analytics."""

class Exasol(Dialect):
    """Exasol dialect for high-performance analytics."""

class Materialize(Dialect):
    """Materialize dialect for streaming SQL."""

class RisingWave(Dialect):
    """RisingWave dialect for stream processing."""

class SingleStore(Dialect):
    """SingleStore dialect for distributed SQL."""

class Tableau(Dialect):
    """Tableau dialect for business intelligence."""

class Druid(Dialect):
    """Apache Druid dialect for real-time analytics."""

class Dune(Dialect):
    """Dune Analytics dialect for blockchain data."""

class PRQL(Dialect):
    """PRQL (Pipelined Relational Query Language) dialect."""

import sqlglot
from sqlglot.dialects import BigQuery, Snowflake, Spark

# Parse using specific dialect
bigquery = BigQuery()
expressions = bigquery.parse("SELECT EXTRACT(YEAR FROM date_col) FROM table")

# Generate SQL for specific dialect  
snowflake = Snowflake()
sql = snowflake.generate(expressions[0])

# Use dialect names with main functions
spark_sql = "SELECT explode(split('a,b,c', ',')) as item"
postgres_sql = sqlglot.transpile(spark_sql, read="spark", write="postgres")[0]

import sqlglot

# BigQuery array and struct syntax
bq_query = """
SELECT 
    arr[OFFSET(0)] as first_element,
    struct_col.field_name,
    PARSE_DATE('%Y-%m-%d', date_string)
FROM table_name
"""
standard_sql = sqlglot.transpile(bq_query, read="bigquery", write="postgres")[0]

# Snowflake variant data type
sf_query = """
SELECT 
    variant_col:field::string as extracted_field,
    FLATTEN(array_col) as flattened
FROM table_name
"""

# Spark SQL window functions with specific syntax
spark_query = """
SELECT 
    col1,
    LAG(col1, 1) OVER (PARTITION BY col2 ORDER BY col3 
                       ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) as prev_val
FROM table
"""

from sqlglot import exp
from sqlglot.dialects.dialect import Dialect
from sqlglot.generator import Generator
from sqlglot.tokens import Tokenizer, TokenType

class CustomDialect(Dialect):
    class Tokenizer(Tokenizer):
        QUOTES = ["'", '"']  # String delimiters
        IDENTIFIERS = ["`"]  # Identifier delimiters
        
        KEYWORDS = {
            **Tokenizer.KEYWORDS,
            "INT64": TokenType.BIGINT,
            "FLOAT64": TokenType.DOUBLE,
        }
    
    class Generator(Generator):
        # Custom SQL generation rules
        TRANSFORMS = {
            exp.Array: lambda self, e: f"[{self.expressions(e)}]",
        }
        
        # Data type mappings
        TYPE_MAPPING = {
            exp.DataType.Type.TINYINT: "INT64",
            exp.DataType.Type.SMALLINT: "INT64", 
            exp.DataType.Type.INT: "INT64",
            exp.DataType.Type.BIGINT: "INT64",
            exp.DataType.Type.FLOAT: "FLOAT64",
            exp.DataType.Type.DOUBLE: "FLOAT64",
        }

# Use custom dialect
custom = CustomDialect()
parsed = custom.parse("SELECT col FROM table WHERE int_col > 5")
generated = custom.generate(parsed[0])

import sqlglot
from sqlglot.dialects import Dialect

# Get available dialects
available_dialects = [
    "bigquery", "snowflake", "spark", "postgres", "mysql", 
    "oracle", "duckdb", "clickhouse", "presto", "trino"
]

# Convert between multiple dialects
source_sql = "SELECT EXTRACT(YEAR FROM date_col) FROM table"

for target_dialect in ["postgres", "mysql", "snowflake"]:
    try:
        converted = sqlglot.transpile(
            source_sql, 
            read="bigquery", 
            write=target_dialect
        )[0]
        print(f"{target_dialect}: {converted}")
    except Exception as e:
        print(f"Error converting to {target_dialect}: {e}")

import sqlglot
from sqlglot.optimizer import optimize

# Optimize for specific target dialect
sql = """
SELECT user_id, COUNT(*) as cnt
FROM users 
WHERE created_date >= '2023-01-01'
GROUP BY user_id
HAVING COUNT(*) > 5
"""

# Optimize for BigQuery
optimized_bq = optimize(sql, dialect="bigquery")
bq_sql = optimized_bq.sql(dialect="bigquery", pretty=True)

# Optimize for Snowflake  
optimized_sf = optimize(sql, dialect="snowflake")
sf_sql = optimized_sf.sql(dialect="snowflake", pretty=True)

print("BigQuery:", bq_sql)
print("Snowflake:", sf_sql)

class Dialect:
    """Base dialect class with extensible components."""
    
    class Tokenizer:
        """Dialect-specific tokenization rules."""
        QUOTES: List[str]      # String quote characters
        IDENTIFIERS: List[str] # Identifier quote characters  
        KEYWORDS: Dict[str, TokenType] # Keyword mappings
        
    class Parser:
        """Dialect-specific parsing rules."""
        FUNCTIONS: Dict[str, Callable] # Function parsers
        
    class Generator:
        """Dialect-specific SQL generation rules."""
        TRANSFORMS: Dict[Type, Callable] # Expression transformers
        TYPE_MAPPING: Dict[Type, str]    # Data type mappings
        
    # Core dialect methods
    def parse(self, sql: str, **opts) -> List[Optional[Expression]]: ...
    def generate(self, expression: Expression, **opts) -> str: ...
    def tokenize(self, sql: str) -> List[Token]: ...

# Type alias for dialect specification
DialectType = Union[str, Dialect, Type[Dialect]]