TCPDashboard/docs/modules/database_operations.md

Database Operations Documentation

Overview

The Database Operations module (database/operations.py) provides a clean, centralized interface for all database interactions using the Repository Pattern. This approach abstracts SQL complexity from business logic, ensuring maintainable, testable, and consistent database operations across the entire application.

Key Benefits

🏗️ Clean Architecture

• Repository Pattern: Separates data access logic from business logic
• Centralized Operations: All database interactions go through well-defined APIs
• No Raw SQL: Business logic never contains direct SQL queries
• Consistent Interface: Standardized methods across all database operations

🛡️ Reliability & Safety

• Automatic Transaction Management: Sessions and commits handled automatically
• Error Handling: Custom exceptions with proper context
• Connection Pooling: Efficient database connection management
• Session Cleanup: Automatic session management and cleanup

🔧 Maintainability

• Easy Testing: Repository methods can be easily mocked for testing
• Database Agnostic: Can change database implementations without affecting business logic
• Type Safety: Full type hints for better IDE support and error detection
• Logging Integration: Built-in logging for monitoring and debugging

Architecture

┌─────────────────────────────────────────────────────────────┐
│                DatabaseOperations                           │
│  ┌─────────────────────────────────────────────────────┐    │
│  │              Health Check & Stats                   │    │
│  │  • Connection health monitoring                     │    │
│  │  • Database statistics                              │    │
│  │  • Performance metrics                              │    │
│  └─────────────────────────────────────────────────────┘    │
│                           │                                 │
│  ┌─────────────────┐  ┌─────────────────┐  ┌──────────────┐ │
│  │MarketDataRepo   │  │RawTradeRepo     │  │   BotRepo    │ │
│  │                 │  │                 │  │              │ │
│  │ • upsert_candle │  │ • insert_data   │  │ • add        │ │
│  │ • get_candles   │  │ • get_trades    │  │ • get_by_id  │ │
│  │ • get_latest    │  │ • raw_websocket │  │ • update/delete│ │
│  └─────────────────┘  └─────────────────┘  └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
                              │
                    ┌─────────────────┐
                    │ BaseRepository  │
                    │                 │
                    │ • Session Mgmt  │
                    │ • Error Logging │
                    │ • DB Connection │
                    └─────────────────┘

Quick Start

Basic Usage

from database.operations import get_database_operations
from data.common.data_types import OHLCVCandle
from datetime import datetime, timezone

# Get the database operations instance (singleton)
db = get_database_operations()

# Check database health
if not db.health_check():
    print("Database connection issue!")
    return

# Store a candle
candle = OHLCVCandle(
    exchange="okx",
    symbol="BTC-USDT", 
    timeframe="5s",
    open=50000.0,
    high=50100.0,
    low=49900.0,
    close=50050.0,
    volume=1.5,
    trade_count=25,
    start_time=datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc),
    end_time=datetime(2024, 1, 1, 12, 0, 5, tzinfo=timezone.utc)
)

# Store candle (with duplicate handling)
success = db.market_data.upsert_candle(candle, force_update=False)
if success:
    print("Candle stored successfully!")

With Data Collectors

import asyncio
from data.exchanges.okx import OKXCollector
from data.base_collector import DataType
from database.operations import get_database_operations

async def main():
    # Initialize database operations
    db = get_database_operations()
    
    # The collector automatically uses the database operations module
    collector = OKXCollector(
        symbols=['BTC-USDT'],
        data_types=[DataType.TRADE],
        store_raw_data=True,  # Stores raw WebSocket data
        force_update_candles=False  # Ignore duplicate candles
    )
    
    await collector.start()
    await asyncio.sleep(60)  # Collect for 1 minute
    await collector.stop()
    
    # Check statistics
    stats = db.get_stats()
    print(f"Total bots: {stats['bot_count']}")
    print(f"Total candles: {stats['candle_count']}")
    print(f"Total raw trades: {stats['raw_trade_count']}")

asyncio.run(main())

API Reference

DatabaseOperations

Main entry point for all database operations.

Methods

health_check() -> bool

Test database connection health.

db = get_database_operations()
if db.health_check():
    print("✅ Database is healthy")
else:
    print("❌ Database connection issues")

get_stats() -> Dict[str, Any]

Get comprehensive database statistics.

stats = db.get_stats()
print(f"Bots: {stats['bot_count']:,}")
print(f"Candles: {stats['candle_count']:,}")
print(f"Raw trades: {stats['raw_trade_count']:,}")
print(f"Health: {stats['healthy']}")

MarketDataRepository

Repository for market_data table operations (candles/OHLCV data).

