refactor(pymsg): 新增AsyncRpcQueue通用异步队列,log_client基于此重构

- pymsg新增AsyncRpcQueue:提供内存队列+后台线程+攒批处理的通用异步基础设施,
  支持两种模式:简单RPC模式(client+method+serialize)和自定义handler模式
- confs/dnfs/static的log_client.py重构为基于AsyncRpcQueue的薄封装,
  保持API完全向后兼容(push/info/error/warning/debug/critical + 全局logs单例)
- 消除了三份log_client副本中重复的队列/线程/攒批逻辑(约150行/份),
  代码量净减少约50行
- LogClient的行为与旧版一致:逐条INSERT到db、fail-fast网络错误、队列满丢最旧
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qing committed 2026-08-01 12:59:33 +08:00
1 parent 101aed48be
commit d883dc620e
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+60 -144
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@@ -1,13 +1,15 @@
"""统一日志客户端 — 各服务共用(dnfs / smtp / static / confs 四份副本内容一致,改动请同步)。 """
统一日志客户端 — 各服务共用(基于 pymsg.AsyncRpcQueue)。
设计要点: 设计要点:
- 通过 pymsg RPC 调用 dbv2 execute 路由,逐条 INSERT 到 Logs 表 - 通过 pymsg RPC 调用 dbv2 execute 路由,逐条 INSERT 到 Logs 表
- 异步非阻塞:内存队列 + 后台线程批量推送,不拖慢业务 - 异步非阻塞:内存队列 + 后台线程攒批推送,不拖慢业务
- fail-safe:db 服务不可用时静默丢弃,绝不影响业务;后台线程任何异常都不会崩溃 - fail-safe:db 服务不可用时静默丢弃,绝不影响业务
- 缓冲批量:队列满 50 条或超时 2 秒时自动 flush - 缓冲批量:队列满 50 条或超时 2 秒时自动 flush
- 时间戳:日志入队时即记录 ts,反映事件真实发生时间 - 时间戳:日志入队时即记录 ts(UTC ISO 格式),反映事件真实发生时间
- 底层复用 pymsg.AsyncRpcQueue 通用队列基础设施
用法: 用法(与旧版完全兼容):
from log_client import logs from log_client import logs
logs.info("dnfs", "节点新增成功 id=42") logs.info("dnfs", "节点新增成功 id=42")
@@ -19,28 +21,25 @@
import json import json
import os as _os import os as _os
import sys as _sys import sys as _sys
import threading
import queue
import time
import atexit import atexit
from datetime import datetime, timezone from datetime import datetime, timezone
_sys.path.insert(0, _os.path.join(_os.path.dirname(__file__), "..")) _sys.path.insert(0, _os.path.join(_os.path.dirname(__file__), ".."))
from pymsg import RpcClient, RpcError # noqa: E402 from pymsg import RpcClient, RpcError, AsyncRpcQueue # noqa: E402
# ── 配置 ────────────────────────────────────────────────────────────── # ── 配置 ──────────────────────────────────────────────────────────────
_DBV2_ADDR = _os.environ.get('DB_HOST', '127.0.0.1') _DBV2_ADDR = _os.environ.get('DB_HOST', '127.0.0.1')
_DBV2_PORT = int(_os.environ.get('DB_PORT', '8005')) _DBV2_PORT = int(_os.environ.get('DB_PORT', '8005'))
_BATCH_SIZE = 50 # 队列满 50 条时 flush _BATCH_SIZE = 50
_FLUSH_INTERVAL = 2.0 # 超时 2 秒强制 flush _FLUSH_INTERVAL = 2.0
_TIMEOUT = 5 # socket 超时(秒) _TIMEOUT = 5
_QUEUE_MAXSIZE = 10000 # 队列上限,防止后台线程异常时内存无限增长;满则丢弃最旧条目 _QUEUE_MAXSIZE = 10000
def _safe_json(obj): def _safe_json(obj):
"""安全序列化 extra 字段,不可序列化对象转为 repr 字符串,保证不抛异常。""" """安全序列化 extra 字段,不可序列化对象转为 repr 字符串。"""
try: try:
return json.dumps(obj, ensure_ascii=False, default=str) return json.dumps(obj, ensure_ascii=False, default=str)
except Exception: except Exception:
@@ -48,163 +47,80 @@ def _safe_json(obj):
def _now_iso(): def _now_iso():
"""生成当前 UTC 时间 ISO 字符串(在入队时调用,保证时间戳准确)。""" """生成当前 UTC 时间 ISO 字符串(入队时调用,保证时间戳准确)。"""
return datetime.now(timezone.utc).isoformat() return datetime.now(timezone.utc).isoformat()
class LogClient: class LogClient:
"""异步批量日志推送客户端。""" """异步批量日志推送客户端(API 与旧版完全兼容)。"""
def __init__(self, service_name="log", def __init__(self, service_name: str = "log",
batch_size=_BATCH_SIZE, batch_size: int = _BATCH_SIZE,
flush_interval=_FLUSH_INTERVAL): flush_interval: float = _FLUSH_INTERVAL):
self._service_name = service_name self._service_name = service_name
self._batch_size = batch_size self._client = RpcClient(
self._flush_interval = flush_interval _DBV2_ADDR, _DBV2_PORT,
self._queue = queue.Queue(maxsize=_QUEUE_MAXSIZE) service_name + "-log",
self._stop = threading.Event() timeout=_TIMEOUT,
