"""
Regression tests for analytics.py.

Bugs covered:
- Concurrent DuckDB queries via asyncio.gather returned empty/wrong results
  because _conn.execute() is not thread-safe. Fixed by using _conn.cursor()
  per asyncio.to_thread call (each cursor is independently usable from any
  single thread).
- DuckDB normalizes unquoted column identifiers to lowercase; analytics
  queries and callers must use lowercase names.
"""
import asyncio

import duckdb
import pytest


# ── Fixtures ────────────────────────────────────────────────────────────────


@pytest.fixture
def analytics_duckdb(tmp_path):
    """Temporary DuckDB with serving.commodity_metrics: 11 global rows + 5 country rows."""
    db_path = str(tmp_path / "test.duckdb")
    conn = duckdb.connect(db_path)
    conn.execute("CREATE SCHEMA serving")
    conn.execute(
        """
        CREATE TABLE serving.commodity_metrics (
            commodity_code     INTEGER,
            commodity_name     TEXT,
            country_code       TEXT,
            country_name       TEXT,
            market_year        INTEGER,
            ingest_date        DATE,
            production         DOUBLE,
            imports            DOUBLE,
            exports            DOUBLE,
            total_distribution DOUBLE,
            ending_stocks      DOUBLE,
            net_supply         DOUBLE,
            trade_balance      DOUBLE,
            supply_demand_balance DOUBLE,
            stock_to_use_ratio_pct DOUBLE,
            production_yoy_pct DOUBLE
        )
        """
    )
    # 11 global rows (2015–2025)
    for year in range(2015, 2026):
        conn.execute(
            """INSERT INTO serving.commodity_metrics VALUES
               (711100, 'Coffee', NULL, 'Global', ?, '2025-01-01',
                100.0, 10.0, 20.0, 90.0, 30.0, 90.0, 10.0, 10.0, 33.3, 1.0)""",
            [year],
        )
    # 5 country rows for latest year
    for code, name in [
        ("BR", "Brazil"), ("VN", "Vietnam"), ("CO", "Colombia"),
        ("ID", "Indonesia"), ("ET", "Ethiopia"),
    ]:
        conn.execute(
            """INSERT INTO serving.commodity_metrics VALUES
               (711100, 'Coffee', ?, ?, 2025, '2025-01-01',
                50.0, 5.0, 10.0, 45.0, 15.0, 45.0, 5.0, 5.0, 33.3, 2.0)""",
            [code, name],
        )
    conn.commit()
    conn.close()
    yield duckdb.connect(db_path, read_only=True)


@pytest.fixture(autouse=False)
def patched_analytics(analytics_duckdb, monkeypatch):
    """Patch analytics._conn with the temp DuckDB connection."""
    from beanflows import analytics
    monkeypatch.setattr(analytics, "_conn", analytics_duckdb)
    yield analytics_duckdb


# ── Concurrency regression ───────────────────────────────────────────────────


@pytest.mark.asyncio
async def test_concurrent_queries_all_return_data(patched_analytics):
    """
    Regression: asyncio.gather fires analytics queries concurrently via
    asyncio.to_thread. Using _conn.execute() from multiple threads simultaneously
    corrupted internal cursor state — callers silently received 0 rows.

    Fix: _query() obtains its own _conn.cursor() so each thread has an
    independent execution context.
    """
    from beanflows import analytics

    ts, top, stu, bal, yoy = await asyncio.gather(
        analytics.get_global_time_series(
            711100, ["production", "exports", "imports", "ending_stocks", "total_distribution"]
        ),
        analytics.get_top_countries(711100, "production", limit=10),
        analytics.get_stock_to_use_trend(711100),
        analytics.get_supply_demand_balance(711100),
        analytics.get_production_yoy_by_country(711100, limit=15),
    )

    assert len(ts) == 11,  f"time_series: expected 11, got {len(ts)}"
    assert len(top) == 5,  f"top_producers: expected 5, got {len(top)}"
    assert len(stu) == 11, f"stu_trend: expected 11, got {len(stu)}"
    assert len(bal) == 11, f"balance: expected 11, got {len(bal)}"
    assert len(yoy) == 5,  f"yoy: expected 5, got {len(yoy)}"


@pytest.mark.asyncio
async def test_repeated_concurrent_runs_are_stable(patched_analytics):
    """Concurrent queries should return consistent row counts across multiple runs."""
    from beanflows import analytics

    for _ in range(3):
        ts, top = await asyncio.gather(
            analytics.get_global_time_series(711100, ["production"]),
            analytics.get_top_countries(711100, "production", limit=10),
        )
        assert len(ts) == 11
        assert len(top) == 5


# ── Column name regression ───────────────────────────────────────────────────


@pytest.mark.asyncio
async def test_result_column_names_are_lowercase(patched_analytics):
    """
    Regression: DuckDB normalizes unquoted identifiers to lowercase in physical
    tables. Templates and analytics callers must use lowercase column names.
    """
    from beanflows import analytics

    ts = await analytics.get_global_time_series(
        711100, ["production", "exports", "total_distribution"]
    )
    assert ts, "Expected rows"
    row = ts[0]
    for col in ("market_year", "production", "exports", "total_distribution"):
        assert col in row, f"Column '{col}' missing — DuckDB should return lowercase"
    # Ensure no legacy mixed-case keys leaked through
    for bad in ("Production", "Exports", "Total_Distribution", "Market_Year"):
        assert bad not in row, f"Mixed-case key '{bad}' found — column casing regression"


@pytest.mark.asyncio
async def test_stu_trend_column_name_lowercase(patched_analytics):
    """stock_to_use_ratio_pct must be lowercase (was Stock_to_Use_Ratio_pct in SQL)."""
    from beanflows import analytics

    rows = await analytics.get_stock_to_use_trend(711100)
    assert rows
    assert "stock_to_use_ratio_pct" in rows[0]
    assert "Stock_to_Use_Ratio_pct" not in rows[0]


# ── Global filter regression ─────────────────────────────────────────────────


@pytest.mark.asyncio
async def test_global_time_series_excludes_country_rows(patched_analytics):
    """get_global_time_series must filter country_name = 'Global' only."""
    from beanflows import analytics

    rows = await analytics.get_global_time_series(711100, ["production"])
    assert all(r["market_year"] in range(2015, 2026) for r in rows)
    assert len(rows) == 11  # 11 global rows, 0 country rows