Apollo 应用与源码分析：Monitor监控-软件监控-时间延迟监控

 

 

目录

 

代码

分析

RunOnce 函数分析

UpdateState函数分析

发送时间延迟报告函数分析

备注

---

 

代码

class LatencyMonitor : public RecurrentRunner {
 public:
  LatencyMonitor();
  void RunOnce(const double current_time) override;
  bool GetFrequency(const std::string& channel_name, double* freq);
 
 private:
  void UpdateStat(
      const std::shared_ptr& records);
  void PublishLatencyReport();
  void AggregateLatency();
 
  apollo::common::LatencyReport latency_report_;
  std::unordered_map<uint64_t,
                     std::setuint64_t, uint64_t, std::string>>>
      track_map_;
  std::unordered_mapdouble> freq_map_;
  double flush_time_ = 0.0;
};
 
void LatencyMonitor::RunOnce(const double current_time) {
  static auto reader =
      MonitorManager::Instance()->CreateReader(
          FLAGS_latency_recording_topic);
  reader->SetHistoryDepth(FLAGS_latency_reader_capacity);
  reader->Observe();
 
  static std::string last_processed_key;
  std::string first_key_of_current_round;
  for (auto it = reader->Begin(); it != reader->End(); ++it) {
    const std::string current_key =
        absl::StrCat((*it)->module_name(), (*it)->header().sequence_num());
    if (it == reader->Begin()) {
      first_key_of_current_round = current_key;
    }
    if (current_key == last_processed_key) {
      break;
    }
    UpdateStat(*it);
  }
  last_processed_key = first_key_of_current_round;
 
  if (current_time - flush_time_ > FLAGS_latency_report_interval) {
    flush_time_ = current_time;
    if (!track_map_.empty()) {
      PublishLatencyReport();
    }
  }
}

分析

分析之前先回忆一下，之前模块channel 的之间的时间延迟就是通过LatencyMonitor 实现的，所以LatencyMonitor的工作就是收集各种时间延迟，并且汇总形成一个报告。

RunOnce 函数分析

订阅latency_recording_topic，消息体是LatencyRecordMap

message LatencyRecord {
  optional uint64 begin_time = 1;
  optional uint64 end_time = 2;
  optional uint64 message_id = 3;
};
 
message LatencyRecordMap {
  optional apollo.common.Header header = 1;
  optional string module_name = 2;
  repeated LatencyRecord latency_records = 3;
};

遍历订阅到的所有的信息，然后使用UpdateStat 进行状态更新

UpdateState函数分析

void LatencyMonitor::UpdateStat(
    const std::shared_ptr& records) {
  const auto module_name = records->module_name();
  for (const auto& record : records->latency_records()) {
    track_map_[record.message_id()].emplace(record.begin_time(),
                                            record.end_time(), module_name);
  }
 
  if (!records->latency_records().empty()) {
    const auto begin_time = records->latency_records().begin()->begin_time();
    const auto end_time = records->latency_records().rbegin()->end_time();
    if (end_time > begin_time) {
      freq_map_[module_name] =
          records->latency_records().size() /
          apollo::cyber::Time(end_time - begin_time).ToSecond();
    }
  }
}

1. 保存每个 msg 的耗时信息到 track_map_
2. 更新 freq_map 中模块的频率信息

发送时间延迟报告函数分析

void LatencyMonitor::PublishLatencyReport() {
  static auto writer = MonitorManager::Instance()->CreateWriter(
      FLAGS_latency_reporting_topic);
  apollo::common::util::FillHeader("LatencyReport", &latency_report_);
  AggregateLatency();
  writer->Write(latency_report_);
  latency_report_.clear_header();
  track_map_.clear();
  latency_report_.clear_modules_latency();
  latency_report_.clear_e2es_latency();
}
void LatencyMonitor::AggregateLatency() {
  static const std::string kE2EStartPoint = FLAGS_pointcloud_topic;
  std::unordered_mapuint64_t>> modules_track;
  std::unordered_mapuint64_t>> e2es_track;
  std::unordered_set all_modules;
 
  // Aggregate modules latencies
  std::string module_name;
  uint64_t begin_time = 0, end_time = 0;
  for (const auto& message : track_map_) {
    auto iter = message.second.begin();
    while (iter != message.second.end()) {
      std::tie(begin_time, end_time, module_name) = *iter;
      modules_track[module_name].push_back(end_time - begin_time);
      all_modules.emplace(module_name);
      ++iter;
    }
  }
  // Aggregate E2E latencies
  std::unordered_mapuint64_t> e2e_latencies;
  for (const auto& message : track_map_) {
    uint64_t e2e_begin_time = 0;
    auto iter = message.second.begin();
    e2e_latencies.clear();
    while (iter != message.second.end()) {
      std::tie(begin_time, std::ignore, module_name) = *iter;
      if (e2e_begin_time == 0 && module_name == kE2EStartPoint) {
        e2e_begin_time = begin_time;
      } else if (module_name != kE2EStartPoint && e2e_begin_time != 0 &&
                 e2e_latencies.find(module_name) == e2e_latencies.end()) {
        const auto duration = begin_time - e2e_begin_time;
        e2e_latencies[module_name] = duration;
        e2es_track[module_name].push_back(duration);
      }
      ++iter;
    }
  }
 
  // The results could be in the following fromat:
  // e2e latency:
  // pointcloud -> perception: min(500), max(600), average(550),
  // sample_size(1500) pointcloud -> planning: min(800), max(1000),
  // average(900), sample_size(1500) pointcloud -> control: min(1200),
  // max(1300), average(1250), sample_size(1500)
  // ...
  // modules latency:
  // perception: min(5), max(50), average(30), sample_size(1000)
  // prediction: min(500), max(5000), average(2000), sample_size(800)
  // control: min(500), max(800), average(600), sample_size(800)
  // ...
 
  auto* modules_latency = latency_report_.mutable_modules_latency();
  for (const auto& module : modules_track) {
    SetLatency(module.first, module.second, modules_latency);
  }
  auto* e2es_latency = latency_report_.mutable_e2es_latency();
  for (const auto& e2e : e2es_track) {
    SetLatency(absl::StrCat(kE2EStartPoint, " -> ", e2e.first), e2e.second,
               e2es_latency);
  }
}

可以看出主要是两个部分的时间延迟：

1. 所有模块的时间延迟
2. E2E 的时间延迟

这里的E2E就是端到端的时间延迟，在apollo 中指的是电晕信息到各个模块输出的时间。

E2E Latency 的逻辑：

1. 记录第一条点云数据的开始时间
2. 依次记录那些不是点云数据的记录的开始时间，计算它们之间的差值，就成了这一个测试周期的 E2E 时延。

备注

Latency 的运行基于依靠于 CyberRT 的通信，所以，也必须保证 CyberRT 的 Channel 通信机制足够可靠，不然会产生误差。