datus-clickhouse 0.1.0

ClickHouse database adapter for Datus

datus-clickhouse

ClickHouse database adapter for Datus.

Installation

pip install datus-clickhouse

This will automatically install the required dependencies:

• datus-agent
• datus-sqlalchemy
• clickhouse-sqlalchemy

Usage

The adapter is automatically registered with Datus when installed. Configure your database connection in your Datus configuration:

database:
  type: clickhouse
  host: localhost
  port: 8123
  username: default
  password: your_password
  database: your_database

Or use programmatically:

from datus_clickhouse import ClickHouseConfig, ClickHouseConnector

# Using config object
config = ClickHouseConfig(
    host="localhost",
    port=8123,
    username="default",
    password="your_password",
    database="mydb",
)
connector = ClickHouseConnector(config)

# Or using dict
connector = ClickHouseConnector({
    "host": "localhost",
    "port": 8123,
    "username": "default",
    "password": "your_password",
    "database": "mydb",
})

# Test connection
connector.test_connection()

# Execute query
result = connector.execute({"sql_query": "SELECT * FROM users LIMIT 10"})
print(result.sql_return)

# Get table list
tables = connector.get_tables()
print(f"Tables: {tables}")

# Get table schema
schema = connector.get_schema(table_name="users")
for column in schema:
    print(f"{column['name']}: {column['type']}")

Configuration Options

Option	Type	Default	Description
host	str	"localhost"	ClickHouse server host
port	int	8123	ClickHouse HTTP port
username	str	(required)	Username
password	str	""	Password
database	str	None	Default database
timeout_seconds	int	30	Connection timeout

Features

• Query execution via ClickHouse SQL (SELECT)
• DDL execution (CREATE, ALTER, DROP)
• DML operations (INSERT, ALTER TABLE UPDATE, DELETE)
• Metadata retrieval (databases, tables, views, columns)
• Sample data extraction
• Multiple result formats (pandas, arrow, csv, list)
• Connection pooling and management
• Comprehensive error handling

Testing

Quick Start

cd datus-clickhouse

# Unit tests (no database required)
uv run pytest tests/ -m "not integration" -v

# All tests with coverage
uv run pytest tests/ -v --cov=datus_clickhouse --cov-report=term-missing

Integration Tests (Requires ClickHouse Server)

# Start ClickHouse container
docker compose up -d

# Wait for container to become healthy (~15s)
docker compose ps

# Run integration tests
uv run pytest tests/integration/ -v

# Run only TPC-H tests
uv run pytest tests/integration/test_tpch.py -v

# Run acceptance tests (core functionality)
uv run pytest tests/ -m acceptance -v

# Stop ClickHouse
docker compose down

TPC-H Test Data

Integration tests include TPC-H benchmark data for realistic query testing. The tpch_setup fixture (session-scoped) automatically creates 5 tables with sample data:

Table	Rows	Description
tpch_region	5	Standard TPC-H regions
tpch_nation	25	Standard TPC-H nations
tpch_customer	10	Simplified customer data
tpch_orders	15	Simplified order data
tpch_supplier	5	Simplified supplier data

Tables are created at the start of the test session and dropped after all tests complete.

Initialize TPC-H Data Manually

To create TPC-H data for use with Datus (outside of tests):

# Basic usage
uv run python scripts/init_tpch_data.py

# Drop existing tables and re-create
uv run python scripts/init_tpch_data.py --drop

# Custom connection
uv run python scripts/init_tpch_data.py --host 192.168.1.100 --port 8123 --username admin --password secret

Test Statistics

• Unit Tests: 45 tests (config validation, connector logic, identifiers)
• Integration Tests: 20 tests (connection, metadata, SQL execution)
• TPC-H Tests: 9 tests (metadata queries, joins, aggregations, CSV format)
• Total: 74 tests

Test Markers

Marker	Description
integration	Requires running ClickHouse server
acceptance	Core functionality validation for CI/CD

Development

Setup

# From workspace root
uv sync --all-packages

# Or install in editable mode
uv pip install -e .

Code Quality

black datus_clickhouse tests
isort datus_clickhouse tests
ruff check datus_clickhouse tests

ClickHouse SQL Notes

ClickHouse has some syntax differences from standard SQL:

• UPDATE: Use ALTER TABLE <table> UPDATE ... WHERE ... instead of UPDATE <table> SET ...
• DELETE: Supports lightweight deletes with DELETE FROM <table> WHERE ...
• Identifiers: Use backticks for quoting: `database`.`table`
• No schema layer: Databases serve as schemas; there is no separate schema concept

Requirements

• Python >= 3.12
• ClickHouse >= 20.1
• datus-agent > 0.2.1
• datus-sqlalchemy >= 0.1.0
• clickhouse-sqlalchemy >= 0.3.2

License

Apache License 2.0