import sys import os import sqlite3 import subprocess from urllib.parse import urlparse BATCH_SIZE = 100000 # Insert in chunks of 100k to save RAM # Embedded schema to make the script standalone[span_1](start_span)[span_1](end_span) SCHEMA_SQL = """ -- 1. The Payload Table (Standard ROWID table) -- 'id' becomes a direct alias for the SQLite rowid. CREATE TABLE payload_data ( id INTEGER PRIMARY KEY, json_record TEXT ); -- 2. The Lookup Table (WITHOUT ROWID) -- Clustered directly by SURT for extremely fast prefix/exact lookups. CREATE TABLE surt_lookup ( surt TEXT, capture_time INTEGER, payload_id INTEGER, PRIMARY KEY (surt, capture_time) ) WITHOUT ROWID; """ if len(sys.argv) < 2: print("Usage: uv run import_cdx.py ") sys.exit(1) source = sys.argv[1] # --- 1. Parse File Info and Compression --- parsed_url = urlparse(source) original_file_name = os.path.basename(parsed_url.path) # Determine compression based strictly on the parsed path is_zst = original_file_name.endswith('.zst') # --- 2. Determine Database Name --- if is_zst: base_name = original_file_name[:-4] else: base_name = original_file_name db_name = f"{base_name}.db" print(f"Input source: {source}") print(f"Target database: {db_name}") # --- 3. Initialize Database and Schema --- if not os.path.exists(db_name): print(f"Initializing {db_name} with embedded schema...") init_conn = sqlite3.connect(db_name) init_conn.executescript(SCHEMA_SQL) init_conn.close() # --- 4. Setup Streaming Pipeline (curl/cat -> zstdcat) --- is_url = source.startswith("http://") or source.startswith("https://") fetch_process = None decompress_process = None if is_url: fetch_process = subprocess.Popen(["curl", "-sL", source], stdout=subprocess.PIPE) raw_stream = fetch_process.stdout else: raw_stream = open(source, "rb") if is_zst: decompress_process = subprocess.Popen(["zstdcat"], stdin=raw_stream, stdout=subprocess.PIPE) stream = decompress_process.stdout else: stream = raw_stream # --- 5. Process and Insert Data --- connection = sqlite3.connect(db_name) cursor = connection.cursor() cursor.execute("PRAGMA journal_mode = WAL;") cursor.execute("PRAGMA synchronous = NORMAL;") cursor.execute("SELECT IFNULL(MAX(id), 0) FROM payload_data") current_id = cursor.fetchone()[0] payload_batch = [] lookup_batch = [] # Variables to track the last seen record for deduplication last_surt = None last_capture_time = None cursor.execute("BEGIN TRANSACTION;") print("Importing data (with duplicate filtering)...") for line_bytes in stream: line = line_bytes.decode('utf-8', errors='replace').strip() if not line: continue parts = line.split(' ', 2) if len(parts) != 3: continue surt = parts[0] try: capture_time = int(parts[1]) except ValueError: continue json_record = parts[2] # Deduplication check: skip if it matches the previous row's keys if surt == last_surt and capture_time == last_capture_time: continue # Update trackers for the next iteration last_surt = surt last_capture_time = capture_time current_id += 1 payload_batch.append((current_id, json_record)) lookup_batch.append((surt, capture_time, current_id)) if len(payload_batch) >= BATCH_SIZE: cursor.executemany("INSERT INTO payload_data (id, json_record) VALUES (?, ?)", payload_batch) cursor.executemany("INSERT INTO surt_lookup (surt, capture_time, payload_id) VALUES (?, ?, ?)", lookup_batch) payload_batch.clear() lookup_batch.clear() print(f"Inserted up to ID {current_id} in {db_name}...") # Cleanup leftover batch if payload_batch: cursor.executemany("INSERT INTO payload_data (id, json_record) VALUES (?, ?)", payload_batch) cursor.executemany("INSERT INTO surt_lookup (surt, capture_time, payload_id) VALUES (?, ?, ?)", lookup_batch) connection.commit() connection.close() # Close processes and files safely if decompress_process: decompress_process.wait() if fetch_process: fetch_process.wait() elif not is_url: raw_stream.close() print(f"Import fully complete for {db_name}!")