Top Related Projects
Npgsql is the .NET data provider for PostgreSQL.
.NET Transactional Document DB and Event Store on PostgreSQL
Fast, Simple, Typed ORM for .NET
Fluent migrations framework for .NET
EF Core is a modern object-database mapper for .NET. It supports LINQ queries, change tracking, updates, and schema migrations.
Quick Overview
Dapper is a high-performance micro-ORM for .NET, providing a simple object mapper for ADO.NET. It offers fast execution of database queries and efficient mapping to .NET objects, making it an excellent choice for developers who need raw SQL performance with the convenience of an ORM.
Pros
- Extremely fast performance, often outperforming other ORMs
- Lightweight and simple to use, with a minimal learning curve
- Supports multiple databases (SQL Server, PostgreSQL, SQLite, etc.)
- Allows for fine-grained control over SQL queries
Cons
- Limited automated query generation compared to full-featured ORMs
- Requires more manual SQL writing, which can be error-prone
- Lacks advanced features like change tracking or lazy loading
- Not ideal for complex object graphs or heavily relational data models
Code Examples
Querying a single object:
using (var connection = new SqlConnection(connectionString))
{
var user = connection.QuerySingle<User>("SELECT * FROM Users WHERE Id = @Id", new { Id = 1 });
}
Executing a parameterized query:
using (var connection = new SqlConnection(connectionString))
{
var users = connection.Query<User>("SELECT * FROM Users WHERE Age > @Age", new { Age = 18 });
}
Performing an insert operation:
using (var connection = new SqlConnection(connectionString))
{
var affectedRows = connection.Execute("INSERT INTO Users (Name, Email) VALUES (@Name, @Email)",
new { Name = "John Doe", Email = "john@example.com" });
}
Getting Started
-
Install Dapper via NuGet:
Install-Package Dapper
-
Add the following using statement to your C# file:
using Dapper;
-
Create a database connection and start querying:
using System.Data.SqlClient; using Dapper; string connectionString = "Your connection string here"; using (var connection = new SqlConnection(connectionString)) { connection.Open(); var result = connection.Query<YourType>("SELECT * FROM YourTable"); }
Competitor Comparisons
Npgsql is the .NET data provider for PostgreSQL.
Pros of Npgsql
- Specialized for PostgreSQL, offering deep integration and support for PostgreSQL-specific features
- Provides asynchronous operations out of the box, enhancing performance for I/O-bound operations
- Includes built-in connection pooling, optimizing database connection management
Cons of Npgsql
- Limited to PostgreSQL databases, lacking the multi-database support of Dapper
- Steeper learning curve due to its focus on PostgreSQL-specific features
- May require more boilerplate code for simple CRUD operations compared to Dapper's lightweight approach
Code Comparison
Npgsql:
using (var conn = new NpgsqlConnection(connectionString))
{
await conn.OpenAsync();
using (var cmd = new NpgsqlCommand("SELECT * FROM users WHERE id = @id", conn))
{
cmd.Parameters.AddWithValue("id", userId);
using (var reader = await cmd.ExecuteReaderAsync())
{
// Process results
}
}
}
Dapper:
using (var conn = new SqlConnection(connectionString))
{
var user = await conn.QuerySingleOrDefaultAsync<User>("SELECT * FROM users WHERE id = @id", new { id = userId });
// Process results
}
.NET Transactional Document DB and Event Store on PostgreSQL
Pros of Marten
- Built-in document storage and querying capabilities for PostgreSQL
- Supports event sourcing and projections out of the box
- Offers advanced features like multi-tenancy and batch operations
Cons of Marten
- Limited to PostgreSQL, while Dapper supports multiple databases
- Steeper learning curve due to more complex features
- Potentially slower performance for simple CRUD operations
Code Comparison
Marten (Document storage):
var user = new User { Name = "John Doe", Email = "john@example.com" };
session.Store(user);
await session.SaveChangesAsync();
var retrievedUser = await session.LoadAsync<User>(user.Id);
Dapper (SQL query):
var user = new User { Name = "John Doe", Email = "john@example.com" };
await connection.ExecuteAsync("INSERT INTO Users (Name, Email) VALUES (@Name, @Email)", user);
var retrievedUser = await connection.QuerySingleOrDefaultAsync<User>("SELECT * FROM Users WHERE Id = @Id", new { Id = user.Id });
Marten excels in document-based scenarios and complex domain models, while Dapper shines in performance-critical, SQL-centric applications. Choose based on your project's specific requirements and database preferences.
