# -*- coding: utf-8 -*-
"""
DWD 第一阶段重构 — 属性测试

Feature: dwd-phase1-refactor
测试位置：monorepo 根目录 tests/

使用 hypothesis + FakeCursor 验证重构后 DwdLoadTask 的核心正确性属性。
"""
from __future__ import annotations

import re
import sys
import logging
from datetime import datetime, timedelta, timezone
from pathlib import Path
from types import SimpleNamespace
from typing import Any, Dict, List, Sequence, Tuple
from unittest.mock import MagicMock

from hypothesis import given, settings, assume
import hypothesis.strategies as st

# ── 将 ETL 模块加入 sys.path ──
_ETL_ROOT = Path(__file__).resolve().parent.parent / "apps" / "etl" / "connectors" / "feiqiu"
if str(_ETL_ROOT) not in sys.path:
    sys.path.insert(0, str(_ETL_ROOT))

from tasks.dwd.dwd_load_task import DwdLoadTask


# ── 测试用 FakeCursor（扩展自 task_test_utils，增加 fetchmany 支持）──

class PropertyTestCursor:
    """捕获 SQL 语句的极简游标，用于属性测试验证 SQL 结构。"""

    def __init__(self):
        self.executed: List[Dict[str, Any]] = []
        self._fetchall_rows: List[Tuple] = []
        self.rowcount = 0
        self.connection = SimpleNamespace(encoding="UTF8")

    def execute(self, sql: str, params=None):
        sql_text = sql.decode("utf-8", errors="ignore") if isinstance(sql, (bytes, bytearray)) else str(sql)
        self.executed.append({"sql": sql_text.strip(), "params": params})
        self._fetchall_rows = []

        lowered = sql_text.lower()
        # information_schema 查询返回假列信息
        if "from information_schema.columns" in lowered:
            table_name = params[1] if params and len(params) >= 2 else None
            self._fetchall_rows = self._fake_columns(table_name)
            return
        if "from information_schema.table_constraints" in lowered:
            self._fetchall_rows = []
            return
        self.rowcount = 0

    def fetchone(self):
        return None

    def fetchall(self):
        return list(self._fetchall_rows)

    def fetchmany(self, size=None):
        """_merge_fact_increment 使用 fetchmany 读取 RETURNING 结果。"""
        # 返回空列表表示无更多行，终止循环
        return []

    def mogrify(self, template, args):
        return b"(?)"

    @staticmethod
    def _fake_columns(table_name: str | None) -> List[Tuple[str, str, str]]:
        """返回假列信息，确保包含 fetched_at 和常见列。"""
        return [
            ("id", "bigint", "int8"),
            ("site_id", "bigint", "int8"),
            ("record_index", "integer", "int4"),
            ("content_hash", "text", "text"),
            ("fetched_at", "timestamp with time zone", "timestamptz"),
            ("payload", "jsonb", "jsonb"),
        ]

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc, tb):
        return False


# ── 辅助：构造最小可用的 DwdLoadTask 实例 ──

def _make_task(fact_upsert: bool = True) -> DwdLoadTask:
    """构造一个用于测试的 DwdLoadTask，使用 mock config/db/api/logger。"""
    config = MagicMock()
    config.get = lambda key, default=None: {
        "app.store_id": 1,
        "app.timezone": "Asia/Shanghai",
        "dwd.fact_upsert": fact_upsert,
    }.get(key, default)

    db = MagicMock()
    api = MagicMock()
    logger = logging.getLogger("test_dwd_property")
    logger.setLevel(logging.WARNING)  # 抑制测试噪音

    return DwdLoadTask(config, db, api, logger)


# ── Hypothesis 策略 ──

@st.composite
def window_pair(draw):
    """生成有效的 (window_start, window_end) 时间对，确保 start < end。"""
    base = draw(st.datetimes(
        min_value=datetime(2020, 1, 1),
        max_value=datetime(2030, 12, 31),
        timezones=st.just(timezone.utc),
    ))
    delta_minutes = draw(st.integers(min_value=1, max_value=10080))  # 1 分钟 ~ 7 天
    end = base + timedelta(minutes=delta_minutes)
    return base, end


# 从 TABLE_MAP 中提取所有事实表（dwd_ 前缀）
_FACT_TABLES = [
    (dwd, ods)
    for dwd, ods in DwdLoadTask.TABLE_MAP.items()
    if dwd.split(".")[-1].startswith("dwd_")
]


