market_sync/app/tasks/task_kline_daily.py

"""同步任务：全市场日 K 线。

数据流：
  1. 选主源（按 priority 选第一个 is_available 的源）
  2. 增量判断：读 kline_stock.MAX(trade_date) 决定每只股票 start
  3. **Producer-Consumer 模式**：
     - N 个 fetcher 线程并发拉 K 线，把 (code, rows) 塞到 Queue
     - 1 个 writer 线程攒满 BATCH_SIZE 就 executemany + commit 一次
  4. 更新 stocks.kline_synced_at

设计要点：
  - 避免每只股票 commit（5000 只 = 5000 次 commit 太慢）
  - 减少并发数（避免新浪限流 / MySQL 连接数爆）
  - fetcher 间用 Queue 解耦，写库串行避免锁竞争
"""
from __future__ import annotations

import os
import queue
import threading
import time
from datetime import datetime, timedelta
from typing import Any

import pandas as pd

from app.core.db import ops as db_ops
from app.core.datasource.base import registry as ds_registry
from app.core.datasource.utils import is_a_share_code, to_code6, to_hermes
from app.core.sync.base import SyncTask, _effective_sync_end
from app.core.utils.logging import get_logger

logger = get_logger("sync.kline_daily")

# 主源优先级：mairui（5 RPS，免费 + 稳定 + 单接口含5min）→ 雪球 → Baostock → 新浪
# 经验：sina 在单日窗口 (start=end=某天) 会返回空，雪球更可靠
PRIMARY_PRIORITY = ["datasource_xueqiu", "datasource_mairui", "datasource_xinlang"]
DEFAULT_START = "2018-01-01"
# 写库批量：攒够 BATCH_SIZE 行就 executemany 一次 + commit
BATCH_SIZE = 2000
# fetcher 并发：实测单线程最稳（5000 只 × 0.2s = 17 分钟，足够）
# 雪球 token 限速 10 RPS，单线程 0.1s/只 已经打满。多 worker 会触发风控 hang
DEFAULT_FETCH_WORKERS = 1


class SyncKlineDaily(SyncTask):
    dataset_id = "kline_daily"

    def _run(
        self,
        *,
        trigger_source: str = "manual",
        start: str | None = None,
        end: str | None = None,
        codes: list[str] | None = None,
        incremental: bool = True,
        max_workers: int = DEFAULT_FETCH_WORKERS,
        **kwargs,
    ) -> dict[str, Any]:
        # 1. 选主源
        primary = self._pick_primary()
        if primary is None:
            return {"status": "error", "message": "所有 K 线数据源均不可用"}

        self._progress(
            message=f"使用 {primary.name} 同步日K线 (fetcher={max_workers}, batch={BATCH_SIZE})...",
            current_step=primary.key,
        )

        # 2. 拉股票列表
        if codes:
            stock_codes = [to_code6(c) for c in codes]
        else:
            stock_codes = [
                c for c in db_ops.iter_stock_codes(active_only=True)
                if is_a_share_code(c)
            ]
        limit_raw = os.environ.get("MARKET_DATA_STOCK_LIMIT", "").strip()
        if limit_raw.isdigit() and int(limit_raw) > 0:
            stock_codes = stock_codes[: int(limit_raw)]

        if not stock_codes:
            return {"status": "error", "message": "无股票代码（stocks 表为空？）"}

        # 3. 增量判断
        _end = end or _effective_sync_end()
        end_date_obj = datetime.strptime(_end, "%Y-%m-%d").date()
        jobs: list[tuple[str, str]] = []
        if incremental and not start:
            for c6 in stock_codes:
                last = db_ops.get_stock_kline_max_date(c6)
                if last is None:
                    fetch_start = DEFAULT_START
                elif datetime.strptime(last, "%Y-%m-%d").date() >= end_date_obj:
                    continue
                else:
                    fetch_start = (datetime.strptime(last, "%Y-%m-%d").date() - timedelta(days=5)).strftime("%Y-%m-%d")
                jobs.append((c6, fetch_start))
        else:
            fetch_start = start or DEFAULT_START
            jobs = [(c, fetch_start) for c in stock_codes]

        total = len(jobs)
        skipped = len(stock_codes) - total
        self._progress(
            message=f"开始同步 {total} 只股票 (跳过 {skipped} 只已最新)",
            current=0, total=total,
        )