Fast, Simple, Typed ORM for .NET
Pros of ServiceStack.OrmLite
- More feature-rich, offering a wider range of database operations and utilities
- Supports multiple databases out of the box (SQLite, SQL Server, PostgreSQL, etc.)
- Provides a fluent API for complex queries and joins
Cons of ServiceStack.OrmLite
- Steeper learning curve due to its extensive feature set
- Larger footprint and potentially slower performance for simple queries
- Part of a larger framework, which may be overkill for small projects
Code Comparison
Dapper:
var user = connection.QuerySingle<User>("SELECT * FROM Users WHERE Id = @Id", new { Id = 1 });
ServiceStack.OrmLite:
var user = db.Single<User>(u => u.Id == 1);
Both libraries offer simple ways to query data, but ServiceStack.OrmLite provides a more object-oriented approach with its fluent API. Dapper excels in raw SQL execution, while ServiceStack.OrmLite offers a higher level of abstraction.
ServiceStack.OrmLite is better suited for projects requiring a full-featured ORM with support for multiple databases, while Dapper is ideal for developers who prefer writing raw SQL and need high performance for simple queries.
Fluent migrations framework for .NET
Pros of FluentMigrator
- Provides a fluent interface for database migrations, making them more readable and maintainable
- Supports multiple database providers out of the box
- Offers a command-line tool for executing migrations
Cons of FluentMigrator
- Steeper learning curve compared to Dapper's simpler approach
- May introduce additional complexity for smaller projects
- Limited to database schema migrations, unlike Dapper's broader data access capabilities
Code Comparison
FluentMigrator:
public class AddUserTable : Migration
{
public override void Up()
{
Create.Table("Users")
.WithColumn("Id").AsInt32().PrimaryKey().Identity()
.WithColumn("Name").AsString(100).NotNullable();
}
}
Dapper:
connection.Execute(@"
CREATE TABLE Users (
Id INT PRIMARY KEY IDENTITY(1,1),
Name NVARCHAR(100) NOT NULL
)");
FluentMigrator focuses on defining database schema changes using a fluent API, while Dapper excels at executing raw SQL queries and mapping results to objects. FluentMigrator is specifically designed for managing database migrations, whereas Dapper is a more general-purpose micro-ORM for data access.
EF Core is a modern object-database mapper for .NET. It supports LINQ queries, change tracking, updates, and schema migrations.
Pros of Entity Framework Core
- Rich ORM features including change tracking, lazy loading, and migrations
- Supports multiple database providers (SQL Server, PostgreSQL, SQLite, etc.)
- Seamless integration with ASP.NET Core and other .NET Core libraries
Cons of Entity Framework Core
- Steeper learning curve compared to Dapper
- Can be slower for simple queries due to overhead
- More complex setup and configuration required
Code Comparison
Dapper:
var user = connection.QuerySingle<User>("SELECT * FROM Users WHERE Id = @Id", new { Id = 1 });
Entity Framework Core:
var user = dbContext.Users.FirstOrDefault(u => u.Id == 1);
Summary
Dapper is a lightweight, high-performance micro-ORM that excels in simplicity and raw SQL execution. It's ideal for developers who prefer writing SQL queries directly and need maximum performance.
Entity Framework Core is a full-featured ORM that provides a higher level of abstraction and more built-in features. It's better suited for complex data models and scenarios where developer productivity is prioritized over raw performance.
The choice between Dapper and Entity Framework Core depends on project requirements, performance needs, and developer preferences. Many projects benefit from using both in different parts of the application.
