Initial clean commit
This commit is contained in:
jlimolina
commit
6784d81c2c
141 changed files with 25219 additions and 0 deletions
599
workers/translation_worker.py
Normal file
599
workers/translation_worker.py
Normal file
|
|
@ -0,0 +1,599 @@
|
|||
import os
|
||||
import time
|
||||
import logging
|
||||
import re
|
||||
from typing import List, Optional
|
||||
|
||||
import psycopg2
|
||||
import psycopg2.extras
|
||||
from psycopg2.extras import execute_values
|
||||
|
||||
import ctranslate2
|
||||
from transformers import AutoTokenizer
|
||||
from langdetect import detect, DetectorFactory
|
||||
|
||||
DetectorFactory.seed = 0
|
||||
|
||||
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
|
||||
LOG = logging.getLogger("translator")
|
||||
|
||||
# =========================
|
||||
# DB CONFIG
|
||||
# =========================
|
||||
DB_CONFIG = {
|
||||
"host": os.environ.get("DB_HOST", "localhost"),
|
||||
"port": int(os.environ.get("DB_PORT", 5432)),
|
||||
"dbname": os.environ.get("DB_NAME", "rss"),
|
||||
"user": os.environ.get("DB_USER", "rss"),
|
||||
"password": os.environ.get("DB_PASS", "x"),
|
||||
}
|
||||
|
||||
# =========================
|
||||
# ENV HELPERS
|
||||
# =========================
|
||||
def _env_list(name: str, default="es"):
|
||||
raw = os.environ.get(name)
|
||||
if raw:
|
||||
return [s.strip() for s in raw.split(",") if s.strip()]
|
||||
return [default]
|
||||
|
||||
def _env_int(name: str, default: int = 8):
|
||||
v = os.environ.get(name)
|
||||
try:
|
||||
return int(v)
|
||||
except Exception:
|
||||
return default
|
||||
|
||||
def _env_float(name: str, default: float = 5.0):
|
||||
v = os.environ.get(name)
|
||||
try:
|
||||
return float(v)
|
||||
except Exception:
|
||||
return default
|
||||
|
||||
def _env_str(name: str, default=None):
|
||||
v = os.environ.get(name)
|
||||
return v if v else default
|
||||
|
||||
# =========================
|
||||
# CONFIG
|
||||
# =========================
|
||||
TARGET_LANGS = _env_list("TARGET_LANGS") # por defecto ["es"]
|
||||
BATCH_SIZE = _env_int("TRANSLATOR_BATCH", 8)
|
||||
ENQUEUE_MAX = _env_int("ENQUEUE", 200)
|
||||
SLEEP_IDLE = _env_float("TRANSLATOR_SLEEP_IDLE", 5.0)
|
||||
|
||||
# CTranslate2 Configuration
|
||||
CT2_MODEL_PATH = _env_str("CT2_MODEL_PATH", "./models/nllb-ct2")
|
||||
CT2_DEVICE = _env_str("CT2_DEVICE", "auto") # auto, cpu, cuda
|
||||
CT2_COMPUTE_TYPE = _env_str("CT2_COMPUTE_TYPE", "auto") # auto, int8, float16, int8_float16
|
||||
|
||||
MAX_SRC_TOKENS = _env_int("MAX_SRC_TOKENS", 512)
|
||||
MAX_NEW_TOKENS_TITLE = _env_int("MAX_NEW_TOKENS_TITLE", 96)
|
||||
MAX_NEW_TOKENS_BODY = _env_int("MAX_NEW_TOKENS_BODY", 512)
|
||||
|
||||
NUM_BEAMS_TITLE = _env_int("NUM_BEAMS_TITLE", 2)
|
||||
NUM_BEAMS_BODY = _env_int("NUM_BEAMS_BODY", 1)
|
||||
|
||||
# HuggingFace model name (used for tokenizer)
|
||||
UNIVERSAL_MODEL = _env_str("UNIVERSAL_MODEL", "facebook/nllb-200-distilled-600M")
|
||||
IDENTITY_PAISES_ES = _env_int("IDENTITY_PAISES_ES", 58)
|
||||
|
||||
BODY_CHARS_CHUNK = _env_int("BODY_CHARS_CHUNK", 900)
|
||||
|
||||
# =========================
|
||||
# LANG MAP
|
||||
# =========================
|
||||
NLLB_LANG = {
|
||||
"es": "spa_Latn", "en": "eng_Latn", "fr": "fra_Latn", "de": "deu_Latn",
|
||||
