SourceGenerator 生成db to class代码优化结果记录 二

优化

在上一篇留下的 Dapper AOT 还有什么特别优化点的问题

在仔细阅读生成代码和源码之后，终于得到了答案

个人之前一直以为 Dapper AOT 只用了迭代器去实现，所以理应差不多实现代码却又极大差距，思维陷入了僵局，一度以为有什么黑魔法

结果 Dapper AOT 没有用迭代器去实现!!! 靠北啦，还以为迭代器有新姿势可以优化了

不再使用迭代器

List<BenchmarkTest.Dog> results = new();
try
{
    while (reader.Read())
    {
        results.Add(ReadOne(reader, readOnlyTokens));
    }
    return results;
}

当然就只能要求 用户必须使用 AsList 方法，因为 ToList 会导致复制list的问题， 导致负优化，

像这样

 connection.Query<Dog>("select * from dog").AsList();

// AsList 实现
public static List<T> AsList<T>(this IEnumerable<T>? source) => source switch
{
    null => null!,
    List<T> list => list,
    _ => Enumerable.ToList(source),
};

使用 span

再没有了迭代器方法限制， span 就可以放飞自我，随意使用了

public static BenchmarkTest.Dog ReadOne(this IDataReader reader, ref ReadOnlySpan<int> ss)
{
    var d = new BenchmarkTest.Dog();
    for (int j = 0; j < ss.Length; j++)
    {

使用 ArrayPool 减少内存占用

public Span<int> GetTokens()
{
    FieldCount = Reader!.FieldCount;
    if (Tokens is null || Tokens.Length < FieldCount)
    {
        // no leased array, or existing lease is not big enough; rent a new array
        if (Tokens is not null) ArrayPool<int>.Shared.Return(Tokens);
        Tokens = ArrayPool<int>.Shared.Rent(FieldCount);
    }
    return MemoryMarshal.CreateSpan(ref MemoryMarshal.GetArrayDataReference(Tokens), FieldCount);
}

数据小时使用栈分配

 var s = reader.FieldCount <= 64 ? MemoryMarshal.CreateSpan(ref MemoryMarshal.GetReference(stackalloc int[reader.FieldCount]), reader.FieldCount) :  state.GetTokens();

提前生成部分 hashcode 进行比较

因为比较现在也并不耗时了， 所以 缓存也没有必要了， 也一并移除

public static void GenerateReadTokens(this IDataReader reader, Span<int> s)
{
    for (int i = 0; i < reader.FieldCount; i++)
    {
        var name = reader.GetName(i);
        var type = reader.GetFieldType(i);
        switch (EntitiesGenerator.NormalizedHash(name))
        {

            case 742476188U:
                s[i] = type == typeof(int) ? 1 : 2;
                break;

            case 2369371622U:
                s[i] = type == typeof(string) ? 3 : 4;
                break;

            case 1352703673U:
                s[i] = type == typeof(float) ? 5 : 6;
                break;

            default:
                break;
        }
    }
}

性能测试说明

BenchmarkDotNet

这里特别说明一下

使用的 BenchmarkDotNet ，其本身已经考虑了 jit优化等等方面， 有预热，超多次执行，

结果值也是按照统计学有考虑结果集分布情况处理，移除变差大的值(比如少数的孤立的极大极小值)， 差异不大情况，一般显示平均值，有大差异时还会显示 中位值

感兴趣的童鞋可以去 https://github.com/dotnet/BenchmarkDotNet 了解

chole 有点棘手，为了方便mock，所以 copy了部分源码，只比较实体映射部分

DapperAOT 和 纯 dapper 很难一起运行，所以不再比较了，反正 dapper 肯定慢

测试数据

测试数据 正如之前说过， 采用 手动 mock 方式，避免 db 驱动 、db 执行、mock库 等等 带来的执行差异影响

class

非常简单的类，当然不能代表所有情况，不过简单测试够用了

public class Dog
{
    public int? Age { get; set; }
    public string Name { get; set; }
    public float? Weight { get; set; }
}

mock 数据

 public class TestDbConnection : DbConnection
 {
     public int RowCount { get; set; }

    public IDbCommand CreateCommand()
    {
        return new TestDbCommand() { RowCount = RowCount };
    }
}

public class TestDbCommand : DbCommand
{
    public int RowCount { get; set; }

    public IDataParameterCollection Parameters { get; } = new TestDataParameterCollection();

   public IDbDataParameter CreateParameter()
      {
         return new TestDataParameter();
      }

        protected override DbDataReader ExecuteDbDataReader(CommandBehavior behavior)
        {
            return new TestDbDataReader() { RowCount = RowCount };
        }
}

    public class TestDbDataReader : DbDataReader
    {
        public int RowCount { get; set; }
        private int calls = 0;
        public override object this[int ordinal]
        {
            get
            {
                switch (ordinal)
                {
                    case 0:
                        return "XX";
                    case 1:
                        return 2;
                    case 2:
                        return 3.3f;
                    default:
                        return null;
                }
            }

        }
      public override int FieldCount => 3;

      public override Type GetFieldType(int ordinal)
      {
          switch (ordinal)
          {
              case 0:
                  return typeof(string);
              case 1:
                  return typeof(int);
              case 2:
                  return typeof(float);
              default:
                  return null;
          }
      }

      public override float GetFloat(int ordinal)
      {
          switch (ordinal)
          {
              case 2:
                  return 3.3f;
              default:
                  return 0;
          }
      }
        public override int GetInt32(int ordinal)
        {
            switch (ordinal)
            {
                case 1:
                    return 2;
                default:
                    return 0;
            }
        }
        public override string GetName(int ordinal)
        {
            switch (ordinal)
            {
                case 0:
                    return "Name";
                case 1:
                    return "Age";
                case 2:
                    return "Weight";
                default:
                    return null;
            }
        }
        public override string GetString(int ordinal)
        {
            switch (ordinal)
            {
                case 0:
                    return "XX";
                default:
                    return null;
            }
        }

        public override object GetValue(int ordinal)
        {
            switch (ordinal)
            {
                case 0:
                    return "XX";
                case 1:
                    return 2;
                case 2:
                    return 3.3f;
                default:
                    return null;
            }
        }

        public override bool Read()
        {
            calls++;
            return calls <= RowCount;
        }
}

Benchmark 代码

    [MemoryDiagnoser, Orderer(summaryOrderPolicy: SummaryOrderPolicy.FastestToSlowest), GroupBenchmarksBy(BenchmarkLogicalGroupRule.ByCategory), CategoriesColumn]
    public class ObjectMappingTest
    {
        [Params(1, 1000, 10000, 100000, 1000000)]
        public int RowCount { get; set; }

        [Benchmark(Baseline = true)]
        public void SetClass()
        {
            var connection = new TestDbConnection() { RowCount = RowCount };
            var dogs = new List<Dog>();
            try
            {
                connection.Open();
                var cmd = connection.CreateCommand();
                cmd.CommandText = "select ";
                using (var reader = cmd.ExecuteReader(CommandBehavior.Default))
                {
                    while (reader.Read())
                    {
                        var dog = new Dog();
                        dogs.Add(dog);
                        dog.Name = reader.GetString(0);
                        dog.Age = reader.GetInt32(1);
                        dog.Weight = reader.GetFloat(2);
                    }
                }
            }
            finally
            {
                connection.Close();
            }
        }

        [Benchmark]
        public void DapperAOT()
        {
            var connection = new TestDbConnection() { RowCount = RowCount };
            var dogs = connection.Query<Dog>("select * from dog").AsList();
        }

        [Benchmark]
        public void SourceGenerator()
        {
            var connection = new TestDbConnection() { RowCount = RowCount };
            List<Dog> dogs;
            try
            {
                connection.Open();
                var cmd = connection.CreateCommand();
                cmd.CommandText = "select ";
                using (var reader = cmd.ExecuteReader(CommandBehavior.Default))
                {
                    dogs = reader.ReadTo<Dog>().AsList();
                }
            }
            finally
            {
                connection.Close();
            }
        }

        [Benchmark]
        public void Chloe()
        {
            var connection = new TestDbConnection() { RowCount = RowCount };
            try
            {
                connection.Open();
                var cmd = connection.CreateCommand();
                var dogs = new InternalSqlQuery<Dog>(cmd, "select").AsList();
            }
            finally
            {
                connection.Close();
            }
        }
    }

完整代码可以参考 https://github.com/fs7744/SlowestEM

测试结果

BenchmarkDotNet v0.13.12, Windows 10 (10.0.19045.4651/22H2/2022Update)
Intel Core i7-10700 CPU 2.90GHz, 1 CPU, 16 logical and 8 physical cores
.NET SDK 9.0.100-preview.5.24307.3
  [Host]     : .NET 8.0.6 (8.0.624.26715), X64 RyuJIT AVX2
  DefaultJob : .NET 8.0.6 (8.0.624.26715), X64 RyuJIT AVX2

Method	RowCount	Mean	Error	StdDev	Ratio	RatioSD	Gen0	Gen1	Gen2	Allocated	Alloc Ratio
DapperAOT	1	446.3 ns	8.81 ns	8.65 ns	0.60	0.03	0.0525	0.0515	-	440 B	1.00
SourceGenerator	1	690.0 ns	13.72 ns	32.34 ns	0.95	0.07	0.0525	0.0515	-	440 B	1.00
SetClass	1	728.3 ns	14.59 ns	37.41 ns	1.00	0.00	0.0525	0.0515	-	440 B	1.00
Chloe	1	909.7 ns	17.49 ns	22.75 ns	1.25	0.06	0.1020	0.1011	-	856 B	1.95
SetClass	1000	8,593.3 ns	169.90 ns	390.38 ns	1.00	0.00	6.7902	1.6937	-	56912 B	1.00
SourceGenerator	1000	16,967.8 ns	310.02 ns	258.88 ns	1.91	0.08	6.7749	1.6785	-	56912 B	1.00
DapperAOT	1000	18,299.7 ns	267.72 ns	250.43 ns	2.06	0.09	6.7749	1.3428	-	56912 B	1.00
Chloe	1000	116,049.4 ns	297.71 ns	263.91 ns	13.06	0.54	6.8359	1.7090	-	57328 B	1.01
SetClass	10000	309,255.1 ns	3,945.26 ns	3,294.47 ns	1.00	0.00	83.0078	82.5195	41.5039	662782 B	1.00
DapperAOT	10000	402,700.7 ns	7,676.45 ns	7,180.56 ns	1.31	0.03	83.0078	82.5195	41.5039	662782 B	1.00
SourceGenerator	10000	414,226.2 ns	8,149.22 ns	10,007.97 ns	1.34	0.04	83.0078	82.5195	41.5039	662782 B	1.00
Chloe	10000	1,453,166.1 ns	19,660.10 ns	17,428.16 ns	4.70	0.07	82.0313	80.0781	41.0156	663199 B	1.00
SetClass	100000	2,176,860.4 ns	42,449.84 ns	63,536.93 ns	1.00	0.00	496.0938	496.0938	496.0938	6098015 B	1.00
SourceGenerator	100000	3,045,760.4 ns	59,378.23 ns	63,534.04 ns	1.39	0.05	496.0938	496.0938	496.0938	6098015 B	1.00
DapperAOT	100000	3,053,510.0 ns	35,015.61 ns	29,239.62 ns	1.40	0.04	496.0938	496.0938	496.0938	6098015 B	1.00
Chloe	100000	13,152,653.6 ns	65,400.49 ns	51,060.40 ns	6.02	0.14	484.3750	484.3750	484.3750	6098433 B	1.00
SetClass	1000000	105,420,410.0 ns	2,093,734.23 ns	3,380,990.50 ns	1.00	0.00	6800.0000	6800.0000	2200.0000	56780029 B	1.00
SourceGenerator	1000000	115,534,043.8 ns	1,828,036.86 ns	1,795,376.62 ns	1.09	0.03	6800.0000	6800.0000	2200.0000	56780118 B	1.00
DapperAOT	1000000	115,751,485.5 ns	2,120,239.39 ns	2,603,844.38 ns	1.10	0.04	6800.0000	6800.0000	2200.0000	56780029 B	1.00
Chloe	1000000	208,295,919.3 ns	4,031,590.18 ns	4,481,101.81 ns	1.97	0.06	6666.6667	6666.6667	2333.3333	56781907 B	1.00

SourceGenerator 基本等同 DapperAOT 了， 除了没有使用 Interceptor， 以及各种情况细节没有考虑之外， 两者性能一样

SourceGenerator 肯定现在性能优化最佳方式，毕竟可以生成代码文件，上手难度其实比 emit 之类小多了