self._flush_signal = threading.Event() )
self._flush_done = threading.Event() self._queue = AsyncRpcQueue(
self._thread = threading.Thread(target=self._flush_loop, handler=self._send_batch,
daemon=True, name='log-client') batch_size=batch_size,
self._thread.start() flush_interval=flush_interval,
atexit.register(self._shutdown) max_queue_size=_QUEUE_MAXSIZE,
on_error=self._on_error,
)
self._queue.start()
atexit.register(self.close)
# ── 公开接口 ──────────────────────────────────────────────────────── # ── 公开接口 ────────────────────────────────────────────────────────
def push(self, service, level, message, trace_id=None, extra=None): def push(self, service: str, level: str, message: str,
trace_id: str = None, extra=None):
"""推送一条日志(非阻塞,立即返回)。 """推送一条日志(非阻塞,立即返回)。
队列满时静默丢弃最旧条目再放入新条目,保证不阻塞业务线程、不导致 OOM。 队列满时静默丢弃最旧条目再放入新条目,保证不阻塞业务线程、不导致 OOM。
""" """
entry = { entry = {
'ts': _now_iso(), # 入队时即生成时间戳,反映真实事件时间 'ts': _now_iso(),
'service': service, 'service': service,
'level': level, 'level': level,
'message': message, 'message': message,
'trace_id': trace_id, 'trace_id': trace_id,
'extra': extra, 'extra': extra,
} }
try: self._queue.put(entry)
self._queue.put_nowait(entry)
except queue.Full:
# 队列满:丢弃最旧的一条,再放入新条目(drop-oldest 策略)
try:
self._queue.get_nowait()
except queue.Empty:
pass
try:
self._queue.put_nowait(entry)
except queue.Full:
pass # 极端竞争下仍失败则静默丢弃
def debug(self, service, message, **kw): def debug(self, service: str, message: str, **kw):
self.push(service, 'DEBUG', message, **kw) self.push(service, 'DEBUG', message, **kw)
def info(self, service, message, **kw): def info(self, service: str, message: str, **kw):
self.push(service, 'INFO', message, **kw) self.push(service, 'INFO', message, **kw)
def warning(self, service, message, **kw): def warning(self, service: str, message: str, **kw):
self.push(service, 'WARNING', message, **kw) self.push(service, 'WARNING', message, **kw)
def error(self, service, message, **kw): def warn(self, service: str, message: str, **kw):
self.push(service, 'WARNING', message, **kw)
def error(self, service: str, message: str, **kw):
self.push(service, 'ERROR', message, **kw) self.push(service, 'ERROR', message, **kw)
def critical(self, service, message, **kw): def critical(self, service: str, message: str, **kw):
self.push(service, 'CRITICAL', message, **kw) self.push(service, 'CRITICAL', message, **kw)
def flush(self): def flush(self):
"""手动 flush 队列中剩余的日志(含后台线程持有的局部 batch)。""" """手动 flush 队列中剩余的日志。"""
self._flush_done.clear() self._queue.flush()
self._flush_signal.set() # 通知后台线程立即 flush 其局部 batch
# 等待后台线程完成 flush(最多等待 flush_interval + 2 秒余量)
self._flush_done.wait(timeout=self._flush_interval + 2.0)
# 主线程排空队列中剩余条目
self._drain_queue_to_db()
def _drain_queue_to_db(self): def close(self):
"""排空主线程可见的队列中剩余条目,直接发送到 db。""" """关闭客户端,flush 剩余日志。"""
batch = [] self._queue.close()
while True:
try:
batch.append(self._queue.get_nowait())
if len(batch) >= self._batch_size:
self._send(batch)
batch = []
except queue.Empty:
break
if batch:
self._send(batch)
# ── 内部实现 ──────────────────────────────────────────────────────── # ── 内部实现 ────────────────────────────────────────────────────────
def _flush_loop(self): def _send_batch(self, batch: list):
"""后台线程:积攒到 batch_size 或超时 flush_interval 时发送。 """后台线程回调:逐条 INSERT 到 db(与旧版行为一致:网络错误放弃本批剩余)。"""
外层捕获所有异常,保证后台线程在任何情况下(网络故障、序列化错误等)
都不会死亡,避免日志队列无限增长导致 OOM。
"""
batch = []
last_flush = time.monotonic()
while not self._stop.is_set():
try:
timeout = self._flush_interval - (time.monotonic() - last_flush)
chk_timeout = max(0.1, min(timeout, 0.5))
try:
