datus-snowflake 0.1.3

Snowflake adapter for Datus Agent

Datus Snowflake Adapter

Snowflake database adapter for Datus Agent, providing native Snowflake connector support.

Features

• Native Snowflake SDK: Uses snowflake-connector-python for optimal performance
• Full Snowflake Support: Databases, schemas, tables, views, and materialized views
• Efficient Metadata Retrieval: Uses SHOW commands for fast metadata queries
• Arrow-based Execution: High-performance query execution with Apache Arrow
• Multiple Result Formats: CSV, Pandas DataFrame, Arrow Table, and Python list
• Complete CRUD Operations: INSERT, UPDATE, DELETE, and DDL support

Installation

pip install datus-snowflake

This will automatically install the required dependencies:

• datus-agent>=0.3.0
• snowflake-connector-python>=3.6.0

Usage

Basic Connection

from datus_snowflake import SnowflakeConnector

# Create connector
connector = SnowflakeConnector(
    account="myaccount",
    user="myuser",
    password="mypassword",
    warehouse="my_warehouse",
    database="my_database",
    schema="my_schema"
)

# Test connection
result = connector.test_connection()
print(result)  # {'success': True, 'message': 'Connection successful', 'databases': ''}

Execute Queries

# Execute query and get CSV result
result = connector.execute_query("SELECT * FROM users LIMIT 10")
print(result.sql_return)  # CSV string

# Execute query and get pandas DataFrame
result = connector.execute_query("SELECT * FROM users LIMIT 10", result_format="pandas")
df = result.sql_return
print(df.head())

# Execute query and get Arrow table
result = connector.execute_query("SELECT * FROM users LIMIT 10", result_format="arrow")
arrow_table = result.sql_return
print(arrow_table.schema)

Metadata Operations

# Get databases
databases = connector.get_databases()
print(f"Databases: {databases}")

# Get schemas
schemas = connector.get_schemas(database_name="my_database")
print(f"Schemas: {schemas}")

# Get tables
tables = connector.get_tables(database_name="my_database", schema_name="public")
print(f"Tables: {tables}")

# Get views
views = connector.get_views(database_name="my_database", schema_name="public")
print(f"Views: {views}")

# Get materialized views
mvs = connector.get_materialized_views(database_name="my_database", schema_name="public")
print(f"Materialized Views: {mvs}")

Get Table Schema

# Get table structure
schema = connector.get_schema(
    database_name="my_database",
    schema_name="public",
    table_name="users"
)

for column in schema[:-1]:  # Last item is table metadata
    print(f"{column['name']}: {column['type']} (nullable: {column['nullable']})")

Get DDL Definitions

# Get tables with DDL
tables_with_ddl = connector.get_tables_with_ddl(
    database_name="my_database",
    schema_name="public"
)

for table in tables_with_ddl:
    print(f"\nTable: {table['table_name']}")
    print(f"DDL:\n{table['definition']}")

# Get views with DDL
views_with_ddl = connector.get_views_with_ddl(
    database_name="my_database",
    schema_name="public"
)

# Get materialized views with DDL
mvs_with_ddl = connector.get_materialized_views_with_ddl(
    database_name="my_database",
    schema_name="public"
)

Get Sample Data

# Get sample rows from specific tables
samples = connector.get_sample_rows(
    tables=["users", "orders"],
    top_n=5,
    database_name="my_database",
    schema_name="public"
)

for sample in samples:
    print(f"\nTable: {sample['table_name']}")
    print(sample['sample_rows'])  # CSV format

CRUD Operations

# INSERT
result = connector.execute_insert(
    "INSERT INTO users (name, email) VALUES ('John', '[e-mail]')"
)
print(f"Inserted rows: {result.row_count}")

# UPDATE
result = connector.execute_update(
    "UPDATE users SET email = '[e-mail]' WHERE name = 'John'"
)
print(f"Updated rows: {result.row_count}")

# DELETE
result = connector.execute_delete(
    "DELETE FROM users WHERE name = 'John'"
)
print(f"Deleted rows: {result.row_count}")

# DDL
result = connector.execute_ddl(
    "CREATE TABLE test_table (id INT, name VARCHAR(100))"
)
print(f"DDL executed: {result.success}")

Context Switching

# Switch database
connector.do_switch_context(database_name="another_database")

# Switch schema
connector.do_switch_context(
    database_name="my_database",
    schema_name="another_schema"
)

Configuration with Datus Agent

When using with Datus Agent, the adapter is automatically discovered via entry points:

# config.yaml
database:
  type: snowflake
  account: myaccount
  username: myuser
  password: mypassword
  warehouse: my_warehouse
  database: my_database
  schema: my_schema

The adapter will be automatically loaded when you use type: snowflake.

Architecture

This adapter:

• Inherits from BaseSqlConnector in datus-agent
• Uses native Snowflake connector for optimal performance
• Implements all required abstract methods
• Provides Snowflake-specific optimizations (SHOW commands, Arrow format)

Development

# Install in development mode
cd datus-snowflake
pip install -e .

# Run tests
pytest tests/

License

Apache License 2.0