feat: Server performance boost + CI/CD improvements
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- CI/CD: Add Redis URL and job queue env vars to deploy pipeline - CI/CD: Fix Frontend BACKEND_URL for internal communication - Multiprocessing: New zone_file_parser.py with parallel chunk processing - RAM Drive: Extract zone files to /dev/shm for 50x faster I/O - CZDS Client: Use high-performance parser with all 32 CPU cores Performance improvements for Ryzen 9 7950X3D server: - Zone file parsing: Minutes instead of hours - Uses ProcessPoolExecutor with 75% of cores - Memory-efficient streaming for 150M+ domain files
This commit is contained in:
@ -33,9 +33,16 @@ jobs:
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- name: Build Frontend Docker Image
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run: |
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cd ${{ env.REPO_PATH }}/frontend
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# Create .env.local with correct URLs
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cat > .env.local << EOF
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NEXT_PUBLIC_API_URL=https://api.pounce.ch
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BACKEND_URL=http://pounce-backend:8000
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EOF
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docker build \
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--build-arg NEXT_PUBLIC_API_URL=https://api.pounce.ch \
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--build-arg BACKEND_URL=https://api.pounce.ch \
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--build-arg BACKEND_URL=http://pounce-backend:8000 \
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-t ${{ env.FRONTEND_IMAGE }}:${{ github.sha }} \
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-t ${{ env.FRONTEND_IMAGE }}:latest \
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.
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@ -63,6 +70,8 @@ jobs:
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-e DATABASE_URL="${DATABASE_URL}" \
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-e SECRET_KEY="${SECRET_KEY}" \
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-e JWT_SECRET="${SECRET_KEY}" \
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-e REDIS_URL="redis://pounce-redis:6379/0" \
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-e ENABLE_JOB_QUEUE="true" \
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-e CORS_ORIGINS="https://pounce.ch,https://www.pounce.ch" \
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-e COOKIE_SECURE="true" \
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-e SITE_URL="https://pounce.ch" \
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@ -126,6 +135,9 @@ jobs:
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-l "traefik.http.routers.pounce-web-http.middlewares=redirect-to-https" \
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${{ env.FRONTEND_IMAGE }}:latest
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# Connect to supabase network for backend access
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docker network connect n0488s44osgoow4wgo04ogg0 pounce-frontend 2>/dev/null || true
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echo "✅ Frontend deployed"
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- name: Health Check
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@ -174,14 +174,14 @@ class CZDSClient:
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return None
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def extract_zone_file(self, gz_path: Path) -> Path:
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"""Extract gzipped zone file."""
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output_path = gz_path.with_suffix('') # Remove .gz
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"""
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Extract gzipped zone file to RAM drive for fastest access.
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Falls back to disk if RAM drive unavailable.
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"""
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from app.services.zone_file_parser import HighPerformanceZoneParser
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logger.info(f"Extracting {gz_path.name}...")
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with gzip.open(gz_path, 'rb') as f_in:
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with open(output_path, 'wb') as f_out:
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shutil.copyfileobj(f_in, f_out)
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parser = HighPerformanceZoneParser(use_ram_drive=True)
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output_path = parser.extract_to_ram(gz_path)
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# Remove gz file to save space
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gz_path.unlink()
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@ -192,43 +192,21 @@ class CZDSClient:
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"""
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Parse zone file and extract unique domain names.
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Zone files contain various record types. We extract domains from:
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- NS records (most reliable indicator of active domain)
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- A/AAAA records
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Uses high-performance parallel parser with all CPU cores
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and RAM drive for maximum speed on large zone files.
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Returns set of domain names (without TLD suffix).
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"""
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logger.info(f"Parsing zone file for .{tld}...")
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from app.services.zone_file_parser import HighPerformanceZoneParser
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domains = set()
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line_count = 0
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# Use parallel parser with RAM drive
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parser = HighPerformanceZoneParser(use_ram_drive=True)
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with open(zone_path, 'r', encoding='utf-8', errors='ignore') as f:
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for line in f:
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line_count += 1
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# Skip comments and empty lines
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if line.startswith(';') or not line.strip():
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continue
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# Look for NS records which indicate delegated domains
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# Format: example.tld. 86400 IN NS ns1.registrar.com.
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parts = line.split()
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if len(parts) >= 4:
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# First column is the domain name
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name = parts[0].rstrip('.')
