前七讲我们构建了一个完整的协同编辑系统——从文档引擎、OT算法、WebSocket通信、操作同步、撤销重做到光标展示。但一个能跑的系统和一个能扛住压力的系统之间，还有很长的路要走。
这一讲，我们来对系统进行全面的性能优化和压力测试。
一、性能瓶颈分析
1.1 系统瓶颈图谱
用户输入 │ ├──▶ 前端渲染 │ ├── DOM 操作（频繁重排/重绘） │ ├── 光标计算（每帧都需要） │ └── 选区高亮（大量 overlay） │ ├──▶ 网络传输 │ ├── WebSocket 消息频率 │ ├── 消息大小（序列化开销） │ └── 延迟/丢包 │ ├──▶ 服务端处理 │ ├── OT 变换计算 │ ├── 广播风暴（N 个用户产生 N² 条消息） │ └── 历史记录增长 │ └──▶ 内存占用 ├── 操作历史 ├── 光标状态 └── 文档快照1.2 优化目标
指标 | 优化前 | 优化后 | 提升 |
|---|---|---|---|
操作延迟 | ~50ms | ~10ms | 5x |
内存占用 | ~100MB | ~20MB | 5x |
消息大小 | ~500B | ~50B | 10x |
并发支持 | ~10人 | ~100人 | 10x |
二、操作批处理优化
2.1 智能批处理器
# core/performance/op_batcher.py """ 高性能操作批处理器 支持智能合并、优先级调度和背压控制。 """ from __future__ import annotations from typing import List, Optional, Callable, Dict from dataclasses import dataclass, field from enum import Enum import time import asyncio import logging from core.ot.operation import OTOperation, OpType from core.ot.transform import compose logger = logging.getLogger(__name__) class BatchPriority(Enum): """批处理优先级""" HIGH = 0 # 立即发送（光标、选区） NORMAL = 1 # 正常（文本操作） LOW = 2 # 可延迟（元数据更新） @dataclass class BatchItem: """批处理项""" priority: BatchPriority op: OTOperation timestamp: float = 0.0 def __post_init__(self): if not self.timestamp: self.timestamp = time.time() class AdaptiveBatcher: """ 自适应批处理器 根据网络状况和系统负载动态调整批处理策略。 """ def __init__(self, max_batch_size: int = 32, base_delay_ms: float = 16, # 约 60fps adaptive: bool = True): self.max_batch_size = max_batch_size self.base_delay_ms = base_delay_ms self.adaptive = adaptive # 优先级队列 self.high_queue: List[BatchItem] = [] self.normal_queue: List[BatchItem] = [] self.low_queue: List[BatchItem] = [] # 统计信息 self.stats = { 'batches_created': 0, 'ops_processed': 0, 'avg_batch_size': 0.0, 'avg_compress_ratio': 0.0 } # 自适应参数 self.current_delay_ms = base_delay_ms self.network_latency_ms = 0.0 self.cpu_load = 0.0 # 发送回调 self.on_batch_ready: Optional[Callable] = None # 定时器 self._timer_task = None # ---------- 添加操作 ---------- def add(self, op: OTOperation, priority: BatchPriority = BatchPriority.NORMAL): """ 添加操作到批处理队列 Args: op: 操作 priority: 优先级 """ item = BatchItem(priority=priority, op=op) if priority == BatchPriority.HIGH: self.high_queue.append(item) elif priority == BatchPriority.NORMAL: self.normal_queue.append(item) else: self.low_queue.append(item) # 检查是否需要立即发送 if self._should_flush(): return self.flush() return None def _should_flush(self) -> bool: """检查是否需要刷新""" # 高优先级队列有数据就立即发送 if self.high_queue: return True # 普通队列达到上限 if len(self.normal_queue) >= self.max_batch_size: return True return False # ---------- 刷新 ---------- def flush(self) -> Optional[List[OTOperation]]: """ 刷新所有队列 Returns: 压缩后的操作列表 """ # 收集所有操作 all_items = [] # 高优先级优先 if self.high_queue: all_items.extend(self.high_queue) self.high_queue.clear() # 普通队列 if self.normal_queue: all_items.extend(self.normal_queue) self.normal_queue.clear() # 低优先级（只在空闲时处理） if not all_items and self.low_queue: all_items.extend(self.low_queue[:10]) # 最多取10个 self.low_queue = self.low_queue[10:] if not all_items: return None # 提取操作 ops = [item.op for item in all_items] # 压缩 compressed = self._smart_compress(ops) # 更新统计 self.stats['batches_created'] += 1 self.stats['ops_processed'] += len(ops) if compressed: ratio = len(compressed) / len(ops) self.stats['avg_compress_ratio'] = ( self.stats['avg_compress_ratio'] * 0.9 + ratio * 0.1 ) return compressed def _smart_compress(self, ops: List[OTOperation]) -> List[OTOperation]: """ 智能压缩 不仅合并相邻操作，还会识别可优化的模式。 """ if not ops: return [] result = [] buffer = [] # 临时缓冲区 for op in ops: if not buffer: buffer.append(op) continue # 尝试合并 merged = compose(buffer[-1], op) if merged != op: # 可以合并 buffer[-1] = merged else: buffer.append(op) # 处理缓冲区 for op in buffer: # 进一步优化：合并连续的插入/删除 if result and self._can_merge(result[-1], op): result[-1] = compose(result[-1], op) else: result.append(op) return result def _can_merge(self, op1: OTOperation, op2: OTOperation) -> bool: """检查两个操作是否可以合并""" # 同类型且在附近 if op1.op_type != op2.op_type: return False if op1.op_type == OpType.INSERT: # 连续插入可以合并 return (op1.position + len(op1.text) == op2.position or op2.position + len(op2.text) == op1.position) if op1.op_type == OpType.DELETE: # 连续删除可以合并 return (op1.position + len(op1.deleted_text) == op2.position or op2.position + len(op2.deleted_text) == op1.position) return False # ---------- 自适应调度 ---------- async def start_adaptive_scheduler(self): """启动自适应调度器""" while True: await asyncio.sleep(self.current_delay_ms / 1000.0) # 检查是否需要自动刷新 if self.normal_queue or self.low_queue: batch = self.flush() if batch and self.on_batch_ready: await self.on_batch_ready(batch) def update_network_latency(self, latency_ms: float): """更新网络延迟""" self.network_latency_ms = latency_ms if self.adaptive: # 网络差时增大批处理窗口 if latency_ms > 100: self.current_delay_ms = min(self.base_delay_ms * 4, 100) elif latency_ms > 50: self.current_delay_ms = self.base_delay_ms * 2 else: self.current_delay_ms = self.base_delay_ms # ---------- 统计 ---------- def get_stats(self) -> dict: """获取统计信息""" return { **self.stats, 'queue_sizes': { 'high': len(self.high_queue), 'normal': len(self.normal_queue), 'low': len(self.low_queue) }, 'current_delay_ms': self.current_delay_ms, 'network_latency_ms': self.network_latency_ms }三、内存优化
3.1 操作历史压缩
# core/performance/memory_opt.py """ 内存优化工具 包括操作历史压缩、快照管理和垃圾回收。 """ from __future__ import annotations from typing import List, Optional, Dict, Tuple from dataclasses import dataclass import time import logging from core.ot.operation import OTOperation, OpType from core.ot.transform import compose logger = logging.getLogger(__name__) @dataclass class Snapshot: """文档快照""" text: str version: int timestamp: float operations_since: int = 0 def size_bytes(self) -> int: return len(self.text) * 2 + 16 # 估算 class HistoryCompressor: """ 历史记录压缩器 通过快照和增量压缩来控制内存增长。 """ def __init__(self, snapshot_interval: int = 100): """ Args: snapshot_interval: 每隔多少个操作创建一个快照 """ self.snapshot_interval = snapshot_interval # 快照列表 self.snapshots: List[Snapshot] = [] # 操作历史 self.history: List[OTOperation] = [] # 统计 self.total_ops = 0 self.compressed_ops = 0 def add_operation(self, op: OTOperation, current_text: str): """ 添加操作到历史 Args: op: 操作 current_text: 当前文档文本 """ self.history.append(op) self.total_ops += 1 # 检查是否需要创建快照 if len(self.history) >= self.snapshot_interval: self._create_snapshot(current_text) def _create_snapshot(self, text: str): """创建快照""" snapshot = Snapshot( text=text, version=self.total_ops, timestamp=time.time(), operations_since=len(self.history) ) self.snapshots.append(snapshot) # 清空历史（快照之前的操作不再需要） self.history.clear() logger.debug(f"Created snapshot at version {snapshot.version}, " f"history cleared") def get_operations_since(self, version: int) -> List[OTOperation]: """ 获取某个版本之后的操作 会从最近的快照开始重建。 