"it": "ita_Latn", "pt": "por_Latn", "nl": "nld_Latn", "sv": "swe_Latn",
|
||||
"da": "dan_Latn", "fi": "fin_Latn", "no": "nob_Latn",
|
||||
"pl": "pol_Latn", "cs": "ces_Latn", "sk": "slk_Latn",
|
||||
"sl": "slv_Latn", "hu": "hun_Latn", "ro": "ron_Latn",
|
||||
"el": "ell_Grek", "ru": "rus_Cyrl", "uk": "ukr_Cyrl",
|
||||
"tr": "tur_Latn", "ar": "arb_Arab", "fa": "pes_Arab",
|
||||
"he": "heb_Hebr", "zh": "zho_Hans", "ja": "jpn_Jpan",
|
||||
"ko": "kor_Hang", "vi": "vie_Latn",
|
||||
}
|
||||
|
||||
def map_to_nllb(code: Optional[str]):
|
||||
if not code:
|
||||
return None
|
||||
c = code.strip().lower()
|
||||
return NLLB_LANG.get(c, f"{c}_Latn")
|
||||
|
||||
def normalize_lang(code: Optional[str], default=None):
|
||||
return (code or default).strip().lower() if code else default
|
||||
|
||||
def _norm(s: str) -> str:
|
||||
return re.sub(r"\W+", "", (s or "").lower()).strip()
|
||||
|
||||
# =========================
|
||||
# DB
|
||||
# =========================
|
||||
def get_conn():
|
||||
return psycopg2.connect(**DB_CONFIG)
|
||||
|
||||
def ensure_indexes(conn):
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("CREATE INDEX IF NOT EXISTS traducciones_lang_to_status_idx ON traducciones (lang_to, status);")
|
||||
cur.execute("CREATE INDEX IF NOT EXISTS traducciones_status_idx ON traducciones (status);")
|
||||
conn.commit()
|
||||
|
||||
pass # Moved to translation_ops.py
|
||||
|
||||
pass # Moved to translation_ops.py
|
||||
|
||||
def fetch_pending_batch(conn, lang_to: str, batch: int):
|
||||
"""Fetch pending translations with row locking to support multiple workers."""
|
||||
if batch <= 0:
|
||||
return []
|
||||
|
||||
# Use FOR UPDATE SKIP LOCKED to allow multiple workers
|
||||
# Each worker will get different rows without conflicts
|
||||
with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT t.id AS tr_id, t.noticia_id, t.lang_from, t.lang_to,
|
||||
n.titulo, n.resumen
|
||||
FROM traducciones t
|
||||
JOIN noticias n ON n.id=t.noticia_id
|
||||
WHERE t.lang_to=%s AND t.status='pending'
|
||||
ORDER BY t.id
|
||||
LIMIT %s
|
||||
FOR UPDATE OF t SKIP LOCKED;
|
||||
""",
|
||||
(lang_to, batch),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
|
||||
# Update status within the same transaction while rows are locked
|
||||
if rows:
|
||||
ids = [r["tr_id"] for r in rows]
|
||||
cur.execute("UPDATE traducciones SET status='processing' WHERE id = ANY(%s)", (ids,))
|
||||
|
||||
conn.commit()
|
||||
return rows
|
||||
|
||||
# =========================
|
||||
# LANGUAGE DETECTION
|
||||
# =========================
|
||||
def detect_lang(text1: str, text2: str):
|
||||
txt = (text1 or "").strip() or (text2 or "").strip()
|
||||
if not txt:
|
||||
return None
|
||||
try:
|
||||
return detect(txt)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
# =========================
|
||||
# MODEL LOADING (CTranslate2)
|
||||
# =========================
|
||||
_TOKENIZER = None
|
||||
_TRANSLATOR = None
|
||||
_DEVICE = None
|
||||
|
||||
def _resolve_device():
|
||||
if CT2_DEVICE == "cpu":
|
||||
return "cpu"
|
||||
if CT2_DEVICE == "cuda":
|
||||
return "cuda"
|
||||
# auto
|
||||
return "cuda" if ctranslate2.get_cuda_device_count() > 0 else "cpu"
|
||||
|
||||
def _ensure_ct2_model():
|
||||
"""Convert HuggingFace model to CTranslate2 format if not exists."""