# ══════════════════════════════════════════════════════════════
# Property 1: 事实表增量 SQL 始终使用窗口范围条件
# Feature: dwd-phase1-refactor, Property 1: 事实表增量 SQL 始终使用窗口范围条件
# ══════════════════════════════════════════════════════════════

# 匹配双边窗口条件：fetched_at >= %s AND fetched_at < %s
_WINDOW_PATTERN = re.compile(
    r'"fetched_at"\s*>=\s*%s\s+AND\s+"fetched_at"\s*<\s*%s',
    re.IGNORECASE,
)

# 匹配单边水位线条件：fetched_at > %s（不跟 AND fetched_at < %s）
_SINGLE_WATERMARK_PATTERN = re.compile(
    r'"fetched_at"\s*>\s*%s(?!\s+AND\s+"fetched_at"\s*<\s*%s)',
    re.IGNORECASE,
)


@given(
    table_pair=st.sampled_from(_FACT_TABLES),
    window=window_pair(),
)
@settings(max_examples=100)
def test_fact_increment_sql_uses_window_range(
    table_pair: Tuple[str, str],
    window: Tuple[datetime, datetime],
):
    """
    Property 1: 事实表增量 SQL 始终使用窗口范围条件

    对于任意事实表和任意 window_start/window_end 时间对，
    _merge_fact_increment() 生成的 SQL 的 WHERE 子句：
    - 必须包含 fetched_at >= %s AND fetched_at < %s
    - 不得包含单边水位线条件 fetched_at > %s

    **Validates: Requirements 1.1, 1.2**
    """
    dwd_table, ods_table = table_pair
    window_start, window_end = window

    task = _make_task()
    cur = PropertyTestCursor()

    # 获取 FakeCursor 返回的列名（模拟 information_schema 查询结果）
    fake_cols = [row[0] for row in PropertyTestCursor._fake_columns(None)]

    # 获取该表的 FACT_MAPPINGS 中的目标列
    mapping_entries = task.FACT_MAPPINGS.get(dwd_table) or []
    mapping_dest = [dst for dst, _, _ in mapping_entries]

    # 构建 dwd_cols 和 ods_cols（合并映射列和假列）
    dwd_cols = list(set(fake_cols + mapping_dest))
    ods_cols = list(fake_cols)

    # 构建类型字典
    fake_types = {row[0]: row[1] for row in PropertyTestCursor._fake_columns(None)}
    dwd_types = dict(fake_types)
    ods_types = dict(fake_types)
    # 为映射目标列补充类型
    for col in mapping_dest:
        if col not in dwd_types:
            dwd_types[col] = "text"

    result = task._merge_fact_increment(
        cur=cur,
        dwd_table=dwd_table,
        ods_table=ods_table,
        dwd_cols=dwd_cols,
        ods_cols=ods_cols,
        dwd_types=dwd_types,
        ods_types=ods_types,
        window_start=window_start,
        window_end=window_end,
    )

    # 找到主 INSERT SQL（排除 information_schema 查询）
    insert_sqls = [
        entry["sql"]
        for entry in cur.executed
        if entry["sql"].upper().startswith("INSERT INTO")
    ]

    assert len(insert_sqls) == 1, (
        f"期望恰好 1 条 INSERT SQL，实际 {len(insert_sqls)} 条，"
        f"表: {dwd_table}"
    )

    sql = insert_sqls[0]

    # 验证：包含双边窗口条件
    assert _WINDOW_PATTERN.search(sql), (
        f"INSERT SQL 缺少双边窗口条件 (fetched_at >= %s AND fetched_at < %s)，"
        f"表: {dwd_table}\nSQL: {sql[:500]}"
    )

    # 验证：不包含单边水位线条件
    assert not _SINGLE_WATERMARK_PATTERN.search(sql), (
        f"INSERT SQL 包含单边水位线条件 (fetched_at > %s)，"
        f"表: {dwd_table}\nSQL: {sql[:500]}"
    )

    # 验证：SQL 参数包含 window_start 和 window_end
    insert_entry = [
        entry for entry in cur.executed
        if entry["sql"].upper().startswith("INSERT INTO")
    ][0]
    params = insert_entry["params"]
    assert params is not None, f"INSERT SQL 参数为 None，表: {dwd_table}"
    assert window_start in params, (
        f"SQL 参数缺少 window_start={window_start}，"
        f"表: {dwd_table}, params={params}"
    )
    assert window_end in params, (
        f"SQL 参数缺少 window_end={window_end}，"
        f"表: {dwd_table}, params={params}"
    )