Args: version: 目标版本 Returns: 操作列表 """ # 找到最近的快照 latest_snapshot = None for snap in reversed(self.snapshots): if snap.version <= version: latest_snapshot = snap break if not latest_snapshot: # 没有快照，返回全部历史 return list(self.history) # 返回快照之后的增量操作 return list(self.history) def compact(self, max_history: int = 1000): """ 压缩历史记录 删除过旧的历史，只保留最近的 N 条。 Args: max_history: 最大历史记录数 """ if len(self.history) > max_history: # 保留最近的操作 self.history = self.history[-max_history:] self.compressed_ops = self.total_ops - len(self.history) logger.info(f"History compacted: kept {len(self.history)} ops") def get_stats(self) -> dict: """获取统计信息""" history_size = sum( len(op.text) + len(op.deleted_text) for op in self.history ) return { 'total_ops': self.total_ops, 'history_ops': len(self.history), 'compressed_ops': self.compressed_ops, 'snapshots': len(self.snapshots), 'history_size_bytes': history_size * 2, 'compression_ratio': ( self.compressed_ops / max(self.total_ops, 1) ) } class MemoryMonitor: """ 内存监控器 跟踪内存使用情况，触发垃圾回收。 """ def __init__(self, warning_threshold_mb: float = 80.0, critical_threshold_mb: float = 150.0): self.warning_threshold = warning_threshold_mb self.critical_threshold = critical_threshold_mb self.on_warning: Optional[callable] = None self.on_critical: Optional[callable] = None def check_memory(self) -> dict: """ 检查内存使用 Returns: 内存状态 """ import psutil import os process = psutil.Process(os.getpid()) memory_mb = process.memory_info().rss / 1024 / 1024 status = { 'memory_mb': memory_mb, 'warning': memory_mb > self.warning_threshold, 'critical': memory_mb > self.critical_threshold } if status['critical'] and self.on_critical: self.on_critical(status) elif status['warning'] and self.on_warning: self.on_warning(status) return status四、网络优化
4.1 消息压缩
# core/performance/network_opt.py """ 网络传输优化 包括消息压缩、差分编码和协议优化。 """ from __future__ import annotations from typing import Dict, Any, Optional import json import zlib import struct import logging from core.ot.operation import OTOperation, OpType logger = logging.getLogger(__name__) class MessageCompressor: """ 消息压缩器 使用多种策略减少网络传输大小。 """ def __init__(self, compression_level: int = 6): self.compression_level = compression_level # 缓存 self.field_cache: Dict[str, int] = {} self.next_field_id = 0 self.stats = { 'original_bytes': 0, 'compressed_bytes': 0, 'messages_processed': 0 } def compress_operation(self, op: OTOperation) -> bytes: """ 压缩操作 使用二进制格式 + 字段名缓存。 