Dapper is a single file you can drop in to your project that will extend your IDbConnection interface.
It provides 3 helpers:
Note: all extension methods assume the connection is already open, they will fail if the connection is closed.
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 });
dog.Count()
.IsEqualTo(1);
dog.First().Age
.IsNull();
dog.First().Id
.IsEqualTo(guid);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");
((int)rows[0].A)
.IsEqualTo(1);
((int)rows[0].B)
.IsEqualTo(2);
((int)rows[1].A)
.IsEqualTo(3);
((int)rows[1].B)
.IsEqualTo(4);public static int Execute(this IDbConnection cnn, string sql, object param = null, SqlTransaction transaction = null)Example usage:
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 })
.IsEqualTo(2);The same signature also allows you to conveniently and efficiently execute a command multiple times (for example to bulk-load data)
Example usage:
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 } }
).IsEqualTo(3); // 3 rows inserted: "1,1", "2,2" and "3,3"This works for any parameter that implements IEnumerable for some T.
A key feature of Dapper is performance. The following metrics show how long it takes to execute 500 SELECT statements against a DB and map the data returned to objects.
The performance tests are broken in to 3 lists:
- POCO serialization for frameworks that support pulling static typed objects from the DB. Using raw SQL.
- Dynamic serialization for frameworks that support returning dynamic lists of objects.
- Typical framework usage. Often typical framework usage differs from the optimal usage performance wise. Often it will not involve writing SQL.
| Method | Duration | Remarks |
|---|---|---|
Hand coded (using a SqlDataReader) |
47ms | Can be faster |
Dapper ExecuteMapperQuery |
49ms | |
| ServiceStack.OrmLite (QueryById) | 50ms | |
| PetaPoco | 52ms | |
| BLToolkit | 80ms | |
| SubSonic CodingHorror | 107ms | |
| NHibernate SQL | 104ms | |
Linq 2 SQL ExecuteQuery |
181ms | |
Entity framework ExecuteStoreQuery |
631ms |
| Method | Duration | Remarks |
|---|---|---|
Dapper ExecuteMapperQuery (dynamic) |
48ms | |
| Massive | 52ms | |
| Simple.Data | 95ms |
| Method | Duration | Remarks |
|---|---|---|
| Linq 2 SQL CompiledQuery | 81ms | Not super typical involves complex code |
| NHibernate HQL | 118ms | |
| Linq 2 SQL | 559ms | |
| Entity framework | 859ms | |
| SubSonic ActiveRecord.SingleOrDefault | 3619ms |
Performance benchmarks are available here
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)
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 Query analyzer.
new {A = 1, B = "b"} // A will be mapped to the param @A, B to the param @B Dapper allow you to pass in IEnumerable 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 = 3Dapper'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.
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:
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();
post.Content.IsEqualTo("Sams Post1");
post.Id.IsEqualTo(1);
post.Owner.Name.IsEqualTo("Sam");
post.Owner.Id.IsEqualTo(99);important note Dapper assumes your Id columns are named "Id" or "id", if your primary key is different or you would like to split the wide row at point other than "Id", use the optional 'splitOn' parameter.
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();
...
} Dapper supports fully stored procs:
var user = cnn.Query<User>("spGetUser", new {Id = 1},
commandType: CommandType.StoredProcedure).First();}}}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"); 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.
Dapper caches information about every query it runs, this allow it to materialize objects quickly and process parameters quickly. The current implementation caches this information in a ConcurrentDictionary object. The objects it stores are never flushed. If you are generating SQL strings on the fly without using parameters it is possible you will hit memory issues. We may convert the dictionaries to an LRU Cache.
Dapper's simplicity means that many feature that ORMs ship with are stripped out, there is no identity map, there are no helpers for update / select and so on.
Dapper does not manage your connection's lifecycle, it assumes the connection it gets is open AND has no existing datareaders enumerating (unless MARS is enabled)
Dapper has no DB specific implementation details, it works across all .net ado providers including sqlite, sqlce, firebird, oracle, MySQL and SQL Server
Dapper has a comprehensive test suite in the test project
Dapper is in production use at:
(if you would like to be listed here let me know)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent
