backend: store adaptive rate changes as events and serve an audit of them per line
This commit is contained in:
justme committed
1 parent
d63c3bff74
commit
9ad34acbac
9 files changed
+371
No files matched your search
@@ -0,0 +1,155 @@
|
||||
using System.Text.Json;
|
||||
using Fleet.Core;
|
||||
using MoShiro.Protocol;
|
||||
|
||||
namespace MoShiro.Backend.Application;
|
||||
|
||||
/// <summary>一个时间桶里一种采集方法的自适应探测频率：各状态各占多少秒、时间加权的平均频率、状态变化次数、变化时的最大丢包率。</summary>
|
||||
public sealed record AdaptiveBucket(long StartMs, double QuietSeconds, double WatchSeconds, double ActiveSeconds, double DeadSeconds, double? AverageHz, int Changes, double MaxLossPct);
|
||||
|
||||
public sealed record AdaptiveKind(string Kind, IReadOnlyList<AdaptiveBucket> Buckets);
|
||||
|
||||
public sealed record AdaptiveResult(long FromMs, long ToMs, long StepMs, IReadOnlyList<AdaptiveKind> Kinds, IReadOnlyList<RateChange> Changes);
|
||||
|
||||
/// <summary>
|
||||
/// 自适应探测频率的审计：节点把每一次状态变化（Quiet / Watch / Active / Dead、频率、原因、丢包）随心跳上报，Backend 存成事件（保留期和别的事件一样），
|
||||
/// 这里把它们还原成「某个时段里各状态占多久、平均频率多少」，所以几天之后也能回答「晚高峰的时候自适应是什么状态」。
|
||||
/// </summary>
|
||||
public sealed class AdaptiveAuditService
|
||||
{
|
||||
private readonly IEventStore _events;
|
||||
|
||||
public AdaptiveAuditService(IEventStore events)
|
||||
{
|
||||
_events = events;
|
||||
}
|
||||
|
||||
public async ValueTask<AdaptiveResult> GetAsync(LineKey line, long fromMs, long toMs, long stepMs, CancellationToken ct)
|
||||
{
|
||||
// 往前多取一段：区间开头的状态要由这之前最近的一次变化决定
|
||||
IReadOnlyList<EventRecord> records = await _events.ListAsync(line.Listener, AdaptiveAudit.EventKind, fromMs - AdaptiveAudit.LookBackMs, toMs, null, 20_000, ct);
|
||||
List<RateChange> changes = [];
|
||||
foreach (EventRecord record in records.OrderBy(r => r.AtMs).ThenBy(r => r.Id))
|
||||
{
|
||||
RateChange? change = AdaptiveAudit.Parse(record);
|
||||
if (change is not null && change.Dialer == line.Dialer.Value && (change.IpVersion ?? "ipv4") == line.IpVersion && change.Listener == line.Listener.Value)
|
||||
{
|
||||
changes.Add(change);
|
||||
}
|
||||
}
|
||||
|
||||
return AdaptiveAudit.Summarize(changes, fromMs, toMs, stepMs);
|
||||
}
|
||||
}
|
||||
|
||||
public static class AdaptiveAudit
|
||||
{
|
||||
public const string EventKind = "rate_change";
|
||||
|
||||
/// <summary>区间开头之前最多往回看多久，找那时的状态。</summary>
|
||||
public const long LookBackMs = 24 * 3_600_000L;
|
||||
|
||||
public static RateChange? Parse(EventRecord record)
|
||||
{
|
||||
if (record.Detail is null)
|
||||
{
|
||||
return null;
|
||||
}
|
||||
|
||||
try
|
||||
{
|
||||
RateChange? change = JsonSerializer.Deserialize(record.Detail, MoShiroJsonContext.Default.RateChange);
|
||||
if (change is not null)
|
||||
{
|
||||
change.AtMs = record.AtMs;
|
||||
}
|
||||
|
||||
return change;
|
||||
}
|
||||
catch (JsonException)
|
||||
{
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// 把按时间排好的状态变化还原成每个时间桶里各状态的秒数。一次变化之后到下一次变化之前，是变化后的状态和频率；
|
||||
/// 区间里第一次变化之前，状态是这次变化的「从」（频率不知道，不计入平均）。一种采集方法一条时间线（同一条线路上每种方法各自变化）。