# ══════════════════════════════════════════════════════════════
# Property 2: 事实表增量不执行回补 SQL
# Feature: dwd-phase1-refactor, Property 2: 事实表增量不执行回补 SQL
# ══════════════════════════════════════════════════════════════

# 匹配回补 SQL 特征：LEFT JOIN ... WHERE ... IS NULL
_BACKFILL_PATTERN = re.compile(
    r"LEFT\s+JOIN\b.*?\bWHERE\b.*?\bIS\s+NULL\b",
    re.IGNORECASE | re.DOTALL,
)


@given(
    table_pair=st.sampled_from(_FACT_TABLES),
    window=window_pair(),
)
@settings(max_examples=100)
def test_fact_increment_no_backfill(
    table_pair: Tuple[str, str],
    window: Tuple[datetime, datetime],
):
    """
    Property 2: 事实表增量不执行回补 SQL

    对于任意事实表和任意 window_start/window_end 时间对，
    _merge_fact_increment() 执行的所有 SQL 语句中不得包含
    LEFT JOIN ... WHERE ... IS NULL 模式（回补特征）。

    **Validates: Requirements 2.3**
    """
    dwd_table, ods_table = table_pair
    window_start, window_end = window

    task = _make_task()
    cur = PropertyTestCursor()

    # 构建列信息（复用 Property 1 的逻辑）
    fake_cols = [row[0] for row in PropertyTestCursor._fake_columns(None)]
    mapping_entries = task.FACT_MAPPINGS.get(dwd_table) or []
    mapping_dest = [dst for dst, _, _ in mapping_entries]

    dwd_cols = list(set(fake_cols + mapping_dest))
    ods_cols = list(fake_cols)

    fake_types = {row[0]: row[1] for row in PropertyTestCursor._fake_columns(None)}
    dwd_types = dict(fake_types)
    ods_types = dict(fake_types)
    for col in mapping_dest:
        if col not in dwd_types:
            dwd_types[col] = "text"

    result = task._merge_fact_increment(
        cur=cur,
        dwd_table=dwd_table,
        ods_table=ods_table,
        dwd_cols=dwd_cols,
        ods_cols=ods_cols,
        dwd_types=dwd_types,
        ods_types=ods_types,
        window_start=window_start,
        window_end=window_end,
    )

    # 检查所有执行的 SQL（排除 information_schema 元数据查询）
    all_sqls = [
        entry["sql"]
        for entry in cur.executed
        if "information_schema" not in entry["sql"].lower()
    ]

    for sql in all_sqls:
        assert not _BACKFILL_PATTERN.search(sql), (
            f"检测到回补 SQL（LEFT JOIN ... IS NULL 模式），"
            f"表: {dwd_table}\nSQL: {sql[:500]}"
        )


# ══════════════════════════════════════════════════════════════
# Property 4: 维度表始终走 SCD2 路径
# Feature: dwd-phase1-refactor, Property 4: 维度表始终走 SCD2 路径
# ══════════════════════════════════════════════════════════════

# 从 TABLE_MAP 中提取所有维度表（dim_ 前缀）
_DIM_TABLES = [
    (dwd, ods)
    for dwd, ods in DwdLoadTask.TABLE_MAP.items()
    if dwd.split(".")[-1].startswith("dim_")
]


@given(
    table_pair=st.sampled_from(_DIM_TABLES),
    now=st.datetimes(
        min_value=datetime(2020, 1, 1),
        max_value=datetime(2030, 12, 31),
        timezones=st.just(timezone.utc),
    ),
)
@settings(max_examples=100)
def test_dim_always_scd2(
    table_pair: Tuple[str, str],
    now: datetime,
):
    """
    Property 4: 维度表始终走 SCD2 路径

    对于任意维度表（TABLE_MAP 中 dim_ 前缀），调用 _merge_dim()
    始终委托给 _merge_dim_scd2()，不经过任何条件分支判断。

    **Validates: Requirements 3.8, 5.1**
    """
    from unittest.mock import patch, sentinel

    dwd_table, ods_table = table_pair

    task = _make_task()
    cur = PropertyTestCursor()

