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FLV: Detect duplicated data by using hashes
TODO: fix test data
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@ -1,8 +1,13 @@
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using System;
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using System.Buffers;
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using System.Buffers.Binary;
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using System.Collections.Generic;
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using System.IO;
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using System.Linq;
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using System.Text;
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using BililiveRecorder.Flv;
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using BililiveRecorder.Flv.Pipeline.Actions;
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using FastHashes;
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using StructLinq;
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namespace BililiveRecorder.Flv.Pipeline.Rules
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@ -12,10 +17,11 @@ namespace BililiveRecorder.Flv.Pipeline.Rules
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/// </summary>
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public class RemoveDuplicatedChunkRule : ISimpleProcessingRule
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{
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private const int MAX_HISTORY = 8;
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private const int MAX_HISTORY = 16;
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private const string QUEUE_KEY = "DeDuplicationQueue";
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private static readonly ProcessingComment comment = new ProcessingComment(CommentType.RepeatingData, "发现了重复的 Flv Chunk");
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private static readonly FarmHash64 farmHash64 = new();
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private static readonly ProcessingComment comment = new ProcessingComment(CommentType.RepeatingData, "重复数据");
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public void Run(FlvProcessingContext context, Action next)
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{
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@ -27,67 +33,56 @@ namespace BililiveRecorder.Flv.Pipeline.Rules
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{
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if (action is PipelineDataAction data)
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{
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var feature = new List<long>(data.Tags.Count * 2 + 1)
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{
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data.Tags.Count
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};
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var tagHashs = new MemoryStream(4 + data.Tags.Count * 16);
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unchecked
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{
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// TODO: 改成用 Hash 判断
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// 计算一个特征码
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// 此处并没有遵循什么特定的算法,只是随便取了一些代表性比较强的值,用简单又尽量可靠的方式糅合到一起而已
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var buffer = ArrayPool<byte>.Shared.Rent(4);
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try
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{
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BinaryPrimitives.WriteInt32BigEndian(buffer, data.Tags.Count);
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tagHashs.Write(buffer, 0, 4);
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}
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finally
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{
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ArrayPool<byte>.Shared.Return(buffer);
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}
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}
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foreach (var tag in data.Tags)
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{
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var f = 0L;
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f ^= tag.Type switch
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var tagHash = tag.DataHash ?? tag.UpdateDataHash();
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if (tagHash is not null)
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{
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TagType.Audio => 0b01,
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TagType.Video => 0b10,
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TagType.Script => 0b11,
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_ => 0b00,
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};
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f <<= 3;
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f ^= (int)tag.Flag & ((1 << 3) - 1);
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f <<= 32;
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f ^= tag.Timestamp;
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f <<= 32 - 5;
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f ^= tag.Size & ((1 << (32 - 5)) - 1);
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feature.Add(f);
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var bytes = Encoding.UTF8.GetBytes(tagHash);
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tagHashs.Write(bytes, 0, bytes.Length);
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}
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}
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if (tag.Nalus == null)
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feature.Add(long.MinValue);
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else
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{
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long n = tag.Nalus.Count << 32;
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foreach (var nalu in tag.Nalus)
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n ^= (((int)nalu.Type) << 16) ^ ((int)nalu.FullSize);
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feature.Add(n);
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}
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}
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}
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var hash = farmHash64.ComputeHash(tagHashs.GetBuffer(), (int)tagHashs.Length);
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// 存储最近 MAX_HISTORY 个 Data Chunk 的特征的 Queue
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Queue<List<long>> history;
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if (context.SessionItems.TryGetValue(QUEUE_KEY, out var obj) && obj is Queue<List<long>> q)
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history = q;
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Queue<byte[]> hashHistory;
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if (context.SessionItems.TryGetValue(QUEUE_KEY, out var obj) && obj is Queue<byte[]> q)
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hashHistory = q;
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else
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{
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history = new Queue<List<long>>(MAX_HISTORY + 1);
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context.SessionItems[QUEUE_KEY] = history;
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hashHistory = new Queue<byte[]>(MAX_HISTORY + 1);
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context.SessionItems[QUEUE_KEY] = hashHistory;
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}
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// 对比历史特征
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if (history.ToStructEnumerable().Any(x => x.SequenceEqual(feature), x => x))
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if (hashHistory.ToStructEnumerable().Any(x => x.SequenceEqual(hash), x => x))
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{
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// 重复数据
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context.AddComment(comment);
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}
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else
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{
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history.Enqueue(feature);
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// 新数据
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hashHistory.Enqueue(hash);
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while (history.Count > MAX_HISTORY)
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history.Dequeue();
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while (hashHistory.Count > MAX_HISTORY)
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hashHistory.Dequeue();
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yield return action;
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}
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