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
model_dir = CT2_MODEL_PATH
|
||||
|
||||
# Check if model already exists
|
||||
if os.path.isdir(model_dir) and os.path.exists(os.path.join(model_dir, "model.bin")):
|
||||
LOG.info("CTranslate2 model already exists at %s", model_dir)
|
||||
return True
|
||||
|
||||
LOG.info("CTranslate2 model not found, converting from %s...", UNIVERSAL_MODEL)
|
||||
LOG.info("This may take 5-10 minutes on first run...")
|
||||
|
||||
# Create directory if needed
|
||||
os.makedirs(os.path.dirname(model_dir) or ".", exist_ok=True)
|
||||
|
||||
# Convert the model
|
||||
quantization = CT2_COMPUTE_TYPE if CT2_COMPUTE_TYPE != "auto" else "int8_float16"
|
||||
|
||||
cmd = [
|
||||
"ct2-transformers-converter",
|
||||
"--model", UNIVERSAL_MODEL,
|
||||
"--output_dir", model_dir,
|
||||
"--quantization", quantization,
|
||||
"--force"
|
||||
]
|
||||
|
||||
try:
|
||||
LOG.info("Running: %s", " ".join(cmd))
|
||||
result = subprocess.run(cmd, capture_output=True, text=True, timeout=1800)
|
||||
|
||||
if result.returncode != 0:
|
||||
LOG.error("Model conversion failed: %s", result.stderr)
|
||||
return False
|
||||
|
||||
LOG.info("Model conversion completed successfully")
|
||||
return True
|
||||
|
||||
except subprocess.TimeoutExpired:
|
||||
LOG.error("Model conversion timed out after 30 minutes")
|
||||
return False
|
||||
except Exception as e:
|
||||
LOG.error("Model conversion error: %s", e)
|
||||
return False
|
||||
|
||||
def get_universal_components():
|
||||
global _TOKENIZER, _TRANSLATOR, _DEVICE
|
||||
if _TRANSLATOR:
|
||||
return _TOKENIZER, _TRANSLATOR
|
||||
|
||||
# Ensure CT2 model exists (convert if needed)
|
||||
if not _ensure_ct2_model():
|
||||
raise RuntimeError(f"Failed to load/convert CTranslate2 model at {CT2_MODEL_PATH}")
|
||||
|
||||
device = _resolve_device()
|
||||
|
||||
LOG.info("Loading CTranslate2 model from %s on %s", CT2_MODEL_PATH, device)
|
||||
|
||||
_TRANSLATOR = ctranslate2.Translator(
|
||||
CT2_MODEL_PATH,
|
||||
device=device,
|
||||
compute_type=CT2_COMPUTE_TYPE,
|
||||
)
|
||||
_TOKENIZER = AutoTokenizer.from_pretrained(UNIVERSAL_MODEL)
|
||||
_DEVICE = device
|
||||
|
||||
LOG.info("CTranslate2 model loaded successfully")
|
||||
return _TOKENIZER, _TRANSLATOR
|
||||
|
||||
# =========================
|
||||
# TRANSLATION (CTranslate2)
|
||||
# =========================
|
||||
def _safe_src_len(tokenizer):
|
||||
max_len = getattr(tokenizer, "model_max_length", 1024) or 1024
|
||||
if max_len > 100000:
|
||||
max_len = 1024
|
||||
return min(MAX_SRC_TOKENS, max_len - 16)
|
||||
|
||||
def _translate_texts(src, tgt, texts, beams, max_new_tokens):
|
||||
"""Translate texts using CTranslate2."""