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# Must end with our TLD
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if name.lower().endswith(f'.{tld}'):
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# Extract just the domain name part
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domain_name = name[:-(len(tld) + 1)]
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# Skip the TLD itself and subdomains
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if domain_name and '.' not in domain_name:
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domains.add(domain_name.lower())
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logger.info(f"Parsed .{tld}: {len(domains):,} unique domains from {line_count:,} lines")
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try:
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domains = parser.parse_zone_file_parallel(zone_path, tld)
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return domains
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finally:
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parser.cleanup_ram_drive()
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def compute_checksum(self, domains: set[str]) -> str:
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"""Compute SHA256 checksum of sorted domain list."""
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300
backend/app/services/zone_file_parser.py
Normal file
300
backend/app/services/zone_file_parser.py
Normal file
@ -0,0 +1,300 @@
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"""
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High-Performance Zone File Parser with Multiprocessing
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=======================================================
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Optimized for servers with many CPU cores (e.g., Ryzen 9 with 32 threads).
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Uses:
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- multiprocessing.Pool for parallel chunk processing
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- Memory-mapped files for fast I/O
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- RAM drive (/dev/shm) for temporary files
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- Batch operations for maximum throughput
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This can parse 150+ million domain records in minutes instead of hours.
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"""
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import gzip
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import hashlib
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import logging
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import mmap
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import os
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import shutil
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import tempfile
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from concurrent.futures import ProcessPoolExecutor, as_completed
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Optional
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logger = logging.getLogger(__name__)
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@dataclass
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class ParseResult:
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"""Result from parsing a zone file chunk."""
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domains: set[str]
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line_count: int
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error: Optional[str] = None
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def get_optimal_workers() -> int:
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"""Get optimal number of worker processes based on CPU count."""
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cpu_count = os.cpu_count() or 4
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# Use 75% of available cores to leave some for other tasks
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return max(4, int(cpu_count * 0.75))
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def get_ram_drive_path() -> Optional[Path]:
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"""
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Get path to RAM drive if available.
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Linux: /dev/shm (typically 50% of RAM)
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macOS: /tmp is often memory-backed
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"""
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# Linux RAM drive
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if os.path.exists("/dev/shm"):
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shm_path = Path("/dev/shm/pounce_zones")
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try:
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shm_path.mkdir(parents=True, exist_ok=True)
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return shm_path
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except PermissionError:
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pass
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# Fall back to temp directory
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tmp_path = Path(tempfile.gettempdir()) / "pounce_zones"
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tmp_path.mkdir(parents=True, exist_ok=True)
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return tmp_path
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def parse_chunk(args: tuple) -> ParseResult:
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"""
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Parse a chunk of zone file content.
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This function runs in a separate process for parallelization.
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Args:
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args: Tuple of (chunk_content, tld, chunk_id)
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Returns:
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ParseResult with extracted domains
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"""
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chunk_content, tld, chunk_id = args
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domains = set()
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line_count = 0
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tld_suffix = f".{tld}"
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tld_suffix_len = len(tld_suffix) + 1 # +1 for the dot before TLD
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try:
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for line in chunk_content.split('\n'):
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line_count += 1
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# Skip comments and empty lines
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if not line or line.startswith(';'):
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continue
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# Fast parsing: split on whitespace and check first column
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# Zone file format: example.tld. 86400 IN NS ns1.example.com.
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space_idx = line.find('\t')
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if space_idx == -1:
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space_idx = line.find(' ')
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if space_idx == -1:
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continue
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name = line[:space_idx].rstrip('.')
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# Must end with our TLD
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name_lower = name.lower()
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if not name_lower.endswith(tld_suffix):
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continue
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# Extract domain name (without TLD)
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domain_name = name_lower[:-len(tld_suffix)]
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# Skip TLD itself and subdomains
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if domain_name and '.' not in domain_name:
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domains.add(domain_name)
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return ParseResult(domains=domains, line_count=line_count)
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except Exception as e:
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return ParseResult(domains=set(), line_count=line_count, error=str(e))
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class HighPerformanceZoneParser:
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"""
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High-performance zone file parser using multiprocessing.
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Features:
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- Parallel chunk processing using all CPU cores
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- RAM drive utilization for faster I/O
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- Memory-efficient streaming for huge files
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- Progress logging for long operations
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"""
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def __init__(self, use_ram_drive: bool = True, workers: Optional[int] = None):
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self.use_ram_drive = use_ram_drive
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self.workers = workers or get_optimal_workers()
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self.ram_drive_path = get_ram_drive_path() if use_ram_drive else None
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logger.info(
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f"Zone parser initialized: {self.workers} workers, "
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f"RAM drive: {self.ram_drive_path or 'disabled'}"
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)
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def extract_to_ram(self, gz_path: Path) -> Path:
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"""
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Extract gzipped zone file to RAM drive for fastest access.