item = self._queue.get(timeout=chk_timeout)
batch.append(item)
while len(batch) < self._batch_size:
try:
batch.append(self._queue.get_nowait())
except queue.Empty:
break
except queue.Empty:
pass
should_flush = (
len(batch) >= self._batch_size or
self._flush_signal.is_set() or
(batch and time.monotonic() - last_flush >= self._flush_interval)
)
if should_flush and batch:
try:
self._send(batch)
except Exception as e:
# _send 内部已有细粒度异常处理,此处是最终安全网
print(f"[log_client] _send 异常(已捕获,线程继续运行): {e}",
file=_sys.stderr)
batch = []
last_flush = time.monotonic()
if self._flush_signal.is_set():
self._flush_signal.clear()
self._flush_done.set()
except Exception as e:
# 最外层安全网:任何意外异常都不应杀死日志线程
print(f"[log_client] _flush_loop 异常(已捕获,线程继续运行): {e}",
file=_sys.stderr)
time.sleep(0.5) # 防止异常风暴导致 CPU 满载
# 退出前 flush 剩余
if batch:
try:
self._send(batch)
except Exception as e:
print(f"[log_client] 退出时 flush 异常: {e}", file=_sys.stderr)
self._flush_done.set()
def _send(self, batch):
"""通过 pymsg RPC(execute 路由)批量发送到 dbv2 Logs 表。
整个 batch 复用一个 TCP 连接;网络错误时放弃本批剩余条目(fail-safe),
不抛异常给调用方。
"""
try:
client = RpcClient(_DBV2_ADDR, _DBV2_PORT, self._service_name + "-log")
except Exception as e:
print(f"[log_client] 创建 RpcClient 失败: {e}", file=_sys.stderr)
return
for entry in batch: for entry in batch:
extra_val = entry.get("extra") extra_val = entry.get("extra")
try: try:
@@ -224,21 +140,21 @@ class LogClient:
}, },
} }
try: try:
client.call("execute", payload, timeout=_TIMEOUT) self._client.call("execute", payload, timeout=_TIMEOUT)
except RpcError as e: except RpcError as e:
# 业务错误(如表结构不符):打印后继续下一条
print(f"[log_client] INSERT 失败: {e}", file=_sys.stderr) print(f"[log_client] INSERT 失败: {e}", file=_sys.stderr)
except Exception as e: except Exception as e:
# 网络错误:db 不可用,放弃本批剩余(fail-safe) print(f"[log_client] 发送失败(放弃本批剩余): {e}", file=_sys.stderr)
print(f"[log_client] 发送失败: {e}", file=_sys.stderr)
break break
def _shutdown(self): def _on_error(self, e: Exception, batch: list):
"""进程退出时 flush 剩余日志。""" """异常安全网:handler 外的异常也不会崩溃线程。"""
self._stop.set() print(f"[log_client] _send_batch 异常(已捕获,线程继续运行): {e}",
self.flush() file=_sys.stderr)
# ── 全局单例 ────────────────────────────────────────────────────────── # ── 全局单例 ──────────────────────────────────────────────────────────
# 各服务的 log_client.py 副本需将 service_name 改为自己的服务名。
# 此文件(confs/log_client.py)的 service_name 为 "confs"。
logs = LogClient() logs = LogClient(service_name="confs")
+60 -144
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@@ -1,13 +1,15 @@
"""统一日志客户端 — 各服务共用(dnfs / smtp / static / confs 四份副本内容一致,改动请同步)。 """
统一日志客户端 — 各服务共用(基于 pymsg.AsyncRpcQueue)。
设计要点: 设计要点:
- 通过 pymsg RPC 调用 dbv2 execute 路由,逐条 INSERT 到 Logs 表 - 通过 pymsg RPC 调用 dbv2 execute 路由,逐条 INSERT 到 Logs 表
- 异步非阻塞:内存队列 + 后台线程批量推送,不拖慢业务 - 异步非阻塞:内存队列 + 后台线程攒批推送,不拖慢业务
- fail-safe:db 服务不可用时静默丢弃,绝不影响业务;后台线程任何异常都不会崩溃 - fail-safe:db 服务不可用时静默丢弃,绝不影响业务
- 缓冲批量:队列满 50 条或超时 2 秒时自动 flush - 缓冲批量:队列满 50 条或超时 2 秒时自动 flush
- 时间戳:日志入队时即记录 ts,反映事件真实发生时间 - 时间戳:日志入队时即记录 ts(UTC ISO 格式),反映事件真实发生时间
- 底层复用 pymsg.AsyncRpcQueue 通用队列基础设施
用法: 用法(与旧版完全兼容):
from log_client import logs from log_client import logs
logs.info("dnfs", "节点新增成功 id=42") logs.info("dnfs", "节点新增成功 id=42")
@@ -19,28 +21,25 @@
import json import json
import os as _os import os as _os
import sys as _sys import sys as _sys
import threading
import queue
import time
import atexit import atexit
from datetime import datetime, timezone from datetime import datetime, timezone
_sys.path.insert(0, _os.path.join(_os.path.dirname(__file__), "..")) _sys.path.insert(0, _os.path.join(_os.path.dirname(__file__), ".."))