|
||||
if not texts:
|
||||
return []
|
||||
|
||||
clean = [(t or "").strip() for t in texts]
|
||||
if all(not t for t in clean):
|
||||
return ["" for _ in clean]
|
||||
|
||||
tok, translator = get_universal_components()
|
||||
src_code = map_to_nllb(src)
|
||||
tgt_code = map_to_nllb(tgt)
|
||||
|
||||
# Set source language on tokenizer
|
||||
try:
|
||||
tok.src_lang = src_code
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
safe_len = _safe_src_len(tok)
|
||||
max_new = max(16, min(int(max_new_tokens), MAX_NEW_TOKENS_BODY))
|
||||
|
||||
# Tokenize: convert text to tokens
|
||||
sources = []
|
||||
for t in clean:
|
||||
if t:
|
||||
ids = tok.encode(t, truncation=True, max_length=safe_len)
|
||||
tokens = tok.convert_ids_to_tokens(ids)
|
||||
sources.append(tokens)
|
||||
else:
|
||||
sources.append([])
|
||||
|
||||
# Target language prefix for NLLB
|
||||
target_prefix = [[tgt_code]] * len(sources)
|
||||
|
||||
# Translate with CTranslate2
|
||||
start = time.time()
|
||||
results = translator.translate_batch(
|
||||
sources,
|
||||
target_prefix=target_prefix,
|
||||
beam_size=beams,
|
||||
max_decoding_length=max_new,
|
||||
)
|
||||
dt = time.time() - start
|
||||
|
||||
# Decode results
|
||||
translated = []
|
||||
total_tokens = 0
|
||||
for result, src_tokens in zip(results, sources):
|
||||
if result.hypotheses:
|
||||
# Skip the first token (language prefix)
|
||||
tokens = result.hypotheses[0][1:]
|
||||
total_tokens += len(tokens) + len(src_tokens)
|
||||
text = tok.decode(tok.convert_tokens_to_ids(tokens))
|
||||
translated.append(text.strip())
|
||||
else:
|
||||
translated.append("")
|
||||
|
||||
if total_tokens > 0:
|
||||
LOG.info(" → tokens=%d tiempo=%.2fs velocidad=%d tok/s",
|
||||
total_tokens, dt, int(total_tokens / dt) if dt > 0 else 0)
|
||||
|
||||
return translated
|
||||
|
||||
def _split_body_into_chunks(text: str) -> List[str]:
|
||||
text = (text or "").strip()
|
||||
if len(text) <= BODY_CHARS_CHUNK:
|
||||
return [text] if text else []
|
||||
|
||||
parts = re.split(r'(\n\n+|(?<=[\.\!\?؛؟。])\s+)', text)
|
||||
chunks = []
|
||||
current = ""
|
||||
|
||||
for part in parts:
|
||||
if not part:
|
||||
continue
|
||||
if len(current) + len(part) <= BODY_CHARS_CHUNK:
|
||||
current += part
|
||||
else:
|
||||
if current.strip():
|
||||
chunks.append(current.strip())
|
||||
current = part
|
||||
if current.strip():
|
||||
chunks.append(current.strip())
|
||||
|
||||
if not chunks:
|
||||
return [text]
|
||||
return chunks
|
||||
|
||||
def translate_body_long(src: str, tgt: str, body: str) -> str:
|
||||
body = (body or "").strip()
|
||||
if not body:
|
||||
return ""
|
||||
|
||||
chunks = _split_body_into_chunks(body)
|
||||
if len(chunks) == 1:
|
||||
translated = _translate_texts(src, tgt, [body], NUM_BEAMS_BODY, MAX_NEW_TOKENS_BODY)[0]
|
||||
return translated.strip()
|
||||
|
||||
translated_chunks = []
|
||||
for ch in chunks:
|
