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"""
双写协调服务 - 生产级实现
保证MySQL、Milvus、MinIO三存储的数据一致性
"""
import time
import uuid
import threading
from typing import Optional, List, Dict, Any
from dataclasses import dataclass, field
from enum import Enum
from concurrent.futures import ThreadPoolExecutor, as_completed
import logging
import traceback
logger = logging.getLogger(__name__)
class WriteOperation(Enum):
"""写操作类型"""
INSERT = "insert"
UPDATE = "update"
DELETE = "delete"
SYNC = "sync"
class ConsistencyLevel(Enum):
"""一致性等级"""
STRONG = "strong"
EVENTUAL = "eventual"
CAUSAL = "causal"
@dataclass
class WriteResult:
"""写操作结果"""
success: bool
operation: WriteOperation
target: str
data_id: str
error: Optional[str] = None
latency_ms: float = 0
@dataclass
class DualWriteResult:
"""双写操作的聚合结果"""
operation_id: str
overall_success: bool
consistency_achieved: bool
results: List[WriteResult] = field(default_factory=list)
compensation_needed: bool = False
total_latency_ms: float = 0
class DualWriteCoordinator:
"""
双写协调器
核心功能：
1. 编排多存储写入顺序
2. 处理部分失败场景
3. 触发补偿机制
4. 保证最终一致性
"""
def __init__(
self,
mysql_client,
milvus_client,
minio_client,
consistency_level: ConsistencyLevel = ConsistencyLevel.EVENTUAL,
max_retries: int = 3,
retry_delay_base: float = 1.0,
enable_compensation_queue: bool = True
):
self.mysql = mysql_client
self.milvus = milvus_client
self.minio = minio_client
self.consistency_level = consistency_level
self.max_retries = max_retries
self.retry_delay_base = retry_delay_base
self.enable_compensation_queue = enable_compensation_queue
self.compensation_queue = []
self._queue_lock = threading.Lock()
self.stats = {
'total_operations': 0,
'successful_operations': 0,
'compensations_triggered': 0,
'avg_latency_ms': 0,
}
def execute_dual_write(
self,
operation: WriteOperation,
mysql_data: Dict = None,
milvus_data: List[Dict] = None,
minio_file_path: str = None,
minio_file_content: bytes = None,
transaction_context: Dict = None
) -> DualWriteResult:
"""
执行双写操作
参数:
operation: 操作类型
mysql_data: 要写入MySQL的数据字典
milvus_data: 要写入Milvus的数据列表
minio_file_path: MinIO对象路径
minio_file_content: 文件内容字节
transaction_context: 事务上下文（用于日志追踪）
"""
op_id = str(uuid.uuid4())[:8]
start_time = time.time()
logger.info(f"[{op_id}] 开始双写操作: {operation.value}")
results = []
all_success = True
compensation_needed = False
try:
if operation == WriteOperation.INSERT:
results = self._execute_insert(
op_id, mysql_data, milvus_data,
minio_file_path, minio_file_content
)
elif operation == WriteOperation.UPDATE:
results = self._execute_update(
op_id, mysql_data, milvus_data, minio_file_path, minio_file_content
)
elif operation == WriteOperation.DELETE:
results = self._execute_delete(op_id, mysql_data, milvus_data, minio_file_path)
failed_targets = [r for r in results if not r.success]
all_success = len(failed_targets) == 0
if not all_success:
compensation_needed = True
logger.warning(
f"[{op_id}] 部分写入失败: "
f"{[f'{r.target}({r.error})' for r in failed_targets]}"
)
if self.enable_compensation_queue:
self._enqueue_compensation(
op_id, operation, mysql_data, milvus_data,
minio_file_path, minio_file_content, failed_targets
)
except Exception as e:
logger.error(f"[{op_id}] 双写异常: {e}\n{traceback.format_exc()}")
all_success = False
compensation_needed = True
results.append(WriteResult(
success=False,
operation=operation,
target="coordinator",
data_id=op_id,
error=str(e)
))
elapsed_ms = (time.time() - start_time) * 1000
self.stats['total_operations'] += 1
if all_success:
self.stats['successful_operations'] += 1
result = DualWriteResult(
operation_id=op_id,
overall_success=all_success,
consistency_achieved=all_success or self.consistency_level != ConsistencyLevel.STRONG,
results=results,
compensation_needed=compensation_needed,
total_latency_ms=elapsed_ms
)
logger.info(
f"[{op_id}] 双写完成: success={all_success}, "
f"latency={elapsed_ms:.0f}ms, targets={len(results)}"
)
return result
def _execute_insert(
self,
op_id: str,
mysql_data: Dict,
milvus_data: List[Dict],
minio_path: str,
minio_content: bytes
) -> List[WriteResult]:
"""
执行插入操作（推荐顺序: MinIO → MySQL → Milvus）
"""
results = []
if minio_path and minio_content:
result = self._safe_write(
op_id, "minio", WriteOperation.INSERT,
lambda: self._upload_to_minio(minio_path, minio_content),
data_id=minio_path
)
results.append(result)
if not result.success:
return results
if mysql_data:
result = self._safe_write(
op_id, "mysql", WriteOperation.INSERT,
lambda: self._insert_to_mysql(mysql_data),
data_id=mysql_data.get('doc_id', '')
)
results.append(result)
if not result.success:
if minio_path:
try:
self.minio.remove_object('rag-knowledge-base', minio_path)
except:
pass
return results
if milvus_data:
result = self._safe_write(
op_id, "milvus", WriteOperation.INSERT,
lambda: self._insert_to_milvus(milvus_data),
data_id=mysql_data.get('doc_id', '') if mysql_data else ''
)
results.append(result)
return results
def _safe_write(
self,
op_id: str,
target: str,
operation: WriteOperation,
write_func,
data_id: str,
retry: int = 0
) -> WriteResult:
"""
安全执行写操作（带重试）
"""
start = time.time()
try:
write_func()
latency = (time.time() - start) * 1000
return WriteResult(
success=True,
operation=operation,
target=target,
data_id=data_id,
latency_ms=latency
)
except Exception as e:
latency = (time.time() - start) * 1000
if retry < self.max_retries:
delay = self.retry_delay_base * (2 ** retry)
logger.warning(
f"[{op_id}] {target} 写入失败(第{retry+1}次), "
f"{delay}s后重试: {e}"
)
time.sleep(delay)
return self._safe_write(
op_id, target, operation, write_func,
data_id, retry + 1
)
else:
logger.error(f"[{op_id}] {target} 写入最终失败: {e}")
return WriteResult(
success=False,
operation=operation,
target=target,
data_id=data_id,
error=str(e),
latency_ms=latency
)
def _upload_to_minio(self, path: str, content: bytes):
"""上传文件到MinIO"""
self.minio.put_object(
bucket_name="rag-knowledge-base",
object_name=path,
data=content,
length=len(content)
)
def _insert_to_mysql(self, data: Dict):
"""插入数据到MySQL"""
sql = """
INSERT INTO documents
(doc_id, title, source_type, business_tag, oss_key, status)
VALUES (%s, %s, %s, %s, %s, %s)
"""
self.mysql.execute(sql, (
data['doc_id'],
data['title'],
data['source_type'],
data.get('business_tag', ''),
data.get('oss_key', ''),
'completed'
))
def _insert_to_milvus(self, data_list: List[Dict]):
"""批量插入数据到Milvus"""
if data_list:
self.milvus.insert(
collection_name="rag_vectors",
data=data_list
)
def _enqueue_compensation(
self,
op_id: str,
operation: WriteOperation,
mysql_data: Dict,
milvus_data: List[Dict],
minio_path: str,
minio_content: bytes,
failed_targets: List[WriteResult]
):
"""将失败操作加入补偿队列"""
with self._queue_lock:
compensation_item = {
'operation_id': op_id,
'operation': operation,
'mysql_data': mysql_data,
'milvus_data': milvus_data,
'minio_path': minio_path,
'minio_content': minio_content,
'failed_targets': [t.target for t in failed_targets],
'created_at': time.time(),
'retry_count': 0
}
self.compensation_queue.append(compensation_item)
self.stats['compensations_triggered'] += 1
logger.info(f"[{op_id}] 已加入补偿队列, 当前队列长度: {len(self.compensation_queue)}")
def process_compensation_queue(self, max_items: int = 100):
"""
处理补偿队列中的失败操作
应该由后台定时任务调用
"""
with self._queue_lock:
items_to_process = self.compensation_queue[:max_items]
self.compensation_queue = self.compensation_queue[max_items:]
processed = 0
success_count = 0
for item in items_to_process:
if item['retry_count'] >= self.max_retries:
logger.error(
f"补偿操作达到最大重试次数, 放弃: {item['operation_id']}"
)
continue
item['retry_count'] += 1
try:
result = self.execute_dual_write(
operation=item['operation'],
mysql_data=item['mysql_data'],
milvus_data=item['milvus_data'],
minio_file_path=item['minio_path'],
minio_file_content=item['minio_content']
)
if result.overall_success:
success_count += 1
logger.info(f"补偿成功: {item['operation_id']}")
else:
with self._queue_lock:
self.compensation_queue.append(item)
except Exception as e:
logger.error(f"补偿执行异常: {item['operation_id']}, {e}")
with self._queue_lock:
self.compensation_queue.append(item)
processed += 1
logger.info(
f"补偿队列处理完成: 处理={processed}, 成功={success_count}, "
f"剩余队列长度={len(self.compensation_queue)}"
)
return {'processed': processed, 'success': success_count}
def get_statistics(self) -> Dict:
"""获取统计信息"""
return {
**self.stats,
'compensation_queue_size': len(self.compensation_queue),
'success_rate': (
self.stats['successful_operations'] / max(1, self.stats['total_operations']) * 100
)
}
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