    # 构建最小列集合
    fake_cols = [row[0] for row in PropertyTestCursor._fake_columns(None)]
    dwd_cols = list(fake_cols)
    ods_cols = list(fake_cols)

    # mock _merge_dim_scd2，返回 sentinel 以验证委托关系
    expected_return = {"processed": 0, "inserted": 0, "updated": 0, "skipped": 0}

    with patch.object(task, "_merge_dim_scd2", return_value=expected_return) as mock_scd2:
        result = task._merge_dim(cur, dwd_table, ods_table, dwd_cols, ods_cols, now)

        # 验证 _merge_dim_scd2 被调用了恰好一次
        assert mock_scd2.call_count == 1, (
            f"期望 _merge_dim_scd2 被调用 1 次，实际 {mock_scd2.call_count} 次，"
            f"表: {dwd_table}"
        )

        # 验证传入参数正确
        call_args = mock_scd2.call_args
        assert call_args[0] == (cur, dwd_table, ods_table, dwd_cols, ods_cols, now), (
            f"_merge_dim_scd2 调用参数不匹配，表: {dwd_table}\n"
            f"期望: (cur, {dwd_table}, {ods_table}, {dwd_cols}, {ods_cols}, {now})\n"
            f"实际: {call_args[0]}"
        )

        # 验证返回值直接透传
        assert result is expected_return, (
            f"_merge_dim() 返回值未直接透传 _merge_dim_scd2() 的结果，"
            f"表: {dwd_table}"
        )


# ══════════════════════════════════════════════════════════════
# Property 3: 事实表主增量 SQL 结构等价性
# Feature: dwd-phase1-refactor, Property 3: 事实表主增量 SQL 结构等价性
# ══════════════════════════════════════════════════════════════


class _PkAwareCursor(PropertyTestCursor):
    """扩展 PropertyTestCursor，对 table_constraints 查询返回主键信息。

    _merge_fact_increment 通过 _get_primary_keys 查询 information_schema
    获取主键列。基础 PropertyTestCursor 返回空列表，导致无法生成
    ON CONFLICT 子句。此子类注入 pk_cols 以完整测试 SQL 结构。
    """

    def __init__(self, pk_cols: Sequence[str]):
        super().__init__()
        self._pk_cols = list(pk_cols)

    def execute(self, sql: str, params=None):
        sql_text = sql.decode("utf-8", errors="ignore") if isinstance(sql, (bytes, bytearray)) else str(sql)
        lowered = sql_text.lower()
        # 拦截主键查询，返回注入的 pk_cols
        if "from information_schema.table_constraints" in lowered:
            self.executed.append({"sql": sql_text.strip(), "params": params})
            self._fetchall_rows = [
                {"column_name": col} for col in self._pk_cols
            ]
            return
        super().execute(sql, params)


# INSERT INTO "dwd"."<table>" (...) — 实际 SQL 使用引号包裹 schema 和表名
_INSERT_INTO_PATTERN = re.compile(
    r'INSERT\s+INTO\s+"?dwd"?\."?\w+"?\s*\(',
    re.IGNORECASE,
)

# SELECT ... FROM "ods"."<table>"
_SELECT_FROM_ODS_PATTERN = re.compile(
    r'SELECT\s+.+?\s+FROM\s+"?ods"?\."?\w+"?',
    re.IGNORECASE | re.DOTALL,
)

# ON CONFLICT (...) DO UPDATE SET 或 DO NOTHING
_ON_CONFLICT_PATTERN = re.compile(
    r"ON\s+CONFLICT\s*\([^)]+\)\s+DO\s+(UPDATE\s+SET|NOTHING)",
    re.IGNORECASE,
)

# IS DISTINCT FROM（变更检测）
_IS_DISTINCT_FROM_PATTERN = re.compile(
    r"IS\s+DISTINCT\s+FROM",
    re.IGNORECASE,
)

# RETURNING (xmax = 0) AS inserted
_RETURNING_PATTERN = re.compile(
    r"RETURNING\s+\(xmax\s*=\s*0\)\s+AS\s+inserted",
    re.IGNORECASE,
)


@given(
    table_pair=st.sampled_from(_FACT_TABLES),
    window=window_pair(),
    fact_upsert=st.booleans(),
)
@settings(max_examples=100)
def test_fact_increment_sql_structure(
    table_pair: Tuple[str, str],
    window: Tuple[datetime, datetime],
    fact_upsert: bool,
):
    """
    Property 3: 事实表主增量 SQL 结构等价性