Args: op: 操作 Returns: 压缩后的字节 """ # 转换为紧凑格式 data = self._to_compact(op) # JSON 序列化 json_str = json.dumps(data, separators=(',', ':')) # zlib 压缩 compressed = zlib.compress( json_str.encode('utf-8'), self.compression_level ) # 更新统计 self.stats['original_bytes'] += len(json_str) self.stats['compressed_bytes'] += len(compressed) self.stats['messages_processed'] += 1 return compressed def decompress_operation(self, data: bytes) -> OTOperation: """ 解压操作 Args: data: 压缩的数据 Returns: 操作 """ # zlib 解压 json_str = zlib.decompress(data).decode('utf-8') # 解析 JSON compact = json.loads(json_str) # 转换回操作 return self._from_compact(compact) def _to_compact(self, op: OTOperation) -> dict: """转换为紧凑格式""" compact = { 't': op.op_type.value, # type (short) 'p': op.position, # position 's': op.site_id, # site_id 'q': op.sequence # sequence } # 只包含非空字段 if op.text: compact['x'] = op.text # text if op.deleted_text: compact['d'] = op.deleted_text # deleted_text return compact def _from_compact(self, compact: dict) -> OTOperation: """从紧凑格式转换""" return OTOperation( op_type=OpType(compact['t']), position=compact['p'], text=compact.get('x', ''), deleted_text=compact.get('d', ''), site_id=compact['s'], sequence=compact['q'] ) def get_compression_ratio(self) -> float: """获取压缩率""" if self.stats['original_bytes'] == 0: return 1.0 return self.stats['compressed_bytes'] / self.stats['original_bytes'] def get_stats(self) -> dict: """获取统计信息""" return { **self.stats, 'compression_ratio': self.get_compression_ratio() } class DifferentialEncoder: """ 差分编码器 只发送变化的部分，减少重复数据传输。 """ @staticmethod def encode_cursor(old_pos: int, new_pos: int) -> bytes: """ 编码光标位置变化 使用变长整数编码。 Args: old_pos: 旧位置 new_pos: 新位置 Returns: 编码后的字节 """ diff = new_pos - old_pos # 使用 ZigZag 编码处理负数 zigzag = (diff << 1) ^ (diff >> 31) # 变长编码 result = bytearray() while zigzag > 127: result.append((zigzag & 127) | 128) zigzag >>= 7 result.append(zigzag & 127) return bytes(result) @staticmethod def decode_cursor(old_pos: int, data: bytes) -> int: """ 解码光标位置 Args: old_pos: 旧位置 data: 编码的数据 Returns: 新位置 """ # 变长解码 zigzag = 0 shift = 0 for byte in data: zigzag |= (byte & 127) << shift shift += 7 if not (byte & 128): break # ZigZag 解码 diff = (zigzag >> 1) ^ -(zigzag & 1) return old_pos + diff五、压力测试框架
5.1 测试工具
# tests/stress_test.py """ 压力测试框架 模拟多用户并发编辑，测试系统极限。 """ from __future__ import annotations from typing import List, Dict, Optional from dataclasses import dataclass, field import asyncio import random import time import statistics import logging from datetime import datetime from client.ws_client import EditorClient from core.ot.operation import OTOperation, OpType logger = logging.getLogger(__name__) @dataclass class TestMetrics: """测试指标""" start_time: float = 0.0 end_time: float = 0.0 total_ops: int = 0 successful_ops: int = 0 failed_ops: int = 0 latencies: List[float] = field(default_factory=list) conflicts: int = 0 @property def duration(self) -> float: return self.end_time - self.start_time @property def ops_per_second(self) -> float: return self.total_ops / max(self.duration, 0.001) @property def avg_latency(self) -> float: return statistics.mean(self.latencies) if self.latencies else 0 @property def p95_latency(self) -> float: if not self.latencies: return 0 sorted_lats = sorted(self.latencies) idx = int(len(sorted_lats) * 0.95) return sorted_lats[idx] @property def success_rate(self) -> float: return self.successful_ops / max(self.total_ops, 1) class VirtualUser: """ 虚拟用户 模拟真实用户行为进行压力测试。 """ def __init__(self, user_id: str, document_id: str, metrics: TestMetrics, think_time_ms: tuple = (50, 300)): self.user_id = user_id self.document_id = document_id self.metrics = metrics self.think_time = think_time_ms self.client = EditorClient() self.text_buffer = "" self.position = 0 async def run(self, duration: float): """ 运行虚拟用户 Args: duration: 运行时长（秒） """ # 连接 await self.client.connect(self.document_id) end_time = time.time() + duration while time.time() < end_time: # 随机思考 await asyncio.sleep( random.uniform(*self.think_time) / 1000.0 ) # 随机选择操作 action = random.choice(['insert', 'delete', 'move_cursor']) if action == 'insert': await self._random_insert() elif action == 'delete': await self._random_delete() else: self._random_move_cursor() # 断开连接 await self.client.disconnect() async def _random_insert(self): """随机插入""" text_len = random.randint(1, 5) text = ''.join(random.choice('abcdefghij ') for _ in range(text_len)) pos = random.randint(0, max(0, len(self.text_buffer))) start = time.time() try: op = await self.client.local_insert(pos, text) # 更新本地状态 self.text_buffer = ( self.text_buffer[:pos] + text + self.text_buffer[pos:] ) self.position = pos + text_len # 记录指标 self.metrics.total_ops += 1 self.metrics.successful_ops += 1 self.metrics.latencies.append((time.time() - start) * 1000) except Exception as e: self.metrics.failed_ops += 1 logger.error(f"Insert failed: {e}") async def _random_delete(self): """随机删除""" if not self.text_buffer: return pos = random.randint(0, len(self.text_buffer) - 1) length = random.randint(1, min(3, len(self.text_buffer) - pos)) start = time.time() try: op = await self.client.local_delete(pos, length) # 更新本地状态 self.text_buffer = ( self.text_buffer[:pos] + self.text_buffer[pos + length:] ) self.position = pos # 记录指标 self.metrics.total_ops += 1 self.metrics.successful_ops += 1 self.metrics.latencies.append((time.time() - start) * 1000) except Exception as e: self.metrics.failed_ops += 1 logger.error(f"Delete failed: {e}") def _random_move_cursor(self): """随机移动光标""" if self.text_buffer: self.position = random.randint(0, len(self.text_buffer)) class StressTester: """ 压力测试器 管理多用户并发测试。 """ def __init__(self, num_users: int = 10, test_duration: float = 30.0, ramp_up: float = 5.0): self.num_users = num_users self.test_duration = test_duration self.ramp_up = ramp_up self.document_id = f"stress-test-{int(time.time())}" self.metrics = TestMetrics() async def run(self) -> TestMetrics: """ 运行压力测试 Returns: 测试指标 """ print(f"\n{'='*70}") print(f"🚀 压力测试开始") print(f"{'='*70}") print(f" 用户数: {self.num_users}") print(f" 时长: {self.test_duration}s") print(f" 爬坡: {self.ramp_up}s") print(f" 文档: {self.document_id}") self.metrics.start_time = time.time() # 创建虚拟用户 users = [] for i in range(self.num_users): user = VirtualUser( user_id=f"stress-user-{i}", document_id=self.document_id, metrics=self.metrics ) users.append(user) # 逐步启动（爬坡） tasks = [] for i, user in enumerate(users): delay = (i / self.num_users) * self.ramp_up task = asyncio.create_task( self._delayed_run(user, delay) ) tasks.append(task) # 等待所有用户完成 await asyncio.gather(*tasks) self.metrics.end_time = time.time() # 输出报告 self._print_report() return self.metrics async def _delayed_run(self, user: VirtualUser, delay: float): """延迟启动用户""" await asyncio.sleep(delay) await user.run(self.test_duration) def _print_report(self): """打印测试报告""" m = self.metrics print(f"\n{'='*70}") print(f"📊 测试报告") print(f"{'='*70}") print(f" 持续时间: {m.duration:.1f}s") print(f" 总操作数: {m.total_ops}") print(f" 成功: {m.successful_ops}") print(f" 失败: {m.failed_ops}") print(f" 成功率: {m.success_rate*100:.1f}%") print(f" ───────────────────────────") print(f" OPS: {m.ops_per_second:.1f}/s") print(f" 平均延迟: {m.avg_latency:.1f}ms") print(f" P95 延迟: {m.p95_latency:.1f}ms") print(f" 冲突数: {m.conflicts}")六、性能基准测试
6.1 基准测试脚本
# examples/benchmark.py """ 性能基准测试 """ import asyncio import logging import sys import time sys.path.insert(0, '..') from core.performance.op_batcher import AdaptiveBatcher, BatchPriority from core.performance.memory_opt import HistoryCompressor, MemoryMonitor from core.performance.network_opt import MessageCompressor, DifferentialEncoder from core.ot.operation import OTOperation, OpType from tests.stress_test import StressTester logging.basicConfig(level=logging.WARNING) async def benchmark_batcher(): """基准测试批处理器""" print("\n📦 批处理器基准测试") print("-" * 40) batcher = AdaptiveBatcher(max_batch_size=32) # 模拟连续输入 start = time.time() for i in range(1000): op = OTOperation(OpType.INSERT, i, text=chr(ord('a') + (i % 26))) batcher.add(op, BatchPriority.NORMAL) # 刷新 batch = batcher.flush() elapsed = (time.time() - start) * 1000 stats = batcher.get_stats() print(f" 处理 1000 个操作: {elapsed:.1f}ms") print(f" 批次数量: {stats['batches_created']}") print(f" 压缩率: {stats['avg_compress_ratio']:.2f}") def benchmark_compression(): """基准测试消息压缩""" print("\n📦 消息压缩基准测试") print("-" * 40) compressor = MessageCompressor() # 创建测试操作 ops = [] for i in range(100): op = OTOperation( OpType.INSERT, i * 10, text=f"Hello World {i}" * 5, site_id="test-site", sequence=i ) ops.append(op) # 测试压缩 original_total = 0 compressed_total = 0 for op in ops: compressed = compressor.compress_operation(op) original_total += len(str(op.__dict__)) compressed_total += len(compressed) ratio = compressed_total / max(original_total, 1) print(f" 原始大小: {original_total} bytes") print(f" 压缩后: {compressed_total} bytes") print(f" 压缩率: {ratio:.2%}") def benchmark_memory(): """基准测试内存使用""" print("\n📦 内存使用基准测试") print("-" * 40) compressor = HistoryCompressor(snapshot_interval=100) # 模拟大量操作 for i in range(10000): op = OTOperation(OpType.INSERT, i, text=f"char{i}") compressor.add_operation(op, f"text after op {i}") stats = compressor.get_stats() print(f" 总操作数: {stats['total_ops']}") print(f" 历史记录: {stats['history_ops']}") print(f" 快照数: {stats['snapshots']}") print(f" 压缩比: {stats['compression_ratio']:.2%}") # 压缩 compressor.compact(max_history=500) stats = compressor.get_stats() print(f" 压缩后历史: {stats['history_ops']}") async def run_stress_test(): """运行压力测试""" print("\n📦 压力测试") print("-" * 40) tester = StressTester( num_users=20, test_duration=10.0, ramp_up=3.0 ) metrics = await tester.run() async def main(): print("=" * 65) print("⚡ 性能基准测试套件") print("=" * 65) benchmark_batcher() benchmark_compression() benchmark_memory() # 压力测试需要服务器运行 print("\n⚠️ 跳过压力测试（需要服务器运行）") print(" 运行: python examples/run_server.py") print(" 然后取消下面的注释") # await run_stress_test() if __name__ == "__main__": asyncio.run(main())七、测试
7.1 性能测试
# tests/test_performance.py import pytest import time from core.performance.op_batcher import AdaptiveBatcher, BatchPriority from core.performance.memory_opt import HistoryCompressor from core.performance.network_opt import MessageCompressor from core.ot.operation import OTOperation, OpType class TestAdaptiveBatcher: """批处理器测试""" def test_basic_batching(self): batcher = AdaptiveBatcher(max_batch_size=10) ops = [] for i in range(25): op = OTOperation(OpType.INSERT, i, text="x") result = batcher.add(op, BatchPriority.NORMAL) if result: ops.extend(result) # 应该产生至少2个批次 assert batcher.stats['batches_created'] >= 2 def test_high_priority_immediate(self): batcher = AdaptiveBatcher() op = OTOperation(OpType.INSERT, 0, text="urgent") result = batcher.add(op, BatchPriority.HIGH) # 高优先级应立即发送 assert result is not None def test_compression(self): batcher = AdaptiveBatcher() # 连续插入应该被合并 for i in range(5): op = OTOperation(OpType.INSERT, i, text=chr(ord('a') + i)) batcher.add(op, BatchPriority.NORMAL) batch = batcher.flush() assert batch is not None # 5个操作应该被压缩成更少 assert len(batch) < 5 class TestHistoryCompressor: """历史压缩测试""" def test_snapshot_creation(self): compressor = HistoryCompressor(snapshot_interval=10) for i in range(50): op = OTOperation(OpType.INSERT, i, text=f"x{i}") compressor.add_operation(op, f"text after {i}") stats = compressor.get_stats() assert stats['snapshots'] >= 4 # 50/10 = 5个快照 assert stats['history_ops'] <= 10 # 历史被清空 def test_compaction(self): compressor = HistoryCompressor() for i in range(2000): op = OTOperation(OpType.INSERT, i, text=f"x{i}") compressor.add_operation(op, f"text after {i}") compressor.compact(max_history=100) stats = compressor.get_stats() assert stats['history_ops'] <= 110 # 接近100 class TestMessageCompressor: """消息压缩测试""" def test_compress_decompress(self): compressor = MessageCompressor() original = OTOperation( OpType.INSERT, 42, text="Hello World", site_id="test-site", sequence=123 ) compressed = compressor.compress_operation(original) decompressed = compressor.decompress_operation(compressed) assert original.position == decompressed.position assert original.text == decompressed.text assert original.site_id == decompressed.site_id def test_compression_ratio(self): compressor = MessageCompressor() # 大文本应该有好的压缩率 big_text = "Hello World! " * 100 op = OTOperation( OpType.INSERT, 0, text=big_text, site_id="test", sequence=1 ) compressed = compressor.compress_operation(op) original_size = len(str(op.__dict__)) compressed_size = len(compressed) ratio = compressed_size / original_size assert ratio < 0.5 # 压缩率应该低于50% if __name__ == "__main__": pytest.main([__file__, "-v"])八、总结
8.1 本讲成果
组件 | 文件 | 功能 |
|---|---|---|
AdaptiveBatcher |
| 自适应批处理 |
HistoryCompressor |
| 历史压缩与快照 |
MemoryMonitor |
| 内存监控 |
MessageCompressor |
| 消息压缩 |
DifferentialEncoder |
| 差分编码 |
StressTester |
| 压力测试框架 |
8.2 优化效果
优化项 | 技术手段 | 效果 |
|---|---|---|
批处理 | 智能合并 + 优先级调度 | 减少90%网络消息 |
内存 | 快照 + 历史压缩 | 降低80%内存占用 |
网络 | zlib压缩 + 差分编码 | 减少70%传输大小 |
并发 | 自适应调度 | 支持100+并发用户 |
8.3 下一讲预告
第9讲：部署与运维
我们将把系统部署到生产环境：
Docker 容器化
Kubernetes 编排
监控与告警
日志聚合
灰度发布
准备好了吗？让我们在第9讲再见！
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