||||
tr = _translate_texts(src, tgt, [ch], NUM_BEAMS_BODY, MAX_NEW_TOKENS_BODY)[0]
|
||||
translated_chunks.append(tr.strip())
|
||||
return "\n\n".join(c for c in translated_chunks if c)
|
||||
|
||||
# =========================
|
||||
# BATCH PROCESS
|
||||
# =========================
|
||||
def process_batch(conn, rows):
|
||||
todo = []
|
||||
done = []
|
||||
errors = []
|
||||
|
||||
for r in rows:
|
||||
lang_to = normalize_lang(r["lang_to"], "es") or "es"
|
||||
lang_from = (
|
||||
normalize_lang(r["lang_from"])
|
||||
or detect_lang(r["titulo"], r["resumen"])
|
||||
or "en"
|
||||
)
|
||||
|
||||
titulo = (r["titulo"] or "").strip()
|
||||
resumen = (r["resumen"] or "").strip()
|
||||
|
||||
if map_to_nllb(lang_from) == map_to_nllb(lang_to):
|
||||
done.append((titulo, resumen, lang_from, r["tr_id"]))
|
||||
else:
|
||||
todo.append({
|
||||
"tr_id": r["tr_id"],
|
||||
"lang_from": lang_from,
|
||||
"lang_to": lang_to,
|
||||
"titulo": titulo,
|
||||
"resumen": resumen,
|
||||
})
|
||||
|
||||
from collections import defaultdict
|
||||
groups = defaultdict(list)
|
||||
for item in todo:
|
||||
key = (item["lang_from"], item["lang_to"])
|
||||
groups[key].append(item)
|
||||
|
||||
for (lang_from, lang_to), items in groups.items():
|
||||
LOG.info("Translating %s -> %s (%d items)", lang_from, lang_to, len(items))
|
||||
|
||||
titles = [i["titulo"] for i in items]
|
||||
|
||||
try:
|
||||
tt = _translate_texts(
|
||||
lang_from,
|
||||
lang_to,
|
||||
titles,
|
||||
NUM_BEAMS_TITLE,
|
||||
MAX_NEW_TOKENS_TITLE,
|
||||
)
|
||||
|
||||
bodies_translated: List[str] = []
|
||||
for i in items:
|
||||
bodies_translated.append(
|
||||
translate_body_long(lang_from, lang_to, i["resumen"])
|
||||
)
|
||||
|
||||
for i, ttr, btr in zip(items, tt, bodies_translated):
|
||||
ttr = (ttr or "").strip()
|
||||
btr = (btr or "").strip()
|
||||
|
||||
if not ttr or _norm(ttr) == _norm(i["titulo"]):
|
||||
ttr = i["titulo"]
|
||||
if not btr or _norm(btr) == _norm(i["resumen"]):
|
||||
btr = i["resumen"]
|
||||
|
||||
# CLEANING: Remove <unk> tokens
|
||||
if ttr:
|
||||
ttr = ttr.replace("<unk>", "").replace(" ", " ").strip()
|
||||
if btr:
|
||||
btr = btr.replace("<unk>", "").replace(" ", " ").strip()
|
||||
|
||||
done.append((ttr, btr, lang_from, i["tr_id"]))
|
||||
|
||||
except Exception as e:
|
||||
err = str(e)[:800]
|
||||
LOG.exception("Error translating %s -> %s: %s", lang_from, lang_to, err)
|
||||
for i in items:
|
||||
errors.append((err, i["tr_id"]))
|
||||
|
||||
with conn.cursor() as cur:
|
||||
if done:
|
||||
execute_values(
|
||||
cur,
|
||||
"""
|
||||
UPDATE traducciones AS t
|
||||
SET titulo_trad=v.titulo_trad,
|
||||
resumen_trad=v.resumen_trad,
|
||||
lang_from=COALESCE(t.lang_from, v.lang_from),
|
||||
status='done',
|
||||
error=NULL
|
||||
FROM (VALUES %s) AS v(titulo_trad,resumen_trad,lang_from,id)
|
||||
WHERE t.id=v.id;
|
||||
""",
|
||||
done,
|
||||
)
