Dapper - a simple object mapper for .Net

Release Notes

Located at stackexchange.github.io/Dapper

Packages

MyGet Pre-release feed: https://www.myget.org/gallery/dapper

Package	NuGet Stable	NuGet Pre-release	Downloads	MyGet
Dapper
Dapper.Contrib
Dapper.EntityFramework
Dapper.EntityFramework.StrongName
Dapper.Rainbow
Dapper.SqlBuilder
Dapper.StrongName

Features

Dapper is a NuGet library that you can add in to your project that will extend your IDbConnection interface.

It provides 3 helpers:

Execute a query and map the results to a strongly typed List

public static IEnumerable<T> Query<T>(this IDbConnection cnn, string sql, object param = null, SqlTransaction transaction = null, bool buffered = true)

Example usage:

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

    public int IgnoredProperty { get { return 1; } }
}

var guid = Guid.NewGuid();
var dog = connection.Query<Dog>("select Age = @Age, Id = @Id", new { Age = (int?)null, Id = guid });

Assert.Equal(1,dog.Count());
Assert.Null(dog.First().Age);
Assert.Equal(guid, dog.First().Id);

Execute a query and map it to a list of dynamic objects

public static IEnumerable<dynamic> Query (this IDbConnection cnn, string sql, object param = null, SqlTransaction transaction = null, bool buffered = true)

This method will execute SQL and return a dynamic list.

Example usage:

var rows = connection.Query("select 1 A, 2 B union all select 3, 4");

Assert.Equal(1, (int)rows[0].A);
Assert.Equal(2, (int)rows[0].B);
Assert.Equal(3, (int)rows[1].A);
Assert.Equal(4, (int)rows[1].B);

Execute a Command that returns no results

public static int Execute(this IDbConnection cnn, string sql, object param = null, SqlTransaction transaction = null)

Example usage:

var count = connection.Execute(@"
  set nocount on
  create table #t(i int)
  set nocount off
  insert #t
  select @a a union all select @b
  set nocount on
  drop table #t", new {a=1, b=2 });
Assert.Equal(2, count);

Execute a Command multiple times

The same signature also allows you to conveniently and efficiently execute a command multiple times (for example to bulk-load data)

Example usage:

var count = connection.Execute(@"insert MyTable(colA, colB) values (@a, @b)",
    new[] { new { a=1, b=1 }, new { a=2, b=2 }, new { a=3, b=3 } }
  );
Assert.Equal(3, count); // 3 rows inserted: "1,1", "2,2" and "3,3"

This works for any parameter that implements IEnumerable for some T.

Performance

A key feature of Dapper is performance. The following metrics show how long it takes to execute a SELECT statement against a DB (in various config, each labeled) and map the data returned to objects.

The benchmarks can be found in Dapper.Tests.Performance (contributions welcome!) and can be run once compiled via:

Dapper.Tests.Performance.exe -f * --join

Output from the latest run is:

BenchmarkDotNet=v0.11.1, OS=Windows 10.0.17134.254 (1803/April2018Update/Redstone4)
Intel Core i7-7700HQ CPU 2.80GHz (Kaby Lake), 1 CPU, 8 logical and 4 physical cores
Frequency=2742188 Hz, Resolution=364.6723 ns, Timer=TSC
  [Host] : .NET Framework 4.7.2 (CLR 4.0.30319.42000), 64bit RyuJIT-v4.7.3163.0
  Dry    : .NET Framework 4.7.2 (CLR 4.0.30319.42000), 64bit RyuJIT-v4.7.3163.0

