Add 20230429 db snapshot as parquet files

README.md

@@ -1,3 +1,7 @@
 ---
 license: mit
 ---
+
+This repository contains the datasets for hacker news, used by https://github.com/anantn/hn-chatgpt-plugin
+
+As of June 2025, these are now exported as parquet files instead of sqlite for space efficiency

hn-sqlite-20230420.db.zst → embeddings.parquet

@@ -1,3 +1,3 @@
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-size 5406260876
+oid sha256:cc3d78a063936b88cdd8cceec278035d816429c1125296f6d09cc0fd8b4652aa
+size 9322735434

hn-sqlite-20230420_embeddings.db.zst → items.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:167adc5b0f2d969e2027cffdcb6c89975f5e5dee9d2db4f03d1412c36d32c67c
-size 8635136123
+oid sha256:8d59e99f80ce0855c000d997ba91238d9708f45b6b2add45a586b3faca53fb54
+size 8523867315

hn-sqlite-20230429.db.zst → kids.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 6448492710
+oid sha256:f43aa51e13b16d4afde757c650a11dfd1d9bbe006b4266c4bea320f1730b8637
+size 210842922

sql2parquet.py

@@ -0,0 +1,103 @@
+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)

hn-sqlite-20230429_embeddings.db.zst → users.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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