        # 4. 并发 fetch + 攒 batch 写库（线程安全）
        # mairui 等外部 API 无并发问题；DB 写串行加锁
        from concurrent.futures import ThreadPoolExecutor, as_completed
        t0 = time.time()
        batch_lock = threading.Lock()
        batch_rows: list[dict] = []
        pending_codes: dict[str, str] = {}
        ok_lock = threading.Lock()
        ok_cnt = [0]
        fail_cnt = [0]
        rows_cnt = [0]

        def _flush():
            with batch_lock:
                if not batch_rows:
                    return
                rows_to_write = list(batch_rows)
                codes_to_update = dict(pending_codes)
                batch_rows.clear()
                pending_codes.clear()
            try:
                db_ops.upsert_kline_stock(rows_to_write)
            except Exception as e:
                logger.error(f"batch upsert 失败 ({len(rows_to_write)} 行): {e}")
                return
            for c6, latest in codes_to_update.items():
                try:
                    db_ops.update_stock_kline_synced_at(c6, latest)
                except Exception as e:
                    logger.warning(f"update synced_at 失败 {c6}: {e}")

        def _fetch_one(code6: str, fetch_start: str) -> tuple[str, list[dict] | None, str | None]:
            try:
                df = primary.fetch_kline_daily(code6, fetch_start, _end)
            except Exception as e:
                return (code6, None, str(e)[:120])
            if df is None or df.empty:
                return (code6, None, "no data")
            # 写库用 hermes（带 SH/SZ 前缀）—— 2026-07-01 重构：历史上 kline_stock 用
            # 6位 code 与 stocks.code(SH600519) 格式不一致，跨表 JOIN 失败。现统一。
            hermes = to_hermes(code6)
            rows = []
            for _, r in df.iterrows():
                td = r["trade_date"]
                rows.append({
                    "stock_code": hermes,
                    "trade_date": td.strftime("%Y-%m-%d") if hasattr(td, "strftime") else str(td)[:10],
                    "open": float(r["open"]),
                    "high": float(r["high"]),
                    "low": float(r["low"]),
                    "close": float(r["close"]),
                    "volume": float(r["volume"]),
                })
            latest = df["trade_date"].max()
            latest = latest.strftime("%Y-%m-%d") if hasattr(latest, "strftime") else str(latest)[:10]
            return (code6, rows, latest)

        total = len(jobs)
        with ThreadPoolExecutor(max_workers=max_workers) as pool:
            futures = {pool.submit(_fetch_one, c6, fs): c6 for c6, fs in jobs}
            done_cnt = 0
            for future in as_completed(futures):
                done_cnt += 1
                code6, rows, latest = future.result()
                if rows is None:
                    with ok_lock:
                        fail_cnt[0] += 1
                    logger.warning(f"[kline {code6}] {latest}")
                    continue
                with batch_lock:
                    batch_rows.extend(rows)
                    pending_codes[code6] = latest
                    cur_size = len(batch_rows)
                with ok_lock:
                    ok_cnt[0] += 1
                    rows_cnt[0] += len(rows)

                if cur_size >= BATCH_SIZE:
                    _flush()

                if done_cnt % 50 == 0 or done_cnt == total:
                    elapsed = time.time() - t0
                    rate = done_cnt / elapsed if elapsed > 0 else 0
                    self._progress(
                        message=f"进度 {done_cnt}/{total} OK:{ok_cnt[0]} FAIL:{fail_cnt[0]} {rate:.1f}只/秒",
                        current=done_cnt, total=total, current_step=code6,
                    )

        # 收尾 flush
        _flush()

        elapsed = round(time.time() - t0, 1)
        msg = f"日K线 {ok_cnt[0]}成 {fail_cnt[0]}败 {skipped}跳 共{rows_cnt[0]}行, {elapsed}s"
        return {
            "status": "ok" if fail_cnt[0] == 0 else "warning",
            "message": msg,
            "ok": ok_cnt[0],
            "fail": fail_cnt[0],
            "skip": skipped,
            "rows": rows_cnt[0],
            "elapsed_sec": elapsed,
            "primary_source": primary.key,
        }

    def _pick_primary(self):
        from app.core.datasource.registry import is_source_ready, run_health_check, get_health_status
        if not get_health_status():
            run_health_check()
        for key in PRIMARY_PRIORITY:
            ok, _ = is_source_ready(key)
            if ok:
                return ds_registry.get(key)
        return None

    def _sync_one(self, primary, code6: str, start: str, end: str) -> dict:
        """保留这个方法以兼容外部调用（已不用，但单只测试可能用到）"""
        try:
            df = primary.fetch_kline_daily(code6, start, end)
        except Exception as e:
            return {"status": "fail", "error": str(e)}
        if df is None or df.empty:
            return {"status": "fail", "error": "no data"}
        hermes = to_hermes(code6)
        rows = []
        for _, r in df.iterrows():
            td = r["trade_date"]
            rows.append({
                "stock_code": hermes,
                "trade_date": td.strftime("%Y-%m-%d") if hasattr(td, "strftime") else str(td)[:10],
                "open": float(r["open"]),
                "high": float(r["high"]),
                "low": float(r["low"]),
                "close": float(r["close"]),
                "volume": float(r["volume"]),
            })
        try:
            db_ops.upsert_kline_stock(rows)
        except Exception as e:
            return {"status": "fail", "error": f"db write: {e}"}
        if rows:
            latest = max(r["trade_date"] for r in rows)
            try:
                db_ops.update_stock_kline_synced_at(code6, latest)
            except Exception:
                pass
        return {"status": "ok", "rows": len(rows)}