ORM	Method	Return	Mean	StdDev	Error	Gen 0	Gen 1	Allocated
HandCoded	SqlCommand	Post	86.76 us	1.573 us	2.379 us	3.8000	-	12.24 KB
PetaPoco	'Fetch<Post> (Fast)'	Post	95.43 us	2.434 us	3.681 us	4.4000	-	13.65 KB
HandCoded	DataTable	dynamic	98.18 us	5.242 us	7.925 us	2.2000	0.6000	12.45 KB
PetaPoco	Fetch<Post>	Post	98.18 us	2.252 us	3.404 us	4.6000	-	14.59 KB
Massive	'Query (dynamic)'	dynamic	98.47 us	3.102 us	4.690 us	4.6000	-	14.21 KB
Belgrade	ExecuteReader	Post	99.14 us	15.731 us	23.783 us	3.6000	1.0000	11.37 KB
Dapper	'Query<dynamic> (buffered)'	dynamic	100.42 us	5.311 us	8.030 us	4.4000	-	13.88 KB
Dapper	'Contrib Get<T>'	Post	102.55 us	9.289 us	14.044 us	4.6000	-	14.45 KB
Dapper	'Query<T> (buffered)'	Post	104.85 us	10.278 us	15.539 us	4.4000	-	13.79 KB
ServiceStack	SingleById	Post	107.83 us	5.144 us	7.778 us	5.6000	-	17.53 KB
Dapper	QueryFirstOrDefault<dynamic>	dynamic	109.78 us	7.689 us	11.624 us	4.2000	-	13.51 KB
Dapper	QueryFirstOrDefault<T>	Post	112.97 us	4.081 us	6.170 us	4.2000	-	13.47 KB
Susanoo	'Mapping Static'	Post	118.91 us	8.786 us	13.282 us	4.8000	-	14.99 KB
Susanoo	'Mapping Static (dynamic)'	dynamic	119.57 us	6.985 us	10.560 us	4.8000	-	14.97 KB
Susanoo	'Mapping Cache'	Post	123.68 us	6.693 us	10.118 us	6.8000	-	20.9 KB
Dapper	'Query<dynamic> (unbuffered)'	dynamic	127.06 us	8.748 us	13.225 us	4.4000	-	13.87 KB
Dapper	'Query<T> (unbuffered)'	Post	132.28 us	9.350 us	14.136 us	4.4000	-	13.84 KB
Susanoo	'Mapping Cache (dynamic)'	dynamic	133.45 us	4.179 us	6.318 us	6.6000	-	20.41 KB
Linq2Sql	Compiled	Post	133.46 us	6.137 us	9.278 us	3.0000	-	9.82 KB
EFCore	Compiled	Post	180.70 us	65.498 us	99.024 us	5.2000	-	16.08 KB
EF6	SqlQuery	Post	187.09 us	136.314 us	206.088 us	9.0000	-	27.86 KB
NHibernate	Get<T>	Post	202.34 us	10.271 us	15.528 us	10.4000	-	32.5 KB
NHibernate	HQL	Post	216.95 us	26.889 us	40.653 us	11.2000	-	35 KB
EFCore	'No Tracking'	Post	243.18 us	59.359 us	89.742 us	6.8000	-	21.36 KB
EFCore	Normal	Post	245.60 us	72.618 us	109.788 us	6.4000	-	20.25 KB
Linq2Sql	ExecuteQuery	Post	246.66 us	6.215 us	9.396 us	13.6000	-	42.34 KB
NHibernate	Criteria	Post	252.51 us	18.303 us	27.672 us	21.2000	-	65.37 KB
EFCore	SqlQuery	Post	263.34 us	73.919 us	111.755 us	6.6000	-	20.75 KB
EF6	Normal	Post	308.46 us	154.203 us	233.133 us	15.6000	-	48.29 KB
NHibernate	SQL	Post	322.13 us	9.530 us	14.408 us	32.8000	-	101.06 KB
EF6	'No Tracking'	Post	324.12 us	134.870 us	203.905 us	17.8000	-	55.1 KB
Linq2Sql	Normal	Post	370.55 us	238.033 us	359.871 us	4.6000	1.4000	14.68 KB
NHibernate	LINQ	Post	1,110.87 us	36.863 us	55.731 us	20.2000	-	62.13 KB

Feel free to submit patches that include other ORMs - when running benchmarks, be sure to compile in Release and not attach a debugger (Ctrl+F5).

Alternatively, you might prefer Frans Bouma's RawDataAccessBencher test suite or OrmBenchmark.

Parameterized queries

Parameters are passed in as anonymous classes. This allow you to name your parameters easily and gives you the ability to simply cut-and-paste SQL snippets and run them in your db platform's Query analyzer.

new {A = 1, B = "b"} // A will be mapped to the param @A, B to the param @B

List Support

Dapper allows you to pass in IEnumerable<int> and will automatically parameterize your query.

For example:

connection.Query<int>("select * from (select 1 as Id union all select 2 union all select 3) as X where Id in @Ids", new { Ids = new int[] { 1, 2, 3 } });

Will be translated to:

select * from (select 1 as Id union all select 2 union all select 3) as X where Id in (@Ids1, @Ids2, @Ids3)" // @Ids1 = 1 , @Ids2 = 2 , @Ids2 = 3

Literal replacements

Dapper supports literal replacements for bool and numeric types.

connection.Query("select * from User where UserId = {=Id}", new {Id = 1}));

The literal replacement is not sent as a parameter; this allows better plans and filtered index usage but should usually be used sparingly and after testing. This feature is particularly useful when the value being injected is actually a fixed value (for example, a fixed "category id", "status code" or "region" that is specific to the query). For live data where you are considering literals, you might also want to consider and test provider-specific query hints like OPTIMIZE FOR UNKNOWN with regular parameters.

Buffered vs Unbuffered readers

Dapper's default behavior is to execute your SQL and buffer the entire reader on return. This is ideal in most cases as it minimizes shared locks in the db and cuts down on db network time.

However when executing huge queries you may need to minimize memory footprint and only load objects as needed. To do so pass, buffered: false into the Query method.

Multi Mapping

Dapper allows you to map a single row to multiple objects. This is a key feature if you want to avoid extraneous querying and eager load associations.

Example:

Consider 2 classes: Post and User

class Post
{
    public int Id { get; set; }
    public string Title { get; set; }
    public string Content { get; set; }
    public User Owner { get; set; }
}

class User
{
    public int Id { get; set; }
    public string Name { get; set; }
}

Now let us say that we want to map a query that joins both the posts and the users table. Until now if we needed to combine the result of 2 queries, we'd need a new object to express it but it makes more sense in this case to put the User object inside the Post object.