Methods

upsert_candle(candle: OHLCVCandle, force_update: bool = False) -> bool

Store or update candle data with configurable duplicate handling.

Parameters:

• candle: OHLCVCandle object to store
• force_update: If True, overwrites existing data; if False, ignores duplicates

Returns: True if successful, False otherwise

Duplicate Handling:

• force_update=False: Uses ON CONFLICT DO NOTHING (preserves existing candles)
• force_update=True: Uses ON CONFLICT DO UPDATE SET (overwrites existing candles)

# Store new candle, ignore if duplicate exists
db.market_data.upsert_candle(candle, force_update=False)

# Store candle, overwrite if duplicate exists  
db.market_data.upsert_candle(candle, force_update=True)

get_candles(symbol: str, timeframe: str, start_time: datetime, end_time: datetime, exchange: str = "okx") -> List[Dict[str, Any]]

Retrieve historical candle data.

from datetime import datetime, timezone

candles = db.market_data.get_candles(
    symbol="BTC-USDT",
    timeframe="5s", 
    start_time=datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc),
    end_time=datetime(2024, 1, 1, 13, 0, 0, tzinfo=timezone.utc),
    exchange="okx"
)

for candle in candles:
    print(f"{candle['timestamp']}: O={candle['open']} H={candle['high']} L={candle['low']} C={candle['close']}")

get_latest_candle(symbol: str, timeframe: str, exchange: str = "okx") -> Optional[Dict[str, Any]]

Get the most recent candle for a symbol/timeframe combination.

latest = db.market_data.get_latest_candle("BTC-USDT", "5s")
if latest:
    print(f"Latest 5s candle: {latest['close']} at {latest['timestamp']}")
else:
    print("No candles found")

BotRepository

Repository for bots table operations.

Methods

add(bot_data: Dict[str, Any]) -> Bot

Adds a new bot to the database.

Parameters:

• bot_data: Dictionary containing the bot's attributes (name, strategy_name, etc.)

Returns: The newly created Bot object.

from decimal import Decimal

bot_data = {
    "name": "MyTestBot",
    "strategy_name": "SimpleMACD",
    "symbol": "BTC-USDT",
    "timeframe": "1h",
    "status": "inactive",
    "virtual_balance": Decimal("10000"),
}
new_bot = db.bots.add(bot_data)
print(f"Added bot with ID: {new_bot.id}")

get_by_id(bot_id: int) -> Optional[Bot]

Retrieves a bot by its unique ID.

bot = db.bots.get_by_id(1)
if bot:
    print(f"Found bot: {bot.name}")

get_by_name(name: str) -> Optional[Bot]

Retrieves a bot by its unique name.

bot = db.bots.get_by_name("MyTestBot")
if bot:
    print(f"Found bot with ID: {bot.id}")

update(bot_id: int, update_data: Dict[str, Any]) -> Optional[Bot]

Updates an existing bot's attributes.

from datetime import datetime, timezone

update_payload = {"status": "active", "last_heartbeat": datetime.now(timezone.utc)}
updated_bot = db.bots.update(1, update_payload)
if updated_bot:
    print(f"Bot status updated to: {updated_bot.status}")

delete(bot_id: int) -> bool

Deletes a bot from the database.

Returns: True if deletion was successful, False otherwise.

success = db.bots.delete(1)
if success:
    print("Bot deleted successfully.")

RawTradeRepository

Repository for raw_trades table operations (raw WebSocket data).

Methods

insert_market_data_point(data_point: MarketDataPoint) -> bool

Store raw market data from WebSocket streams.

from data.base_collector import MarketDataPoint, DataType
from datetime import datetime, timezone

data_point = MarketDataPoint(
    exchange="okx",
    symbol="BTC-USDT",
    timestamp=datetime.now(timezone.utc),
    data_type=DataType.TRADE,
    data={"price": 50000, "size": 0.1, "side": "buy"}
)

success = db.raw_trades.insert_market_data_point(data_point)

insert_raw_websocket_data(exchange: str, symbol: str, data_type: str, raw_data: Dict[str, Any], timestamp: Optional[datetime] = None) -> bool

Store raw WebSocket data for debugging purposes.