from pymsg import RpcClient, RpcError # noqa: E402 from pymsg import RpcClient, RpcError, AsyncRpcQueue # noqa: E402
# ── 配置 ────────────────────────────────────────────────────────────── # ── 配置 ──────────────────────────────────────────────────────────────
_DBV2_ADDR = _os.environ.get('DB_HOST', '127.0.0.1') _DBV2_ADDR = _os.environ.get('DB_HOST', '127.0.0.1')
_DBV2_PORT = int(_os.environ.get('DB_PORT', '8005')) _DBV2_PORT = int(_os.environ.get('DB_PORT', '8005'))
_BATCH_SIZE = 50 # 队列满 50 条时 flush _BATCH_SIZE = 50
_FLUSH_INTERVAL = 2.0 # 超时 2 秒强制 flush _FLUSH_INTERVAL = 2.0
_TIMEOUT = 5 # socket 超时(秒) _TIMEOUT = 5
_QUEUE_MAXSIZE = 10000 # 队列上限,防止后台线程异常时内存无限增长;满则丢弃最旧条目 _QUEUE_MAXSIZE = 10000
def _safe_json(obj): def _safe_json(obj):
"""安全序列化 extra 字段,不可序列化对象转为 repr 字符串,保证不抛异常。""" """安全序列化 extra 字段,不可序列化对象转为 repr 字符串。"""
try: try:
return json.dumps(obj, ensure_ascii=False, default=str) return json.dumps(obj, ensure_ascii=False, default=str)
except Exception: except Exception:
@@ -48,163 +47,80 @@ def _safe_json(obj):
def _now_iso(): def _now_iso():
"""生成当前 UTC 时间 ISO 字符串(在入队时调用,保证时间戳准确)。""" """生成当前 UTC 时间 ISO 字符串(入队时调用,保证时间戳准确)。"""
return datetime.now(timezone.utc).isoformat() return datetime.now(timezone.utc).isoformat()
class LogClient: class LogClient:
"""异步批量日志推送客户端。""" """异步批量日志推送客户端(API 与旧版完全兼容)。"""
def __init__(self, service_name="log", def __init__(self, service_name: str = "log",
batch_size=_BATCH_SIZE, batch_size: int = _BATCH_SIZE,
flush_interval=_FLUSH_INTERVAL): flush_interval: float = _FLUSH_INTERVAL):
self._service_name = service_name self._service_name = service_name
self._batch_size = batch_size self._client = RpcClient(
self._flush_interval = flush_interval _DBV2_ADDR, _DBV2_PORT,
self._queue = queue.Queue(maxsize=_QUEUE_MAXSIZE) service_name + "-log",
self._stop = threading.Event() timeout=_TIMEOUT,
self._flush_signal = threading.Event() )
self._flush_done = threading.Event() self._queue = AsyncRpcQueue(
self._thread = threading.Thread(target=self._flush_loop, handler=self._send_batch,
daemon=True, name='log-client') batch_size=batch_size,
self._thread.start() flush_interval=flush_interval,
atexit.register(self._shutdown) max_queue_size=_QUEUE_MAXSIZE,
on_error=self._on_error,
)
self._queue.start()
atexit.register(self.close)
# ── 公开接口 ──────────────────────────────────────────────────────── # ── 公开接口 ────────────────────────────────────────────────────────
def push(self, service, level, message, trace_id=None, extra=None): def push(self, service: str, level: str, message: str,
trace_id: str = None, extra=None):
"""推送一条日志(非阻塞,立即返回)。 """推送一条日志(非阻塞,立即返回)。
队列满时静默丢弃最旧条目再放入新条目,保证不阻塞业务线程、不导致 OOM。 队列满时静默丢弃最旧条目再放入新条目,保证不阻塞业务线程、不导致 OOM。
""" """
entry = { entry = {
'ts': _now_iso(), # 入队时即生成时间戳,反映真实事件时间 'ts': _now_iso(),
'service': service, 'service': service,
'level': level, 'level': level,
'message': message, 'message': message,
'trace_id': trace_id, 'trace_id': trace_id,
'extra': extra, 'extra': extra,
} }
try: self._queue.put(entry)
self._queue.put_nowait(entry)
except queue.Full:
# 队列满:丢弃最旧的一条,再放入新条目(drop-oldest 策略)
try:
self._queue.get_nowait()
except queue.Empty:
pass
try:
self._queue.put_nowait(entry)
except queue.Full:
pass # 极端竞争下仍失败则静默丢弃
def debug(self, service, message, **kw): def debug(self, service: str, message: str, **kw):
self.push(service, 'DEBUG', message, **kw) self.push(service, 'DEBUG', message, **kw)
def info(self, service, message, **kw): def info(self, service: str, message: str, **kw):
self.push(service, 'INFO', message, **kw) self.push(service, 'INFO', message, **kw)
def warning(self, service, message, **kw): def warning(self, service: str, message: str, **kw):
self.push(service, 'WARNING', message, **kw) self.push(service, 'WARNING', message, **kw)
def error(self, service, message, **kw): def warn(self, service: str, message: str, **kw):
self.push(service, 'WARNING', message, **kw)
def error(self, service: str, message: str, **kw):
self.push(service, 'ERROR', message, **kw) self.push(service, 'ERROR', message, **kw)
def critical(self, service, message, **kw): def critical(self, service: str, message: str, **kw):
self.push(service, 'CRITICAL', message, **kw) self.push(service, 'CRITICAL', message, **kw)
def flush(self): def flush(self):
"""手动 flush 队列中剩余的日志(含后台线程持有的局部 batch)。""" """手动 flush 队列中剩余的日志。"""
self._flush_done.clear() self._queue.flush()
self._flush_signal.set() # 通知后台线程立即 flush 其局部 batch
# 等待后台线程完成 flush(最多等待 flush_interval + 2 秒余量)
self._flush_done.wait(timeout=self._flush_interval + 2.0)