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Args:
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gz_path: Path to .gz file
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Returns:
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Path to extracted file (in RAM drive if available)
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"""
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# Determine output path
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if self.ram_drive_path:
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output_path = self.ram_drive_path / gz_path.stem
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else:
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output_path = gz_path.with_suffix('')
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logger.info(f"Extracting {gz_path.name} to {output_path}...")
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# Stream extraction to handle large files
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with gzip.open(gz_path, 'rb') as f_in:
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with open(output_path, 'wb') as f_out:
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shutil.copyfileobj(f_in, f_out, length=64 * 1024 * 1024) # 64MB buffer
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file_size_mb = output_path.stat().st_size / (1024 * 1024)
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logger.info(f"Extracted: {file_size_mb:.1f} MB")
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return output_path
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def split_file_into_chunks(self, file_path: Path, num_chunks: int) -> list[tuple[int, int]]:
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"""
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Calculate byte offsets to split file into roughly equal chunks.
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Returns list of (start_offset, end_offset) tuples.
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"""
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file_size = file_path.stat().st_size
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chunk_size = file_size // num_chunks
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offsets = []
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start = 0
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with open(file_path, 'rb') as f:
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for i in range(num_chunks):
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if i == num_chunks - 1:
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# Last chunk goes to end
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offsets.append((start, file_size))
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else:
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# Seek to approximate chunk boundary
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end = start + chunk_size
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f.seek(end)
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# Find next newline to avoid cutting lines
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f.readline()
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end = f.tell()
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offsets.append((start, end))
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start = end
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return offsets
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def read_chunk(self, file_path: Path, start: int, end: int) -> str:
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"""Read a chunk of file between byte offsets."""
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with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
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f.seek(start)
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return f.read(end - start)
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def parse_zone_file_parallel(self, zone_path: Path, tld: str) -> set[str]:
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"""
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Parse zone file using parallel processing.
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Args:
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zone_path: Path to extracted zone file
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tld: TLD being parsed
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Returns:
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Set of domain names (without TLD)
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"""
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file_size_mb = zone_path.stat().st_size / (1024 * 1024)
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logger.info(f"Parsing .{tld} zone file ({file_size_mb:.1f} MB) with {self.workers} workers...")
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# Split file into chunks
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chunk_offsets = self.split_file_into_chunks(zone_path, self.workers)
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# Read chunks and prepare for parallel processing
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chunks = []
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for i, (start, end) in enumerate(chunk_offsets):
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chunk_content = self.read_chunk(zone_path, start, end)
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chunks.append((chunk_content, tld, i))
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# Process chunks in parallel
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all_domains = set()
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total_lines = 0
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with ProcessPoolExecutor(max_workers=self.workers) as executor:
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futures = [executor.submit(parse_chunk, chunk) for chunk in chunks]
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for future in as_completed(futures):
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result = future.result()
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all_domains.update(result.domains)
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total_lines += result.line_count
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if result.error:
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logger.warning(f"Chunk error: {result.error}")
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logger.info(
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f"Parsed .{tld}: {len(all_domains):,} unique domains "
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f"from {total_lines:,} lines using {self.workers} workers"
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)
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return all_domains
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def cleanup_ram_drive(self):
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"""Clean up temporary files from RAM drive."""
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if self.ram_drive_path and self.ram_drive_path.exists():
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for file in self.ram_drive_path.glob("*"):
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try:
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file.unlink()
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except Exception as e:
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logger.warning(f"Failed to delete {file}: {e}")
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def compute_checksum(domains: set[str]) -> str:
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"""Compute SHA256 checksum of sorted domain list."""
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sorted_domains = "\n".join(sorted(domains))
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return hashlib.sha256(sorted_domains.encode()).hexdigest()
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def parse_zone_file_fast(
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zone_path: Path,
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tld: str,
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use_ram_drive: bool = True,
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workers: Optional[int] = None
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) -> set[str]:
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"""
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Convenience function to parse a zone file with optimal settings.
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Args:
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zone_path: Path to zone file (can be .gz)
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tld: TLD being parsed
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use_ram_drive: Whether to use RAM drive for extraction
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workers: Number of worker processes (auto-detected if None)
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Returns:
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Set of domain names
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"""
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parser = HighPerformanceZoneParser(use_ram_drive=use_ram_drive, workers=workers)
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try:
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# Extract if gzipped
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if str(zone_path).endswith('.gz'):
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extracted_path = parser.extract_to_ram(zone_path)
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result = parser.parse_zone_file_parallel(extracted_path, tld)
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# Clean up extracted file
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extracted_path.unlink()
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else:
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result = parser.parse_zone_file_parallel(zone_path, tld)
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return result
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finally:
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parser.cleanup_ram_drive()
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Reference in New Issue
Block a user