db.raw_trades.insert_raw_websocket_data(
    exchange="okx",
    symbol="BTC-USDT",
    data_type="raw_trade",
    raw_data={"instId": "BTC-USDT", "px": "50000", "sz": "0.1"},
    timestamp=datetime.now(timezone.utc)
)

get_raw_trades(symbol: str, data_type: str, start_time: datetime, end_time: datetime, exchange: str = "okx", limit: Optional[int] = None) -> List[Dict[str, Any]]

Retrieve raw trade data for analysis.

trades = db.raw_trades.get_raw_trades(
    symbol="BTC-USDT",
    data_type="trade",
    start_time=datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc),
    end_time=datetime(2024, 1, 1, 13, 0, 0, tzinfo=timezone.utc),
    limit=1000
)

Error Handling

The database operations module includes comprehensive error handling with custom exceptions.

DatabaseOperationError

Custom exception for database operation failures.

from database.operations import DatabaseOperationError

try:
    db.market_data.upsert_candle(candle)
except DatabaseOperationError as e:
    logger.error(f"Database operation failed: {e}")
    # Handle the error appropriately

Best Practices

1. Always Handle Exceptions: Wrap database operations in try-catch blocks
2. Check Health First: Use health_check() before critical operations
3. Monitor Performance: Use get_stats() to monitor database growth
4. Use Appropriate Repositories: Use market_data for candles, raw_trades for raw data
5. Handle Duplicates Appropriately: Choose the right force_update setting

Configuration

Force Update Behavior

The force_update_candles parameter in collectors controls duplicate handling:

# In OKX collector configuration
collector = OKXCollector(
    symbols=['BTC-USDT'],
    force_update_candles=False  # Default: ignore duplicates
)

# Or enable force updates
collector = OKXCollector(
    symbols=['BTC-USDT'], 
    force_update_candles=True   # Overwrite existing candles
)

Logging Integration

Database operations automatically integrate with the application's logging system:

import logging
from database.operations import get_database_operations

logger = logging.getLogger(__name__)
db = get_database_operations(logger)

# All database operations will now log through your logger
db.market_data.upsert_candle(candle)  # Logs: "Stored candle: BTC-USDT 5s at ..."

Migration from Direct SQL

If you have existing code using direct SQL, here's how to migrate:

Before (Direct SQL - ❌ Don't do this)

# OLD WAY - direct SQL queries
from database.connection import get_db_manager
from sqlalchemy import text

db_manager = get_db_manager()
with db_manager.get_session() as session:
    session.execute(text("""
        INSERT INTO market_data (exchange, symbol, timeframe, ...)
        VALUES (:exchange, :symbol, :timeframe, ...)
    """), {'exchange': 'okx', 'symbol': 'BTC-USDT', ...})
    session.commit()

After (Repository Pattern - ✅ Correct way)

# NEW WAY - using repository pattern
from database.operations import get_database_operations
from data.common.data_types import OHLCVCandle

db = get_database_operations()
candle = OHLCVCandle(...) # Create candle object
success = db.market_data.upsert_candle(candle)

Performance Considerations

Connection Pooling

The database operations module automatically manages connection pooling through the underlying DatabaseManager.

Batch Operations

For high-throughput scenarios, consider batching operations:

# Store multiple candles efficiently
candles = [candle1, candle2, candle3, ...]

for candle in candles:
    db.market_data.upsert_candle(candle)

Monitoring

Monitor database performance using the built-in statistics:

import time

# Monitor database load
while True:
    stats = db.get_stats()
    print(f"Candles: {stats['candle_count']:,}, Health: {stats['healthy']}")
    time.sleep(30)

Troubleshooting

Common Issues

1. Connection Errors

if not db.health_check():
    logger.error("Database connection failed - check connection settings")

2. Duplicate Key Errors

# Use force_update=False to ignore duplicates
db.market_data.upsert_candle(candle, force_update=False)

3. Transaction Errors

The repository automatically handles session management, but if you encounter issues:

try:
    db.market_data.upsert_candle(candle)
except DatabaseOperationError as e:
    logger.error(f"Transaction failed: {e}")

Debug Mode

Enable database query logging for debugging:

# Set environment variable
import os
os.environ['DEBUG'] = 'true'

# This will log all SQL queries
db = get_database_operations()

Related Documentation

• Database Connection - Connection pooling and configuration
• Data Collectors - How collectors use database operations
• Architecture Overview - System design patterns

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This documentation covers the repository pattern implementation in database/operations.py. For database schema details, see the Architecture Documentation.