原文链接:  https://*.com/questions/9350467/how-do-i-write-one-to-many-query-in-dapper-net

This post shows how to query a highly normalised SQL database, and map the result into a set of highly nested C# POCO objects.

Ingredients:

• 8 lines of C#.
• Some reasonably simple SQL that uses some joins.
• Two awesome libraries.

The insight that allowed me to solve this problem is to separate the MicroORM from mapping the result back to the POCO Entities. Thus, we use two separate libraries:

• Dapper as the MicroORM.
• Slapper.Automapper for mapping.

Essentially, we use Dapper to query the database, then use Slapper.Automapper to map the result straight into our POCOs.

Advantages

• Simplicity. Its less than 8 lines of code. I find this a lot easier to understand, debug, and change.
• Less code. A few lines of code is all Slapper.Automapper needs to handle anything you throw at it, even if we have a complex nested POCO (i.e. POCO contains List<MyClass1> which in turn contains List<MySubClass2>, etc).
• Speed. Both of these libraries have an extraordinary amount of optimization and caching to make them run almost as fast as hand tuned ADO.NET queries.
• Separation of concerns. We can change the MicroORM for a different one, and the mapping still works, and vice-versa.
• Flexibility. Slapper.Automapper handles arbitrarily nested hierarchies, it isn‘t limited to a couple of levels of nesting. We can easily make rapid changes, and everything will still work.
• Debugging. We can first see that the SQL query is working properly, then we can check that the SQL query result is properly mapped back to the target POCO Entities.
• Ease of development in SQL. I find that creating flattened queries with inner joins to return flat results is much easier than creating multiple select statements, with stitching on the client side.
• Optimized queries in SQL. In a highly normalized database, creating a flat query allows the SQL engine to apply advanced optimizations to the whole which would not normally be possible if many small individual queries were constructed and run.
• Trust. Dapper is the back end for *, and, well, Randy Burden is a bit of a superstar. Need I say any more?
• Speed of development. I was able to do some extraordinarily complex queries, with many levels of nesting, and the dev time was quite low.
• Fewer bugs. I wrote it once, it just worked, and this technique is now helping to power a FTSE company. There was so little code that there was no unexpected behavior.

Disadvantages

• Scaling beyond 1,000,000 rows returned. Works well when returning < 100,000 rows. However, if we are bringing back >1,000,000 rows, in order to reduce the traffic between us and SQL server, we should not flatten it out using inner join (which brings back duplicates), we should instead use multiple select statements and stitch everything back together on the client side (see the other answers on this page).
• This technique is query oriented. I haven‘t used this technique to write to the database, but I‘m sure that Dapper is more than capable of doing this with some more extra work, as * itself uses Dapper as its Data Access Layer (DAL).

Performance Testing

In my tests, Slapper.Automapper added a small overhead to the results returned by Dapper, which meant that it was still 10x faster than Entity Framework, and the combination is still pretty darn close to the theoretical maximum speed SQL + C# is capable of.

In most practical cases, most of the overhead would be in a less-than-optimum SQL query, and not with some mapping of the results on the C# side.

Performance Testing Results

Total number of iterations: 1000

• Dapper by itself: 1.889 milliseconds per query, using 3 lines of code to return the dynamic.
• Dapper + Slapper.Automapper: 2.463 milliseconds per query, using an additional 3 lines of code for the query + mapping from dynamic to POCO Entities.

Worked Example

In this example, we have list of Contacts, and each Contact can have one or more phone numbers.

POCO Entities

public class TestContact
{
    public int ContactID { get; set; }
    public string ContactName { get; set; }
    public List<TestPhone> TestPhones { get; set; }
}

public class TestPhone
{
    public int PhoneId { get; set; }
    public int ContactID { get; set; } // foreign key
    public string Number { get; set; }
}

SQL Table TestContact

SQL Table TestPhone

Note that this table has a foreign key ContactID which refers to the TestContact table (this corresponds to the List<TestPhone> in the POCO above).

SQL Which Produces Flat Result

In our SQL query, we use as many JOIN statements as we need to get all of the data we need, in a flat, denormalized form. Yes, this might produce duplicates in the output, but these duplicates will be eliminated automatically when we use Slapper.Automapper to automatically map the result of this query straight into our POCO object map.

USE [MyDatabase];
    SELECT tc.[ContactID] as ContactID
          ,tc.[ContactName] as ContactName
          ,tp.[PhoneId] AS TestPhones_PhoneId
          ,tp.[ContactId] AS TestPhones_ContactId
          ,tp.[Number] AS TestPhones_Number
          FROM TestContact tc
    INNER JOIN TestPhone tp ON tc.ContactId = tp.ContactId

const string sql = @"SELECT tc.[ContactID] as ContactID
          ,tc.[ContactName] as ContactName
          ,tp.[PhoneId] AS TestPhones_PhoneId
          ,tp.[ContactId] AS TestPhones_ContactId
          ,tp.[Number] AS TestPhones_Number
          FROM TestContact tc
    INNER JOIN TestPhone tp ON tc.ContactId = tp.ContactId";

string connectionString = // -- Insert SQL connection string here.

using (var conn = new SqlConnection(connectionString))
{
    conn.Open();    
    // Can set default database here with conn.ChangeDatabase(...)
    {
        // Step 1: Use Dapper to return the  flat result as a Dynamic.
        dynamic test = conn.Query<dynamic>(sql);

        // Step 2: Use Slapper.Automapper for mapping to the POCO Entities.
        // - IMPORTANT: Let Slapper.Automapper know how to do the mapping;
        //   let it know the primary key for each POCO.
        // - Must also use underscore notation ("_") to name parameters in the SQL query;
        //   see Slapper.Automapper docs.
        Slapper.AutoMapper.Configuration.AddIdentifiers(typeof(TestContact), new List<string> { "ContactID" });
        Slapper.AutoMapper.Configuration.AddIdentifiers(typeof(TestPhone), new List<string> { "PhoneID" });

        var testContact = (Slapper.AutoMapper.MapDynamic<TestContact>(test) as IEnumerable<TestContact>).ToList();      

        foreach (var c in testContact)
        {                               
            foreach (var p in c.TestPhones)
            {
                Console.Write("ContactName: {0}: Phone: {1}\n", c.ContactName, p.Number);   
            }
        }
    }
}

Output

POCO Entity Hierarchy

Looking in Visual Studio, We can see that Slapper.Automapper has properly populated our POCO Entities, i.e. we have a List<TestContact>, and each TestContact has a List<TestPhone>.

Notes

Both Dapper and Slapper.Automapper cache everything internally for speed. If you run into memory issues (very unlikely), ensure that you occasionally clear the cache for both of them.

Ensure that you name the columns coming back, using the underscore (_) notation to give Slapper.Automapper clues on how to map the result into the POCO Entities.

Ensure that you give Slapper.Automapper clues on the primary key for each POCO Entity (see the lines Slapper.AutoMapper.Configuration.AddIdentifiers). You can also use Attributes on the POCO for this. If you skip this step, then it could go wrong (in theory), as Slapper.Automapper would not know how to do the mapping properly.

How do I write one to many query in Dapper.Net?