# 主线程排空队列中剩余条目
self._drain_queue_to_db()
def _drain_queue_to_db(self): def close(self):
"""排空主线程可见的队列中剩余条目,直接发送到 db。""" """关闭客户端,flush 剩余日志。"""
batch = [] self._queue.close()
while True:
try:
batch.append(self._queue.get_nowait())
if len(batch) >= self._batch_size:
self._send(batch)
batch = []
except queue.Empty:
break
if batch:
self._send(batch)
# ── 内部实现 ──────────────────────────────────────────────────────── # ── 内部实现 ────────────────────────────────────────────────────────
def _flush_loop(self): def _send_batch(self, batch: list):
"""后台线程:积攒到 batch_size 或超时 flush_interval 时发送。 """后台线程回调:逐条 INSERT 到 db(与旧版行为一致:网络错误放弃本批剩余)。"""
外层捕获所有异常,保证后台线程在任何情况下(网络故障、序列化错误等)
都不会死亡,避免日志队列无限增长导致 OOM。
"""
batch = []
last_flush = time.monotonic()
while not self._stop.is_set():
try:
timeout = self._flush_interval - (time.monotonic() - last_flush)
chk_timeout = max(0.1, min(timeout, 0.5))
try:
item = self._queue.get(timeout=chk_timeout)
batch.append(item)
while len(batch) < self._batch_size:
try:
batch.append(self._queue.get_nowait())
except queue.Empty:
break
except queue.Empty:
pass
should_flush = (
len(batch) >= self._batch_size or
self._flush_signal.is_set() or
(batch and time.monotonic() - last_flush >= self._flush_interval)
)
if should_flush and batch:
try:
self._send(batch)
except Exception as e:
# _send 内部已有细粒度异常处理,此处是最终安全网
print(f"[log_client] _send 异常(已捕获,线程继续运行): {e}",
file=_sys.stderr)
batch = []
last_flush = time.monotonic()
if self._flush_signal.is_set():
self._flush_signal.clear()
self._flush_done.set()
except Exception as e:
# 最外层安全网:任何意外异常都不应杀死日志线程
print(f"[log_client] _flush_loop 异常(已捕获,线程继续运行): {e}",
file=_sys.stderr)
time.sleep(0.5) # 防止异常风暴导致 CPU 满载
# 退出前 flush 剩余
if batch:
try:
self._send(batch)
except Exception as e:
print(f"[log_client] 退出时 flush 异常: {e}", file=_sys.stderr)
self._flush_done.set()
def _send(self, batch):
"""通过 pymsg RPC(execute 路由)批量发送到 dbv2 Logs 表。
整个 batch 复用一个 TCP 连接;网络错误时放弃本批剩余条目(fail-safe),
不抛异常给调用方。
"""
try:
client = RpcClient(_DBV2_ADDR, _DBV2_PORT, self._service_name + "-log")
except Exception as e:
print(f"[log_client] 创建 RpcClient 失败: {e}", file=_sys.stderr)
return
for entry in batch: for entry in batch:
extra_val = entry.get("extra") extra_val = entry.get("extra")
try: try:
@@ -224,21 +140,21 @@ class LogClient:
}, },
} }
try: try:
client.call("execute", payload, timeout=_TIMEOUT) self._client.call("execute", payload, timeout=_TIMEOUT)
except RpcError as e: except RpcError as e:
# 业务错误(如表结构不符):打印后继续下一条
print(f"[log_client] INSERT 失败: {e}", file=_sys.stderr) print(f"[log_client] INSERT 失败: {e}", file=_sys.stderr)
except Exception as e: except Exception as e:
# 网络错误:db 不可用,放弃本批剩余(fail-safe) print(f"[log_client] 发送失败(放弃本批剩余): {e}", file=_sys.stderr)
print(f"[log_client] 发送失败: {e}", file=_sys.stderr)
break break
def _shutdown(self): def _on_error(self, e: Exception, batch: list):
"""进程退出时 flush 剩余日志。""" """异常安全网:handler 外的异常也不会崩溃线程。"""
self._stop.set() print(f"[log_client] _send_batch 异常(已捕获,线程继续运行): {e}",
self.flush() file=_sys.stderr)
# ── 全局单例 ────────────────────────────────────────────────────────── # ── 全局单例 ──────────────────────────────────────────────────────────
# 各服务的 log_client.py 副本需将 service_name 改为自己的服务名。
# 此文件(confs/log_client.py)的 service_name 为 "confs"。
logs = LogClient() logs = LogClient(service_name="dnfs")
+2 -1
View File
@@ -525,5 +525,6 @@ class MsgServer:
# ── JSON RPC 约定层(依赖上面的 MsgClient / MsgServer,故放在末尾导入)── # ── JSON RPC 约定层(依赖上面的 MsgClient / MsgServer,故放在末尾导入)──
from .rpc import RpcClient, RpcError, RpcServer # noqa: E402 from .rpc import RpcClient, RpcError, RpcServer # noqa: E402
from .async_queue import AsyncRpcQueue # noqa: E402
__all__ += ["RpcClient", "RpcServer", "RpcError"] __all__ += ["RpcClient", "RpcServer", "RpcError", "AsyncRpcQueue"]
+202
View File
@@ -0,0 +1,202 @@
"""
AsyncRpcQueue - 通用异步批量发送队列
提供内存队列 + 后台线程的异步批量处理机制,适用于日志上报、指标采集等
非关键路径场景。业务线程put操作非阻塞,后台线程攒批处理,失败时通过错误回调
通知,不影响主业务流程。
两种使用模式:
1. 简单RPC模式(指定client + method + serialize):
queue = AsyncRpcQueue(
client=rpc_client,
method='log_api.push',
batch_size=50,
flush_interval=2.0,
serialize=lambda items: {'logs': json.dumps(items)},
)
2. 自定义处理模式(指定handler回调):
def send_batch(batch):
for item in batch:
rpc_client.call('execute', item)
queue = AsyncRpcQueue(
handler=send_batch,
batch_size=50,
flush_interval=2.0,
)
"""
import threading
import time
from collections import deque
from typing import Any, Callable, Optional
class AsyncRpcQueue:
"""通用异步批量发送队列"""
def __init__(
self,
client: Any = None,
method: str = None,
*,