|
||||
/// </summary>
|
||||
public static AdaptiveResult Summarize(IReadOnlyList<RateChange> changes, long fromMs, long toMs, long stepMs)
|
||||
{
|
||||
int count = (int)Math.Max(1, Math.Ceiling((toMs - fromMs) / (double)stepMs));
|
||||
var kinds = new List<AdaptiveKind>();
|
||||
foreach (IGrouping<string, RateChange> group in changes.GroupBy(c => c.Kind).OrderBy(g => g.Key, StringComparer.Ordinal))
|
||||
{
|
||||
var quiet = new double[count];
|
||||
var watch = new double[count];
|
||||
var active = new double[count];
|
||||
var dead = new double[count];
|
||||
var hzWeighted = new double[count];
|
||||
var hzSeconds = new double[count];
|
||||
var changeCounts = new int[count];
|
||||
var maxLoss = new double[count];
|
||||
RateChange[] ordered = [.. group.OrderBy(c => c.AtMs)];
|
||||
|
||||
// 每一段：[开始, 结束) 内的状态；第一段在第一次变化之前，状态取它的「从」
|
||||
string state = ordered[0].From;
|
||||
double? hz = null;
|
||||
long cursor = Math.Min(ordered[0].AtMs, fromMs);
|
||||
foreach (RateChange change in ordered)
|
||||
{
|
||||
AddSegment(cursor, change.AtMs, state, hz);
|
||||
int slot = (int)((change.AtMs - fromMs) / stepMs);
|
||||
if (change.AtMs >= fromMs && change.AtMs < toMs && slot >= 0 && slot < count)
|
||||
{
|
||||
changeCounts[slot]++;
|
||||
maxLoss[slot] = Math.Max(maxLoss[slot], change.LossPct);
|
||||
}
|
||||
|
||||
cursor = change.AtMs;
|
||||
state = change.To;
|
||||
hz = change.Hz;
|
||||
}
|
||||
|
||||
AddSegment(cursor, toMs, state, hz);
|
||||
var buckets = new List<AdaptiveBucket>(count);
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
buckets.Add(new AdaptiveBucket(fromMs + (i * stepMs), Math.Round(quiet[i], 1), Math.Round(watch[i], 1), Math.Round(active[i], 1), Math.Round(dead[i], 1),
|
||||
hzSeconds[i] > 0 ? Math.Round(hzWeighted[i] / hzSeconds[i], 3) : null, changeCounts[i], maxLoss[i]));
|
||||
}
|
||||
|
||||
kinds.Add(new AdaptiveKind(group.Key, buckets));
|
||||
|
||||
void AddSegment(long start, long end, string segmentState, double? segmentHz)
|
||||
{
|
||||
start = Math.Max(start, fromMs);
|
||||
end = Math.Min(end, toMs);
|
||||
for (long t = start; t < end;)
|
||||
{
|
||||
int slot = (int)((t - fromMs) / stepMs);
|
||||
long slotEnd = Math.Min(end, fromMs + ((slot + 1) * stepMs));
|
||||
double seconds = (slotEnd - t) / 1000.0;
|
||||
switch (segmentState)
|
||||
{
|
||||
case "quiet": quiet[slot] += seconds; break;
|
||||
case "watch": watch[slot] += seconds; break;
|
||||
case "active": active[slot] += seconds; break;
|
||||
case "dead": dead[slot] += seconds; break;
|
||||
}
|
||||
|
||||
if (segmentHz is { } known)
|
||||
{
|
||||
hzWeighted[slot] += known * seconds;
|
||||
hzSeconds[slot] += seconds;
|
||||
}
|
||||
|
||||
t = slotEnd;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
RateChange[] inRange = [.. changes.Where(c => c.AtMs >= fromMs && c.AtMs < toMs).OrderByDescending(c => c.AtMs).Take(200)];
|
||||
return new AdaptiveResult(fromMs, toMs, stepMs, kinds, inRange);
|
||||
}
|
||||
}
|
||||
Reference in new issue
Block a user