docs: gotcha de OOM por fuentes grandes en KNOWN-ISSUES

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

KNOWN-ISSUES.md

@@ -60,3 +60,24 @@ hit a consent wall from EU IPs, and the inner `AU_yqL` id is only resolvable via
 Google's private batchexecute API. Do not retry. The news seed uses Bing News
 RSS instead (`ENABLE_NEWS_SEED`, real publisher URL in the `?url=` param of
 apiclick.aspx — unwrapped by `_unwrap_news_link`).
+
+## Large sources can OOM-kill the pod
+
+On 2026-07-10 the pod was OOMKilled (memory limit was 1Gi) mid-research: a
+batch of 20 concurrent sources hit a 98k-word document plus several large PDFs
+at once, and pdfplumber's parse spiked RAM past the limit. The in-memory
+research task died with the pod and its session sat in `running` forever.
+
+Mitigations now in place:
+
+- Memory limit raised to 2Gi (`k8s-manifests/researchowl/deployment.yaml`).
+- PDFs capped at 15MB (was 50MB), checked both via Content-Length and actual
+  body size; pdfplumber runs in `run_in_executor` (it is sync + CPU-heavy and
+  also froze the event loop, same class of bug as DDGS) and flushes its page
+  cache per page.
+- Extracted content is truncated to `max_content_length` (300k chars) before
+  hitting `source_contents`.
+- On startup the bot marks orphaned `running` sessions as `interrupted`.
+
+If a research still dies, the scraped sources survive in the DB: `/process`
+re-chunks and scores them without re-scraping.

src/bot/bot.py

@@ -851,6 +851,27 @@ async def cmd_help(update: Update, ctx: ContextTypes.DEFAULT_TYPE):
 
 # ─── Bot setup ────────────────────────────────────────────────────────────────
 
+async def _mark_interrupted_on_startup(app: Application) -> None:
+    """Las tareas de research viven solo en memoria (_active_tasks): un
+    reinicio del pod las mata sin tocar la DB, y sus sesiones quedan en
+    'running' para siempre — parecen activas en /status y get_active_session.
+    """
+    db_conn = await get_db()
+    try:
+        cursor = await db_conn.execute(
+            "UPDATE research_sessions SET status = ?, updated_at = ? WHERE status = ?",
+            (ResearchStatus.INTERRUPTED, time.time(), ResearchStatus.RUNNING),
+        )
+        await db_conn.commit()
+        if cursor.rowcount:
+            logger.info("Orphaned running sessions marked interrupted",
+                        count=cursor.rowcount)
+    except Exception as e:
+        logger.warning("Interrupted-mark failed — bot continues", error=str(e))
+    finally:
+        await db_conn.close()
+
+
 async def _purge_on_startup(app: Application) -> None:
     db_conn = await get_db()
     try:
@@ -994,6 +1015,7 @@ async def _start_scheduler(app: Application) -> None:
 
 
 async def _on_startup(app: Application) -> None:
+    await _mark_interrupted_on_startup(app)
     await _purge_on_startup(app)
     await _start_scheduler(app)
 

src/config.py

@@ -47,6 +47,8 @@ class Settings(BaseSettings):
     request_timeout: int = Field(30)
     request_delay: float = Field(1.0)          # seconds between requests
     min_content_length: int = Field(200)  # chars
+    # Libros/dumps enteros (100k+ palabras) inflan RAM y DB sin aportar al RAG
+    max_content_length: int = Field(300_000)  # chars
 
     # Fuentes opcionales — desactivadas por defecto: la IP del homelab está
     # bloqueada por Reddit (403) y YouTube (transcripts vacíos), eran peso muerto.

src/db/database.py

@@ -18,6 +18,7 @@ class ResearchStatus(str, Enum):
     SATURATED = "saturated"
     FINISHED = "finished"
     ERROR = "error"
+    INTERRUPTED = "interrupted"  # el pod se reinició con el research en marcha
 
 
 class OutputType(str, Enum):

src/scraper/exhaustive.py

@@ -628,6 +628,11 @@ class ExhaustiveScraper:
                                         error="Content too short or empty")
             return
 
+        if len(content) > settings.max_content_length:
+            logger.info("Content truncated", source_id=source_id,
+                        original_length=len(content), url=url[:60])
+            content = content[:settings.max_content_length]
+
         word_count = len(content.split())
 
         await self.db.save_source_content(source_id, content)
@@ -819,30 +824,49 @@ class ExhaustiveScraper:
                     entries=len(entries), added=added)
         return added
 
+    # pdfplumber es síncrono y CPU-intensivo: parsear inline congela el event
+    # loop, y con PDFs grandes el pico de RAM puede matar el pod (OOM con
+    # límite de 1-2Gi). Ejecutar SIEMPRE vía run_in_executor.
+
+    @staticmethod
+    def _parse_pdf_sync(path: str) -> str:
+        import pdfplumber
+
+        with pdfplumber.open(path) as pdf:
+            pages = []
+            for page in pdf.pages[:50]:  # max 50 pages
+                pages.append(page.extract_text() or "")
+                page.flush_cache()  # pdfplumber cachea objetos de página: liberar
+            return "\n\n".join(pages)
+
     async def _extract_pdf(self, url: str) -> tuple[Optional[str], Optional[str]]:
         """Download and extract PDF text"""
-        import pdfplumber
         import tempfile
         import os
 
+        max_pdf_bytes = 15 * 1024 * 1024  # varios PDFs concurrentes en RAM: cap agresivo
+
         http = await self._get_http()
         try:
             async with http.get(url) as resp:
                 if resp.status != 200:
                     return None, None
                 content_length = int(resp.headers.get("content-length", 0))
-                if content_length > 50 * 1024 * 1024:  # skip PDFs > 50MB
+                if content_length > max_pdf_bytes:
                     return None, None
                 pdf_bytes = await resp.read()
+                # Sin Content-Length el check anterior no protege
+                if len(pdf_bytes) > max_pdf_bytes:
+                    return None, None
 
             with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as f:
                 f.write(pdf_bytes)
                 tmp_path = f.name
+            del pdf_bytes
 
             try:
-                with pdfplumber.open(tmp_path) as pdf:
-                    pages = [p.extract_text() or "" for p in pdf.pages[:50]]  # max 50 pages
-                    text = "\n\n".join(pages)
+                loop = asyncio.get_running_loop()
+                text = await loop.run_in_executor(None, self._parse_pdf_sync, tmp_path)
                 return text, url.split("/")[-1]
             finally:
                 os.unlink(tmp_path)