data-collection

name	data-collection
description	Parallel data collection from web sources, APIs, and documentation sites
version	1.0.0
author	Hermes Agent
license	MIT
platforms	["linux"]
metadata	{"hermes":{"tags":["data-collection","web-scraping","parallel-processing","python","multiprocessing"],"related_skills":["web-tool-builder","flask-web-tools","coding-principles"]}}
triggers	["data collection","web scraping","crawl","采集","爬虫","parallel collection"]

Data Collection Patterns

Reusable patterns for collecting data from web sources, APIs, and documentation sites. Optimized for multi-core servers.

Architecture: Multi-Process Parallel Collector

When to use: Collecting from multiple independent sources simultaneously.

Key insight: Use multiprocessing.Process (not threads) for true parallelism on multi-core servers. Each process maintains its own state file, with a shared counter for progress tracking.

from multiprocessing import Process, Manager, Lock, Value
import signal, sys, json
from pathlib import Path

# Shared counter (atomic via Lock)
shared_counter = Value('i', 0)
lock = Lock()
stop_event = multiprocessing.Event()

# Each process has its own seen-state file
def init_process_seen(process_name):
    seen_file = BASE_DIR / f"seen_{process_name}.json"
    if seen_file.exists():
        data = json.loads(seen_file.read_text())
        return set(data.get("urls", [])), set(data.get("contents", []))
    return set(), set()

def save_process_seen(process_name, seen_urls, seen_contents):
    seen_file = BASE_DIR / f"seen_{process_name}.json"
    data = {"urls": list(seen_urls), "contents": list(seen_contents)}
    seen_file.write_text(json.dumps(data, indent=2))

# Process function
def process_source(shared_counter, lock, stop_event):
    process_name = "source_name"
    seen_urls, seen_contents = init_process_seen(process_name)
    session = create_session()
    
    while not stop_event.is_set():
        # Collect data...
        with lock:
            shared_counter.value += 1
        
         shared_counter.value %  == :
            save_process_seen(process_name, seen_urls, seen_contents)
        
        
         _  (interval):
             stop_event.is_set():
                
            time.sleep()


 ():
    stop_event.()
     p  processes:
        p.join(timeout=)
         p.is_alive():
            p.terminate()
    sys.exit()

signal.signal(signal.SIGTERM, signal_handler)
signal.signal(signal.SIGINT, signal_handler)

Why Per-Process State Files?

• Manager().list() returns ListProxy which doesn't have .add() method
• Manager().set() works but adds IPC overhead for every operation
• Per-process state files: no IPC, no lock contention, periodic sync
• Cross-process dedup: check other processes' seen files (optional, adds some overhead)

Pitfalls

1. Python requests + gzip: Don't Manually Decompress

Problem: Manual gzip.decompress(resp.content) fails when server sends plain JSON with Content-Encoding: gzip header.

Solution: Use resp.text — requests handles decompression automatically.

# ❌ WRONG - crashes on plain JSON responses
if resp.headers.get("Content-Encoding") == "gzip":
    content = gzip.decompress(resp.content).decode("utf-8")
else:
    content = resp.text

# ✅ CORRECT - requests handles it
content = resp.text

Why it happens: Some servers (like GitHub API) send Content-Encoding: gzip header but the actual content is plain JSON. requests transparently decompresses when using .text, but .content returns raw bytes.

2. multiprocessing.Manager().list() is ListProxy

Problem: Manager().list() returns a ListProxy object, not a real list.

# ❌ WRONG - ListProxy has no .add()
shared_urls = manager.list()
shared_urls.add(url)  # AttributeError!

# ✅ CORRECT - use per-process state or Manager().dict()
# Option A: Per-process state (recommended for high-throughput)
# Option B: Manager().dict() with manual set operations
shared_urls = manager.dict()
shared_urls[url_hash] = True

3. URL Exhaustion in Loops

Problem: Fixed URL list + hash-based dedup = first round collects everything, subsequent rounds do nothing.

Solution:

• Dynamic URL discovery (extract links from collected content)
• Multiple data sources (API search, trending, blogs, docs)
• Periodic refresh for changing content (GitHub Trending, search results)

Data Source Patterns

GitHub Trending (HTML scraping)

# Extract repo links from trending page
repos = re.findall(r'href=["\'](/[^/]+/[^"\']+)["\']', content)
repos = [r for r in repos if r.count("/") == 2 and not r.startswith("/trending")]
# Then fetch README from raw.githubusercontent.com

GitHub API Search

# Search repositories by topic
url = f"https://api.github.com/search/repositories?q={topic}&sort=stars&per_page=10"
# Rate limit: 10 requests/minute for unauthenticated, 30 for authenticated

Blog/Documentation Crawling

# Extract article links with date patterns
article_patterns = [
    r'/\d{4}/\d{2}/',  # Date format
    r'/blog/', r'/article/', r'/post/', r'/tutorial/',
]

Link Discovery from Collected Content

# Scan collected files for new links
all_links = set()
for doc_file in collected_dir.glob("*.md"):
    content = doc_file.read_text()
    links = extract_links(content, "")
    all_links.update(links)

Performance Tuning

• Workers per process: 4 threads for I/O-bound (network requests)
• Process count: 20 on 2C2G, 50 on 2C4G. Do NOT exceed — 100 processes on 2C2G crashes the server (SSH unreachable, requires hard reboot).
• Request timeout: 15s with 3 retries
• Sleep between requests: 0.5-2s to avoid rate limiting
• Save interval: Every 50 items or 5 minutes

See Also

• multi-process-scraper — More complete reference with watchdog, size monitoring, scaling guide, and pitfalls