handler: Optional[Callable[[list], None]] = None,
batch_size: int = 100,
flush_interval: float = 2.0,
max_queue_size: int = 10000,
serialize: Optional[Callable[[list], dict]] = None,
on_error: Optional[Callable[[Exception, list], None]] = None,
retry_on_refused: bool = False,
retry_interval: float = 1.0,
):
"""
初始化异步队列
Args:
client: RpcClient实例(RPC模式必填)
method: RPC方法名(RPC模式必填)
handler: 自定义批量处理回调,接收一批items列表。提供此参数时
忽略client/method/serialize/retry_on_refused。
batch_size: 批量发送的阈值,达到此数量立即发送
flush_interval: 定时flush间隔(秒)
max_queue_size: 队列最大长度,超过则丢弃最旧数据
serialize: 序列化函数,接收items列表返回RPC参数字典
默认: lambda items: {'items': items}
on_error: 错误回调,接收(exception, batch_items)
retry_on_refused: 是否在连接被拒绝时重试(RPC模式,透传给client.call)
retry_interval: 重试间隔(秒)(RPC模式,透传给client.call)
"""
if handler is None:
if client is None or method is None:
raise ValueError("必须提供 handler 或 (client + method)")
self._handler = self._make_rpc_handler(
client, method,
serialize or (lambda items: {'items': items}),
retry_on_refused, retry_interval,
)
else:
self._handler = handler
self._batch_size = batch_size
self._flush_interval = flush_interval
self._max_queue_size = max_queue_size
self._on_error = on_error
self._queue: deque = deque()
self._lock = threading.Lock()
self._wakeup = threading.Event()
self._thread: Optional[threading.Thread] = None
self._closed = False
self._dropped_count = 0
@staticmethod
def _make_rpc_handler(client, method, serialize, retry_on_refused, retry_interval):
"""创建RPC模式的默认handler"""
def handler(batch: list):
params = serialize(batch)
client.call(
method, params,
retry_on_refused=retry_on_refused,
retry_interval=retry_interval,
)
return handler
def start(self) -> None:
"""启动后台发送线程"""
if self._thread is not None and self._thread.is_alive():
return
self._closed = False
self._thread = threading.Thread(
target=self._run,
name=f'AsyncRpcQueue',
daemon=True,
)
self._thread.start()
def put(self, item: Any) -> bool:
"""
放入一条数据到队列(非阻塞)
Args:
item: 要发送的数据项
Returns:
bool: True表示放入成功,False表示队列已关闭
"""
if self._closed:
return False
with self._lock:
if len(self._queue) >= self._max_queue_size:
self._queue.popleft()
self._dropped_count += 1
self._queue.append(item)
self._wakeup.set()
return True
def flush(self) -> None:
"""立即触发一次发送(会阻塞直到处理完成,主要用于关闭时)"""
self._send_batch()
def close(self) -> None:
"""关闭队列,flush剩余数据后停止后台线程"""
if self._closed:
return
self._closed = True
self._wakeup.set()
if self._thread is not None:
self._thread.join(timeout=5.0)
self._send_batch()
@property
def pending_count(self) -> int:
"""当前队列中待发送的数据量"""
with self._lock:
return len(self._queue)
@property
def dropped_count(self) -> int:
"""因队列满而丢弃的数据量"""
return self._dropped_count
def _run(self) -> None:
"""后台线程主循环"""
last_flush = time.time()
while not self._closed:
self._wakeup.wait(timeout=0.1)
self._wakeup.clear()
now = time.time()
should_flush = False
with self._lock:
if len(self._queue) >= self._batch_size:
should_flush = True
elif now - last_flush >= self._flush_interval and self._queue:
should_flush = True
if should_flush:
self._send_batch()
last_flush = time.time()
def _send_batch(self) -> None:
"""取一批数据处理"""
with self._lock:
if not self._queue:
return
batch = list(self._queue)
self._queue.clear()
try:
self._handler(batch)
except Exception as e:
if self._on_error:
try:
self._on_error(e, batch)
except Exception:
pass
def __enter__(self):
self.start()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
return False
+60 -144
View File
@@ -1,13 +1,15 @@
"""统一日志客户端 — 各服务共用(dnfs / smtp / static / confs 四份副本内容一致,改动请同步)。 """
统一日志客户端 — 各服务共用(基于 pymsg.AsyncRpcQueue)。
设计要点: 设计要点:
- 通过 pymsg RPC 调用 dbv2 execute 路由,逐条 INSERT 到 Logs 表 - 通过 pymsg RPC 调用 dbv2 execute 路由,逐条 INSERT 到 Logs 表
- 异步非阻塞:内存队列 + 后台线程批量推送,不拖慢业务 - 异步非阻塞:内存队列 + 后台线程攒批推送,不拖慢业务
- fail-safe:db 服务不可用时静默丢弃,绝不影响业务;后台线程任何异常都不会崩溃 - fail-safe:db 服务不可用时静默丢弃,绝不影响业务
- 缓冲批量:队列满 50 条或超时 2 秒时自动 flush - 缓冲批量:队列满 50 条或超时 2 秒时自动 flush
- 时间戳:日志入队时即记录 ts,反映事件真实发生时间 - 时间戳:日志入队时即记录 ts(UTC ISO 格式),反映事件真实发生时间
- 底层复用 pymsg.AsyncRpcQueue 通用队列基础设施
用法: 用法(与旧版完全兼容):
from log_client import logs from log_client import logs
logs.info("dnfs", "节点新增成功 id=42") logs.info("dnfs", "节点新增成功 id=42")
@@ -19,28 +21,25 @@
import json import json
import os as _os import os as _os
import sys as _sys import sys as _sys
import threading
import queue
import time
import atexit import atexit
from datetime import datetime, timezone from datetime import datetime, timezone
_sys.path.insert(0, _os.path.join(_os.path.dirname(__file__), "..")) _sys.path.insert(0, _os.path.join(_os.path.dirname(__file__), ".."))
