import duckdb import os import sqlite3 import sys def convert_sqlite_to_parquet_with_duckdb(sqlite_db_path, output_dir, tables_to_convert=None): """ Converts tables from a SQLite database to Parquet files using DuckDB. Args: sqlite_db_path (str): The path to the SQLite database file. output_dir (str): The directory where the Parquet files will be saved. tables_to_convert (list, optional): A list of table names to convert. If None, all tables will be converted. """ # --- Step 1: Create the output directory if it doesn't exist --- if not os.path.exists(output_dir): os.makedirs(output_dir) print(f"Created output directory: {output_dir}") # --- Step 2: Connect to the SQLite database and get table names --- # We use the built-in sqlite3 library just to get the list of tables. # DuckDB will do the heavy lifting of reading the data. try: conn_sqlite = sqlite3.connect(sqlite_db_path) cursor = conn_sqlite.cursor() # Get a list of all tables. We exclude internal SQLite tables. cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name NOT LIKE 'sqlite_%';") all_tables = [row[0] for row in cursor.fetchall()] conn_sqlite.close() except sqlite3.Error as e: print(f"Error connecting to SQLite database: {e}") return # --- Step 3: Use DuckDB to attach the SQLite database and convert tables --- # Connect to a DuckDB in-memory database. # The 'read_only=False' is important for writing the Parquet files. # You can also use a persistent DuckDB database file here for more complex workflows. con_duckdb = duckdb.connect(database=':memory:', read_only=False) try: # Load the SQLite extension. It's often autoloader, but this ensures it. con_duckdb.execute("INSTALL sqlite; LOAD sqlite;") # Attach the SQLite database. This makes all its tables available to DuckDB. con_duckdb.execute(f"ATTACH '{sqlite_db_path}' AS sqlite_db (TYPE sqlite);") # Determine which tables to convert if tables_to_convert is None: tables_to_process = all_tables else: tables_to_process = tables_to_convert # Filter to ensure the requested tables actually exist in the database tables_to_process = [table for table in tables_to_process if table in all_tables] if not tables_to_process: print("No tables found to convert.") return print(f"Found {len(tables_to_process)} tables to convert: {', '.join(tables_to_process)}") # Loop through the tables and use the efficient COPY command for table_name in tables_to_process: print(f"\nConverting table: '{table_name}'...") # Define the output path for the Parquet file parquet_file_path = os.path.join(output_dir, f'{table_name}.parquet') # Use the powerful COPY statement. This reads directly from the attached # SQLite table and writes directly to the Parquet file. # The 'FORMAT parquet' option specifies the output format. # You can also add options for compression (e.g., COMPRESSION 'zstd'). # The query is `SELECT * FROM attached_db_alias.table_name`. copy_query = f"COPY (SELECT * FROM sqlite_db.\"{table_name}\") TO '{parquet_file_path}' (FORMAT parquet);" try: con_duckdb.execute(copy_query) print(f" -> Successfully saved to '{parquet_file_path}'") except duckdb.Error as copy_err: print(f" -> ERROR converting table '{table_name}': {copy_err}") print(" This may be due to unsupported data types or other schema issues.") finally: # --- Step 4: Clean up (detach the database and close the connection) --- con_duckdb.execute("DETACH sqlite_db;") con_duckdb.close() print("\nConversion process finished.") # --- How to use the function --- if __name__ == '__main__': # Replace 'your_large_database.db' with the path to your actual SQLite file database_file = sys.argv[1] # Replace 'path/to/your/output_folder' with your desired output directory output_folder = '.' # Call the function to perform the conversion # You can also specify a list of tables if you don't want to convert all of them. # For example: tables_to_convert=['table_1', 'another_large_table'] convert_sqlite_to_parquet_with_duckdb(database_file, output_folder)