This is the user case for multi mapping. You tell dapper that the query returns a Post and a User object and then give it a function describing what you want to do with each of the rows containing both a Post and a User object. In our case, we want to take the user object and put it inside the post object. So we write the function:

(post, user) => { post.Owner = user; return post; }

The 3 type arguments to the Query method specify what objects dapper should use to deserialize the row and what is going to be returned. We're going to interpret both rows as a combination of Post and User and we're returning back a Post object. Hence the type declaration becomes

<Post, User, Post>

Everything put together, looks like this:

var sql =
@"select * from #Posts p
left join #Users u on u.Id = p.OwnerId
Order by p.Id";

var data = connection.Query<Post, User, Post>(sql, (post, user) => { post.Owner = user; return post;});
var post = data.First();

Assert.Equal("Sams Post1", post.Content);
Assert.Equal(1, post.Id);
Assert.Equal("Sam", post.Owner.Name);
Assert.Equal(99, post.Owner.Id);

Dapper is able to split the returned row by making an assumption that your Id columns are named Id or id. If your primary key is different or you would like to split the row at a point other than Id, use the optional splitOn parameter.

Multiple Results

Dapper allows you to process multiple result grids in a single query.

Example:

var sql =
@"
select * from Customers where CustomerId = @id
select * from Orders where CustomerId = @id
select * from Returns where CustomerId = @id";

using (var multi = connection.QueryMultiple(sql, new {id=selectedId}))
{
   var customer = multi.Read<Customer>().Single();
   var orders = multi.Read<Order>().ToList();
   var returns = multi.Read<Return>().ToList();
   ...
}

Stored Procedures

Dapper fully supports stored procs:

var user = cnn.Query<User>("spGetUser", new {Id = 1},
        commandType: CommandType.StoredProcedure).SingleOrDefault();

If you want something more fancy, you can do:

var p = new DynamicParameters();
p.Add("@a", 11);
p.Add("@b", dbType: DbType.Int32, direction: ParameterDirection.Output);
p.Add("@c", dbType: DbType.Int32, direction: ParameterDirection.ReturnValue);

cnn.Execute("spMagicProc", p, commandType: CommandType.StoredProcedure);

int b = p.Get<int>("@b");
int c = p.Get<int>("@c");

Ansi Strings and varchar

Dapper supports varchar params, if you are executing a where clause on a varchar column using a param be sure to pass it in this way:

Query<Thing>("select * from Thing where Name = @Name", new {Name = new DbString { Value = "abcde", IsFixedLength = true, Length = 10, IsAnsi = true });

On SQL Server it is crucial to use the unicode when querying unicode and ANSI when querying non unicode.

Type Switching Per Row

Usually you'll want to treat all rows from a given table as the same data type. However, there are some circumstances where it's useful to be able to parse different rows as different data types. This is where IDataReader.GetRowParser comes in handy.

Imagine you have a database table named "Shapes" with the columns: Id, Type, and Data, and you want to parse its rows into Circle, Square, or Triangle objects based on the value of the Type column.

var shapes = new List<IShape>();
using (var reader = connection.ExecuteReader("select * from Shapes"))
{
    // Generate a row parser for each type you expect.
    // The generic type <IShape> is what the parser will return.
    // The argument (typeof(*)) is the concrete type to parse.
    var circleParser = reader.GetRowParser<IShape>(typeof(Circle));
    var squareParser = reader.GetRowParser<IShape>(typeof(Square));
    var triangleParser = reader.GetRowParser<IShape>(typeof(Triangle));

    var typeColumnIndex = reader.GetOrdinal("Type");

    while (reader.Read())
    {
        IShape shape;
        var type = (ShapeType)reader.GetInt32(typeColumnIndex);
        switch (type)
        {
            case ShapeType.Circle:
            	shape = circleParser(reader);
            	break;
            case ShapeType.Square:
            	shape = squareParser(reader);
            	break;
            case ShapeType.Triangle:
            	shape = triangleParser(reader);
            	break;
            default:
            	throw new NotImplementedException();
        }

      	shapes.Add(shape);
    }
}

Limitations and caveats

Dapper caches information about every query it runs, this allows it to materialize objects quickly and process parameters quickly. The current implementation caches this information in a ConcurrentDictionary object. Statements that are only used once are routinely flushed from this cache. Still, if you are generating SQL strings on the fly without using parameters it is possible you may hit memory issues.

Dapper's simplicity means that many feature that ORMs ship with are stripped out. It worries about the 95% scenario, and gives you the tools you need most of the time. It doesn't attempt to solve every problem.

Will Dapper work with my DB provider?

Dapper has no DB specific implementation details, it works across all .NET ADO providers including SQLite, SQL CE, Firebird, Oracle, MySQL, PostgreSQL and SQL Server.

Do you have a comprehensive list of examples?

Dapper has a comprehensive test suite in the test project.

Who is using this?

Dapper is in production use at Stack Overflow.