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Dapper - a simple object mapper for .Net
Release Notes
Located at https://github.com/DapperLib/Dapper/releases
Packages
MyGet Pre-release feed: https://www.myget.org/gallery/dapper
Package | NuGet Stable | NuGet Pre-release | Downloads | MyGet |
---|---|---|---|---|
Dapper | ||||
Dapper.EntityFramework | ||||
Dapper.EntityFramework.StrongName | ||||
Dapper.Rainbow | ||||
Dapper.SqlBuilder | ||||
Dapper.StrongName |
Package Purposes:
- Dapper
- The core library
- Dapper.EntityFramework
- Extension handlers for EntityFramework
- Dapper.EntityFramework.StrongName
- Extension handlers for EntityFramework
- Dapper.Rainbow
- Micro-ORM implemented on Dapper, provides CRUD helpers (readme)
- Dapper.SqlBuilder
- Component for building SQL queries dynamically and composably
Sponsors
Dapper was originally developed for and by Stack Overflow, but is F/OSS. Sponsorship is welcome and invited - see the sponsor link at the top of the page. A huge thanks to everyone (individuals or organisations) who have sponsored Dapper, but a massive thanks in particular to:
- Dapper Plus is a major sponsor and is proud to contribute to the development of Dapper (read more)
- AWS who sponsored Dapper from Oct 2023 via the .NET on AWS Open Source Software Fund
Features
Dapper is a NuGet library that you can add in to your project that will enhance your ADO.NET connections via
extension methods on your DbConnection
instance. This provides a simple and efficient API for invoking SQL, with support for both synchronous and
asynchronous data access, and allows both buffered and non-buffered queries.
It provides multiple helpers, but the key APIs are:
// insert/update/delete etc
var count = connection.Execute(sql [, args]);
// multi-row query
IEnumerable<T> rows = connection.Query<T>(sql [, args]);
// single-row query ({Single|First}[OrDefault])
T row = connection.QuerySingle<T>(sql [, args]);
where args
can be (among other things):
- a simple POCO (including anonyomous types) for named parameters
- a
Dictionary<string,object>
- a
DynamicParameters
instance
Execute a query and map it to a list of typed objects
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
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").AsList();
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
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"
Another example usage when you already have an existing collection:
var foos = new List<Foo>
{
{ new Foo { A = 1, B = 1 } }
{ new Foo { A = 2, B = 2 } }
{ new Foo { A = 3, B = 3 } }
};
var count = connection.Execute(@"insert MyTable(colA, colB) values (@a, @b)", foos);
Assert.Equal(foos.Count, count);
This works for any parameter that implements IEnumerable<T>
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 via:
dotnet run --project .\benchmarks\Dapper.Tests.Performance\ -c Release -f net8.0 -- -f * --join
Output from the latest run is:
BenchmarkDotNet v0.13.7, Windows 10 (10.0.19045.3693/22H2/2022Update)
Intel Core i7-3630QM CPU 2.40GHz (Ivy Bridge), 1 CPU, 8 logical and 4 physical cores
.NET SDK 8.0.100
[Host] : .NET 8.0.0 (8.0.23.53103), X64 RyuJIT AVX
ShortRun : .NET 8.0.0 (8.0.23.53103), X64 RyuJIT AVX
ORM | Method | Return | Mean | StdDev | Error | Gen0 | Gen1 | Gen2 | Allocated |
---|---|---|---|---|---|---|---|---|---|
Dapper cache impact | ExecuteParameters_Cache | Void | 96.75 us | 0.668 us | 1.010 us | 0.6250 | - | - | 2184 B |
