"""Quick DB diagnostics: Avito listings count + recent insert rate."""
from __future__ import annotations

import os
import psycopg

dsn = os.environ["DATABASE_URL"].replace("postgresql+psycopg://", "postgresql://")
conn = psycopg.connect(dsn)
cur = conn.cursor()

cur.execute("SELECT count(*) FROM listings WHERE source = 'avito'")
print("avito total:", cur.fetchone()[0])

cur.execute(
    "SELECT count(*) FROM listings "
    "WHERE source='avito' AND scraped_at > NOW() - interval '20 minutes'"
)
print("avito scraped last 20min:", cur.fetchone()[0])

cur.execute(
    """
    SELECT column_name FROM information_schema.columns
    WHERE table_name='listings' AND column_name IN
      ('created_at','first_seen_at','first_seen','inserted_at')
    """
)
cols = [r[0] for r in cur.fetchall()]
print(f"timestamp cols available: {cols}")
if "first_seen_at" in cols:
    cur.execute(
        "SELECT count(*) FROM listings "
        "WHERE source='avito' AND first_seen_at > NOW() - interval '20 minutes'"
    )
    print("avito first_seen last 20min (new):", cur.fetchone()[0])

cur.execute(
    """
    SELECT date_trunc('minute', scraped_at) AS m, count(*)
    FROM listings
    WHERE source='avito' AND scraped_at > NOW() - interval '30 minutes'
    GROUP BY 1 ORDER BY 1 DESC
    """
)
print("\nper-minute scraped (last 30min):")
for row in cur.fetchall():
    print(f"  {row[0]}  {row[1]}")

cur.execute(
    """
    SELECT rooms, count(*)
    FROM listings
    WHERE source='avito' AND scraped_at > NOW() - interval '30 minutes'
    GROUP BY 1 ORDER BY 1
    """
)
print("\nby rooms (last 30min):")
for row in cur.fetchall():
    print(f"  rooms={row[0]}  count={row[1]}")