创建高性能的WCF服务

I had a WCF service where I wanted to be able to support over a hundred concurrent users, and while most of the service methods had small payloads which returned quickly, the startup sequence needed to pull down 200,000 records. The out of the box WCF service had no ability to support this scenario, but with some effort I was able to squeeze orders of magnitude performance increases out of the service and hit the performance goal.

Initially performance was abysmal and there was talk of ditching WCF entirely ( and as the one pushing WCF technology on the project this didn't seem like a career enhancing change )

 

Here's how performance was optimized. These are listed in the order they were implemented. Some are fairly obvious, others took some time to discover.  Each item represents, a significant increase in latency or scalability from the prior - and although I have internal measurement numbers, I'm not comfortable publishing them as the size of the data increased, and the testing approach changed.

1. Use NetTCP binding
   This helps both throughput and the time it takes to open and close connections
2. Use DataContract Serializer instead of XMLSerializer
   I started out using DataTables - POCO objects via Linq2Sql yielded a 6x increase
   slow: [OperationContract] MyDataTable GetData(...);
   fast: [OperationContract] IEnumerable<MyData> GetData(...);
   
3. Unthrottle your service
   It's quite understanable that WCF is resistant to Denial of Service attacks out of the box, but it's too bad that it's is such a manual operation to hit the "turbo button". It would be nice if the Visual Studio tooling did this for you, or at least had some guidance (MS - hint, hint)
   
   The items to look at here are:
   1. <serviceBehaviors><serviceThrottling ...> set the max values high
   2. <dataContractSerializer maxItemsInObjectGraph="2147483647" />
   3. and under <netTcpBinding> setting the listenBacklog, maxConnections, and maxBuffer* value high
4. Cache your data
   WCF, unlike ASP.Net has no built in facility to cache service responses, so you need to do it by hand. Any cache class will do.
5. Normalize/compress your data
   this doesn't necessarily have to be done in the database, the Linq GroupBy operators make this easy to do in code. To clarify, say your data is kept in a denormalized table
   

   string	Key1
   string	Key2
   string	Key3
   int	val1
   int	val2

   
   the bulk of the result set ends up being duplicate data
   

   LongKeyVal1	LongKeyVal2	LongKeyVal3	10	12
   LongKeyVal1	LongKeyVal2	LongKeyVal3	11	122
   LongKeyVal1	LongKeyVal2	LongKeyVal3	12	212

   so normalize this into
   

   LongKeyVal1

   LongKeyVal2

   LongKeyVal3

   10	12
   11	122
   12	212

   
   

   In code, given the following classes

   public class MyDataDenormalized
   
   {
   
       public string Key1 { get; set; }
   
       public string Key2 { get; set; }
   
       public string Key3 { get; set; }
   
       public int Val1 { get; set; }
   
       public int Val2 { get; set; }
   
   }
   
   public class MyDataGroup
   
   {
   
       public string Key1 { get; set; }
   
       public string Key2 { get; set; }
   
       public string Key3 { get; set; }
   
       public MyDataItem[] Values { get; set; }
   
   }
   
   public class MyDataItem
   
   {
   
       public int Val1 { get; set; }
   
       public int Val2 { get; set; }
   
   }

   you can transform an IEnumerable<MyDataDenormalized> into a IEnumerable<MyDataGroup> via the following

   var keyed = from sourceItem in source
   
              group sourceItem by new
   
              {
   
                  sourceItem.Key1,
   
                  sourceItem.Key2,
   
                  sourceItem.Key3,
   
              } into g
   
              select g;
   
   var groupedList = from kItems in keyed
   
                 let newValues = (from sourceItem in kItems select new MyDataItem() { Val1 = sourceItem.Val1, Val2= sourceItem.Val2 }).ToArray()
   
                 select new MyDataGroup()
   
                 {
   
                     Key1 = kItems.Key.Key1,
   
                     Key2 = kItems.Key.Key2,
   
                     Key3 = kItems.Key.Key3,
   
                     Values = newValues,
   
                 };

6. Use the BinaryFormatter, and cache your serializations
   If you're willing to forgo over the wire type safety, the binary formatter is the way to go for scalability. Data caching has only a limited impact if a significant amount of CPU time is spent serializing it - which is exactly what happens with the DataContract serializer.
   
   The operation contract changes to

7. [OperationContract]
   
   Byte[] GetData(...);

   and the implementation to

   var bf = new BinaryFormatter();
   
   using (var ms = new MemoryStream())
   
   {
   
       bf.Serialize(ms, groupeList);
   
       // and best to cache it too
   
       return ms.GetBuffer();
   
   }

    

Before items 4,5, and 6 the service would max out at about 50 clients ( response time to go way up and CPU usage would hit 80% - on a 8 core box). After these changes were made, the service could handle of 100 + clients and CPU usage flattened out at 30%

Update: Shay Jacoby has reasonably suggested I show some code.

Update2: Brett asks about relative impact. Here's a summary

item	latency	scalability
2) DataContract Serializer	large	large
3) unthrottle	small	large
4) cache data	small
5) normalize data	medium
6) cache serialization	small	large