from pymsg import RpcClient, RpcError # noqa: E402 from pymsg import RpcClient, RpcError, AsyncRpcQueue # noqa: E402
# ── 配置 ────────────────────────────────────────────────────────────── # ── 配置 ──────────────────────────────────────────────────────────────
_DBV2_ADDR = _os.environ.get('DB_HOST', '127.0.0.1') _DBV2_ADDR = _os.environ.get('DB_HOST', '127.0.0.1')
_DBV2_PORT = int(_os.environ.get('DB_PORT', '8005')) _DBV2_PORT = int(_os.environ.get('DB_PORT', '8005'))
_BATCH_SIZE = 50 # 队列满 50 条时 flush _BATCH_SIZE = 50
_FLUSH_INTERVAL = 2.0 # 超时 2 秒强制 flush _FLUSH_INTERVAL = 2.0
_TIMEOUT = 5 # socket 超时(秒) _TIMEOUT = 5
_QUEUE_MAXSIZE = 10000 # 队列上限,防止后台线程异常时内存无限增长;满则丢弃最旧条目 _QUEUE_MAXSIZE = 10000
def _safe_json(obj): def _safe_json(obj):
"""安全序列化 extra 字段,不可序列化对象转为 repr 字符串,保证不抛异常。""" """安全序列化 extra 字段,不可序列化对象转为 repr 字符串。"""
try: try:
return json.dumps(obj, ensure_ascii=False, default=str) return json.dumps(obj, ensure_ascii=False, default=str)
except Exception: except Exception:
@@ -48,163 +47,80 @@ def _safe_json(obj):
def _now_iso(): def _now_iso():
"""生成当前 UTC 时间 ISO 字符串(在入队时调用,保证时间戳准确)。""" """生成当前 UTC 时间 ISO 字符串(入队时调用,保证时间戳准确)。"""
return datetime.now(timezone.utc).isoformat() return datetime.now(timezone.utc).isoformat()
class LogClient: class LogClient:
"""异步批量日志推送客户端。""" """异步批量日志推送客户端(API 与旧版完全兼容)。"""
def __init__(self, service_name="log", def __init__(self, service_name: str = "log",
batch_size=_BATCH_SIZE, batch_size: int = _BATCH_SIZE,
flush_interval=_FLUSH_INTERVAL): flush_interval: float = _FLUSH_INTERVAL):
self._service_name = service_name self._service_name = service_name
self._batch_size = batch_size self._client = RpcClient(
self._flush_interval = flush_interval _DBV2_ADDR, _DBV2_PORT,
self._queue = queue.Queue(maxsize=_QUEUE_MAXSIZE) service_name + "-log",
self._stop = threading.Event() timeout=_TIMEOUT,
self._flush_signal = threading.Event() )
self._flush_done = threading.Event() self._queue = AsyncRpcQueue(
self._thread = threading.Thread(target=self._flush_loop, handler=self._send_batch,
daemon=True, name='log-client') batch_size=batch_size,
self._thread.start() flush_interval=flush_interval,
atexit.register(self._shutdown) max_queue_size=_QUEUE_MAXSIZE,
on_error=self._on_error,
)
self._queue.start()
atexit.register(self.close)
# ── 公开接口 ──────────────────────────────────────────────────────── # ── 公开接口 ────────────────────────────────────────────────────────
def push(self, service, level, message, trace_id=None, extra=None): def push(self, service: str, level: str, message: str,
trace_id: str = None, extra=None):
"""推送一条日志(非阻塞,立即返回)。 """推送一条日志(非阻塞,立即返回)。
队列满时静默丢弃最旧条目再放入新条目,保证不阻塞业务线程、不导致 OOM。 队列满时静默丢弃最旧条目再放入新条目,保证不阻塞业务线程、不导致 OOM。
""" """
entry = { entry = {
'ts': _now_iso(), # 入队时即生成时间戳,反映真实事件时间 'ts': _now_iso(),
'service': service, 'service': service,
'level': level, 'level': level,
'message': message, 'message': message,
'trace_id': trace_id, 'trace_id': trace_id,
'extra': extra, 'extra': extra,
} }
try: self._queue.put(entry)
self._queue.put_nowait(entry)
except queue.Full:
# 队列满:丢弃最旧的一条,再放入新条目(drop-oldest 策略)
try:
self._queue.get_nowait()
except queue.Empty:
pass
try:
self._queue.put_nowait(entry)
except queue.Full:
pass # 极端竞争下仍失败则静默丢弃
def debug(self, service, message, **kw): def debug(self, service: str, message: str, **kw):
self.push(service, 'DEBUG', message, **kw) self.push(service, 'DEBUG', message, **kw)
def info(self, service, message, **kw): def info(self, service: str, message: str, **kw):
self.push(service, 'INFO', message, **kw) self.push(service, 'INFO', message, **kw)
def warning(self, service, message, **kw): def warning(self, service: str, message: str, **kw):
self.push(service, 'WARNING', message, **kw) self.push(service, 'WARNING', message, **kw)
def error(self, service, message, **kw): def warn(self, service: str, message: str, **kw):
self.push(service, 'WARNING', message, **kw)
def error(self, service: str, message: str, **kw):
self.push(service, 'ERROR', message, **kw) self.push(service, 'ERROR', message, **kw)
def critical(self, service, message, **kw): def critical(self, service: str, message: str, **kw):
self.push(service, 'CRITICAL', message, **kw) self.push(service, 'CRITICAL', message, **kw)
def flush(self): def flush(self):
"""手动 flush 队列中剩余的日志(含后台线程持有的局部 batch)。""" """手动 flush 队列中剩余的日志。"""
self._flush_done.clear() self._queue.flush()
self._flush_signal.set() # 通知后台线程立即 flush 其局部 batch
# 等待后台线程完成 flush(最多等待 flush_interval + 2 秒余量)
self._flush_done.wait(timeout=self._flush_interval + 2.0)
# 主线程排空队列中剩余条目
self._drain_queue_to_db()
def _drain_queue_to_db(self): def close(self):
"""排空主线程可见的队列中剩余条目,直接发送到 db。""" """关闭客户端,flush 剩余日志。"""
batch = [] self._queue.close()
while True:
try:
batch.append(self._queue.get_nowait())
if len(batch) >= self._batch_size:
self._send(batch)
batch = []
except queue.Empty:
break
if batch:
self._send(batch)
# ── 内部实现 ──────────────────────────────────────────────────────── # ── 内部实现 ────────────────────────────────────────────────────────
def _flush_loop(self): def _send_batch(self, batch: list):
"""后台线程:积攒到 batch_size 或超时 flush_interval 时发送。 """后台线程回调:逐条 INSERT 到 db(与旧版行为一致:网络错误放弃本批剩余)。"""
外层捕获所有异常,保证后台线程在任何情况下(网络故障、序列化错误等)
都不会死亡,避免日志队列无限增长导致 OOM。
"""
batch = []
last_flush = time.monotonic()
while not self._stop.is_set():
try:
timeout = self._flush_interval - (time.monotonic() - last_flush)
chk_timeout = max(0.1, min(timeout, 0.5))
try:
item = self._queue.get(timeout=chk_timeout)
batch.append(item)
while len(batch) < self._batch_size:
try:
batch.append(self._queue.get_nowait())
except queue.Empty:
break
except queue.Empty:
pass
should_flush = (
len(batch) >= self._batch_size or
self._flush_signal.is_set() or
(batch and time.monotonic() - last_flush >= self._flush_interval)
)
if should_flush and batch:
try:
self._send(batch)
except Exception as e:
# _send 内部已有细粒度异常处理,此处是最终安全网
print(f"[log_client] _send 异常(已捕获,线程继续运行): {e}",
file=_sys.stderr)
batch = []
last_flush = time.monotonic()
if self._flush_signal.is_set():
self._flush_signal.clear()
self._flush_done.set()
except Exception as e:
# 最外层安全网:任何意外异常都不应杀死日志线程
print(f"[log_client] _flush_loop 异常(已捕获,线程继续运行): {e}",
file=_sys.stderr)
time.sleep(0.5) # 防止异常风暴导致 CPU 满载
# 退出前 flush 剩余
if batch:
try:
self._send(batch)
except Exception as e:
print(f"[log_client] 退出时 flush 异常: {e}", file=_sys.stderr)
self._flush_done.set()
def _send(self, batch):
"""通过 pymsg RPC(execute 路由)批量发送到 dbv2 Logs 表。
整个 batch 复用一个 TCP 连接;网络错误时放弃本批剩余条目(fail-safe),
不抛异常给调用方。
"""
try:
client = RpcClient(_DBV2_ADDR, _DBV2_PORT, self._service_name + "-log")
except Exception as e:
print(f"[log_client] 创建 RpcClient 失败: {e}", file=_sys.stderr)
return
for entry in batch: for entry in batch:
extra_val = entry.get("extra") extra_val = entry.get("extra")
try: try:
@@ -224,21 +140,21 @@ class LogClient:
}, },
} }
try: try:
client.call("execute", payload, timeout=_TIMEOUT) self._client.call("execute", payload, timeout=_TIMEOUT)
except RpcError as e: except RpcError as e:
# 业务错误(如表结构不符):打印后继续下一条
print(f"[log_client] INSERT 失败: {e}", file=_sys.stderr) print(f"[log_client] INSERT 失败: {e}", file=_sys.stderr)
except Exception as e: except Exception as e:
# 网络错误:db 不可用,放弃本批剩余(fail-safe) print(f"[log_client] 发送失败(放弃本批剩余): {e}", file=_sys.stderr)
print(f"[log_client] 发送失败: {e}", file=_sys.stderr)
break break
def _shutdown(self): def _on_error(self, e: Exception, batch: list):
"""进程退出时 flush 剩余日志。""" """异常安全网:handler 外的异常也不会崩溃线程。"""
self._stop.set() print(f"[log_client] _send_batch 异常(已捕获,线程继续运行): {e}",
self.flush() file=_sys.stderr)
# ── 全局单例 ────────────────────────────────────────────────────────── # ── 全局单例 ──────────────────────────────────────────────────────────
# 各服务的 log_client.py 副本需将 service_name 改为自己的服务名。
# 此文件(confs/log_client.py)的 service_name 为 "confs"。
logs = LogClient() logs = LogClient(service_name="static")