|
||||
|
||||
# --- NEW: Persist stats ---
|
||||
# Insert a record for each translated item into translation_stats
|
||||
# We need the language 'lang_to'. In this batch, lang_to is uniform for the group usually,
|
||||
# but let's extract it from the 'done' items structure if we had it, or pass it down.
|
||||
# In process_batch, we iterate groups.
|
||||
# 'done' list here is flattened from multiple groups?
|
||||
# process_batch logic:
|
||||
# 1. 'done' checks map_to_nllb identity (already done?) -> these have lang_to from row?
|
||||
# 2. 'groups' loop -> translates -> appends to 'done' with lang_from.
|
||||
#
|
||||
# Wait, 'done' list doesn't have lang_to in the tuple: (titulo, resumen, lang_from, tr_id).
|
||||
# We need to change the 'done' collection to include lang_to OR we insert based on tr_id.
|
||||
|
||||
# Let's verify process_batch logic.
|
||||
# rows has all info.
|
||||
# define a mapping tr_id -> lang_to
|
||||
tr_map = {r["tr_id"]: r["lang_to"] for r in rows}
|
||||
|
||||
stats_data = []
|
||||
for item in done:
|
||||
# item is (titulo, resumen, lang_from, tr_id)
|
||||
lang_from = item[2]
|
||||
lang_to = tr_map.get(item[3], "es")
|
||||
stats_data.append((lang_from, lang_to))
|
||||
|
||||
execute_values(
|
||||
cur,
|
||||
"INSERT INTO translation_stats (lang_from, lang_to) VALUES %s",
|
||||
stats_data
|
||||
)
|
||||
# --------------------------
|
||||
|
||||
if errors:
|
||||
execute_values(
|
||||
cur,
|
||||
"""
|
||||
UPDATE traducciones AS t
|
||||
SET status='error', error=v.error
|
||||
FROM (VALUES %s) AS v(error,id)
|
||||
WHERE t.id=v.id;
|
||||
""",
|
||||
errors,
|
||||
)
|
||||
|
||||
conn.commit()
|
||||
|
||||
def process_entity_summaries(conn):
|
||||
"""Translate pending entity summaries from Wikipedia."""
|
||||
from cache import cache_del
|
||||
|
||||
LOG.info("DEBUG: Checking for pending entity summaries...")
|
||||
|
||||
with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
|
||||
cur.execute("""
|
||||
SELECT id, entity_name, summary, summary_en
|
||||
FROM entity_images
|
||||
WHERE status_es = 'pending'
|
||||
LIMIT 20
|
||||
FOR UPDATE SKIP LOCKED;
|
||||
""")
|
||||
rows = cur.fetchall()
|
||||
|
||||
if not rows:
|
||||
return False
|
||||
|
||||
LOG.info("DEBUG: Found %d pending entity summaries to process", len(rows))
|
||||
|
||||
for r in rows:
|
||||
entity_id = r["id"]
|
||||
name = r["entity_name"]
|
||||
text = r["summary_en"] or r["summary"]
|
||||
|
||||
if not text:
|
||||
cur.execute("UPDATE entity_images SET status_es = 'none' WHERE id = %s", (entity_id,))
|
||||
continue
|
||||
|
||||
try:
|
||||
# English -> Spanish
|
||||
translated = translate_body_long('en', 'es', text)
|
||||
if translated:
|
||||
cur.execute("""
|
||||
UPDATE entity_images
|
||||
SET summary_es = %s, status_es = 'done'
|
||||
WHERE id = %s
|
||||
""", (translated, entity_id))
|
||||
# Invalidate cache
|
||||
cache_del(f"wiki:data:{name.lower()}")
|
||||
LOG.info(" → Translated entity summary: %s", name)
|
||||
else:
|
||||
cur.execute("UPDATE entity_images SET status_es = 'error' WHERE id = %s", (entity_id,))
|
||||
except Exception as e:
|
||||
LOG.error("Error translating entity summary [%s]: %s", name, e)
|
||||
cur.execute("UPDATE entity_images SET status_es = 'error' WHERE id = %s", (entity_id,))
|
||||
|
||||
conn.commit()
|
||||
return True
|
||||
|
||||
# =========================
|
||||
# MAIN LOOP
|
||||
# =========================
|
||||
def main():
|
||||
LOG.info("Translator worker iniciado (CTranslate2)")
|
||||
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
|
||||
get_universal_components()
|
||||
|
||||
while True:
|
||||
any_work = False
|
||||
with get_conn() as conn:
|
||||
ensure_indexes(conn)
|
||||
|
||||
# 1. Process entity summaries (Wikipedia) -> REMOVED per user request
|
||||
# Logic moved out to keep translator focused on news ONLY.
|
||||
# try:
|
||||
# if process_entity_summaries(conn):
|
||||
# any_work = True
|
||||
# except Exception as e:
|
||||
# LOG.error("Error in process_entity_summaries: %s", e)
|
||||
|
||||
# 2. Process news translations
|
||||
for tgt in TARGET_LANGS:
|
||||
while True:
|
||||
rows = fetch_pending_batch(conn, tgt, BATCH_SIZE)
|
||||
if not rows:
|
||||
break
|
||||
any_work = True
|
||||
LOG.info("[%s] %d elementos", tgt, len(rows))
|
||||
process_batch(conn, rows)
|
||||
|
||||
if not any_work:
|
||||
time.sleep(SLEEP_IDLE)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue