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~10ms5x内存占用~100MB~20MB5x消息大小~500B~50B10x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(prioritypriority, opop) 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( texttext, versionself.total_ops, timestamptime.time(), operations_sincelen(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(fCreated snapshot at version {snapshot.version}, fhistory 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(fHistory 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_typeOpType(compact[t]), positioncompact[p], textcompact.get(x, ), deleted_textcompact.get(d, ), site_idcompact[s], sequencecompact[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_factorylist) 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(fInsert 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(fDelete 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 fstress-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_idfstress-user-{i}, document_idself.document_id, metricsself.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(levellogging.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_size32) # 模拟连续输入 start time.time() for i in range(1000): op OTOperation(OpType.INSERT, i, textchr(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, textfHello World {i} * 5, site_idtest-site, sequencei ) 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_interval100) # 模拟大量操作 for i in range(10000): op OTOperation(OpType.INSERT, i, textfchar{i}) compressor.add_operation(op, ftext 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_history500) 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_users20, test_duration10.0, ramp_up3.0 ) 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_size10) ops [] for i in range(25): op OTOperation(OpType.INSERT, i, textx) 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, texturgent) 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, textchr(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_interval10) for i in range(50): op OTOperation(OpType.INSERT, i, textfx{i}) compressor.add_operation(op, ftext 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, textfx{i}) compressor.add_operation(op, ftext after {i}) compressor.compact(max_history100) 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, textHello World, site_idtest-site, sequence123 ) 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, textbig_text, site_idtest, sequence1 ) 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AdaptiveBatchercore/performance/op_batcher.py自适应批处理HistoryCompressorcore/performance/memory_opt.py历史压缩与快照MemoryMonitorcore/performance/memory_opt.py内存监控MessageCompressorcore/performance/network_opt.py消息压缩DifferentialEncodercore/performance/network_opt.py差分编码StressTestertests/stress_test.py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讲再见开发之余的小工具推荐处理 Base64、JWT 解析、JSON 格式化、Crontab 计算、PDF 合并压缩这些碎片需求我常用一个纯前端本地工具箱zz365.top子页 PDF 大师PDF 大师 - 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第8讲：性能优化与压力测试
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~10ms5x内存占用~100MB~20MB5x消息大小~500B~50B10x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(prioritypriority, opop) 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( texttext, versionself.total_ops, timestamptime.time(), operations_sincelen(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(fCreated snapshot at version {snapshot.version}, fhistory 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(fHistory 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_typeOpType(compact[t]), positioncompact[p], textcompact.get(x, ), deleted_textcompact.get(d, ), site_idcompact[s], sequencecompact[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_factorylist) 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(fInsert 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(fDelete 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 fstress-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_idfstress-user-{i}, document_idself.document_id, metricsself.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(levellogging.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_size32) # 模拟连续输入 start time.time() for i in range(1000): op OTOperation(OpType.INSERT, i, textchr(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, textfHello World {i} * 5, site_idtest-site, sequencei ) 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_interval100) # 模拟大量操作 for i in range(10000): op OTOperation(OpType.INSERT, i, textfchar{i}) compressor.add_operation(op, ftext 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_history500) 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_users20, test_duration10.0, ramp_up3.0 ) 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_size10) ops [] for i in range(25): op OTOperation(OpType.INSERT, i, textx) 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, texturgent) 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, textchr(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_interval10) for i in range(50): op OTOperation(OpType.INSERT, i, textfx{i}) compressor.add_operation(op, ftext 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, textfx{i}) compressor.add_operation(op, ftext after {i}) compressor.compact(max_history100) 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, textHello World, site_idtest-site, sequence123 ) 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, textbig_text, site_idtest, sequence1 ) 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AdaptiveBatchercore/performance/op_batcher.py自适应批处理HistoryCompressorcore/performance/memory_opt.py历史压缩与快照MemoryMonitorcore/performance/memory_opt.py内存监控MessageCompressorcore/performance/network_opt.py消息压缩DifferentialEncodercore/performance/network_opt.py差分编码StressTestertests/stress_test.py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讲再见开发之余的小工具推荐处理 Base64、JWT 解析、JSON 格式化、Crontab 计算、PDF 合并压缩这些碎片需求我常用一个纯前端本地工具箱zz365.top子页 PDF 大师PDF 大师 - 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