Dapper cache impact | QueryFirstParameters_Cache | Void | 96.86 us | 0.493 us | 0.746 us | 0.8750 | - | - | 2824 B |
Hand Coded | SqlCommand | Post | 119.70 us | 0.706 us | 1.067 us | 1.3750 | 1.0000 | 0.1250 | 7584 B |
Hand Coded | DataTable | dynamic | 126.64 us | 1.239 us | 1.873 us | 3.0000 | - | - | 9576 B |
SqlMarshal | SqlCommand | Post | 132.36 us | 1.008 us | 1.523 us | 2.0000 | 1.0000 | 0.2500 | 11529 B |
Dapper | QueryFirstOrDefault | Post | 133.73 us | 1.301 us | 2.186 us | 1.7500 | 1.5000 | - | 11608 B |
Mighty | Query | dynamic | 133.92 us | 1.075 us | 1.806 us | 2.0000 | 1.7500 | - | 12710 B |
LINQ to DB | Query | Post | 134.24 us | 1.068 us | 1.614 us | 1.7500 | 1.2500 | - | 10904 B |
RepoDB | ExecuteQuery | Post | 135.83 us | 1.839 us | 3.091 us | 1.7500 | 1.5000 | - | 11649 B |
Dapper | 'Query | Post | 136.14 us | 1.755 us | 2.653 us | 2.0000 | 1.5000 | - | 11888 B |
Mighty | Query | Post | 137.96 us | 1.485 us | 2.244 us | 2.2500 | 1.2500 | - | 12201 B |
Dapper | QueryFirstOrDefault | dynamic | 139.04 us | 1.507 us | 2.279 us | 3.5000 | - | - | 11648 B |
Mighty | SingleFromQuery | dynamic | 139.74 us | 2.521 us | 3.811 us | 2.0000 | 1.7500 | - | 12710 B |
Dapper | 'Query | dynamic | 140.13 us | 1.382 us | 2.090 us | 2.0000 | 1.5000 | - | 11968 B |
ServiceStack | SingleById | Post | 140.76 us | 1.147 us | 2.192 us | 2.5000 | 1.2500 | 0.2500 | 15248 B |
Dapper | 'Contrib Get | Post | 141.09 us | 1.394 us | 2.108 us | 2.0000 | 1.5000 | - | 12440 B |
Mighty | SingleFromQuery | Post | 141.17 us | 1.941 us | 2.935 us | 1.7500 | 1.5000 | - | 12201 B |
Massive | 'Query (dynamic)' | dynamic | 142.01 us | 4.957 us | 7.494 us | 2.0000 | 1.5000 | - | 12342 B |
LINQ to DB | 'First (Compiled)' | Post | 144.59 us | 1.295 us | 1.958 us | 1.7500 | 1.5000 | - | 12128 B |
RepoDB | QueryField | Post | 148.31 us | 1.742 us | 2.633 us | 2.0000 | 1.5000 | 0.5000 | 13938 B |
Norm | 'Read<> (tuples)' | ValueTuple`8 | 148.58 us | 2.172 us | 3.283 us | 2.0000 | 1.7500 | - | 12745 B |
Norm | 'Read<()> (named tuples)' | ValueTuple`8 | 150.60 us | 0.658 us | 1.106 us | 2.2500 | 2.0000 | 1.2500 | 14562 B |
RepoDB | Query | Post | 152.34 us | 2.164 us | 3.271 us | 2.2500 | 1.5000 | 0.2500 | 14106 B |
RepoDB | QueryDynamic | Post | 154.15 us | 4.108 us | 6.210 us | 2.2500 | 1.7500 | 0.5000 | 13930 B |
RepoDB | QueryWhere | Post | 155.90 us | 1.953 us | 3.282 us | 2.5000 | 0.5000 | - | 14858 B |
Dapper cache impact | ExecuteNoParameters_NoCache | Void | 162.35 us | 1.584 us | 2.394 us | - | - | - | 760 B |
Dapper cache impact | ExecuteNoParameters_Cache | Void | 162.42 us | 2.740 us | 4.142 us | - | - | - | 760 B |
Dapper cache impact | QueryFirstNoParameters_Cache | Void | 164.35 us | 1.206 us | 1.824 us | 0.2500 | - | - | 1520 B |
DevExpress.XPO | FindObject | Post | 165.87 us | 1.012 us | 1.934 us | 8.5000 | - | - | 28099 B |
Dapper cache impact | QueryFirstNoParameters_NoCache | Void | 173.87 us | 1.178 us | 1.781 us | 0.5000 | - | - | 1576 B |
LINQ to DB | First | Post | 175.21 us | 2.292 us | 3.851 us | 2.0000 | 0.5000 | - | 14041 B |
EF 6 | SqlQuery | Post | 175.36 us | 2.259 us | 3.415 us | 4.0000 | 0.7500 | - | 24209 B |
Norm | 'Read<> (class)' | Post | 186.37 us | 1.305 us | 2.496 us | 3.0000 | 0.5000 | - | 17579 B |
DevExpress.XPO | GetObjectByKey | Post | 186.78 us | 3.407 us | 5.151 us | 4.5000 | 1.0000 | - | 30114 B |