    对于任意事实表、窗口参数和 fact_upsert 配置，
    _merge_fact_increment() 生成的主增量 SQL 应包含：
    1. INSERT INTO <dwd_table> 结构
    2. SELECT ... FROM <ods_table> 结构
    3. ON CONFLICT (<pk_cols>) DO UPDATE SET 或 DO NOTHING 结构
    4. IS DISTINCT FROM 变更检测（当有 DO UPDATE SET 时）
    5. RETURNING (xmax = 0) AS inserted 结构

    **Validates: Requirements 5.2, 5.5**
    """
    dwd_table, ods_table = table_pair
    window_start, window_end = window

    task = _make_task(fact_upsert=fact_upsert)

    # 构建列信息
    fake_cols = [row[0] for row in PropertyTestCursor._fake_columns(None)]
    mapping_entries = task.FACT_MAPPINGS.get(dwd_table) or []
    mapping_dest = [dst for dst, _, _ in mapping_entries]

    dwd_cols = list(set(fake_cols + mapping_dest))
    ods_cols = list(fake_cols)

    fake_types = {row[0]: row[1] for row in PropertyTestCursor._fake_columns(None)}
    dwd_types = dict(fake_types)
    ods_types = dict(fake_types)
    for col in mapping_dest:
        if col not in dwd_types:
            dwd_types[col] = "text"

    # 使用注入主键的 cursor，确保 ON CONFLICT 子句被生成
    # 选择 "id" 作为主键——它存在于 _fake_columns 返回的列中
    cur = _PkAwareCursor(pk_cols=["id"])

    result = task._merge_fact_increment(
        cur=cur,
        dwd_table=dwd_table,
        ods_table=ods_table,
        dwd_cols=dwd_cols,
        ods_cols=ods_cols,
        dwd_types=dwd_types,
        ods_types=ods_types,
        window_start=window_start,
        window_end=window_end,
    )

    # 提取主 INSERT SQL
    insert_sqls = [
        entry["sql"]
        for entry in cur.executed
        if entry["sql"].upper().startswith("INSERT INTO")
    ]

    assert len(insert_sqls) == 1, (
        f"期望恰好 1 条 INSERT SQL，实际 {len(insert_sqls)} 条，"
        f"表: {dwd_table}"
    )

    sql = insert_sqls[0]

    # 1. 验证 INSERT INTO dwd.<table> 结构
    assert _INSERT_INTO_PATTERN.search(sql), (
        f"SQL 缺少 INSERT INTO dwd.<table> 结构，"
        f"表: {dwd_table}\nSQL: {sql[:500]}"
    )

    # 2. 验证 SELECT ... FROM ods.<table> 结构
    assert _SELECT_FROM_ODS_PATTERN.search(sql), (
        f"SQL 缺少 SELECT ... FROM ods.<table> 结构，"
        f"表: {dwd_table}\nSQL: {sql[:500]}"
    )

    # 3. 验证 ON CONFLICT 结构
    assert _ON_CONFLICT_PATTERN.search(sql), (
        f"SQL 缺少 ON CONFLICT (...) DO UPDATE SET / DO NOTHING 结构，"
        f"表: {dwd_table}\nSQL: {sql[:500]}"
    )

    # 4. 验证 IS DISTINCT FROM 变更检测逻辑
    #    有 DO UPDATE SET 时，必须有 IS DISTINCT FROM 变更检测
    has_do_update = re.search(
        r"ON\s+CONFLICT\s*\([^)]+\)\s+DO\s+UPDATE\s+SET",
        sql, re.IGNORECASE,
    )
    if has_do_update:
        assert _IS_DISTINCT_FROM_PATTERN.search(sql), (
            f"SQL 有 DO UPDATE SET 但缺少 IS DISTINCT FROM 变更检测，"
            f"表: {dwd_table}\nSQL: {sql[:500]}"
        )

    # 5. 验证 RETURNING (xmax = 0) AS inserted 结构
    assert _RETURNING_PATTERN.search(sql), (
        f"SQL 缺少 RETURNING (xmax = 0) AS inserted 结构，"
        f"表: {dwd_table}\nSQL: {sql[:500]}"
    )