Merlin Vs Dapper

Merlin vs Dapper Benchmark Results

Test Environment

• Runtime: .NET 10.0.10
• CPU: Intel Core i7-8086K @ 4.00GHz
• OS: Windows 11
• Dataset: 100,000 rows
• Database: MySQL
• Benchmark: BenchmarkDotNet
• GC: Concurrent Workstation

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Flat Object Mapping

Scenario: 100,000 User (11x Properties) records with no nested objects

Metric	Dapper	Merlin	Difference
Mean Execution Time	349.5 ms	422.6 ms	+73.1 ms
Relative Performance	1.00x	1.21x	21% slower
Allocated Memory	169.72 MB	190.32 MB	+20.60 MB
Allocation Ratio	1.00x	1.12x	12% more
Gen0 Collections	29,000	34,000	+17%
Gen1 Collections	9,000	10,000	+11%
Gen2 Collections	2,000	3,000	+50%

Summary

Merlin introduced approximately:

• 73 ms additional execution time
• 20 MB additional allocations
• ~21% execution overhead
• ~12% allocation overhead

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Nested Object Mapping

Scenario: 100,000 User (11 properties) records with one nested Client object (11 Properties)

Object structure:

Metric	Dapper	Merlin	Difference
Mean Execution Time	726.6 ms	891.6 ms	+165.0 ms
Relative Performance	1.00x	1.23x	23% slower
Allocated Memory	296.21 MB	323.68 MB	+27.47 MB
Allocation Ratio	1.00x	1.09x	9% more
Gen0 Collections	50,000	56,000	+12%
Gen1 Collections	16,000	16,000	No difference
Gen2 Collections	1,000	3,000	+200%

Summary

Merlin introduced approximately:

• 165 ms additional execution time
• 27 MB additional allocations
• ~23% execution overhead
• ~9% allocation overhead

Dapper is faster, but not by much, the trade off is a simpler API

private static readonly QueryEngine DB = new("Local");

[Benchmark(Baseline = true)]
public int Test1_Dapper()
{
    using IDbConnection connection = new MySqlConnection(ConStr);

    string sql = "SELECT * FROM merlin_bench.users U LEFT JOIN merlin_bench.clients C ON U.user_client = C.client_id;";

    var result = connection.Query<UserExtended, Client, UserExtended>( sql, map: (user, profile) => {
            user.Client = profile;
            return user;
        },
        param: new { },
        splitOn: "client_id" // Tells Dapper where the Profile object fields begin
    );

    return result.AsList().Count();
}

[Benchmark]
public int Test2_Merlin()
{
    var q = new GenericQuery("SELECT * FROM merlin_bench.users U LEFT JOIN merlin_bench.clients C ON U.user_client = C.client_id;");

    var data = DB.GetList<UserExtended>(q);

    return data.Count;
}

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Performance Analysis

Key Findings

Flat Object Mapping

Merlin performs approximately 21% slower than Dapper while allocating only 12% more memory.

The overhead is primarily associated with:

• Metadata-driven mapping
• Property lookup
• Conversion handling
• Automatic object hydration

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Nested Object Mapping

With a nested object, Merlin maintains nearly the same performance ratio:

Test	Merlin Overhead
Flat Object	21% slower
Nested Object	23% slower

This indicates that Merlin's object graph hydration does not introduce significant additional overhead as complexity increases.

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Conclusion

The benchmark demonstrates that Merlin trades a small amount of runtime performance for increased mapping flexibility.

Dapper advantages:

• Maximum raw performance
• Minimal abstraction
• Very efficient simple object mapping

Merlin advantages:

• Automatic object hydration
• Nested object support
• Metadata-driven mapping
• Reduced manual mapping code
• Database abstraction without a full ORM tracking model

For business applications where maintainability and development speed are important, a ~20% performance