Dapper | 'Query | dynamic | 194.62 us | 1.335 us | 2.019 us | 1.7500 | 1.5000 | - | 12048 B |
Dapper | 'Query | Post | 195.01 us | 0.888 us | 1.343 us | 2.0000 | 1.5000 | - | 12008 B |
DevExpress.XPO | Query | Post | 199.46 us | 5.500 us | 9.243 us | 10.0000 | - | - | 32083 B |
Belgrade | FirstOrDefault | Task`1 | 228.70 us | 2.181 us | 3.665 us | 4.5000 | 0.5000 | - | 20555 B |
EF Core | 'First (Compiled)' | Post | 265.45 us | 17.745 us | 26.828 us | 2.0000 | - | - | 7521 B |
NHibernate | Get | Post | 276.02 us | 8.029 us | 12.139 us | 6.5000 | 1.0000 | - | 29885 B |
NHibernate | HQL | Post | 277.74 us | 13.032 us | 19.703 us | 8.0000 | 1.0000 | - | 31886 B |
NHibernate | Criteria | Post | 300.22 us | 14.908 us | 28.504 us | 13.0000 | 1.0000 | - | 57562 B |
EF 6 | First | Post | 310.55 us | 27.254 us | 45.799 us | 13.0000 | - | - | 43309 B |
EF Core | First | Post | 317.12 us | 1.354 us | 2.046 us | 3.5000 | - | - | 11306 B |
EF Core | SqlQuery | Post | 322.34 us | 23.990 us | 40.314 us | 5.0000 | - | - | 18195 B |
NHibernate | SQL | Post | 325.54 us | 3.937 us | 7.527 us | 22.0000 | 1.0000 | - | 80007 B |
EF 6 | 'First (No Tracking)' | Post | 331.14 us | 27.760 us | 46.649 us | 12.0000 | 1.0000 | - | 50237 B |
EF Core | 'First (No Tracking)' | Post | 337.82 us | 27.814 us | 46.740 us | 3.0000 | 1.0000 | - | 17986 B |
NHibernate | LINQ | Post | 604.74 us | 5.549 us | 10.610 us | 10.0000 | - | - | 46061 B |
Dapper cache impact | ExecuteParameters_NoCache | Void | 623.42 us | 3.978 us | 6.684 us | 3.0000 | 2.0000 | - | 10001 B |
Dapper cache impact | QueryFirstParameters_NoCache | Void | 630.77 us | 3.027 us | 4.576 us | 3.0000 | 2.0000 | - | 10640 B |
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 usually passed in as anonymous classes. This allows 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
Parameters can also be built up dynamically using the DynamicParameters class. This allows for building a dynamic SQL statement while still using parameters for safety and performance.
var sqlPredicates = new List<string>();
var queryParams = new DynamicParameters();
if (boolExpression)
{
sqlPredicates.Add("column1 = @param1");
queryParams.Add("param1", dynamicValue1, System.Data.DbType.Guid);
} else {
sqlPredicates.Add("column2 = @param2");
queryParams.Add("param2", dynamicValue2, System.Data.DbType.String);
}
DynamicParameters also supports copying multiple parameters from existing objects of different types.
var queryParams = new DynamicParameters(objectOfType1);
queryParams.AddDynamicParams(objectOfType2);
When an object that implements the IDynamicParameters
interface passed into Execute
or Query
functions, parameter values will be extracted via this interface. Obviously, the most likely object class to use for this purpose would be the built-in DynamicParameters
class.
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 UserTypeId = {=Admin}", new { UserTypeId.Admin });
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 use 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);
}
}
User Defined Variables in MySQL
In order to use Non-parameter SQL variables with MySql Connector, you have to add the following option to your connection string:
Allow User Variables=True
Make sure you don't provide Dapper with a property to map.
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 features 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.
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